This document summarizes a research paper that proposes an improved Lion Optimization Algorithm (ILA) to solve the Team Orienteering Problem with Time Windows (TOPTW). The ILA enhances the standard Lion Optimization Algorithm (LA) by improving the process of generating a nomadic lion during territorial defense. Specifically, instead of randomly generating a nomadic lion, the ILA selects the strongest nomadic lion based on various criteria related to the TOPTW, such as time windows and location profits. The ILA is tested on standard benchmarks and shown to obtain competitive results compared to the LA and other state-of-the-art methods for solving the TOPTW.
An energy-efficient flow shop scheduling using hybrid Harris hawks optimizationjournalBEEI
The energy crisis has become an environmental problem, and this has received much attention from researchers. The manufacturing sector is the most significant contributor to energy consumption in the world. One of the significant efforts made in the manufacturing industry to reduce energy consumption is through proper scheduling. Energy-efficient scheduling (EES) is a problem in scheduling to reduce energy consumption. One of the EES problems is in a flow shop scheduling problem (FSSP). This article intends to develop a new approach to solving an EES in the FSSP problem. Hybrid Harris hawks optimization (hybrid HHO) algorithm is offered to resolve the EES issue on FSSP by considering the sequence-dependent setup. Swap and flip procedures are suggested to improve HHO performance. Furthermore, several procedures were used as a comparison to assess hybrid HHO performance. Ten tests were exercised to exhibit the hybrid HHO accomplishment. Based on numerical experimental results, hybrid HHO can solve EES problems. Furthermore, HHO was proven more competitive than other algorithms.
A HYBRID ALGORITHM BASED ON INVASIVE WEED OPTIMIZATION ALGORITHM AND GREY WOL...gerogepatton
This document describes a hybrid algorithm that combines the Invasive Weed Optimization Algorithm (IWO) and Grey Wolf Optimization Algorithm (GWO). IWO is inspired by the colonial behavior of invasive weeds, while GWO is based on the hunting behavior of grey wolves. The hybrid algorithm IWOGWO is proposed to take advantage of the strengths of both algorithms while minimizing their weaknesses. The document provides detailed descriptions of the IWO, GWO, and the hybridization process between them. It is argued that the hybrid algorithm finds the optimal solution in most test functions when compared to the original algorithms.
A HYBRID ALGORITHM BASED ON INVASIVE WEED OPTIMIZATION ALGORITHM AND GREY WOL...ijaia
In this research, two algorithms first, considered to be one of hybrid algorithms. And it is algorithm represents invasive weed optimization. This algorithm is a random numerical algorithm and the second algorithm representing the grey wolves optimization. This algorithm is one of the algorithms of swarm intelligence in intelligent optimization. The algorithm of invasive weed optimization is inspired by nature as the weeds have colonial behavior and were introduced by Mehrabian and Lucas in 2006. Invasive weeds are a serious threat to cultivated plants because of their adaptability and are a threat to the overall planting process. The behavior of these weeds has been studied and applied in the invasive weed algorithm. The algorithm of grey wolves, which is considered as a swarm intelligence algorithm, has been used to reach the goal and reach the best solution. The algorithm was designed by SeyedaliMirijalili in 2014 and taking advantage of the intelligence of the squadrons is to avoid falling into local solutions so the new hybridization process between the previous algorithms GWO and IWO and we will symbolize the new algorithm IWOGWO.Comparing the suggested hybrid algorithm with the original algorithms it results were excellent. The optimum solution was found in most of test functions.
This document discusses advanced optimization techniques used to solve large-scale problems that traditional techniques cannot handle effectively. It introduces several population-based metaheuristic algorithms inspired by natural phenomena, including genetic algorithms, artificial immune algorithms, and differential evolution. Genetic algorithms use operations like selection, crossover and mutation to evolve solutions over generations. Artificial immune algorithms are based on clonal selection to amplify high-affinity antibodies. Differential evolution generates trial vectors through mutation and crossover of randomly selected target vectors.
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.
Two-Stage Eagle Strategy with Differential EvolutionXin-She Yang
The document describes a two-stage optimization strategy called the Eagle Strategy (ES) that combines global and local search algorithms to improve search efficiency. It evaluates applying ES to differential evolution (DE), a popular evolutionary algorithm. ES first uses randomization like Levy flights for global exploration, then switches to DE for intensive local search around promising solutions. The authors validate ES-DE on test functions, finding it requires only 9.7-24.9% of the function evaluations of pure DE. They also apply it to real-world pressure vessel and gearbox design problems, achieving solutions with 14.9-17.7% fewer function evaluations than pure DE.
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.
EFFECTS OF THE DIFFERENT MIGRATION PERIODS ON PARALLEL MULTI-SWARM PSOcscpconf
This document discusses the effects of different migration periods on the Parallel Multi-Swarm Particle Swarm Optimization (PCLPSO) algorithm. PCLPSO is a parallel metaheuristic algorithm based on Particle Swarm Optimization and the Comprehensive Learning Particle Swarm Optimization. It uses multiple swarms that work cooperatively and concurrently. The migration period, which is the frequency at which swarms share information, is an important parameter. The document analyzes PCLPSO performance on benchmark functions using different migration periods, finding the best periods varied with problem dimension and type.
