Swarm intelligence algorithms have been in recent years one of the most used tools for planning the trajectory of a mobile robot. Researchers are applying those algorithms to find the optimal path, which reduces the time required to perform a task by the mobile robot. In this paper, we propose a new method based on the grey wolf optimizer algorithm (GWO) and the improved elephant herding optimization algorithm (IEHO) for planning the optimal trajectory of a mobile robot. The proposed solution consists of developing an IEHO algorithm by improving the basic EHO algorithm and then hybridizing it with the GWO algorithm to take advantage of the exploration and exploitation capabilities of both algorithms. The comparison of the IEHO-GWO hybrid proposed in this work with the GWO, EHO, and cuckoo-search (CS) algorithms via simulation shows its effectiveness in finding an optimal trajectory by avoiding obstacles around the mobile robot.
IJRET : International Journal of Research in Engineering and Technology is an international peer reviewed, online journal published by eSAT Publishing House for the enhancement of research in various disciplines of Engineering and Technology. The aim and scope of the journal is to provide an academic medium and an important reference for the advancement and dissemination of research results that support high-level learning, teaching and research in the fields of Engineering and Technology. We bring together Scientists, Academician, Field Engineers, Scholars and Students of related fields of Engineering and Technology
Smart element aware gate controller for intelligent wheeled robot navigationIJECEIAES
This document presents a modified neuro-controller mechanism for controlling the navigation of an indoor mobile robot. The proposed mechanism uses a modified Elman neural network (MENN) with an effective element aware gate (MEEG) as the neuro-controller. The MEEG controller is able to estimate trajectory and overcome rigid and dynamic barriers intelligently. It was implemented on a Khepera IV mobile robot. Practical results showed the proposed mechanism was more efficient than MENN in providing the shortest distance to reach the goal with maximum velocity, minimizing the error rate by 58.33%. The document describes the system architecture, proposed neuro-controller, training algorithm for the MEEG, and presents analysis of sensor data and practical results.
A REVIEW OF PARTICLE SWARM OPTIMIZATION (PSO) ALGORITHMIAEME Publication
Particle swarm optimization (PSO) is a population-based stochastic optimization technique that is inspired by the intelligent collective behaviour of certain animals, such as flocks of birds or schools of fish. It has undergone numerous improvements since its debut in 1995. As academics became more familiar with the technique, they produced additional versions aimed at different demands, created new applications in a variety of fields, published theoretical analyses of the impacts of various factors, and offered other variants of the algorithm. This paper discusses the PSO's origins and background, as well as its theory analysis. Then, we examine the current state of research and application in algorithm structure, parameter selection, topological structure, discrete and parallel PSO algorithms, multi-objective optimization PSO, and engineering applications. Finally, existing difficulties are discussed, and new study directions are proposed.
AN IMPROVED MULTIMODAL PSO METHOD BASED ON ELECTROSTATIC INTERACTION USING NN...ijaia
In this paper, an improved multimodal optimization (MMO) algorithm,calledLSEPSO,has been proposed. LSEPSO combinedElectrostatic Particle Swarm Optimization (EPSO) algorithm and a local search method and then madesome modification onthem. It has been shown to improve global and local optima finding ability of the algorithm. This algorithm useda modified local search to improve particle's personal best, which usedn-nearest-neighbour instead of nearest-neighbour. Then, by creating n new points among each particle and n nearest particles, it triedto find a point which could be the alternative of particle's personal best. This methodprevented particle's attenuation and following a specific particle by its neighbours. The performed tests on a number of benchmark functions clearly demonstratedthat the improved algorithm is able to solve MMO problems and outperform other tested algorithms in this article.
Comparison of different Ant based techniques for identification of shortest p...IOSR Journals
This document compares different ant colony optimization (ACO) techniques for identifying the shortest path in a distributed network. ACO is based on the behavior of ants finding food sources and uses pheromone trails to probabilistically determine paths. The document reviews several ACO algorithms and techniques, including Max-Min, rank-based, and fuzzy rule-based approaches. It then implements an efficient ACO algorithm that performs better at finding the shortest path compared to other existing ACO techniques.
The document summarizes three algorithms for multi-robot path planning: Bacteria Foraging Optimization (BFO), Ant Colony Optimization (ACO), and Particle Swarm Optimization (PSO). BFO is inspired by how bacteria like E. coli search for food by swimming and tumbling. ACO is based on how ants deposit and follow pheromone trails to find food sources. PSO mimics the movement of bird flocking and fish schooling. The document provides details on the mechanisms and equations used in each algorithm's approach to finding optimal paths for multiple robots.
Application of optimization algorithms for classification problemIJECEIAES
The document discusses using particle swarm optimization (PSO) and firefly algorithms for feature selection in face recognition. In the first setup, PSO and firefly algorithms are separately applied to select features from face images, which are then classified. In the second setup, selected features from both algorithms are fused before classification. Experiments on two face databases show firefly outperforms PSO and Manhattan distance performs best. Feature fusion and increasing training images improve recognition rates for both algorithms up to 99%.
In the recent years the development in communication systems requires the development of low cost, minimal weight, low profile antennas that are capable of maintaining high performance over a wide spectrum of frequencies. This technological trend has focused much effort into the design of a micro strip patch antennas. Nowadays Evolutionary Computation has its growth to extent. Generally electromagnetic optimization problems generally involve a large number of parameters. Synthesis of non-uniform linear antenna arrays is one of the most important electromagnetic optimization problems of the current interest.
IJRET : International Journal of Research in Engineering and Technology is an international peer reviewed, online journal published by eSAT Publishing House for the enhancement of research in various disciplines of Engineering and Technology. The aim and scope of the journal is to provide an academic medium and an important reference for the advancement and dissemination of research results that support high-level learning, teaching and research in the fields of Engineering and Technology. We bring together Scientists, Academician, Field Engineers, Scholars and Students of related fields of Engineering and Technology
Smart element aware gate controller for intelligent wheeled robot navigationIJECEIAES
This document presents a modified neuro-controller mechanism for controlling the navigation of an indoor mobile robot. The proposed mechanism uses a modified Elman neural network (MENN) with an effective element aware gate (MEEG) as the neuro-controller. The MEEG controller is able to estimate trajectory and overcome rigid and dynamic barriers intelligently. It was implemented on a Khepera IV mobile robot. Practical results showed the proposed mechanism was more efficient than MENN in providing the shortest distance to reach the goal with maximum velocity, minimizing the error rate by 58.33%. The document describes the system architecture, proposed neuro-controller, training algorithm for the MEEG, and presents analysis of sensor data and practical results.
A REVIEW OF PARTICLE SWARM OPTIMIZATION (PSO) ALGORITHMIAEME Publication
Particle swarm optimization (PSO) is a population-based stochastic optimization technique that is inspired by the intelligent collective behaviour of certain animals, such as flocks of birds or schools of fish. It has undergone numerous improvements since its debut in 1995. As academics became more familiar with the technique, they produced additional versions aimed at different demands, created new applications in a variety of fields, published theoretical analyses of the impacts of various factors, and offered other variants of the algorithm. This paper discusses the PSO's origins and background, as well as its theory analysis. Then, we examine the current state of research and application in algorithm structure, parameter selection, topological structure, discrete and parallel PSO algorithms, multi-objective optimization PSO, and engineering applications. Finally, existing difficulties are discussed, and new study directions are proposed.
AN IMPROVED MULTIMODAL PSO METHOD BASED ON ELECTROSTATIC INTERACTION USING NN...ijaia
In this paper, an improved multimodal optimization (MMO) algorithm,calledLSEPSO,has been proposed. LSEPSO combinedElectrostatic Particle Swarm Optimization (EPSO) algorithm and a local search method and then madesome modification onthem. It has been shown to improve global and local optima finding ability of the algorithm. This algorithm useda modified local search to improve particle's personal best, which usedn-nearest-neighbour instead of nearest-neighbour. Then, by creating n new points among each particle and n nearest particles, it triedto find a point which could be the alternative of particle's personal best. This methodprevented particle's attenuation and following a specific particle by its neighbours. The performed tests on a number of benchmark functions clearly demonstratedthat the improved algorithm is able to solve MMO problems and outperform other tested algorithms in this article.
Comparison of different Ant based techniques for identification of shortest p...IOSR Journals
This document compares different ant colony optimization (ACO) techniques for identifying the shortest path in a distributed network. ACO is based on the behavior of ants finding food sources and uses pheromone trails to probabilistically determine paths. The document reviews several ACO algorithms and techniques, including Max-Min, rank-based, and fuzzy rule-based approaches. It then implements an efficient ACO algorithm that performs better at finding the shortest path compared to other existing ACO techniques.
The document summarizes three algorithms for multi-robot path planning: Bacteria Foraging Optimization (BFO), Ant Colony Optimization (ACO), and Particle Swarm Optimization (PSO). BFO is inspired by how bacteria like E. coli search for food by swimming and tumbling. ACO is based on how ants deposit and follow pheromone trails to find food sources. PSO mimics the movement of bird flocking and fish schooling. The document provides details on the mechanisms and equations used in each algorithm's approach to finding optimal paths for multiple robots.
Application of optimization algorithms for classification problemIJECEIAES
The document discusses using particle swarm optimization (PSO) and firefly algorithms for feature selection in face recognition. In the first setup, PSO and firefly algorithms are separately applied to select features from face images, which are then classified. In the second setup, selected features from both algorithms are fused before classification. Experiments on two face databases show firefly outperforms PSO and Manhattan distance performs best. Feature fusion and increasing training images improve recognition rates for both algorithms up to 99%.
In the recent years the development in communication systems requires the development of low cost, minimal weight, low profile antennas that are capable of maintaining high performance over a wide spectrum of frequencies. This technological trend has focused much effort into the design of a micro strip patch antennas. Nowadays Evolutionary Computation has its growth to extent. Generally electromagnetic optimization problems generally involve a large number of parameters. Synthesis of non-uniform linear antenna arrays is one of the most important electromagnetic optimization problems of the current interest.
This paper proposes a new methodology to
optimize trajectory of the path for multi-robots using
Improved particle swarm optimization Algorithm (IPSO) in
clutter Environment. IPSO technique is incorporated into
the multi-robot system in a dynamic framework, which will
provide robust performance, self-deterministic cooperation,
and coping with an inhospitable environment
an improver particle optmizacion plan de negociosCarlos Iza
This document proposes an improved particle swarm optimization (IPSO) algorithm to optimize trajectory paths for multiple robots in a cluttered environment. IPSO incorporates independent decision making, coordination, and cooperation between robots to accomplish a shared goal. A path planning scheme using IPSO was developed to determine optimal successive robot positions from initial to target locations. Simulation and experiment results show IPSO outperforms particle swarm optimization and differential evolution algorithms with respect to average total trajectory deviation and average uncovered target distance.
Radio-frequency circular integrated inductors sizing optimization using bio-...IJECEIAES
In this article, a comparative study is accomplished between three of the most used swarm intelligence (SI) techniques; namely artificial bee colony (ABC), ant colony optimization (ACO), and particle swarm optimization (PSO) to carry out the optimal design of radio-frequency (RF) spiral inductors, the three algorithms are applied to the cost function of RF circular inductors for 180 nm beyond 2.50 GHz, the aim is to ensure optimal performance with less error in inductance, and a high-quality factor when compared to electromagnetic simulation. Simulation experiments are achieved and performances regarding convergence velocity, robustness, and computing time are checked. Also, this paper shows an impact study of technological parameters and geometric features on the inductance and the quality factor of the studied integrated inductor. The building method of constraints design with algorithms used has given good results and electromagnetic simulations are of good accuracy with an error of 2.31% and 4.15% on the quality factor and inductance respectively. The simulation shows that ACO provides more accuracy in circuit size and fewer errors than ABC and PSO, while PSO and ABC are better in terms of convergence velocity.
Path Planning of Mobile aco fuzzy-presentation.pptxssuserf6b378
The document describes a study on path planning for mobile robots using fuzzy logic and ant colony algorithms in complex dynamic environments. It proposes a new approach that combines these methods. The study models the robot's workspace as a grid with fixed and moving obstacles. It explains how the ant colony algorithm and fuzzy logic are applied to determine optimal paths between start and end points while avoiding obstacles. Simulation results show the combined approach finds shorter paths in less time compared to using each method individually.
The document proposes a hybrid algorithm combining genetic algorithm and cuckoo search optimization to solve job shop scheduling problems. It aims to minimize makespan (completion time of all jobs) by scheduling jobs on machines. The genetic algorithm is used to explore the search space but can get trapped in local optima. Cuckoo search optimization performs local search faster than genetic algorithm and helps avoid local optima. Experimental results on benchmark problems show the hybrid algorithm yields better solutions in terms of makespan and runtime compared to genetic algorithm and ant colony optimization algorithms.
A hybrid optimization algorithm based on genetic algorithm and ant colony opt...ijaia
In optimization problem, Genetic Algorithm (GA) and Ant Colony Optimization Algorithm (ACO) have
been known as good alternative techniques. GA is designed by adopting the natural evolution process,
while ACO is inspired by the foraging behaviour of ant species. This paper presents a hybrid GA-ACO for
Travelling Salesman Problem (TSP), called Genetic Ant Colony Optimization (GACO). In this method, GA
will observe and preserve the fittest ant in each cycle in every generation and only unvisited cities will be
assessed by ACO. From experimental result, GACO performance is significantly improved and its time
complexity is fairly equal compared to the GA and ACO.
PSO APPLIED TO DESIGN OPTIMAL PD CONTROL FOR A UNICYCLE MOBILE ROBOTJaresJournal
In this work, we propose a Particle Swarm Optimization (PSO) to design Proportional Derivative
controllers (PD) for the control of Unicycle Mobile Robot. To stabilize and drive the robot precisely with
the predefined trajectory, a decentralized control structure is adopted where four PD controllers are used.