An energy-efficient flow shop scheduling using hybrid Harris hawks optimizationjournalBEEI
The energy crisis has become an environmental problem, and this has received much attention from researchers. The manufacturing sector is the most significant contributor to energy consumption in the world. One of the significant efforts made in the manufacturing industry to reduce energy consumption is through proper scheduling. Energy-efficient scheduling (EES) is a problem in scheduling to reduce energy consumption. One of the EES problems is in a flow shop scheduling problem (FSSP). This article intends to develop a new approach to solving an EES in the FSSP problem. Hybrid Harris hawks optimization (hybrid HHO) algorithm is offered to resolve the EES issue on FSSP by considering the sequence-dependent setup. Swap and flip procedures are suggested to improve HHO performance. Furthermore, several procedures were used as a comparison to assess hybrid HHO performance. Ten tests were exercised to exhibit the hybrid HHO accomplishment. Based on numerical experimental results, hybrid HHO can solve EES problems. Furthermore, HHO was proven more competitive than other algorithms.
A HYBRID ALGORITHM BASED ON INVASIVE WEED OPTIMIZATION ALGORITHM AND GREY WOL...gerogepatton
This document describes a hybrid algorithm that combines the Invasive Weed Optimization Algorithm (IWO) and Grey Wolf Optimization Algorithm (GWO). IWO is inspired by the colonial behavior of invasive weeds, while GWO is based on the hunting behavior of grey wolves. The hybrid algorithm IWOGWO is proposed to take advantage of the strengths of both algorithms while minimizing their weaknesses. The document provides detailed descriptions of the IWO, GWO, and the hybridization process between them. It is argued that the hybrid algorithm finds the optimal solution in most test functions when compared to the original algorithms.
A HYBRID ALGORITHM BASED ON INVASIVE WEED OPTIMIZATION ALGORITHM AND GREY WOL...ijaia
In this research, two algorithms first, considered to be one of hybrid algorithms. And it is algorithm represents invasive weed optimization. This algorithm is a random numerical algorithm and the second algorithm representing the grey wolves optimization. This algorithm is one of the algorithms of swarm intelligence in intelligent optimization. The algorithm of invasive weed optimization is inspired by nature as the weeds have colonial behavior and were introduced by Mehrabian and Lucas in 2006. Invasive weeds are a serious threat to cultivated plants because of their adaptability and are a threat to the overall planting process. The behavior of these weeds has been studied and applied in the invasive weed algorithm. The algorithm of grey wolves, which is considered as a swarm intelligence algorithm, has been used to reach the goal and reach the best solution. The algorithm was designed by SeyedaliMirijalili in 2014 and taking advantage of the intelligence of the squadrons is to avoid falling into local solutions so the new hybridization process between the previous algorithms GWO and IWO and we will symbolize the new algorithm IWOGWO.Comparing the suggested hybrid algorithm with the original algorithms it results were excellent. The optimum solution was found in most of test functions.
This document discusses advanced optimization techniques used to solve large-scale problems that traditional techniques cannot handle effectively. It introduces several population-based metaheuristic algorithms inspired by natural phenomena, including genetic algorithms, artificial immune algorithms, and differential evolution. Genetic algorithms use operations like selection, crossover and mutation to evolve solutions over generations. Artificial immune algorithms are based on clonal selection to amplify high-affinity antibodies. Differential evolution generates trial vectors through mutation and crossover of randomly selected target vectors.
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.
Two-Stage Eagle Strategy with Differential EvolutionXin-She Yang
The document describes a two-stage optimization strategy called the Eagle Strategy (ES) that combines global and local search algorithms to improve search efficiency. It evaluates applying ES to differential evolution (DE), a popular evolutionary algorithm. ES first uses randomization like Levy flights for global exploration, then switches to DE for intensive local search around promising solutions. The authors validate ES-DE on test functions, finding it requires only 9.7-24.9% of the function evaluations of pure DE. They also apply it to real-world pressure vessel and gearbox design problems, achieving solutions with 14.9-17.7% fewer function evaluations than pure DE.
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.
EFFECTS OF THE DIFFERENT MIGRATION PERIODS ON PARALLEL MULTI-SWARM PSOcscpconf
This document discusses the effects of different migration periods on the Parallel Multi-Swarm Particle Swarm Optimization (PCLPSO) algorithm. PCLPSO is a parallel metaheuristic algorithm based on Particle Swarm Optimization and the Comprehensive Learning Particle Swarm Optimization. It uses multiple swarms that work cooperatively and concurrently. The migration period, which is the frequency at which swarms share information, is an important parameter. The document analyzes PCLPSO performance on benchmark functions using different migration periods, finding the best periods varied with problem dimension and type.
Effects of The Different Migration Periods on Parallel Multi-Swarm PSO csandit
In recent years, there has been an increasing inter
est in parallel computing. In parallel
computing, multiple computing resources are used si
multaneously in solving a problem. There
are multiple processors that will work concurrently
and the program is divided into different
tasks to be simultaneously solved. Recently, a cons
iderable literature has grown up around the
theme of metaheuristic algorithms. Particle swarm o
ptimization (PSO) algorithm is a popular
metaheuristic algorithm. The parallel comprehensive
learning particle swarm optimization
(PCLPSO) algorithm based on PSO has multiple swarms
based on the master-slave paradigm
and works cooperatively and concurrently. The migra
tion period is an important parameter in
PCLPSO and affects the efficiency of the algorithm.