Their parameters are given simultaneously by the proposed algorithm (PSO). The performance of the
system from its desired behavior is quantified by an objective function (SE). Simulation results are
presented to show the efficiency of the method. ).
The results are very conclusive and satisfactory in terms of stability and trajectory tracking of unicycle
mobile robot
Swarm flip-crossover algorithm: a new swarm-based metaheuristic enriched with...IJECEIAES
A new swarm-based metaheuristic that is also enriched with the crossover technique called swarm flip-crossover algorithm (SFCA) is introduced in this work. SFCA uses swarm intelligence as its primary technique and the crossover as its secondary one. It consists of three searches in every iteration. The swarm member walks toward the best member as the first search. The central point of the swarm becomes the target in the second search. There are two walks in the second search. The first walk is getting closer to the target, while the second is avoiding the target. The better result between these two walks becomes the candidate for the replacement. In the third search, the swarm member performs balance arithmetic crossover with the central point of the space or jumps to the opposite location within the area (flipping). The assessment is taken by confronting SFCA with five new metaheuristics: slime mold algorithm (SMA), golden search optimization (GSO), osprey optimization algorithm (OOA), coati optimization algorithm (COA), and walrus optimization algorithm (WaOA) in handling the set of 23 functions. The result shows that SFCA performs consecutively better than SMA, GSO, OOA, COA, and WaOA in 20, 23, 17, 17, and 17 functions.
Optimizing Mobile Robot Path Planning and Navigation by Use of Differential E...IOSR Journals
Abstract: Path planning and navigation is essential for an autonomous robot which can move avoiding the
static obstacles in a real world and to reach the specific target. Optimizing path for the robot movement gives
the optimal distance from the source to the target and save precious time as well. With the development of
various evolutionary algorithms, the differential evolution is taking the pace in comparison to genetic algorithm.
Differential evolution has been deployed quite successfully for solving global optimization problem. Differential
evolution is a very simple yet powerful metaheuristics type problem solving method. In this paper we are
proposing a Differential Evolution based path navigation algorithm for mobile path navigation and analyze its
efficiency with other developed approaches. The proposed algorithm optimized the robot path and navigates the
robot to the proper target efficiently.
Kinematics modeling of six degrees of freedom humanoid robot arm using impro...IJECEIAES
The robotic arm has functioned as an arm in the humanoid robot and is generally used to perform grasping tasks. Accordingly, kinematics modeling both forward and inverse kinematics is required to calculate the end-effector position in the cartesian space before performing grasping activities. This research presents the kinematics modeling of six degrees of freedom (6-DOF) robotic arm of the T-FLoW humanoid robot for the grasping mechanism of visual grasping systems on the robot operating system (ROS) platform and CoppeliaSim. Kinematic singularity is a common problem in the inverse kinematics model of robots, but. However, other problems are mechanical limitations and computational time. The work uses the homogeneous transformation matrix (HTM) based on the Euler system of the robot for the forward kinematics and demonstrates the capability of an improved damped least squares (I-DLS) method for the inverse kinematics. The I-DLS method was obtained by improving the original DLS method with the joint limits and clamping techniques. The I-DLS performs better than the original DLS during the experiments yet increases the calculation iteration by 10.95%, with a maximum error position between the endeffector and target positions in path planning of 0.1 cm.
Recent research in finding the optimal path by ant colony optimizationjournalBEEI
The computation of the optimal path is one of the critical problems in graph theory. It has been utilized in various practical ranges of real world applications including image processing, file carving and classification problem. Numerous techniques have been proposed in finding optimal path solutions including using ant colony optimization (ACO). This is a nature-inspired metaheuristic algorithm, which is inspired by the foraging behavior of ants in nature. Thus, this paper study the improvement made by many researchers on ACO in finding optimal path solution. Finally, this paper also identifies the recent trends and explores potential future research directions in file carving.
Reliable and accurate estimation of software has always been a matter of concern for industry and
academia. Numerous estimation models have been proposed by researchers, but no model is suitable for all
types of datasets and environments. Since the motive of estimation model is to minimize the gap between
actual and estimated effort, the effort estimation process can be viewed as an optimization problem to tune
the parameters. In this paper, evolutionary computing techniques, including, Bee colony optimization,
Particle swarm optimization and Ant colony optimization have been employed to tune the parameters of
COCOMO Model. The performance of these techniques has been analysed by established performance
measure. The results obtained have been validated by using data of Interactive voice response (IVR)
projects. Evolutionary techniques have been found to be more accurate than existing estimation models.
EVOLUTIONARY COMPUTING TECHNIQUES FOR SOFTWARE EFFORT ESTIMATIONijcsit
Reliable and accurate estimation of software has always been a matter of concern for industry and academia. Numerous estimation models have been proposed by researchers, but no model is suitable for all types of datasets and environments. Since the motive of estimation model is to minimize the gap between actual and estimated effort, the effort estimation process can be viewed as an optimization problem to tune
the parameters. In this paper, evolutionary computing techniques, including, Bee colony optimization, Particle swarm optimization and Ant colony optimization have been employed to tune the parameters of COCOMO Model. The performance of these techniques has been analysed by established performance measure. The results obtained have been validated by using data of Interactive voice response (IVR)
projects. Evolutionary techniques have been found to be more accurate than existing estimation models.
The document summarizes research using evolutionary computing techniques like particle swarm optimization, ant colony optimization, and bee colony optimization to improve software effort estimation compared to the COCOMO model. The techniques are applied to tune the parameters of the COCOMO model. The models are validated using a dataset of 48 interactive voice response projects, and the bee colony optimization technique produces the lowest mean magnitude of relative error (MMRE) of 0.11 and root mean squared error (RMSE) of 7.85, outperforming the other techniques and COCOMO model. Evolutionary computing techniques are found to provide more accurate effort estimates than existing estimation models.
A CRITICAL STUDY ON TEN NONTRADITIONAL OPTIMIZATION METHODS IN SOLVING ENGINE...IAEME Publication
The objective functions used in Engineering Optimization are complex in nature with
many variables and constraints. Conventional optimization tools sometimes fail to give
global optima point. Very popular methods like Genetic Algorithm, Pattern Search,
Simulated Annealing, and Gradient Search are useful methods to find global optima
related to engineering problems. This paper attempts to review new nontraditional
optimization algorithms which are used to solve such complicated engineering problems
to obtain global optimum solutions
Optimized Robot Path Planning Using Parallel Genetic Algorithm Based on Visib...IJERA Editor
An analysis is made for optimized path planning for mobile robot by using parallel genetic algorithm. The
parallel genetic algorithm (PGA) is applied on the visible midpoint approach to find shortest path for mobile
robot. The hybrid ofthese two algorithms provides a better optimized solution for smooth and shortest path for
mobile robot. In this problem, the visible midpoint approach is used to make the effectiveness for avoiding
local minima. It gives the optimum paths which are always consisting on free trajectories. But the
proposedhybrid parallel genetic algorithm converges very fast to obtain the shortest route from source to
destination due to the sharing of population. The total population is partitioned into a number subgroups to
perform the parallel GA. The master thread is the center of information exchange and making selection with
fitness evaluation.The cell to cell crossover makes the algorithm significantly good. The problem converges
quickly with in a less number of iteration.
The assembly process is one of the most time consuming and expensive manufacturing activities.
Determination of a correct and stable assembly sequence is necessary for automated assembly system. The objective of
the present work is to generate feasible, stable and optimal assembly sequence with minimum assembly time.
Automated assembly has the advantage of greater process capability and scalability. It is faster, more efficient and
precise than any conventional process. Ant Colony Optimization (ACO) method is used for generation of stable
assembly sequence. This method has been applied to a Planetary Gearbox.
Comparative and comprehensive study of linear antenna arrays’ synthesisIJECEIAES
In this paper, a comparative and comprehensive study of synthesizing linear antenna array (LAA) designs, is presented. Different desired objectives are considered in this paper; reducing the maximum sidelobe radiation pattern (i.e., pencil-beam pattern), controlling the first null beamwidth (FNBW), and imposing nulls at specific angles in some designs, which are accomplished by optimizing different array parameters (feed current amplitudes, feed current phase, and array elements positions). Three different optimization algorithms are proposed in order to achieve the wanted goals; grasshopper optimization algorithms (GOA), ant lion optimization (ALO), and a new hybrid optimization algorithm based on GOA and ALO. The obtained results show the effectiveness and robustness of the proposed algorithms to achieve the wanted targets. In most experiments, the proposed algorithms outperform other well-known optimization methods, such as; Biogeography based optimization (BBO), particle swarm optimization (PSO), firefly algorithm (FA), cuckoo search (CS) algorithm, genetic algorithm (GA), Taguchi method, self-adaptive differential evolution (SADE), modified spider monkey optimization (MSMO), symbiotic organisms search (SOS), enhanced firefly algorithm (EFA), bat flower pollination (BFP) and tabu search (TS) algorithm.
Iaetsd modified artificial potential fields algorithm for mobile robot path ...Iaetsd Iaetsd
This document presents a modified artificial potential fields algorithm for mobile robot path planning in unknown and dynamic environments. The algorithm uses artificial potential fields to iteratively find optimal points to form a collision-free path from the start to destination. For static obstacles, potential values are used to identify clusters of points around the start and goal, and find a connecting midpoint. This process is repeated iteratively. For dynamic obstacles, Markov models are used to analyze obstacle behavior from sensor data and predict collision points. The robot's path is replanned as needed to avoid collisions based on feedback from sensors and odometry. Simulation results show the algorithm can efficiently plan paths in unknown environments and avoid both static and dynamic obstacles.
Cuckoo algorithm with great deluge local-search for feature selection problemsIJECEIAES
Feature selection problem is concerned with searching in a dataset for a set of features aiming to reduce the training time and enhance the accuracy of a classification method. Therefore, feature selection algorithms are proposed to choose important features from large and complex datasets. The cuckoo search (CS) algorithm is a type of natural-inspired optimization algorithms and is widely implemented to find the optimum solution for a specified problem. In this work, the cuckoo search algorithm is hybridized with a local search algorithm to find a satisfactory solution for the problem of feature selection. The great deluge (GD) algorithm is an iterative search procedure, that can accept some worse moves to find better solutions for the problem, also to increase the exploitation ability of CS. The comparison is also provided to examine the performance of the proposed method and the original CS algorithm. As result, using the UCI datasets the proposed algorithm outperforms the original algorithm and produces comparable results compared with some of the results from the literature.
Redefining brain tumor segmentation: a cutting-edge convolutional neural netw...IJECEIAES
Medical image analysis has witnessed significant advancements with deep learning techniques. In the domain of brain tumor segmentation, the ability to
precisely delineate tumor boundaries from magnetic resonance imaging (MRI)
scans holds profound implications for diagnosis. This study presents an ensemble convolutional neural network (CNN) with transfer learning, integrating
the state-of-the-art Deeplabv3+ architecture with the ResNet18 backbone. The
model is rigorously trained and evaluated, exhibiting remarkable performance
metrics, including an impressive global accuracy of 99.286%, a high-class accuracy of 82.191%, a mean intersection over union (IoU) of 79.900%, a weighted
IoU of 98.620%, and a Boundary F1 (BF) score of 83.303%. Notably, a detailed comparative analysis with existing methods showcases the superiority of
our proposed model. These findings underscore the model’s competence in precise brain tumor localization, underscoring its potential to revolutionize medical
image analysis and enhance healthcare outcomes. This research paves the way
for future exploration and optimization of advanced CNN models in medical
imaging, emphasizing addressing false positives and resource efficiency.
Embedded machine learning-based road conditions and driving behavior monitoringIJECEIAES
Car accident rates have increased in recent years, resulting in losses in human lives, properties, and other financial costs. An embedded machine learning-based system is developed to address this critical issue. The system can monitor road conditions, detect driving patterns, and identify aggressive driving behaviors. The system is based on neural networks trained on a comprehensive dataset of driving events, driving styles, and road conditions. The system effectively detects potential risks and helps mitigate the frequency and impact of accidents. The primary goal is to ensure the safety of drivers and vehicles. Collecting data involved gathering information on three key road events: normal street and normal drive, speed bumps, circular yellow speed bumps, and three aggressive driving actions: sudden start, sudden stop, and sudden entry. The gathered data is processed and analyzed using a machine learning system designed for limited power and memory devices. The developed system resulted in 91.9% accuracy, 93.6% precision, and 92% recall. The achieved inference time on an Arduino Nano 33 BLE Sense with a 32-bit CPU running at 64 MHz is 34 ms and requires 2.6 kB peak RAM and 139.9 kB program flash memory, making it suitable for resource-constrained embedded systems.
More Related Content
Similar to A novel improved elephant herding optimization for path planning of a mobile robot
This paper proposes a new methodology to
optimize trajectory of the path for multi-robots using
Improved particle swarm optimization Algorithm (IPSO) in
clutter Environment. IPSO technique is incorporated into
the multi-robot system in a dynamic framework, which will
provide robust performance, self-deterministic cooperation,
and coping with an inhospitable environment
an improver particle optmizacion plan de negociosCarlos Iza
This document proposes an improved particle swarm optimization (IPSO) algorithm to optimize trajectory paths for multiple robots in a cluttered environment. IPSO incorporates independent decision making, coordination, and cooperation between robots to accomplish a shared goal. A path planning scheme using IPSO was developed to determine optimal successive robot positions from initial to target locations. Simulation and experiment results show IPSO outperforms particle swarm optimization and differential evolution algorithms with respect to average total trajectory deviation and average uncovered target distance.