We used the well-known benchmark
functions in the experiments and analysed the perfo
rmance of PCLPSO using different
migration periods.
This document discusses various artificial intelligence techniques for robot path planning, including ant colony optimization. It provides background on particle swarm optimization, genetic algorithms, tabu search, simulated annealing, reactive search optimization, and ant colony algorithms. It then proposes a solution for robotic path planning that uses ant colony optimization. The proposed solution involves defining a source and destination point for the robot, moving it forward one step at a time while checking for obstacles, having it take three steps back if an obstacle is encountered, and applying ant colony optimization algorithms to help the robot find an optimal path to bypass obstacles and reach the destination point.
Artificial Intelligence in Robot Path Planningiosrjce
IOSR Journal of Computer Engineering (IOSR-JCE) is a double blind peer reviewed International Journal that provides rapid publication (within a month) of articles in all areas of computer engineering and its applications. The journal welcomes publications of high quality papers on theoretical developments and practical applications in computer technology. Original research papers, state-of-the-art reviews, and high quality technical notes are invited for publications.
This document provides an overview of a survey of multi-objective evolutionary algorithms for data mining tasks. It discusses key concepts in multi-objective optimization and evolutionary algorithms. It also reviews common data mining tasks like feature selection, classification, clustering, and association rule mining that are often formulated as multi-objective problems and solved using multi-objective evolutionary algorithms. The survey focuses on reviewing applications of multi-objective evolutionary algorithms for feature selection and classification in part 1, and applications for clustering, association rule mining and other tasks in part 2.
This document discusses using genetic algorithms to solve a multi-objective traveling salesman problem (MOTSP) that considers both cost and CO2 emissions. It provides background on genetic algorithms and the traveling salesman problem. The study aims to identify how tuning genetic operators and parameters can improve the efficiency of genetic algorithms in solving the MOTSP with CO2 emissions. Empirical results show that performance improves with some combinations of parameters and operators.
This document summarizes an article that proposes improvements to an existing algorithm for resource scheduling in cloud computing environments. The existing algorithm uses a hybrid of ant colony optimization and particle swarm optimization. The proposed improvements add an initial phase that uses an enhanced fish swarm search algorithm to help find more global optimal solutions. This global optimal solution found by fish swarm search is then used to guide the existing ant colony optimization and particle swarm optimization hybrid to find more locally optimal solutions. The document provides background on resource scheduling, metaheuristic algorithms, and describes the specific implementations of the improved fish swarm search algorithm and the overall proposed methodology.
This document provides an introduction to multi-objective optimization using evolutionary algorithms. It discusses how evolutionary algorithms are well-suited for multi-objective optimization problems as they use a population approach to find multiple non-dominated solutions simultaneously. The document outlines the basic principles of evolutionary optimization for single-objective problems and then describes the key concepts and operating principles of evolutionary multi-objective optimization.
Hybrid iterated local search algorithm for optimization route of airplane tr...IJECEIAES
The traveling salesman problem (TSP) is a very popular combinatorics problem. This problem has been widely applied to various real problems. The TSP problem has been classified as a Non-deterministic Polynomial Hard (NP-Hard), so a non-deterministic algorithm is needed to solve this problem. However, a non-deterministic algorithm can only produce a fairly good solution but does not guarantee an optimal solution. Therefore, there are still opportunities to develop new algorithms with better optimization results. This research develops a new algorithm by hybridizing three local search algorithms, namely, iterated local search (ILS) with simulated annealing (SA) and hill climbing (HC), to get a better optimization result. This algorithm aimed to solve TSP problems in the transportation sector, using a case study from the Traveling Salesman Challenge 2.0 (TSC 2.0). The test results show that the developed algorithm can optimize better by 15.7% on average and 11.4% based on the best results compared to previous studies using the TabuSA algorithm.
Towards a formal analysis of the multi-robot task allocation problem using se...journalBEEI
Nowadays, the multi-robot task allocation problem is one of the most challenging problems in multi-robot systems. It concerns the optimal assignment of a set of tasks to several robots while optimizing a given criterion subject to some constraints. This problem is very complex, particularly when handling large groups of robots and tasks. We propose a formal analysis of the task allocation problem in a multi-robot system, based on set theory concepts. We believe that this analysis will help researchers understand the nature of the problem, its time complexity, and consequently develop efficient solutions. Also, we used that formal analysis to formulate two well-known taxonomies of multi-robot task allocation problems. Finally, a generic solving scheme of multi-robot task allocation problems is proposed and illustrated on assigning papers to reviewers within a journal.
Applications and Analysis of Bio-Inspired Eagle Strategy for Engineering Opti...Xin-She Yang
This document discusses applying an eagle strategy inspired by nature to engineering optimization problems. The eagle strategy uses a two-stage approach combining global exploration with local exploitation. Global exploration uses Lèvy flights for random walks to diversify solutions. Promising solutions are then locally optimized using an efficient local search algorithm like particle swarm optimization. The document analyzes random walk models like Lèvy flights and how they can maintain diversity in swarm intelligence algorithms. It applies the eagle strategy to four engineering design problems, finding Lèvy flights can effectively reduce computational efforts.