Radio-frequency circular integrated inductors sizing optimization using bio-...IJECEIAES
In this article, a comparative study is accomplished between three of the most used swarm intelligence (SI) techniques; namely artificial bee colony (ABC), ant colony optimization (ACO), and particle swarm optimization (PSO) to carry out the optimal design of radio-frequency (RF) spiral inductors, the three algorithms are applied to the cost function of RF circular inductors for 180 nm beyond 2.50 GHz, the aim is to ensure optimal performance with less error in inductance, and a high-quality factor when compared to electromagnetic simulation. Simulation experiments are achieved and performances regarding convergence velocity, robustness, and computing time are checked. Also, this paper shows an impact study of technological parameters and geometric features on the inductance and the quality factor of the studied integrated inductor. The building method of constraints design with algorithms used has given good results and electromagnetic simulations are of good accuracy with an error of 2.31% and 4.15% on the quality factor and inductance respectively. The simulation shows that ACO provides more accuracy in circuit size and fewer errors than ABC and PSO, while PSO and ABC are better in terms of convergence velocity.
Path Planning of Mobile aco fuzzy-presentation.pptxssuserf6b378
The document describes a study on path planning for mobile robots using fuzzy logic and ant colony algorithms in complex dynamic environments. It proposes a new approach that combines these methods. The study models the robot's workspace as a grid with fixed and moving obstacles. It explains how the ant colony algorithm and fuzzy logic are applied to determine optimal paths between start and end points while avoiding obstacles. Simulation results show the combined approach finds shorter paths in less time compared to using each method individually.
The document proposes a hybrid algorithm combining genetic algorithm and cuckoo search optimization to solve job shop scheduling problems. It aims to minimize makespan (completion time of all jobs) by scheduling jobs on machines. The genetic algorithm is used to explore the search space but can get trapped in local optima. Cuckoo search optimization performs local search faster than genetic algorithm and helps avoid local optima. Experimental results on benchmark problems show the hybrid algorithm yields better solutions in terms of makespan and runtime compared to genetic algorithm and ant colony optimization algorithms.
A hybrid optimization algorithm based on genetic algorithm and ant colony opt...ijaia
In optimization problem, Genetic Algorithm (GA) and Ant Colony Optimization Algorithm (ACO) have
been known as good alternative techniques. GA is designed by adopting the natural evolution process,
while ACO is inspired by the foraging behaviour of ant species. This paper presents a hybrid GA-ACO for
Travelling Salesman Problem (TSP), called Genetic Ant Colony Optimization (GACO). In this method, GA
will observe and preserve the fittest ant in each cycle in every generation and only unvisited cities will be
assessed by ACO. From experimental result, GACO performance is significantly improved and its time
complexity is fairly equal compared to the GA and ACO.
PSO APPLIED TO DESIGN OPTIMAL PD CONTROL FOR A UNICYCLE MOBILE ROBOTJaresJournal
In this work, we propose a Particle Swarm Optimization (PSO) to design Proportional Derivative
controllers (PD) for the control of Unicycle Mobile Robot. To stabilize and drive the robot precisely with
the predefined trajectory, a decentralized control structure is adopted where four PD controllers are used.
Their parameters are given simultaneously by the proposed algorithm (PSO). The performance of the
system from its desired behavior is quantified by an objective function (SE). Simulation results are
presented to show the efficiency of the method. ).
The results are very conclusive and satisfactory in terms of stability and trajectory tracking of unicycle
mobile robot
Swarm flip-crossover algorithm: a new swarm-based metaheuristic enriched with...IJECEIAES
A new swarm-based metaheuristic that is also enriched with the crossover technique called swarm flip-crossover algorithm (SFCA) is introduced in this work. SFCA uses swarm intelligence as its primary technique and the crossover as its secondary one. It consists of three searches in every iteration. The swarm member walks toward the best member as the first search. The central point of the swarm becomes the target in the second search. There are two walks in the second search. The first walk is getting closer to the target, while the second is avoiding the target. The better result between these two walks becomes the candidate for the replacement. In the third search, the swarm member performs balance arithmetic crossover with the central point of the space or jumps to the opposite location within the area (flipping). The assessment is taken by confronting SFCA with five new metaheuristics: slime mold algorithm (SMA), golden search optimization (GSO), osprey optimization algorithm (OOA), coati optimization algorithm (COA), and walrus optimization algorithm (WaOA) in handling the set of 23 functions. The result shows that SFCA performs consecutively better than SMA, GSO, OOA, COA, and WaOA in 20, 23, 17, 17, and 17 functions.
Optimizing Mobile Robot Path Planning and Navigation by Use of Differential E...IOSR Journals
Abstract: Path planning and navigation is essential for an autonomous robot which can move avoiding the
static obstacles in a real world and to reach the specific target. Optimizing path for the robot movement gives
the optimal distance from the source to the target and save precious time as well. With the development of
various evolutionary algorithms, the differential evolution is taking the pace in comparison to genetic algorithm.
Differential evolution has been deployed quite successfully for solving global optimization problem. Differential
evolution is a very simple yet powerful metaheuristics type problem solving method. In this paper we are
proposing a Differential Evolution based path navigation algorithm for mobile path navigation and analyze its
efficiency with other developed approaches. The proposed algorithm optimized the robot path and navigates the
robot to the proper target efficiently.
Kinematics modeling of six degrees of freedom humanoid robot arm using impro...IJECEIAES
The robotic arm has functioned as an arm in the humanoid robot and is generally used to perform grasping tasks. Accordingly, kinematics modeling both forward and inverse kinematics is required to calculate the end-effector position in the cartesian space before performing grasping activities. This research presents the kinematics modeling of six degrees of freedom (6-DOF) robotic arm of the T-FLoW humanoid robot for the grasping mechanism of visual grasping systems on the robot operating system (ROS) platform and CoppeliaSim. Kinematic singularity is a common problem in the inverse kinematics model of robots, but. However, other problems are mechanical limitations and computational time. The work uses the homogeneous transformation matrix (HTM) based on the Euler system of the robot for the forward kinematics and demonstrates the capability of an improved damped least squares (I-DLS) method for the inverse kinematics. The I-DLS method was obtained by improving the original DLS method with the joint limits and clamping techniques. The I-DLS performs better than the original DLS during the experiments yet increases the calculation iteration by 10.95%, with a maximum error position between the endeffector and target positions in path planning of 0.1 cm.
Recent research in finding the optimal path by ant colony optimizationjournalBEEI
The computation of the optimal path is one of the critical problems in graph theory. It has been utilized in various practical ranges of real world applications including image processing, file carving and classification problem. Numerous techniques have been proposed in finding optimal path solutions including using ant colony optimization (ACO). This is a nature-inspired metaheuristic algorithm, which is inspired by the foraging behavior of ants in nature. Thus, this paper study the improvement made by many researchers on ACO in finding optimal path solution. Finally, this paper also identifies the recent trends and explores potential future research directions in file carving.
Reliable and accurate estimation of software has always been a matter of concern for industry and
academia. Numerous estimation models have been proposed by researchers, but no model is suitable for all
types of datasets and environments. Since the motive of estimation model is to minimize the gap between
actual and estimated effort, the effort estimation process can be viewed as an optimization problem to tune
the parameters. In this paper, evolutionary computing techniques, including, Bee colony optimization,
Particle swarm optimization and Ant colony optimization have been employed to tune the parameters of
COCOMO Model. The performance of these techniques has been analysed by established performance
measure. The results obtained have been validated by using data of Interactive voice response (IVR)
projects. Evolutionary techniques have been found to be more accurate than existing estimation models.
EVOLUTIONARY COMPUTING TECHNIQUES FOR SOFTWARE EFFORT ESTIMATIONijcsit
Reliable and accurate estimation of software has always been a matter of concern for industry and academia. Numerous estimation models have been proposed by researchers, but no model is suitable for all types of datasets and environments. Since the motive of estimation model is to minimize the gap between actual and estimated effort, the effort estimation process can be viewed as an optimization problem to tune
the parameters. In this paper, evolutionary computing techniques, including, Bee colony optimization, Particle swarm optimization and Ant colony optimization have been employed to tune the parameters of COCOMO Model. The performance of these techniques has been analysed by established performance measure. The results obtained have been validated by using data of Interactive voice response (IVR)
projects. Evolutionary techniques have been found to be more accurate than existing estimation models.
The document summarizes research using evolutionary computing techniques like particle swarm optimization, ant colony optimization, and bee colony optimization to improve software effort estimation compared to the COCOMO model. The techniques are applied to tune the parameters of the COCOMO model. The models are validated using a dataset of 48 interactive voice response projects, and the bee colony optimization technique produces the lowest mean magnitude of relative error (MMRE) of 0.11 and root mean squared error (RMSE) of 7.85, outperforming the other techniques and COCOMO model. Evolutionary computing techniques are found to provide more accurate effort estimates than existing estimation models.
A CRITICAL STUDY ON TEN NONTRADITIONAL OPTIMIZATION METHODS IN SOLVING ENGINE...IAEME Publication
The objective functions used in Engineering Optimization are complex in nature with
many variables and constraints. Conventional optimization tools sometimes fail to give
global optima point. Very popular methods like Genetic Algorithm, Pattern Search,
Simulated Annealing, and Gradient Search are useful methods to find global optima
related to engineering problems. This paper attempts to review new nontraditional
optimization algorithms which are used to solve such complicated engineering problems
to obtain global optimum solutions
Optimized Robot Path Planning Using Parallel Genetic Algorithm Based on Visib...IJERA Editor
An analysis is made for optimized path planning for mobile robot by using parallel genetic algorithm. The
parallel genetic algorithm (PGA) is applied on the visible midpoint approach to find shortest path for mobile
robot. The hybrid ofthese two algorithms provides a better optimized solution for smooth and shortest path for
mobile robot. In this problem, the visible midpoint approach is used to make the effectiveness for avoiding
local minima. It gives the optimum paths which are always consisting on free trajectories. But the
proposedhybrid parallel genetic algorithm converges very fast to obtain the shortest route from source to
destination due to the sharing of population. The total population is partitioned into a number subgroups to
perform the parallel GA. The master thread is the center of information exchange and making selection with
fitness evaluation.The cell to cell crossover makes the algorithm significantly good. The problem converges
quickly with in a less number of iteration.
The assembly process is one of the most time consuming and expensive manufacturing activities.
Determination of a correct and stable assembly sequence is necessary for automated assembly system. The objective of
the present work is to generate feasible, stable and optimal assembly sequence with minimum assembly time.
Automated assembly has the advantage of greater process capability and scalability. It is faster, more efficient and
precise than any conventional process. Ant Colony Optimization (ACO) method is used for generation of stable
assembly sequence. This method has been applied to a Planetary Gearbox.
Comparative and comprehensive study of linear antenna arrays’ synthesisIJECEIAES
In this paper, a comparative and comprehensive study of synthesizing linear antenna array (LAA) designs, is presented. Different desired objectives are considered in this paper; reducing the maximum sidelobe radiation pattern (i.e., pencil-beam pattern), controlling the first null beamwidth (FNBW), and imposing nulls at specific angles in some designs, which are accomplished by optimizing different array parameters (feed current amplitudes, feed current phase, and array elements positions). Three different optimization algorithms are proposed in order to achieve the wanted goals; grasshopper optimization algorithms (GOA), ant lion optimization (ALO), and a new hybrid optimization algorithm based on GOA and ALO. The obtained results show the effectiveness and robustness of the proposed algorithms to achieve the wanted targets. In most experiments, the proposed algorithms outperform other well-known optimization methods, such as; Biogeography based optimization (BBO), particle swarm optimization (PSO), firefly algorithm (FA), cuckoo search (CS) algorithm, genetic algorithm (GA), Taguchi method, self-adaptive differential evolution (SADE), modified spider monkey optimization (MSMO), symbiotic organisms search (SOS), enhanced firefly algorithm (EFA), bat flower pollination (BFP) and tabu search (TS) algorithm.
Iaetsd modified artificial potential fields algorithm for mobile robot path ...Iaetsd Iaetsd
This document presents a modified artificial potential fields algorithm for mobile robot path planning in unknown and dynamic environments. The algorithm uses artificial potential fields to iteratively find optimal points to form a collision-free path from the start to destination. For static obstacles, potential values are used to identify clusters of points around the start and goal, and find a connecting midpoint. This process is repeated iteratively. For dynamic obstacles, Markov models are used to analyze obstacle behavior from sensor data and predict collision points. The robot's path is replanned as needed to avoid collisions based on feedback from sensors and odometry. Simulation results show the algorithm can efficiently plan paths in unknown environments and avoid both static and dynamic obstacles.
Cuckoo algorithm with great deluge local-search for feature selection problemsIJECEIAES
Feature selection problem is concerned with searching in a dataset for a set of features aiming to reduce the training time and enhance the accuracy of a classification method. Therefore, feature selection algorithms are proposed to choose important features from large and complex datasets. The cuckoo search (CS) algorithm is a type of natural-inspired optimization algorithms and is widely implemented to find the optimum solution for a specified problem. In this work, the cuckoo search algorithm is hybridized with a local search algorithm to find a satisfactory solution for the problem of feature selection. The great deluge (GD) algorithm is an iterative search procedure, that can accept some worse moves to find better solutions for the problem, also to increase the exploitation ability of CS. The comparison is also provided to examine the performance of the proposed method and the original CS algorithm. As result, using the UCI datasets the proposed algorithm outperforms the original algorithm and produces comparable results compared with some of the results from the literature.