The optimization of running queries in relational databases using ant colony ...ijdms
The issue of optimizing queries is a cost-sensitive
process and with respect to the number of associat
ed
tables in a query, its number of permutations grows
exponentially. On one hand, in comparison with oth
er
operators in relational database, join operator is
the most difficult and complicated one in terms of
optimization for reducing its runtime. Accordingly,
various algorithms have so far been proposed to so
lve
this problem. On the other hand, the success of any
database management system (DBMS) means
exploiting the query model. In the current paper, t
he heuristic ant algorithm has been proposed to sol
ve this
problem and improve the runtime of join operation.
Experiments and observed results reveal the efficie
ncy
of this algorithm compared to its similar algorithm
s.
Spider Monkey optimization (SMO) algorithm is newest addition in class of swarm intelligence. SMO is a population based stochastic meta-heuristic. It is motivated by intelligent foraging behaviour of fission fusion structured social creatures. SMO is a very good option for complex optimization problems. This paper proposed a modified strategy in order to enhance performance of original SMO. This paper introduces a position update strategy in SMO and modifies both local leader and global leader phase. The proposed strategy is named as Modified Position Update in Spider Monkey Optimization (MPU-SMO) algorithm. The proposed algorithm tested over benchmark problems and results show that it gives better results for considered unbiased problems.
Solving np hard problem artificial bee colony algorithmIAEME Publication
The document discusses using an artificial bee colony (ABC) algorithm to solve the NP-hard shortest common supersequence problem. It begins by introducing ABC algorithms and their inspiration from honey bee behavior. It then discusses previous approaches to solving the shortest common supersequence problem and outlines the proposed ABC approach. The ABC approach generates candidate solutions, calculates their fitness by comparing them to input strings, and iteratively improves solutions until termination criteria are met. Experimental results show the ABC approach finds solutions close to optimal.
Solving np hard problem using artificial bee colony algorithmIAEME Publication
The document presents an artificial bee colony (ABC) algorithm to solve the NP-hard shortest common supersequence problem. The ABC algorithm is inspired by the foraging behavior of honey bees. It represents solutions as food sources and uses employed, onlooker, and scout bees to explore the search space. The algorithm calculates character frequencies in input strings to guide random supersequence generation. Fitness is evaluated by comparing sequences using a modified merge algorithm. Results show the ABC approach finds near-optimal solutions compared to other algorithms for solving shortest common supersequences.
Solving np hard problem artificial bee colony algorithmIAEME Publication
The document discusses using an artificial bee colony (ABC) algorithm to solve the NP-hard shortest common supersequence problem. It begins by introducing ABC algorithms and their inspiration from honey bee behavior. It then discusses previous approaches to solving the shortest common supersequence problem and outlines the proposed ABC approach. The ABC approach calculates character frequencies, generates candidate solutions randomly based on frequencies, evaluates candidates against input strings, and iteratively improves candidates until termination criteria are met. Experimental results show the ABC approach finds solutions close to optimal.
Solving np hard problem using artificial bee colony algorithmIAEME Publication
The document presents an artificial bee colony (ABC) algorithm to solve the NP-hard shortest common supersequence problem. The ABC algorithm is inspired by the foraging behavior of honey bees. It represents solutions as food sources and uses employed, onlooker, and scout bees to explore the search space. The algorithm calculates character frequencies in input strings to guide random supersequence generation. Fitness is evaluated by comparing sequences using a modified merge algorithm. Results show the ABC approach finds near-optimal solutions compared to other algorithms for solving shortest common supersequences.
International Journal of Computational Engineering Research (IJCER) is dedicated to protecting personal information and will make every reasonable effort to handle collected information appropriately. All information collected, as well as related requests, will be handled as carefully and efficiently as possible in accordance with IJCER standards for integrity and objectivity.
Simulation-based Optimization of a Real-world Travelling Salesman Problem Usi...CSCJournals
This paper presents a real-world case study of optimizing waste collection in Sweden. The problem, involving approximately 17,000 garbage bins served by three bin lorries, is approached as a travelling salesman problem and solved using simulation-based optimization and an evolutionary algorithm. To improve the performance of the evolutionary algorithm, it is enhanced with a repair function that adjusts its genome values so that shorter routes are found more quickly. The algorithm is tested using two crossover operators, i.e., the order crossover and heuristic crossover, combined with different mutation rates. The results indicate that the order crossover is superior to the heuristics crossover, but that the driving force of the search process is the mutation operator combined with the repair function.
Bat Algorithm is Better Than Intermittent Search StrategyXin-She Yang
This document compares the bat algorithm to the intermittent search strategy for balancing exploration and exploitation in metaheuristic optimization algorithms. It reviews several metaheuristic algorithms and analyzes the theoretical basis for optimal balancing of exploration and exploitation phases. Equations are presented for the optimal ratio of exploration and exploitation phases in 2D problems based on the intermittent search strategy. The bat algorithm is described and its ability to achieve near-optimal balancing is demonstrated through numerical experiments on test functions. The document concludes higher dimensional problems require more exploration effort to find global optima with limited computations.
Convergence tendency of genetic algorithms and artificial immune system in so...ijcsity
By the advances in the Evolution Algorithms (EAs) and the intelligent optimization metho
ds we witness the
big revolutions in solving the optimization problems. The application of the evolution algorithms are not
only not limited to the combined optimization problems, but also are vast in domain to the continuous
optimization problems. In this
paper we analyze and study the Genetic Algorithm (GA) and the Artificial
Immune System (AIS)
algorithm
which are capable in escaping the local optimization and also fastening
reaching the global optimization and to show the efficiency of the GA and AIS th
e application of them in
Solving Continuous Optimization Functions (SCOFs) are studied. Because of the multi variables and the
multi
-
dimensional spaces in SCOFs the use of the classic optimization methods, is generally non
-
efficient
and high cost. In other
words the use of the classic optimization methods for SCOFs generally leads to a
local optimized solution. A possible solution for SCOFs is to use the EAs which are high in probability of
succeeding reaching the local optimized solution. The results in pa
per show that GA is more efficient than
AIS in reaching the optimized solution in SCOFs.