Similar to A novel improved elephant herding optimization for path planning of a mobile robot (20)
Redefining brain tumor segmentation: a cutting-edge convolutional neural netw...IJECEIAES
Medical image analysis has witnessed significant advancements with deep learning techniques. In the domain of brain tumor segmentation, the ability to
precisely delineate tumor boundaries from magnetic resonance imaging (MRI)
scans holds profound implications for diagnosis. This study presents an ensemble convolutional neural network (CNN) with transfer learning, integrating
the state-of-the-art Deeplabv3+ architecture with the ResNet18 backbone. The
model is rigorously trained and evaluated, exhibiting remarkable performance
metrics, including an impressive global accuracy of 99.286%, a high-class accuracy of 82.191%, a mean intersection over union (IoU) of 79.900%, a weighted
IoU of 98.620%, and a Boundary F1 (BF) score of 83.303%. Notably, a detailed comparative analysis with existing methods showcases the superiority of
our proposed model. These findings underscore the model’s competence in precise brain tumor localization, underscoring its potential to revolutionize medical
image analysis and enhance healthcare outcomes. This research paves the way
for future exploration and optimization of advanced CNN models in medical
imaging, emphasizing addressing false positives and resource efficiency.
Embedded machine learning-based road conditions and driving behavior monitoringIJECEIAES
Car accident rates have increased in recent years, resulting in losses in human lives, properties, and other financial costs. An embedded machine learning-based system is developed to address this critical issue. The system can monitor road conditions, detect driving patterns, and identify aggressive driving behaviors. The system is based on neural networks trained on a comprehensive dataset of driving events, driving styles, and road conditions. The system effectively detects potential risks and helps mitigate the frequency and impact of accidents. The primary goal is to ensure the safety of drivers and vehicles. Collecting data involved gathering information on three key road events: normal street and normal drive, speed bumps, circular yellow speed bumps, and three aggressive driving actions: sudden start, sudden stop, and sudden entry. The gathered data is processed and analyzed using a machine learning system designed for limited power and memory devices. The developed system resulted in 91.9% accuracy, 93.6% precision, and 92% recall. The achieved inference time on an Arduino Nano 33 BLE Sense with a 32-bit CPU running at 64 MHz is 34 ms and requires 2.6 kB peak RAM and 139.9 kB program flash memory, making it suitable for resource-constrained embedded systems.
Advanced control scheme of doubly fed induction generator for wind turbine us...IJECEIAES
This paper describes a speed control device for generating electrical energy on an electricity network based on the doubly fed induction generator (DFIG) used for wind power conversion systems. At first, a double-fed induction generator model was constructed. A control law is formulated to govern the flow of energy between the stator of a DFIG and the energy network using three types of controllers: proportional integral (PI), sliding mode controller (SMC) and second order sliding mode controller (SOSMC). Their different results in terms of power reference tracking, reaction to unexpected speed fluctuations, sensitivity to perturbations, and resilience against machine parameter alterations are compared. MATLAB/Simulink was used to conduct the simulations for the preceding study. Multiple simulations have shown very satisfying results, and the investigations demonstrate the efficacy and power-enhancing capabilities of the suggested control system.
Neural network optimizer of proportional-integral-differential controller par...IJECEIAES
Wide application of proportional-integral-differential (PID)-regulator in industry requires constant improvement of methods of its parameters adjustment. The paper deals with the issues of optimization of PID-regulator parameters with the use of neural network technology methods. A methodology for choosing the architecture (structure) of neural network optimizer is proposed, which consists in determining the number of layers, the number of neurons in each layer, as well as the form and type of activation function. Algorithms of neural network training based on the application of the method of minimizing the mismatch between the regulated value and the target value are developed. The method of back propagation of gradients is proposed to select the optimal training rate of neurons of the neural network. The neural network optimizer, which is a superstructure of the linear PID controller, allows increasing the regulation accuracy from 0.23 to 0.09, thus reducing the power consumption from 65% to 53%. The results of the conducted experiments allow us to conclude that the created neural superstructure may well become a prototype of an automatic voltage regulator (AVR)-type industrial controller for tuning the parameters of the PID controller.
An improved modulation technique suitable for a three level flying capacitor ...IJECEIAES
This research paper introduces an innovative modulation technique for controlling a 3-level flying capacitor multilevel inverter (FCMLI), aiming to streamline the modulation process in contrast to conventional methods. The proposed
simplified modulation technique paves the way for more straightforward and
efficient control of multilevel inverters, enabling their widespread adoption and
integration into modern power electronic systems. Through the amalgamation of
sinusoidal pulse width modulation (SPWM) with a high-frequency square wave
pulse, this controlling technique attains energy equilibrium across the coupling
capacitor. The modulation scheme incorporates a simplified switching pattern
and a decreased count of voltage references, thereby simplifying the control
algorithm.
A review on features and methods of potential fishing zoneIJECEIAES
This review focuses on the importance of identifying potential fishing zones in seawater for sustainable fishing practices. It explores features like sea surface temperature (SST) and sea surface height (SSH), along with classification methods such as classifiers. The features like SST, SSH, and different classifiers used to classify the data, have been figured out in this review study. This study underscores the importance of examining potential fishing zones using advanced analytical techniques. It thoroughly explores the methodologies employed by researchers, covering both past and current approaches. The examination centers on data characteristics and the application of classification algorithms for classification of potential fishing zones. Furthermore, the prediction of potential fishing zones relies significantly on the effectiveness of classification algorithms. Previous research has assessed the performance of models like support vector machines, naïve Bayes, and artificial neural networks (ANN). In the previous result, the results of support vector machine (SVM) were 97.6% more accurate than naive Bayes's 94.2% to classify test data for fisheries classification. By considering the recent works in this area, several recommendations for future works are presented to further improve the performance of the potential fishing zone models, which is important to the fisheries community.
Electrical signal interference minimization using appropriate core material f...IJECEIAES
As demand for smaller, quicker, and more powerful devices rises, Moore's law is strictly followed. The industry has worked hard to make little devices that boost productivity. The goal is to optimize device density. Scientists are reducing connection delays to improve circuit performance. This helped them understand three-dimensional integrated circuit (3D IC) concepts, which stack active devices and create vertical connections to diminish latency and lower interconnects. Electrical involvement is a big worry with 3D integrates circuits. Researchers have developed and tested through silicon via (TSV) and substrates to decrease electrical wave involvement. This study illustrates a novel noise coupling reduction method using several electrical involvement models. A 22% drop in electrical involvement from wave-carrying to victim TSVs introduces this new paradigm and improves system performance even at higher THz frequencies.
Electric vehicle and photovoltaic advanced roles in enhancing the financial p...IJECEIAES
Climate change's impact on the planet forced the United Nations and governments to promote green energies and electric transportation. The deployments of photovoltaic (PV) and electric vehicle (EV) systems gained stronger momentum due to their numerous advantages over fossil fuel types. The advantages go beyond sustainability to reach financial support and stability. The work in this paper introduces the hybrid system between PV and EV to support industrial and commercial plants. This paper covers the theoretical framework of the proposed hybrid system including the required equation to complete the cost analysis when PV and EV are present. In addition, the proposed design diagram which sets the priorities and requirements of the system is presented. The proposed approach allows setup to advance their power stability, especially during power outages. The presented information supports researchers and plant owners to complete the necessary analysis while promoting the deployment of clean energy. The result of a case study that represents a dairy milk farmer supports the theoretical works and highlights its advanced benefits to existing plants. The short return on investment of the proposed approach supports the paper's novelty approach for the sustainable electrical system. In addition, the proposed system allows for an isolated power setup without the need for a transmission line which enhances the safety of the electrical network
Bibliometric analysis highlighting the role of women in addressing climate ch...IJECEIAES
Fossil fuel consumption increased quickly, contributing to climate change
that is evident in unusual flooding and draughts, and global warming. Over
the past ten years, women's involvement in society has grown dramatically,
and they succeeded in playing a noticeable role in reducing climate change.
A bibliometric analysis of data from the last ten years has been carried out to
examine the role of women in addressing the climate change. The analysis's
findings discussed the relevant to the sustainable development goals (SDGs),
particularly SDG 7 and SDG 13. The results considered contributions made
by women in the various sectors while taking geographic dispersion into
account. The bibliometric analysis delves into topics including women's
leadership in environmental groups, their involvement in policymaking, their
contributions to sustainable development projects, and the influence of
gender diversity on attempts to mitigate climate change. This study's results
highlight how women have influenced policies and actions related to climate
change, point out areas of research deficiency and recommendations on how
to increase role of the women in addressing the climate change and
achieving sustainability. To achieve more successful results, this initiative
aims to highlight the significance of gender equality and encourage
inclusivity in climate change decision-making processes.
Voltage and frequency control of microgrid in presence of micro-turbine inter...IJECEIAES
The active and reactive load changes have a significant impact on voltage
and frequency. In this paper, in order to stabilize the microgrid (MG) against
load variations in islanding mode, the active and reactive power of all
distributed generators (DGs), including energy storage (battery), diesel
generator, and micro-turbine, are controlled. The micro-turbine generator is
connected to MG through a three-phase to three-phase matrix converter, and
the droop control method is applied for controlling the voltage and
frequency of MG. In addition, a method is introduced for voltage and
frequency control of micro-turbines in the transition state from gridconnected mode to islanding mode. A novel switching strategy of the matrix
converter is used for converting the high-frequency output voltage of the
micro-turbine to the grid-side frequency of the utility system. Moreover,
using the switching strategy, the low-order harmonics in the output current
and voltage are not produced, and consequently, the size of the output filter
would be reduced. In fact, the suggested control strategy is load-independent
and has no frequency conversion restrictions. The proposed approach for
voltage and frequency regulation demonstrates exceptional performance and
favorable response across various load alteration scenarios. The suggested
strategy is examined in several scenarios in the MG test systems, and the
simulation results are addressed.
Enhancing battery system identification: nonlinear autoregressive modeling fo...IJECEIAES
Precisely characterizing Li-ion batteries is essential for optimizing their
performance, enhancing safety, and prolonging their lifespan across various
applications, such as electric vehicles and renewable energy systems. This
article introduces an innovative nonlinear methodology for system
identification of a Li-ion battery, employing a nonlinear autoregressive with
exogenous inputs (NARX) model. The proposed approach integrates the
benefits of nonlinear modeling with the adaptability of the NARX structure,
facilitating a more comprehensive representation of the intricate
electrochemical processes within the battery. Experimental data collected
from a Li-ion battery operating under diverse scenarios are employed to
validate the effectiveness of the proposed methodology. The identified
NARX model exhibits superior accuracy in predicting the battery's behavior
compared to traditional linear models. This study underscores the
importance of accounting for nonlinearities in battery modeling, providing
insights into the intricate relationships between state-of-charge, voltage, and
current under dynamic conditions.
Smart grid deployment: from a bibliometric analysis to a surveyIJECEIAES
Smart grids are one of the last decades' innovations in electrical energy.
They bring relevant advantages compared to the traditional grid and
significant interest from the research community. Assessing the field's
evolution is essential to propose guidelines for facing new and future smart
grid challenges. In addition, knowing the main technologies involved in the
deployment of smart grids (SGs) is important to highlight possible
shortcomings that can be mitigated by developing new tools. This paper
contributes to the research trends mentioned above by focusing on two
objectives. First, a bibliometric analysis is presented to give an overview of
the current research level about smart grid deployment. Second, a survey of
the main technological approaches used for smart grid implementation and
their contributions are highlighted. To that effect, we searched the Web of
Science (WoS), and the Scopus databases. We obtained 5,663 documents
from WoS and 7,215 from Scopus on smart grid implementation or
deployment. With the extraction limitation in the Scopus database, 5,872 of
the 7,215 documents were extracted using a multi-step process. These two
datasets have been analyzed using a bibliometric tool called bibliometrix.
The main outputs are presented with some recommendations for future
research.
Use of analytical hierarchy process for selecting and prioritizing islanding ...IJECEIAES
One of the problems that are associated to power systems is islanding
condition, which must be rapidly and properly detected to prevent any
negative consequences on the system's protection, stability, and security.
This paper offers a thorough overview of several islanding detection
strategies, which are divided into two categories: classic approaches,
including local and remote approaches, and modern techniques, including
techniques based on signal processing and computational intelligence.
Additionally, each approach is compared and assessed based on several
factors, including implementation costs, non-detected zones, declining
power quality, and response times using the analytical hierarchy process
(AHP). The multi-criteria decision-making analysis shows that the overall
weight of passive methods (24.7%), active methods (7.8%), hybrid methods
(5.6%), remote methods (14.5%), signal processing-based methods (26.6%),
and computational intelligent-based methods (20.8%) based on the
comparison of all criteria together. Thus, it can be seen from the total weight
that hybrid approaches are the least suitable to be chosen, while signal
processing-based methods are the most appropriate islanding detection
method to be selected and implemented in power system with respect to the
aforementioned factors. Using Expert Choice software, the proposed
hierarchy model is studied and examined.
Enhancing of single-stage grid-connected photovoltaic system using fuzzy logi...IJECEIAES
The power generated by photovoltaic (PV) systems is influenced by
environmental factors. This variability hampers the control and utilization of
solar cells' peak output. In this study, a single-stage grid-connected PV
system is designed to enhance power quality. Our approach employs fuzzy
logic in the direct power control (DPC) of a three-phase voltage source
inverter (VSI), enabling seamless integration of the PV connected to the
grid. Additionally, a fuzzy logic-based maximum power point tracking
(MPPT) controller is adopted, which outperforms traditional methods like
incremental conductance (INC) in enhancing solar cell efficiency and
minimizing the response time. Moreover, the inverter's real-time active and
reactive power is directly managed to achieve a unity power factor (UPF).
The system's performance is assessed through MATLAB/Simulink
implementation, showing marked improvement over conventional methods,
particularly in steady-state and varying weather conditions. For solar
irradiances of 500 and 1,000 W/m2
, the results show that the proposed
method reduces the total harmonic distortion (THD) of the injected current
to the grid by approximately 46% and 38% compared to conventional
methods, respectively. Furthermore, we compare the simulation results with
IEEE standards to evaluate the system's grid compatibility.