Development of depth map from stereo images using sum of absolute differences...nooriasukmaningtyas
This article proposes a framework for the depth map reconstruction using stereo images. Fundamentally, this map provides an important information which commonly used in essential applications such as autonomous vehicle navigation, drone’s navigation and 3D surface reconstruction. To develop an accurate depth map, the framework must be robust against the challenging regions of low texture, plain color and repetitive pattern on the input stereo image. The development of this map requires several stages which starts with matching cost calculation, cost aggregation, optimization and refinement stage. Hence, this work develops a framework with sum of absolute difference (SAD) and the combination of two edge preserving filters to increase the robustness against the challenging regions. The SAD convolves using block matching technique to increase the efficiency of matching process on the low texture and plain color regions. Moreover, two edge preserving filters will increase the accuracy on the repetitive pattern region. The results show that the proposed method is accurate and capable to work with the challenging regions. The results are provided by the Middlebury standard dataset. The framework is also efficiently and can be applied on the 3D surface reconstruction. Moreover, this work is greatly competitive with previously available methods.
Model predictive controller for a retrofitted heat exchanger temperature cont...nooriasukmaningtyas
This paper aims to demonstrate the practical aspects of process control theory for undergraduate students at the Department of Chemical Engineering at the University of Bahrain. Both, the ubiquitous proportional integral derivative (PID) as well as model predictive control (MPC) and their auxiliaries were designed and implemented in a real-time framework. The latter was realized through retrofitting an existing plate-and-frame heat exchanger unit that has been operated using an analog PID temperature controller. The upgraded control system consists of a personal computer (PC), low-cost signal conditioning circuit, national instruments USB 6008 data acquisition card, and LabVIEW software. LabVIEW control design and simulation modules were used to design and implement the PID and MPC controllers. The performance of the designed controllers was evaluated while controlling the outlet temperature of the retrofitted plate-and-frame heat exchanger. The distinguished feature of the MPC controller in handling input and output constraints was perceived in real-time. From a pedagogical point of view, realizing the theory of process control through practical implementation was substantial in enhancing the student’s learning and the instructor’s teaching experience.
More Related Content
Similar to Lion optimization algorithm for team orienteering problem with time window
Effects of The Different Migration Periods on Parallel Multi-Swarm PSO csandit
In recent years, there has been an increasing inter
est in parallel computing. In parallel
computing, multiple computing resources are used si
multaneously in solving a problem. There
are multiple processors that will work concurrently
and the program is divided into different
tasks to be simultaneously solved. Recently, a cons
iderable literature has grown up around the
theme of metaheuristic algorithms. Particle swarm o
ptimization (PSO) algorithm is a popular
metaheuristic algorithm. The parallel comprehensive
learning particle swarm optimization
(PCLPSO) algorithm based on PSO has multiple swarms
based on the master-slave paradigm
and works cooperatively and concurrently. The migra
tion period is an important parameter in
PCLPSO and affects the efficiency of the algorithm.
We used the well-known benchmark
functions in the experiments and analysed the perfo
rmance of PCLPSO using different
migration periods.
This document discusses various artificial intelligence techniques for robot path planning, including ant colony optimization. It provides background on particle swarm optimization, genetic algorithms, tabu search, simulated annealing, reactive search optimization, and ant colony algorithms. It then proposes a solution for robotic path planning that uses ant colony optimization. The proposed solution involves defining a source and destination point for the robot, moving it forward one step at a time while checking for obstacles, having it take three steps back if an obstacle is encountered, and applying ant colony optimization algorithms to help the robot find an optimal path to bypass obstacles and reach the destination point.
Artificial Intelligence in Robot Path Planningiosrjce
IOSR Journal of Computer Engineering (IOSR-JCE) is a double blind peer reviewed International Journal that provides rapid publication (within a month) of articles in all areas of computer engineering and its applications. The journal welcomes publications of high quality papers on theoretical developments and practical applications in computer technology. Original research papers, state-of-the-art reviews, and high quality technical notes are invited for publications.
This document provides an overview of a survey of multi-objective evolutionary algorithms for data mining tasks. It discusses key concepts in multi-objective optimization and evolutionary algorithms. It also reviews common data mining tasks like feature selection, classification, clustering, and association rule mining that are often formulated as multi-objective problems and solved using multi-objective evolutionary algorithms. The survey focuses on reviewing applications of multi-objective evolutionary algorithms for feature selection and classification in part 1, and applications for clustering, association rule mining and other tasks in part 2.
This document discusses using genetic algorithms to solve a multi-objective traveling salesman problem (MOTSP) that considers both cost and CO2 emissions. It provides background on genetic algorithms and the traveling salesman problem. The study aims to identify how tuning genetic operators and parameters can improve the efficiency of genetic algorithms in solving the MOTSP with CO2 emissions. Empirical results show that performance improves with some combinations of parameters and operators.
This document summarizes an article that proposes improvements to an existing algorithm for resource scheduling in cloud computing environments. The existing algorithm uses a hybrid of ant colony optimization and particle swarm optimization. The proposed improvements add an initial phase that uses an enhanced fish swarm search algorithm to help find more global optimal solutions. This global optimal solution found by fish swarm search is then used to guide the existing ant colony optimization and particle swarm optimization hybrid to find more locally optimal solutions. The document provides background on resource scheduling, metaheuristic algorithms, and describes the specific implementations of the improved fish swarm search algorithm and the overall proposed methodology.
This document provides an introduction to multi-objective optimization using evolutionary algorithms. It discusses how evolutionary algorithms are well-suited for multi-objective optimization problems as they use a population approach to find multiple non-dominated solutions simultaneously. The document outlines the basic principles of evolutionary optimization for single-objective problems and then describes the key concepts and operating principles of evolutionary multi-objective optimization.
Hybrid iterated local search algorithm for optimization route of airplane tr...IJECEIAES
The traveling salesman problem (TSP) is a very popular combinatorics problem. This problem has been widely applied to various real problems. The TSP problem has been classified as a Non-deterministic Polynomial Hard (NP-Hard), so a non-deterministic algorithm is needed to solve this problem. However, a non-deterministic algorithm can only produce a fairly good solution but does not guarantee an optimal solution. Therefore, there are still opportunities to develop new algorithms with better optimization results. This research develops a new algorithm by hybridizing three local search algorithms, namely, iterated local search (ILS) with simulated annealing (SA) and hill climbing (HC), to get a better optimization result. This algorithm aimed to solve TSP problems in the transportation sector, using a case study from the Traveling Salesman Challenge 2.0 (TSC 2.0). The test results show that the developed algorithm can optimize better by 15.7% on average and 11.4% based on the best results compared to previous studies using the TabuSA algorithm.
Towards a formal analysis of the multi-robot task allocation problem using se...journalBEEI
Nowadays, the multi-robot task allocation problem is one of the most challenging problems in multi-robot systems. It concerns the optimal assignment of a set of tasks to several robots while optimizing a given criterion subject to some constraints. This problem is very complex, particularly when handling large groups of robots and tasks. We propose a formal analysis of the task allocation problem in a multi-robot system, based on set theory concepts. We believe that this analysis will help researchers understand the nature of the problem, its time complexity, and consequently develop efficient solutions. Also, we used that formal analysis to formulate two well-known taxonomies of multi-robot task allocation problems. Finally, a generic solving scheme of multi-robot task allocation problems is proposed and illustrated on assigning papers to reviewers within a journal.
Applications and Analysis of Bio-Inspired Eagle Strategy for Engineering Opti...Xin-She Yang
This document discusses applying an eagle strategy inspired by nature to engineering optimization problems. The eagle strategy uses a two-stage approach combining global exploration with local exploitation. Global exploration uses Lèvy flights for random walks to diversify solutions. Promising solutions are then locally optimized using an efficient local search algorithm like particle swarm optimization. The document analyzes random walk models like Lèvy flights and how they can maintain diversity in swarm intelligence algorithms. It applies the eagle strategy to four engineering design problems, finding Lèvy flights can effectively reduce computational efforts.
The optimization of running queries in relational databases using ant colony ...ijdms
The issue of optimizing queries is a cost-sensitive
process and with respect to the number of associat
ed
tables in a query, its number of permutations grows
exponentially. On one hand, in comparison with oth
er
operators in relational database, join operator is
the most difficult and complicated one in terms of
optimization for reducing its runtime. Accordingly,
various algorithms have so far been proposed to so
lve
this problem. On the other hand, the success of any
database management system (DBMS) means
exploiting the query model. In the current paper, t
he heuristic ant algorithm has been proposed to sol
ve this
problem and improve the runtime of join operation.
Experiments and observed results reveal the efficie
ncy
of this algorithm compared to its similar algorithm
s.
Spider Monkey optimization (SMO) algorithm is newest addition in class of swarm intelligence. SMO is a population based stochastic meta-heuristic. It is motivated by intelligent foraging behaviour of fission fusion structured social creatures. SMO is a very good option for complex optimization problems. This paper proposed a modified strategy in order to enhance performance of original SMO. This paper introduces a position update strategy in SMO and modifies both local leader and global leader phase. The proposed strategy is named as Modified Position Update in Spider Monkey Optimization (MPU-SMO) algorithm. The proposed algorithm tested over benchmark problems and results show that it gives better results for considered unbiased problems.
Solving np hard problem artificial bee colony algorithmIAEME Publication
The document discusses using an artificial bee colony (ABC) algorithm to solve the NP-hard shortest common supersequence problem. It begins by introducing ABC algorithms and their inspiration from honey bee behavior. It then discusses previous approaches to solving the shortest common supersequence problem and outlines the proposed ABC approach. The ABC approach generates candidate solutions, calculates their fitness by comparing them to input strings, and iteratively improves solutions until termination criteria are met. Experimental results show the ABC approach finds solutions close to optimal.