Enhancing photovoltaic system maximum power point tracking with fuzzy logic-b...IJECEIAES
Photovoltaic systems have emerged as a promising energy resource that
caters to the future needs of society, owing to their renewable, inexhaustible,
and cost-free nature. The power output of these systems relies on solar cell
radiation and temperature. In order to mitigate the dependence on
atmospheric conditions and enhance power tracking, a conventional
approach has been improved by integrating various methods. To optimize
the generation of electricity from solar systems, the maximum power point
tracking (MPPT) technique is employed. To overcome limitations such as
steady-state voltage oscillations and improve transient response, two
traditional MPPT methods, namely fuzzy logic controller (FLC) and perturb
and observe (P&O), have been modified. This research paper aims to
simulate and validate the step size of the proposed modified P&O and FLC
techniques within the MPPT algorithm using MATLAB/Simulink for
efficient power tracking in photovoltaic systems.
Adaptive synchronous sliding control for a robot manipulator based on neural ...IJECEIAES
Robot manipulators have become important equipment in production lines, medical fields, and transportation. Improving the quality of trajectory tracking for
robot hands is always an attractive topic in the research community. This is a
challenging problem because robot manipulators are complex nonlinear systems
and are often subject to fluctuations in loads and external disturbances. This
article proposes an adaptive synchronous sliding control scheme to improve trajectory tracking performance for a robot manipulator. The proposed controller
ensures that the positions of the joints track the desired trajectory, synchronize
the errors, and significantly reduces chattering. First, the synchronous tracking
errors and synchronous sliding surfaces are presented. Second, the synchronous
tracking error dynamics are determined. Third, a robust adaptive control law is
designed,the unknown components of the model are estimated online by the neural network, and the parameters of the switching elements are selected by fuzzy
logic. The built algorithm ensures that the tracking and approximation errors
are ultimately uniformly bounded (UUB). Finally, the effectiveness of the constructed algorithm is demonstrated through simulation and experimental results.
Simulation and experimental results show that the proposed controller is effective with small synchronous tracking errors, and the chattering phenomenon is
significantly reduced.
Remote field-programmable gate array laboratory for signal acquisition and de...IJECEIAES
A remote laboratory utilizing field-programmable gate array (FPGA) technologies enhances students’ learning experience anywhere and anytime in embedded system design. Existing remote laboratories prioritize hardware access and visual feedback for observing board behavior after programming, neglecting comprehensive debugging tools to resolve errors that require internal signal acquisition. This paper proposes a novel remote embeddedsystem design approach targeting FPGA technologies that are fully interactive via a web-based platform. Our solution provides FPGA board access and debugging capabilities beyond the visual feedback provided by existing remote laboratories. We implemented a lab module that allows users to seamlessly incorporate into their FPGA design. The module minimizes hardware resource utilization while enabling the acquisition of a large number of data samples from the signal during the experiments by adaptively compressing the signal prior to data transmission. The results demonstrate an average compression ratio of 2.90 across three benchmark signals, indicating efficient signal acquisition and effective debugging and analysis. This method allows users to acquire more data samples than conventional methods. The proposed lab allows students to remotely test and debug their designs, bridging the gap between theory and practice in embedded system design.
Detecting and resolving feature envy through automated machine learning and m...IJECEIAES
Efficiently identifying and resolving code smells enhances software project quality. This paper presents a novel solution, utilizing automated machine learning (AutoML) techniques, to detect code smells and apply move method refactoring. By evaluating code metrics before and after refactoring, we assessed its impact on coupling, complexity, and cohesion. Key contributions of this research include a unique dataset for code smell classification and the development of models using AutoGluon for optimal performance. Furthermore, the study identifies the top 20 influential features in classifying feature envy, a well-known code smell, stemming from excessive reliance on external classes. We also explored how move method refactoring addresses feature envy, revealing reduced coupling and complexity, and improved cohesion, ultimately enhancing code quality. In summary, this research offers an empirical, data-driven approach, integrating AutoML and move method refactoring to optimize software project quality. Insights gained shed light on the benefits of refactoring on code quality and the significance of specific features in detecting feature envy. Future research can expand to explore additional refactoring techniques and a broader range of code metrics, advancing software engineering practices and standards.
Smart monitoring technique for solar cell systems using internet of things ba...IJECEIAES
Rapidly and remotely monitoring and receiving the solar cell systems status parameters, solar irradiance, temperature, and humidity, are critical issues in enhancement their efficiency. Hence, in the present article an improved smart prototype of internet of things (IoT) technique based on embedded system through NodeMCU ESP8266 (ESP-12E) was carried out experimentally. Three different regions at Egypt; Luxor, Cairo, and El-Beheira cities were chosen to study their solar irradiance profile, temperature, and humidity by the proposed IoT system. The monitoring data of solar irradiance, temperature, and humidity were live visualized directly by Ubidots through hypertext transfer protocol (HTTP) protocol. The measured solar power radiation in Luxor, Cairo, and El-Beheira ranged between 216-1000, 245-958, and 187-692 W/m 2 respectively during the solar day. The accuracy and rapidity of obtaining monitoring results using the proposed IoT system made it a strong candidate for application in monitoring solar cell systems. On the other hand, the obtained solar power radiation results of the three considered regions strongly candidate Luxor and Cairo as suitable places to build up a solar cells system station rather than El-Beheira.
An efficient security framework for intrusion detection and prevention in int...IJECEIAES
Over the past few years, the internet of things (IoT) has advanced to connect billions of smart devices to improve quality of life. However, anomalies or malicious intrusions pose several security loopholes, leading to performance degradation and threat to data security in IoT operations. Thereby, IoT security systems must keep an eye on and restrict unwanted events from occurring in the IoT network. Recently, various technical solutions based on machine learning (ML) models have been derived towards identifying and restricting unwanted events in IoT. However, most ML-based approaches are prone to miss-classification due to inappropriate feature selection. Additionally, most ML approaches applied to intrusion detection and prevention consider supervised learning, which requires a large amount of labeled data to be trained. Consequently, such complex datasets are impossible to source in a large network like IoT. To address this problem, this proposed study introduces an efficient learning mechanism to strengthen the IoT security aspects. The proposed algorithm incorporates supervised and unsupervised approaches to improve the learning models for intrusion detection and mitigation. Compared with the related works, the experimental outcome shows that the model performs well in a benchmark dataset. It accomplishes an improved detection accuracy of approximately 99.21%.
KuberTENes Birthday Bash Guadalajara - K8sGPT first impressionsVictor Morales
K8sGPT is a tool that analyzes and diagnoses Kubernetes clusters. This presentation was used to share the requirements and dependencies to deploy K8sGPT in a local environment.
Using recycled concrete aggregates (RCA) for pavements is crucial to achieving sustainability. Implementing RCA for new pavement can minimize carbon footprint, conserve natural resources, reduce harmful emissions, and lower life cycle costs. Compared to natural aggregate (NA), RCA pavement has fewer comprehensive studies and sustainability assessments.
Comparative analysis between traditional aquaponics and reconstructed aquapon...bijceesjournal
The aquaponic system of planting is a method that does not require soil usage. It is a method that only needs water, fish, lava rocks (a substitute for soil), and plants. Aquaponic systems are sustainable and environmentally friendly. Its use not only helps to plant in small spaces but also helps reduce artificial chemical use and minimizes excess water use, as aquaponics consumes 90% less water than soil-based gardening. The study applied a descriptive and experimental design to assess and compare conventional and reconstructed aquaponic methods for reproducing tomatoes. The researchers created an observation checklist to determine the significant factors of the study. The study aims to determine the significant difference between traditional aquaponics and reconstructed aquaponics systems propagating tomatoes in terms of height, weight, girth, and number of fruits. The reconstructed aquaponics system’s higher growth yield results in a much more nourished crop than the traditional aquaponics system. It is superior in its number of fruits, height, weight, and girth measurement. Moreover, the reconstructed aquaponics system is proven to eliminate all the hindrances present in the traditional aquaponics system, which are overcrowding of fish, algae growth, pest problems, contaminated water, and dead fish.
Harnessing WebAssembly for Real-time Stateless Streaming PipelinesChristina Lin
Traditionally, dealing with real-time data pipelines has involved significant overhead, even for straightforward tasks like data transformation or masking. However, in this talk, we’ll venture into the dynamic realm of WebAssembly (WASM) and discover how it can revolutionize the creation of stateless streaming pipelines within a Kafka (Redpanda) broker. These pipelines are adept at managing low-latency, high-data-volume scenarios.
Batteries -Introduction – Types of Batteries – discharging and charging of battery - characteristics of battery –battery rating- various tests on battery- – Primary battery: silver button cell- Secondary battery :Ni-Cd battery-modern battery: lithium ion battery-maintenance of batteries-choices of batteries for electric vehicle applications.
Fuel Cells: Introduction- importance and classification of fuel cells - description, principle, components, applications of fuel cells: H2-O2 fuel cell, alkaline fuel cell, molten carbonate fuel cell and direct methanol fuel cells.
TIME DIVISION MULTIPLEXING TECHNIQUE FOR COMMUNICATION SYSTEMHODECEDSIET
Time Division Multiplexing (TDM) is a method of transmitting multiple signals over a single communication channel by dividing the signal into many segments, each having a very short duration of time. These time slots are then allocated to different data streams, allowing multiple signals to share the same transmission medium efficiently. TDM is widely used in telecommunications and data communication systems.
### How TDM Works
1. **Time Slots Allocation**: The core principle of TDM is to assign distinct time slots to each signal. During each time slot, the respective signal is transmitted, and then the process repeats cyclically. For example, if there are four signals to be transmitted, the TDM cycle will divide time into four slots, each assigned to one signal.
2. **Synchronization**: Synchronization is crucial in TDM systems to ensure that the signals are correctly aligned with their respective time slots. Both the transmitter and receiver must be synchronized to avoid any overlap or loss of data. This synchronization is typically maintained by a clock signal that ensures time slots are accurately aligned.
3. **Frame Structure**: TDM data is organized into frames, where each frame consists of a set of time slots. Each frame is repeated at regular intervals, ensuring continuous transmission of data streams. The frame structure helps in managing the data streams and maintaining the synchronization between the transmitter and receiver.
4. **Multiplexer and Demultiplexer**: At the transmitting end, a multiplexer combines multiple input signals into a single composite signal by assigning each signal to a specific time slot. At the receiving end, a demultiplexer separates the composite signal back into individual signals based on their respective time slots.
### Types of TDM
1. **Synchronous TDM**: In synchronous TDM, time slots are pre-assigned to each signal, regardless of whether the signal has data to transmit or not. This can lead to inefficiencies if some time slots remain empty due to the absence of data.
2. **Asynchronous TDM (or Statistical TDM)**: Asynchronous TDM addresses the inefficiencies of synchronous TDM by allocating time slots dynamically based on the presence of data. Time slots are assigned only when there is data to transmit, which optimizes the use of the communication channel.
### Applications of TDM
- **Telecommunications**: TDM is extensively used in telecommunication systems, such as in T1 and E1 lines, where multiple telephone calls are transmitted over a single line by assigning each call to a specific time slot.
- **Digital Audio and Video Broadcasting**: TDM is used in broadcasting systems to transmit multiple audio or video streams over a single channel, ensuring efficient use of bandwidth.
- **Computer Networks**: TDM is used in network protocols and systems to manage the transmission of data from multiple sources over a single network medium.
### Advantages of TDM
- **Efficient Use of Bandwidth**: TDM all
The Python for beginners. This is an advance computer language.
A novel improved elephant herding optimization for path planning of a mobile robot
1. International Journal of Electrical and Computer Engineering (IJECE)
Vol. 14, No. 1, February 2024, pp. 206~217
ISSN: 2088-8708, DOI: 10.11591/ijece.v14i1.pp206-217 206
Journal homepage: http://ijece.iaescore.com
A novel improved elephant herding optimization for path
planning of a mobile robot
Ahmed Oultiligh1
, Hassan Ayad1
, Abdeljalil El Kari1
, Mostafa Mjahed2
, Nada El Gmili3
1
Laboratory of Electrical Systems, Energy Efficiency, and Telecommunications (LSEET), Cadi Ayyad University, Marrakesh, Morocco
2
Department of Mathematics and Systems, Royal School of Aeronautics, Marrakesh, Morocco.
3
Laboratory of Materials, Energy and Control Systems (LMECS), Hassan II University, Casablanca, Morocco
Article Info ABSTRACT
Article history:
Received Apr 27, 2023
Revised Apr 30, 2023
Accepted Jun 23, 2023
Swarm intelligence algorithms have been in recent years one of the most
used tools for planning the trajectory of a mobile robot. Researchers are
applying those algorithms to find the optimal path, which reduces the time
required to perform a task by the mobile robot. In this paper, we propose a
new method based on the grey wolf optimizer algorithm (GWO) and the
improved elephant herding optimization algorithm (IEHO) for planning the
optimal trajectory of a mobile robot. The proposed solution consists of
developing an IEHO algorithm by improving the basic EHO algorithm and
then hybridizing it with the GWO algorithm to take advantage of the
exploration and exploitation capabilities of both algorithms. The comparison
of the IEHO-GWO hybrid proposed in this work with the GWO, EHO, and
cuckoo-search (CS) algorithms via simulation shows its effectiveness in
finding an optimal trajectory by avoiding obstacles around the mobile robot.
Keywords:
Cuckoo search
Elephant herding optimization
Grey wolf optimizer
Improved elephant herding
Mobile robot
Optimization
Path planning
This is an open access article under the CC BY-SA license.
Corresponding Author:
Ahmed Oultiligh
Laboratory of Electrical Systems, Energy Efficiency, and Telecommunications (LSEET), Cadi Ayyad
University
Marrakesh, Morocco
Email: a.oultiligh@gmail.com
1. INTRODUCTION
In recent years, research on mobile robots has seen a remarkable evolution because of their large
application. In addition to designing new robots for different tasks, researchers are working on improving and
proposing new methods and intelligent control systems for the autonomous navigation of mobile robots,
whether in industry, agriculture, and autonomous vehicles. Indeed, in most cases, the environment of mobile
robots is either totally unknown or partially unknown, and either static or dynamic, which makes the
autonomous navigation of mobile robots a complicated task. One major task for mobile robot navigation is
path planning, which has become one of the indispensable research axes in the development of mobile robots.