Solving np hard problem using artificial bee colony algorithmIAEME Publication
The document presents an artificial bee colony (ABC) algorithm to solve the NP-hard shortest common supersequence problem. The ABC algorithm is inspired by the foraging behavior of honey bees. It represents solutions as food sources and uses employed, onlooker, and scout bees to explore the search space. The algorithm calculates character frequencies in input strings to guide random supersequence generation. Fitness is evaluated by comparing sequences using a modified merge algorithm. Results show the ABC approach finds near-optimal solutions compared to other algorithms for solving shortest common supersequences.
Solving np hard problem artificial bee colony algorithmIAEME Publication
The document discusses using an artificial bee colony (ABC) algorithm to solve the NP-hard shortest common supersequence problem. It begins by introducing ABC algorithms and their inspiration from honey bee behavior. It then discusses previous approaches to solving the shortest common supersequence problem and outlines the proposed ABC approach. The ABC approach calculates character frequencies, generates candidate solutions randomly based on frequencies, evaluates candidates against input strings, and iteratively improves candidates until termination criteria are met. Experimental results show the ABC approach finds solutions close to optimal.
Solving np hard problem using artificial bee colony algorithmIAEME Publication
The document presents an artificial bee colony (ABC) algorithm to solve the NP-hard shortest common supersequence problem. The ABC algorithm is inspired by the foraging behavior of honey bees. It represents solutions as food sources and uses employed, onlooker, and scout bees to explore the search space. The algorithm calculates character frequencies in input strings to guide random supersequence generation. Fitness is evaluated by comparing sequences using a modified merge algorithm. Results show the ABC approach finds near-optimal solutions compared to other algorithms for solving shortest common supersequences.
International Journal of Computational Engineering Research (IJCER) is dedicated to protecting personal information and will make every reasonable effort to handle collected information appropriately. All information collected, as well as related requests, will be handled as carefully and efficiently as possible in accordance with IJCER standards for integrity and objectivity.
Simulation-based Optimization of a Real-world Travelling Salesman Problem Usi...CSCJournals
This paper presents a real-world case study of optimizing waste collection in Sweden. The problem, involving approximately 17,000 garbage bins served by three bin lorries, is approached as a travelling salesman problem and solved using simulation-based optimization and an evolutionary algorithm. To improve the performance of the evolutionary algorithm, it is enhanced with a repair function that adjusts its genome values so that shorter routes are found more quickly. The algorithm is tested using two crossover operators, i.e., the order crossover and heuristic crossover, combined with different mutation rates. The results indicate that the order crossover is superior to the heuristics crossover, but that the driving force of the search process is the mutation operator combined with the repair function.
Bat Algorithm is Better Than Intermittent Search StrategyXin-She Yang
This document compares the bat algorithm to the intermittent search strategy for balancing exploration and exploitation in metaheuristic optimization algorithms. It reviews several metaheuristic algorithms and analyzes the theoretical basis for optimal balancing of exploration and exploitation phases. Equations are presented for the optimal ratio of exploration and exploitation phases in 2D problems based on the intermittent search strategy. The bat algorithm is described and its ability to achieve near-optimal balancing is demonstrated through numerical experiments on test functions. The document concludes higher dimensional problems require more exploration effort to find global optima with limited computations.
Convergence tendency of genetic algorithms and artificial immune system in so...ijcsity
By the advances in the Evolution Algorithms (EAs) and the intelligent optimization metho
ds we witness the
big revolutions in solving the optimization problems. The application of the evolution algorithms are not
only not limited to the combined optimization problems, but also are vast in domain to the continuous
optimization problems. In this
paper we analyze and study the Genetic Algorithm (GA) and the Artificial
Immune System (AIS)
algorithm
which are capable in escaping the local optimization and also fastening
reaching the global optimization and to show the efficiency of the GA and AIS th
e application of them in
Solving Continuous Optimization Functions (SCOFs) are studied. Because of the multi variables and the
multi
-
dimensional spaces in SCOFs the use of the classic optimization methods, is generally non
-
efficient
and high cost. In other
words the use of the classic optimization methods for SCOFs generally leads to a
local optimized solution. A possible solution for SCOFs is to use the EAs which are high in probability of
succeeding reaching the local optimized solution. The results in pa
per show that GA is more efficient than
AIS in reaching the optimized solution in SCOFs.
Similar to Lion optimization algorithm for team orienteering problem with time window (20)
Development of depth map from stereo images using sum of absolute differences...nooriasukmaningtyas
This article proposes a framework for the depth map reconstruction using stereo images. Fundamentally, this map provides an important information which commonly used in essential applications such as autonomous vehicle navigation, drone’s navigation and 3D surface reconstruction. To develop an accurate depth map, the framework must be robust against the challenging regions of low texture, plain color and repetitive pattern on the input stereo image. The development of this map requires several stages which starts with matching cost calculation, cost aggregation, optimization and refinement stage. Hence, this work develops a framework with sum of absolute difference (SAD) and the combination of two edge preserving filters to increase the robustness against the challenging regions. The SAD convolves using block matching technique to increase the efficiency of matching process on the low texture and plain color regions. Moreover, two edge preserving filters will increase the accuracy on the repetitive pattern region. The results show that the proposed method is accurate and capable to work with the challenging regions. The results are provided by the Middlebury standard dataset. The framework is also efficiently and can be applied on the 3D surface reconstruction. Moreover, this work is greatly competitive with previously available methods.