Trajectory planning is one of the critical tasks when developing a driving system for a mobile robot.
The goal here is to plan a safe trajectory, allowing the robot to avoid obstacles in its environment to reach the
desired goal. Numerous research works in the last decades address this issue by developing methods based on
artificial intelligence. The algorithms and methods proposed by researchers can be classified into three
categories. Algorithms based on heuristics, including fuzzy logic [1], [2], neural networks [2], the neuro-
fuzzy technique [3], [4], probabilistic neuro-fuzzy [3], and the Q-learning [5], [6], which is known in the
planning of trajectories of mobile robots, by its capacity of self-learning without requiring an a priori model
of the environment. Nature-inspired algorithms, used to optimize the length of the path, including, genetic
algorithm (GA) [7], [8], particle swarm optimization (PSO) [9]–[11], ant colony optimization (ACO) [12],
2. Int J Elec & Comp Eng ISSN: 2088-8708
A novel improved elephant herding optimization for path planning of a mobile robot (Ahmed Oultiligh)
207
[13], the grey wolf optimizer (GWO) [14], [15], dragonfly algorithm [16], and cuckoo-search (CS) [17], [18],
the use of those algorithms is based on the optimization of an objective function ensures the search for an
optimal path by taking obstacles into account. Hybrid methods, in which a collaboration between two or
more algorithms is used to improve the search of the optimal solution such as: fuzzy logic-ACO [19], fuzzy-
GA [20], based on an improved genetic algorithm, and a fuzzy obstacle avoidance of a soccer robot, IWO-
PSO based on the use of invasive weed optimization (IWO) and PSO to search for the optimal trajectory for
several robots in a non-stationary environment [21], firefly-fuzzy hybrid algorithm [22] used for trajectory
planning of an unmanned aerial vehicle (UAV), PSO-GWO [23]–[25] used in solving multi-objective path
planning for an autonomous guided robot, and improved PSO-GWO (IPSO-GWO) as an improved method
based on the use of laser range finder, GWO, and PSO [26].
The most common issue solved by these methods is the planning of an optimal trajectory. All
methods select a path among various trajectory possibilities, to allow the robot to reach its goal and to reduce
the navigation time. A successful method or algorithm quickly finds the optimal path, which positively
influences the energy required by a robot to perform such a task, the battery life, the productivity of industrial
mobile robots, the intervention time of robots used in critical areas.
The goal of this research work is to develop a new method based on the use of the elephant herding
optimization algorithm (EHO) for the search for an optimal trajectory for a mobile robot in different
locations. EHO is a recent algorithm belonging to the family of swarm intelligence algorithms, proposed by
[27]. Its principle is based on the simulation of the behavior of elephant herding when solving optimization
problems. This algorithm is known by a small number of parameters and has a very high capacity for space
exploration [28]. In this paper, we have tried to improve some of the basic search principles of the EHO
algorithm by proposing an improved elephant herding optimization (IEHO) algorithm to increase its search
and optimization capabilities. Also, a hybridization of the IEHO algorithm with the GWO has been proposed
to search for the optimal trajectory by taking advantage of the capabilities of both algorithms.
The rest of this paper is presented as follows: Section 2 and Section 3, present respectively the EHO
algorithm and the GWO algorithm. In section 4, we will present the procedure followed to improve the EHO
algorithm as well as the hybrid IEHO-GWO algorithm for optimal trajectory planning. A parametric study of
this hybrid algorithm will be presented in section 5. In section 6 and 7, a comparison and discussion of the
simulation results obtained by the proposed method IEHO-GWO and the application of the GWO, EHO, and
CS algorithms is presented. At the end of this article, conclusions and perspectives are presented in section 8.
2. ELEPHANT HERDING OPTIMIZATION ALGORITHM
Elephant herding optimization (EHO) is an algorithm that belongs to the nature-inspired
optimization algorithms. It is one of the most recent swarm intelligence algorithms proposed by Wang et al.
[27], and its application lies in optimization problems. Even though it is a fairly recent algorithm, EHO has
already been successfully applied to various numerical problems [29]–[31].
In general, solving optimization problems using swarm intelligence algorithms is based on the fact
that the search for the optimal solution requires two capabilities; global search is used to explore the search
space so that the algorithm avoids the local minimum. Local search in which the algorithm tries to find the
best solution in small areas of the search space. The elephant herding optimization algorithm balances these
two capabilities. The behavior of EHO is described as follows: An elephant population is composed of k
clans. Each clan has a leader elephant called the matriarch mi, see Figure 1. In each generation, some male
elephants leave the clans to live alone away from their original clan. The intelligence of swarms in EHO is
therefore based on the fact that the clans represent local search, while the elephants leaving the clan describe
the process of a global search. In terms of optimization, the matriarch is the optimal solution with the best
fitness function value in the clan. On the other hand, male elephants moving away from the clan are the
solutions with the worst fitness function values. The implementation procedure of EHO is generally based on
clan updating and separating operators, as the two major phases of the algorithm.
Figure 1. The social hierarchy of EHO algorithm
3. ISSN: 2088-8708
Int J Elec & Comp Eng, Vol. 14, No. 1, February 2024: 206-217
208
2.1. The clan updating operator
According to the elephants' natural hierarchy, a matriarch is responsible for guiding the elephants of
each clan. The new position of each elephant is always controlled by the matriarch. The clan updating
operator models this behavior as follows: for each clan 𝑐𝑖(𝑖 = 1,2, . . . . , 𝑘) in the population and each
elephant 𝑗(𝑗 = 1,2, . . . . , 𝑛) in the clan 𝑐𝑖, the updating of the old solution 𝑥𝑐𝑖,𝑗with a new solution 𝑥𝑛𝑒𝑤,𝑐𝑖,𝑗 is
done by (1).
𝑥𝑛𝑒𝑤,𝑐𝑖,𝑗 = 𝑥𝑐𝑖,𝑗 + 𝛼(𝑚𝑖 − 𝑥𝑐𝑖,𝑗)𝑟 (1)
where, 𝑥𝑛𝑒𝑤,𝑐𝑖,𝑗 is the new position of elephant 𝑗 in clan 𝑐𝑖, and 𝑥𝑐𝑖,𝑗 its old position, 𝑚𝑖 is the best solution in
the clan 𝑐𝑖 (the matriarch of the clan 𝑐𝑖), 𝛼 ∈ [0,1] designates the influence of the matriarch 𝑚𝑖, and r is a
random number between 0 and 1. Equation (1) models the fact that each elephant in the clan follows its
matriarch to move. The matriarch himself has to look for a new position to continue guiding his clan, so it is
necessary to check for the matriarch by the comparison between 𝑥𝑐𝑖,𝑗 and 𝑚𝑖. If those solutions are equal,
then the new position 𝑥𝑛𝑒𝑤,𝑐𝑖,𝑗 of 𝑚𝑖 is calculated by (2).
𝑥𝑛𝑒𝑤,𝑐𝑖,𝑗 = 𝛽𝑥𝑐𝑒𝑛𝑡𝑒𝑟,𝑐𝑖
(2)
where 𝛽 ∈ [0,1] controls the effect of the center 𝑥𝑐𝑒𝑛𝑡𝑒𝑟,𝑐𝑖
of the clan 𝑐𝑖 on the new position 𝑥𝑛𝑒𝑤,𝑐𝑖,𝑗 of the
matriarch. The center 𝑥𝑐𝑒𝑛𝑡𝑒𝑟,𝑐𝑖
of the clan 𝑐𝑖can be calculated as (3).
𝑥𝑐𝑒𝑛𝑡𝑒𝑟,𝑐𝑖
=
1
𝑛𝑐𝑖
∑ 𝑥𝑐𝑖,𝑗
𝑛𝑐𝑖
𝑗=1
(3)
where 𝑛𝑐𝑖
is the number of elephants in the clan 𝑐𝑖. At this stage of the calculation, it is also necessary to
check the limits of the new solution 𝑥𝑛𝑒𝑤,𝑐𝑖,𝑗 against the limits of the search space.
2.2. The separating operator
According to the social behavior of elephants in the wild, at each generation, the male elephant
leaves his family group to live separately. In terms of optimization, this elephant is considered to be a worst
solution to the optimized problem. This process is modeled by the separation operator as follows; in each
clan of the population, the elephant with the worst fitness function value 𝑥𝑤𝑜𝑟𝑠𝑡,𝑐𝑖
leaving the group is
replaced by another elephant generated randomly using (4).
𝑥𝑤𝑜𝑟𝑠𝑡,𝑐𝑖
= 𝑥𝑚𝑖𝑛 + (𝑥𝑚𝑎𝑥 − 𝑥𝑚𝑖𝑛 + 1) ∗ 𝑟𝑎𝑛𝑑 (4)
where the parameter 𝑟𝑎𝑛𝑑 ∈ [0,1] is a uniform random distribution, while 𝑥𝑚𝑖𝑛 and 𝑥𝑚𝑎𝑥 are respectively
the lower and upper limits of the search space. The pseudocode of the elephant herding optimization
algorithm is given in Table 1.
Table 1. The pseudocode of the EHO algorithm
EHO algorithm
Initialize the population
while (𝑡 < 𝑀𝑎𝑥𝑖𝑡)
Sort all the elephants according to their fitness
for all clans in the population do
for all elephants in the clan 𝑐𝑖 do
Update 𝑥𝑐𝑖,𝑗 and generate 𝑥𝑛𝑒𝑤,𝑐𝑖,𝑗 by (1)
if 𝑚𝑖=𝑥𝑐𝑖,𝑗 then
Update 𝑥𝑐𝑖,𝑗 and generate 𝑥𝑛𝑒𝑤,𝑐𝑖,𝑗 by (2)
end if end for end for
for all clans in the population do
Replace the worst elephant 𝑥𝑤𝑜𝑟𝑠𝑡,𝑐𝑖
in the clan 𝑐𝑖 by (4)
end for
Evaluate the population by the newly updated positions
t=t+1
end while
Return The best-found solution in the population
4. Int J Elec & Comp Eng ISSN: 2088-8708
A novel improved elephant herding optimization for path planning of a mobile robot (Ahmed Oultiligh)
209
3. GREY WOLF OPTIMIZER ALGORITHM
The GWO algorithm is one of the optimization algorithms proposed in recent years, its principle is
based on the simulation of the social hierarchy of gray wolves and their hunting mechanism described by
three main steps; the pursuit of the prey, encircling the prey, and the attack towards the prey. Grey wolves in
general are grouped according to a social hierarchy. The leader of the group alpha (α), followed by a second
category of beta (β) wolves and a third composed of delta (δ) wolves, as well as omegas (ω); the wolves in
the last category in this hierarchy [23].
The solution of the optimization problems by the GWO algorithm is found by considering alpha as
the first best solution, beta as the second-best solution, and delta as the third best solution. The omegas in the
mechanism of the GWO algorithm, present the candidate solutions used to search for the optimal solution in
each iteration. The updating of the position of the omegas (Xω) is based on the positions of the other wolves,
which reflects the fact that each omega in the group follows the alpha, beta, and delta wolves, respecting their
social hierarchy. GWO is known for its high exploration capacity which allows the algorithm to perform a
global search in space avoiding the situations that lead the algorithm to stuck in a local minimum. The
capacity of global search and the limited number of parameters presents the GWO algorithm as one of the
reliable and exploitable methods for the search for the optimal trajectory of a mobile robot. The procedure of
this algorithm is described in [14], [32], [33].
4. HYBRID IEHO-GWO ALGORITHM PROPOSED FOR PATH PLANNING
The basic EHO algorithm as already mentioned above looks for the optimal solution using clan
update and separation as two main operators. The idea of the algorithm is that each elephant in a clan follows
its matriarch Figure 2. In each clan, there is a better solution mi presented by the matriarch. Equation (1)
shows that at each iteration, all the elephants in the group change their position relative to the matriarch's
position, while the same equation does not update the position of the group leader. This is why in the basic
EHO algorithm, this problem is solved using (2). By observing this equation, we notice that at each iteration,
the update of the position of the matriarch takes into account the center of the clan with an influence factor β.
In each generation, the new position of the matriarch mi is chosen between the origin and the clan center
𝑥𝑐𝑒𝑛𝑡𝑒𝑟,𝑐𝑖
as shown Figure 2(a). This step in the algorithm shows an unjustified convergence of matriarch
towards the center of its clan, which can limit the algorithm in terms of space exploration capability and
consequently the search for the optimal solution.
Another weakness of EHO exists in the separation operator. Indeed, with each generation, the male
elephant leaves their clan. In terms of optimization, these elephants present a bad solution to be replaced, to
ensure a reliable convergence of the algorithm towards the optimal solution. This is mathematically
translated by (4). Using this equation to replace randomly those solutions, does not guarantee that in the next
generation, a good candidate solution will be generated. This procedure can influence the algorithm in
general rather than its convergence speed, especially if the randomly generated solution is a bad choice.
The objective of this work is to propose a new method for planning the trajectory of a mobile robot
by trying to improve the EHO algorithm. The improvement that we propose is firstly to develop (2) so that
we can improve the exploration capability and the convergence speed of the EHO algorithm adapted to the
trajectory planning. The principle is based on the fact that the updating of the position of the matriarch in
each clan, is done according to the center of the clan 𝑥𝑐𝑒𝑛𝑡𝑒𝑟,𝑐𝑖
, its current position mi, and the position of the
best global solution mg. This solution presents the matriarch who has the best position among all the
matriarchs in the population. This can be described as follows: each elephant in a clan follows its matriarch
mi, and each matriarch in the population follows the matriarch mg with the best position in the group as
shown in Figure 2(b). Equation (2) becomes:
𝑥𝑛𝑒𝑤,𝑐𝑖,𝑗 = 𝑚𝑖 + 𝛽𝑥𝑐𝑒𝑛𝑡𝑒𝑟,𝑐𝑖
+ 𝛤(𝑚𝑔 − 𝑚𝑖)𝑟 (5)
with 𝑚𝑖 the current position of the matriarch and 𝑥𝑛𝑒𝑤,𝑐𝑖,𝑗 its new position. The term 𝛽𝑥𝑐𝑒𝑛𝑡𝑒𝑟,𝑐𝑖
keeps the
matriarch close to its clan with an influencing factor 𝛽 ∈ [0,1] while 𝛤(𝑚𝑔 − 𝑚𝑖)𝑟 allowing 𝑚𝑖 to track 𝑚𝑔
with an influencing factor 𝛤 ∈ [0,1]; 𝑟 ∈ [0,1] is a uniform random distribution. The second improvement is
the one used to replace the solutions with the worst fitness function values, The 𝑥𝑤𝑜𝑟𝑠𝑡,𝑐𝑖
will be replaced by an
x element calculated based on the procedure of the GWO algorithm. The cooperation between both IEHO and
GWO algorithms consists in that at each generation, the GWO algorithm exploits the elements of each clan as
search agents to search for the best solution x. By using this proposed solution, we will be sure that 𝑥𝑤𝑜𝑟𝑠𝑡,𝑐𝑖
will
be replaced by a better solution, which further influences the exploration ability and convergence speed of the
proposed hybrid IEHO-GWO algorithm. Table 2 presents the pseudocode of the hybrid IEHO-GWO algorithm.
5. ISSN: 2088-8708
Int J Elec & Comp Eng, Vol. 14, No. 1, February 2024: 206-217
210
(a)
(b)
Figure 2. Position updating of the matriarch in: (a) EHO algorithm, and (b) IEHO algorithm
Table 2. The pseudocode of the hybrid IEHO-GWO algorithm
Hybrid IEHO-GWO algorithm
Initialize the population
while (𝑡 < 𝑀𝑎𝑥𝑖𝑡)
Sort all the elephants according to their fitness
for all clans in the population do
for all elephants 𝑗 in the clan 𝑐𝑖do
Update 𝑥𝑐𝑖,𝑗 and generate 𝑥𝑛𝑒𝑤,𝑐𝑖,𝑗 by (1)
if 𝑚𝑖=𝑥𝑐𝑖,𝑗 then
Update 𝑥𝑐𝑖,𝑗 and generate 𝑥𝑛𝑒𝑤,𝑐𝑖,𝑗 by (5)
end if
Implement the GWO algorithm for all elephants in the clan 𝑐𝑖
end for end for
for all clans in the population do
Replace the worst elephant 𝑥𝑤𝑜𝑟𝑠𝑡,𝑐𝑖
in the clan 𝑐𝑖 with the x
found with the GWO algorithm.
end for
Evaluate the population by the newly updated positions
t=t+1
end while
Return mg
5. PARAMETRIC STUDY
The objective of this section is to study the proposed IEHO-GWO algorithm. This study will focus
on the influence of its parameters on its ability to find the optimal path. As already mentioned in the previous
paragraphs, in addition to the number of populations, clans, and iteration, the IEHO-GWO algorithm requires
a set of the three parameters α, β, and 𝛤 for a complete adaptation to find the optimal path planning, see
Figure 3.
6. Int J Elec & Comp Eng ISSN: 2088-8708
A novel improved elephant herding optimization for path planning of a mobile robot (Ahmed Oultiligh)
211
In order to choose adequate values for the three parameters, we observe the evolution of the best
cost (path length) during the iteration for each value of α, β, and 𝛤 as shown in Figure 3(a) to (c). To do so, a
fixed number of populations, clans, and iterations is chosen using an arbitrary environment for the mobile
robot. The analysis of the results obtained in Figure 3, allows us to choose the right value for each parameter.
Taking into consideration that an adequate value of each parameter, allows the algorithm to converge to an
optimal solution as much as possible for a minimum number of iterations which will positively influence the
mobile robot's navigation time. The proposed parameters for the IEHO-GWO algorithm used for the
trajectory planning problem are presented in Table 3.
Table 3. The parameter values considered for the different algorithms
Algorithm Number of iterations Specific parameters
IEHO-GWO 200 Population Size=250
Number of clans=25
α=0.9; β =0.3; 𝛤 =0.2
EHO 200 Population Size=250
Number of clans=25
α=0.9; β =0.3
GWO 200 Number of search agents=250
CS 200 Population Size=250
Abandon probability Pa = 0.25
Lévy flights settings α = 0.1; λ = 1.5
(a) (b)
(c)
Figure 3. The evolution of the best cost for different values of: (a)- α, (b)- β, and (c)- Γ in 200 iterations
7. ISSN: 2088-8708
Int J Elec & Comp Eng, Vol. 14, No. 1, February 2024: 206-217
212
6. RESULTS AND DISCUSSION
6.1. Simulation results
This section consists of validating by simulation the effectiveness of the proposed method for
planning the optimal path of a mobile robot. The simulation results obtained for various environments are
presented in Figures 4-5 using EHO in Figures 4(a)-5(a), GWO in Figures 4(b)-5(b), CS in Figures 4(c)-5(c),
and IEHO-GWO presented in Figures 4(d)-5(d). The tests are performed using MATLAB and V-rep
simulation software considering the parameter values presented in Table 3.
It is shown in Figures 4-6 and Table 4 that the proposed method IEHO-GWO allows the robot to
find an optimal path in the environments considered. This method shows a high capacity in finding the
optimal solution, comparing it with the EHO, GWO, and CS algorithms. The proposed IEHO-GWO method
has successfully reduced both path length and simulation time in scenario-1 and scenario-2 as shown in
Figure 6(a) and (b), respectively.
Table 4. Table comparative of simulation results
Scenario method length of path (m) % of path saved Simulation time (min)
Scenario-1
IEHO-GWO 2.26 ---- 1.15
EHO 2.28 0.88 1.65
GWO 2.50 9.6 2.52
CS 2.59 12.74 2.72
Scenario-2
IEHO-GWO 2.06 ---- 1.04
EHO 2.09 1.43 1.51
GWO 2.24 8.03 2.56
CS 2.36 12.72 2.80
(a) (a)
(b) (b)
(c) (c)
(d) (d)
Figure 4. Paths obtained in scenario-1 using:
(a) EHO, (b) GWO, (c) CS, and (d) IEHO-GWO
algorithms
Figure 5. Paths obtained in scenario-2 using:
(a) EHO, (b) GWO, (c) CS, and (d) IEHO-GWO
algorithms
8. Int J Elec & Comp Eng ISSN: 2088-8708
A novel improved elephant herding optimization for path planning of a mobile robot (Ahmed Oultiligh)
213
Figure 6 shows the evolution of the best solution (the path length) during the iteration run for the
four methods. It can be seen that the IEHO-GWO converges quickly to the optimal solution better than the
basic EHO algorithm which requires more iteration to reach the same solution. Both algorithms GWO and
CS are very slow according to the results obtained, the number of iterations chosen is insufficient to converge
towards the optimal solution, which confirms that the convergence speed of those algorithms is low
compared to the IEHO-GWO and EHO.
To test the proposed method, a comparison of the four algorithms in terms of the trajectory length is
obtained by changing the maximum number of iterations as shown in Table 5 and Figure 7, and the size of
the population in Table 6. Comparing this method with the basic EHO algorithm, it can be seen from
Figure 7 that the EHO needs more iteration in Figure 7(a) as well as more individuals in Figure 7(b) to get
closer to the solution found by the IEHO-GWO which confirms the higher speed of the proposed method
compared to the one of the EHO algorithm. The IEHO-GWO does not require a very large number of
individuals or iterations to find a better solution.
Table 5. Path length for various iterations
Iterations method length of path
(m)
% of path
saved
100 IEHO-
GWO
2.0594 -----
EHO 2.0956 1.73
GWO 2.5259 19.11
CS 2.8947 28.86
150 IEHO-
GWO
2.063 -----
EHO 2.1435 3.75
GWO 2.2377 7.81
CS 2.8243 26.95
200 IEHO-
GWO
2.065 -----
EHO 2.0755 0.51
GWO 2.1661 4.67
CS 2.6759 22.83
250 IEHO-
GWO
2.0594 -----
EHO 2.0844 1.20
GWO 2.184 5.71
CS 2.616 21.28
300 IEHO-
GWO
2.0767 -----
EHO 2.0643 -0.60
GWO 2.2545 7.89
CS 2.6683 22.17
350 IEHO-
GWO
2.0871 -----
EHO 2.0691 -0.87
GWO 2.2318 6.48
CS 2.8325 12.40
400 IEHO-
GWO
2.0666 -----
EHO 2.0838 0.82
GWO 2.1968 5.97
CS 2.377 13.06
450 IEHO-
GWO
2.0842 -----
EHO 2.073 -0.54
GWO 2.1943 5.02
CS 2.3269 10.43
500 IEHO-
GWO
2.0561 ------
EHO 2.154 4.54
GWO 2.0938 1.80
CS 2.4933 17.53
550 IEHO-
GWO
2.0502 -----
EHO 2.0775 1.31
GWO 2.0757 1.23
CS 2.3237 11.77
Table 6. Path length for various population sizes
Individuals method length of path
(m)
% of path
saved
250 IEHO-
GWO
2.0597 -----
EHO 2.1469 4.06
GWO 2.2234 7.36
CS 2.979 30.86
300 IEHO-
GWO
2.0676 -----
EHO 2.0932 1.22
GWO 2.2663 8.77
CS 2.2641 21.21
350 IEHO-
GWO
2.0706 -----
EHO 2.1103 1.88
GWO 2.2377 7.47
CS 2.6106 20.68
400 IEHO-
GWO
2.0934 -----
EHO 2.1031 0.46
GWO 2.1445 2.38
CS 2.6029 19.57
450 IEHO-
GWO
2.0785 -----
EHO 2.1063 1.32
GWO 2.3617 11.99
CS 2.4679 15.78
500 IEHO-
GWO
2.0873 -----
EHO 2.0804 -0.33
GWO 2.1839 4.42
CS 2.5381 17.76
550 IEHO-
GWO
2.0692 ------
EHO 2.0833 0.68
GWO 2.1603 4.22
CS 2.4884 16.84
600 IEHO-
GWO
2.0721 ------
EHO 2.0952 1.10
GWO 2.0867 0.70
CS 2.5212 17.81
650 IEHO-
GWO
2.0582 ----
EHO 2.0865 1.36
GWO 2.075 0.81
CS 2.5316 18.70
700 IEHO-
GWO
2.0565 -----
EHO 2.0782 1.04
GWO 2.0631 0.32
CS 2.4094 14.65
9. ISSN: 2088-8708
Int J Elec & Comp Eng, Vol. 14, No. 1, February 2024: 206-217
214
(a) (b)
Figure 6. BestCost evolution curve for: (a) scenario-1 and (b) scenario-2
(a) (b)
Figure 7. Path length evolution for (a) various iterations and (b) various individuals
7. DISCUSSION
The EHO algorithm has been applied in several works in order to solve optimization problems. The
algorithm's structure, based on the social behavior of elephants in nature, gives the algorithm a good space
exploration capacity. This capacity is still limited and risks minimizing the convergence speed of the
algorithm because of its updating equation [28]. Sharma et al. [34] have developed an improvement of EHO
by proposing a new update equation for EHO. The IEHO-GWO method proposed in this article tries to
improve the basic algorithm in terms of convergence speed, by increasing its space exploration capacity. This
method hybrid the capabilities of both algorithms GWO and IEHO. On one side, the unjustifiable
convergence of the EHO algorithm towards the clan origin at each iteration presents a weakness of the
algorithm, since it limits its space exploration capability and consequently its convergence speed. According
to the modifications applied to the basic equations of EHO, the improved EHO (IEHO) proposed in this
work, allows the algorithm to avoid the local minimum in its search space by leading the search with the best
solution in all the population. On the other side, the implementation of the GWO algorithm in the path
planning of mobile robots has shown, according to several works, that GWO has a high space exploration
capability, which increases the possibility to find the global minimum easily [34], [35]. The PSO-GWO
hybridization presented in [24] shows the utility of GWO in improving the exploration capability of the PSO
10. Int J Elec & Comp Eng ISSN: 2088-8708
A novel improved elephant herding optimization for path planning of a mobile robot (Ahmed Oultiligh)
215
algorithm. A comparison of PSO-GWO and IEHO-GWO has been performed in terms of optimal path
planning in the same environment used in scenario-1. IEHO-GWO has a good performance (L=2.26 m)
compared to PSO-GWO (L=2.36 m). Therefore, PSO-GWO requires more iterations and a larger population
(130 iterations and 300 individuals) to reach a near solution to IEHO-GWO, which requires only 250
individuals and 60 iterations.
The cooperation between both algorithms IEHO and GWO, results in an IEHO-GWO method that
outperforms the EHO algorithm in terms of space exploration and convergence speed. On the one hand, the
IEHO-GWO method exploits the high space exploration capacity provided by GWO to avoid local
minimums. On the other, IEHO affects the convergence speed of IEHO-GWO, making it faster than the EHO
algorithm. IEHO-GWO finds the optimal solution in many iterations and with a lower population number
than those required by EHO and GWO respectively.
8. CONCLUSION
In this paper, based on the EHO algorithm, a hybrid IEHO-GWO method is proposed for planning
the optimal trajectory of a mobile robot in a static environment containing obstacles. The proposed method
has been tested by comparing it with the EHO, GWO, and CS algorithms. In the simulation using MATLAB
and V-rep software, the combination of the GWO's exploration and exploitation capabilities, as well as the
convergence speed of EHO, allows the development of a new IEHO-GWO method that balances between the
different capabilities to find very quickly the best solution in the search space. The results obtained make the
IEHO-GWO algorithm an effective method for the path planning of a mobile robot in an uncertain
environment. The future work consists of developing an intelligent system able to guide and locate a mobile
robot in a dynamic environment.
REFERENCES
[1] B. Elkari, H. Ayad, A. Elkari, M. Mjahed, and C. Pozna, “Design and FPGA implementation of a new Intelligent behaviors fusion
for mobile robot using fuzzy logic,” International Review of Automatic Control (IREACO), vol. 12, 2019, doi:
10.15866/ireaco.v12i1.14802.
[2] B. K. Patle, G. B. L, A. Pandey, D. R. K. Parhi, and A. Jagadeesh, “A review : On path planning strategies for navigation of
mobile robot,” Defence Technology, vol. 15, no. 4, pp. 582–606, 2019, doi: 10.1016/j.dt.2019.04.011.
[3] B. K. Patle, D. R. K. Parhi, A. Jagadeesh, and S. K. Kashyap, “Application of probability to enhance the performance of fuzzy
based mobile robot navigation,” Applied Soft Computing, vol. 75, pp. 265–283, 2019, doi: 10.1016/j.asoc.2018.11.026.
[4] K. El Hamidi, M. Mjahed, A. El Kari, and H. Ayad, “Neural network and fuzzy-logic-based self-tuning PID control for
quadcopter path tracking,” Studies in Informatics and Control, vol. 28, no. 4, pp. 401–412, 2019, doi:
10.24846/v28i4y201904.
[5] E. S. Low, P. Ong, and K. C. Cheah, “Solving the optimal path planning of a mobile robot using improved Q-learning,” Robotics
and Autonomous Systems, vol. 115, pp. 143–161, 2019, doi: 10.1016/j.robot.2019.02.013.
[6] C. Ameur, S. Faquir, A. Yahyaouy, and S. Abdelouahed, “Enhancing hybrid renewable energy performance through deep Q-
learning networks improved by fuzzy reward control,” International Journal of Electrical and Computer Engineering (IJECE),
vol. 12, no. 4, pp. 4302-4314, 2022, doi: 10.11591/ijece.v12i4.pp4302-4314.
[7] H. Ding, “Motion path planning of soccer training auxiliary robot based on genetic algorithm in fixed-point
rotation environment,” Journal of Ambient Intelligence and Humanized Computing, pp. 1–10, 2020, doi: 10.1007/s12652-020-
01877-4.
[8] I. Siti, M. Mjahed, H. Ayad, and A. El Kari, “New trajectory tracking approach for a quadcopter using genetic algorithm and
reference model methods,” Applied Sciences, vol. 9, no. 9, 2019, doi: 10.3390/app9091780.
[9] N. El Gmili, M. Mjahed, A. El Kari, and H. Ayad, “Particle swarm optimization based proportional-derivative parameters for
unmanned tilt-rotor flight control and trajectory tracking,” Automatika, vol. 61, no. 2, pp. 189–206, Apr. 2020, doi:
10.1080/00051144.2019.1698191.
[10] X. Li, D. Wu, J. He, M. Bashir, and M. Liping, “An improved method of particle swarm optimization for path planning of mobile
robot,” Journal of Control Science and Engineering, vol. 2020, 2020, doi: 10.1155/2020/3857894.
[11] M. Dadgar, S. Jafari, and A. Hamzeh, “A PSO-based multi-robot cooperation method for target searching in unknown
environments,” Neurocomputing, vol. 177, pp. 62–74, 2016, doi: 10.1016/j.neucom.2015.11.007.
[12] Y. Zhao, W. Li, X. Wang, and C. Yi, “Path planning of slab library crane based on improved ant colony algorithm,”
Mathematical Problems in Engineering, vol. 2019, 2019, doi: 10.1155/2019/7621464.
[13] H. Ali, D. Gong, M. Wang, and X. Dai, “Path planning of mobile robot with improved ant colony algorithm and MDP to produce
smooth trajectory in grid-based environment,” Frontiers in Neurorobotics, vol. 14, 2020, doi: 10.3389/fnbot.2020.00044.
[14] J. Li and F. Yang, “Task assignment strategy for multi-robot based on improved Grey Wolf Optimizer,” Journal of Ambient
Intelligence and Humanized Computing, vol. 11, no. 12, pp. 6319–6335, 2020, doi: 10.1007/s12652-020-02224-3.
[15] C. Qu, W. Gai, J. Zhang, and M. Zhong, “A novel hybrid grey wolf optimizer algorithm for unmanned aerial vehicle (UAV) path
planning,” Knowledge-Based Systems, p. 105530, 2020, doi: 10.1016/j.knosys.2020.105530.
[16] S. Muthukumaran and R. Sivaramakrishnan, “Optimal path planning for an autonomous mobile robot using dragonfly algorithm,”
International Journal of Simulation Modelling, vol. 18, no. 3, pp. 397–407, 2019, doi: 10.2507/IJSIMM18(3)474.
[17] P. K. Mohanty, “An intelligent navigational strategy for mobile robots in uncertain environments using smart cuckoo search
algorithm,” Journal of Ambient Intelligence and Humanized Computing, vol. 11, no. 12, pp. 6387–6402, 2020, doi:
10.1007/s12652-020-02535-5.
[18] El Gmili, Mjahed, El Kari, and Ayad, “Particle swarm optimization and cuckoo search-based approaches for quadrotor control
and trajectory tracking,” Applied Sciences, vol. 9, no. 8, Apr. 2019, doi: 10.3390/app9081719.
11. ISSN: 2088-8708
Int J Elec & Comp Eng, Vol. 14, No. 1, February 2024: 206-217
216
[19] Q. Song, Q. Zhao, S. Wang, Q. Liu, and X. Chen, “Dynamic path planning for unmanned vehicles based on fuzzy logic and
improved ant colony optimization,” IEEE Access, vol. 8, pp. 62107–62115, 2020, doi: 10.1109/ACCESS.2020.2984695.
[20] D. Chen, “Fuzzy obstacle avoidance optimization of soccer robot based on an improved genetic algorithm,” Journal of Ambient
Intelligence and Humanized Computing, pp. 1–12, 2019, doi: 10.1007/s12652-019-01636-0.
[21] M. R. Panda, P. Das, and S. Pradhan, “Hybridization of IWO and IPSO for mobile robots navigation in a dynamic environment,”
Journal of King Saud University-Computer and Information Sciences, vol. 32, no. 9, pp. 1020–1033, 2020, doi:
10.1016/j.jksuci.2017.12.009.
[22] B. Patel and B. Patle, “Analysis of firefly--fuzzy hybrid algorithm for navigation of quad-rotor unmanned aerial vehicle,”
Inventions, vol. 5, no. 3, p. 48, 2020, doi: 10.3390/inventions5030048.
[23] F. Gul, W. Rahiman, S. S. N. Alhady, A. Ali, I. Mir, and A. Jalil, “Meta-heuristic approach for solving multi-objective path
planning for autonomous guided robot using PSO–GWO optimization algorithm with evolutionary programming,” Journal of
Ambient Intelligence and Humanized Computing, pp. 1–18, 2020, doi: 10.1007/s12652-020-02514-w.
[24] A. Oultiligh, H. Ayad, A. El Kari, M. Mjahed, and N. El Gmili, “A hybrid PSO-GWO algorithm for robot path planning in
uncertain environments,” International Review of Automatic Control (IREACO), vol. 14, no. 6, 2021, doi:
10.15866/ireaco.v14i6.20045.
[25] X. Cheng, J. Li, C. Zheng, J. Zhang, and M. Zhao, “An improved PSO-GWO algorithm with chaos and adaptive inertial weight
for robot path planning,” Frontiers in neurorobotics, vol. 15, p. 770361, 2021, doi: 10.3389/fnbot.2021.770361.
[26] N. Toufan and A. Niknafs, “Robot path planning based on laser range finder and novel objective functions in grey wolf
optimizer,” SN Applied Sciences, vol. 2, no. 8, pp. 1–19, 2020, doi: 10.1007/s42452-020-3093-5.
[27] G.-G. Wang, S. Deb, and L. dos S. Coelho, “Elephant herding optimization,” in 2015 3rd international symposium on
computational and business intelligence (ISCBI), 2015, pp. 1–5. doi: 10.1109/ISCBI.2015.8.
[28] J. Li, L. Guo, Y. Li, and C. Liu, “Enhancing elephant herding optimization with novel individual updating strategies for large-
scale optimization problems,” Mathematics, vol. 7, no. 5, p. 395, 2019, doi: 10.3390/math7050395.
[29] P. Veeramanikandan and S. Selvaperumal, “A fuzzy-elephant herding optimization technique for maximum power point tracking
in the hybrid wind-solar system,” International Transactions on Electrical Energy Systems, vol. 30, no. 3, Art. no. e12214, 2020,
doi: 10.1002/ 2050-7038.12214.
[30] W. Li, G. Wang, and A. H. Alavi, “Learning-based elephant herding optimization algorithm for solving numerical optimization
problems,” Knowledge-Based Systems, vol. 195, p. 105675, 2020, doi: 10.1016/j.knosys.2020.105675.
[31] W. Li and G.-G. Wang, “Improved elephant herding optimization using opposition-based learning and K-means clustering to
solve numerical optimization problems,” Journal of Ambient Intelligence and Humanized Computing, vol. 14, no. 3,
pp. 1753–1784, 2023, doi: 10.1007/s12652-021-03391-7.
[32] K. Albina and S. G. Lee, “Hybrid stochastic exploration using grey wolf optimizer and coordinated multi-robot exploration
algorithms,” IEEE Access, vol. 7, pp. 14246–14255, 2019, doi: 10.1109/ACCESS.2019.2894524.
[33] H. Tang, W. Sun, A. Lin, M. Xue, and X. Zhang, “A GWO-based multi-robot cooperation method for target searching
in unknown environments,” Expert Systems with Applications, vol. 186, Art. no. 115795, 2021, doi:
10.1016/j.eswa.2021.115795.
[34] I. Sharma, V. Kumar, and S. Sharma, “A comprehensive survey on grey wolf optimization,” Recent Advances in Computer
Science and Communications (Formerly: Recent Patents on Computer Science), vol. 15, no. 3, pp. 323–333, 2022, doi: 10.2174/
2666255813999201007165454.
[35] Y. Hou, H. Gao, Z. Wang, and C. Du, “Improved grey wolf optimization algorithm and application,” Sensors, vol. 22, no. 10, Art.
no. 3810, 2022, doi: 10.3390/s22103810.
BIOGRAPHIES OF AUTHORS
Ahmed Oultiligh received his master’s degree in electrical engineering from the
Faculty of Science and Technologies, University Cadi Ayyad, Morocco. He is currently
pursuing a Ph.D. at the same. He is currently working in the Laboratory of Electrical Systems,
Energy Efficiency, and Telecommunications (LSEET), focusing on controlling and path
planning of mobile robots. His research interests include robotics, automatic, and artificial
intelligence. He can be contacted at email: a.oultiligh@gmail.com.
Hassan Ayad obtained his doctorate thesis in 1993 from the University of Le
Havre, France, and his Ph.D. degree from Cadi Ayyad University in 2007. Since 1993, he is a
professor at the Faculty of Science and Technology of Marrakesh, He is a researcher member
of the Electrical Systems, Energy Efficiency, and Telecommunications (LSEET) Laboratory.
He is the author of more than 50 articles and more than 20 international communications. His
research interests include artificial intelligence, automatic control, intelligent control,
intelligent systems, mechatronics, and robotics. He can be contacted at email: h.ayad@uca.ma.
12. Int J Elec & Comp Eng ISSN: 2088-8708
A novel improved elephant herding optimization for path planning of a mobile robot (Ahmed Oultiligh)
217
Abdeljalil El Kari received his Ph.D. degree from the University of Bordeaux, in
1993. In 1994, he joined Cadi Ayyad University, Faculty of Science and Technology,
Marrakesh, Morocco, where he currently is a full professor with the Department of Physics. In
2002, he obtained a Ph.D. degree from Cadi Ayyad University and Reims Champagne-
Ardenne University. He is a researcher member of the Electrical Systems, Energy Efficiency,
and Telecommunications (LSEET) Laboratory. His main areas of research interest are
automatic, robotics, control systems engineering, and artificial intelligence. He has so far
published over 20 papers in different journals and conferences. He can be contacted at email:
a.elkari@uca.ma.
Mostafa Mjahed received his 3ème cycle Doctorate in HEP from the University
of Clermont Ferrand, France, in 1987, and a Ph.D. degree in control and artificial intelligence
from the University of Cadi Ayyad, Marrakech, in 2003. In 1989, he joined the Ecole Royale
de l’Air, Marrakech, Morocco, as an associate professor in the Department of Mathematics and
Systems. Since 2003, he has been a professor in the same institute and department. His current
research interests are conventional and AI-based flight control, pattern recognition, and
classification by GA, PSO, and NN. He can be contacted at: mjahed.mostafa@gmail.com.
Nada El Gmili Ph.D. and Eng. graduated from the Cadi Ayyad University and
laureate of the ENSA of Oujda, Morocco. I has a previous experience of teaching at the
Faculty of Sciences and Techniques (FST) of Marrakesh, Morocco from 2017 to 2020, and
from 2020 I am a professor at the FST of Mohammedia, Hassan II University, Casablanca,
Morocco. My research interests include robotics, automatics, intelligent optimization, artificial
intelligence, embedded systems, and mathematical modeling of complex systems. My research
results have been crowned by several publications indexed in the international Thomson
Reuters and Scopus databases. She can be contacted at email: elgmilinada@gmail.com.