Model predictive controller for a retrofitted heat exchanger temperature cont...nooriasukmaningtyas
This paper aims to demonstrate the practical aspects of process control theory for undergraduate students at the Department of Chemical Engineering at the University of Bahrain. Both, the ubiquitous proportional integral derivative (PID) as well as model predictive control (MPC) and their auxiliaries were designed and implemented in a real-time framework. The latter was realized through retrofitting an existing plate-and-frame heat exchanger unit that has been operated using an analog PID temperature controller. The upgraded control system consists of a personal computer (PC), low-cost signal conditioning circuit, national instruments USB 6008 data acquisition card, and LabVIEW software. LabVIEW control design and simulation modules were used to design and implement the PID and MPC controllers. The performance of the designed controllers was evaluated while controlling the outlet temperature of the retrofitted plate-and-frame heat exchanger. The distinguished feature of the MPC controller in handling input and output constraints was perceived in real-time. From a pedagogical point of view, realizing the theory of process control through practical implementation was substantial in enhancing the student’s learning and the instructor’s teaching experience.
Control of a servo-hydraulic system utilizing an extended wavelet functional ...nooriasukmaningtyas
Servo-hydraulic systems have been extensively employed in various industrial applications. However, these systems are characterized by their highly complex and nonlinear dynamics, which complicates the control design stage of such systems. In this paper, an extended wavelet functional link neural network (EWFLNN) is proposed to control the displacement response of the servo-hydraulic system. To optimize the controller's parameters, a recently developed optimization technique, which is called the modified sine cosine algorithm (M-SCA), is exploited as the training method. The proposed controller has achieved remarkable results in terms of tracking two different displacement signals and handling external disturbances. From a comparative study, the proposed EWFLNN controller has attained the best control precision compared with those of other controllers, namely, a proportional-integralderivative (PID) controller, an artificial neural network (ANN) controller, a wavelet neural network (WNN) controller, and the original wavelet functional link neural network (WFLNN) controller. Moreover, compared to the genetic algorithm (GA) and the original sine cosine algorithm (SCA), the M-SCA has shown better optimization results in finding the optimal values of the controller's parameters.
Decentralised optimal deployment of mobile underwater sensors for covering la...nooriasukmaningtyas
This paper presents the problem of sensing coverage of layers of the ocean in three dimensional underwater environments. We propose distributed control laws to drive mobile underwater sensors to optimally cover a given confined layer of the ocean. By applying this algorithm at first the mobile underwater sensors adjust their depth to the specified depth. Then, they make a triangular grid across a given area. Afterwards, they randomly move to spread across the given grid. These control laws only rely on local information also they are easily implemented and computationally effective as they use some easy consensus rules. The feature of exchanging information just among neighbouring mobile sensors keeps the information exchange minimum in the whole networks and makes this algorithm practicable option for undersea. The efficiency of the presented control laws is confirmed via mathematical proof and numerical simulations.
Evaluation quality of service for internet of things based on fuzzy logic: a ...nooriasukmaningtyas
The development of the internet of thing (IoT) technology has become a major concern in sustainability of quality of service (SQoS) in terms of efficiency, measurement, and evaluation of services, such as our smart home case study. Based on several ambiguous linguistic and standard criteria, this article deals with quality of service (QoS). We used fuzzy logic to select the most appropriate and efficient services. For this reason, we have introduced a new paradigmatic approach to assess QoS. In this regard, to measure SQoS, linguistic terms were collected for identification of ambiguous criteria. This paper collects the results of other work to compare the traditional assessment methods and techniques in IoT. It has been proven that the comparison that traditional valuation methods and techniques could not effectively deal with these metrics. Therefore, fuzzy logic is a worthy method to provide a good measure of QoS with ambiguous linguistic and criteria. The proposed model addresses with constantly being improved, all the main axes of the QoS for a smart home. The results obtained also indicate that the model with its fuzzy performance importance index (FPII) has efficiently evaluate the multiple services of SQoS.
Low power architecture of logic gates using adiabatic techniquesnooriasukmaningtyas
The growing significance of portable systems to limit power consumption in ultra-large-scale-integration chips of very high density, has recently led to rapid and inventive progresses in low-power design. The most effective technique is adiabatic logic circuit design in energy-efficient hardware. This paper presents two adiabatic approaches for the design of low power circuits, modified positive feedback adiabatic logic (modified PFAL) and the other is direct current diode based positive feedback adiabatic logic (DC-DB PFAL). Logic gates are the preliminary components in any digital circuit design. By improving the performance of basic gates, one can improvise the whole system performance. In this paper proposed circuit design of the low power architecture of OR/NOR, AND/NAND, and XOR/XNOR gates are presented using the said approaches and their results are analyzed for powerdissipation, delay, power-delay-product and rise time and compared with the other adiabatic techniques along with the conventional complementary metal oxide semiconductor (CMOS) designs reported in the literature. It has been found that the designs with DC-DB PFAL technique outperform with the percentage improvement of 65% for NOR gate and 7% for NAND gate and 34% for XNOR gate over the modified PFAL techniques at 10 MHz respectively.
A review on techniques and modelling methodologies used for checking electrom...nooriasukmaningtyas
The proper function of the integrated circuit (IC) in an inhibiting electromagnetic environment has always been a serious concern throughout the decades of revolution in the world of electronics, from disjunct devices to today’s integrated circuit technology, where billions of transistors are combined on a single chip. The automotive industry and smart vehicles in particular, are confronting design issues such as being prone to electromagnetic interference (EMI). Electronic control devices calculate incorrect outputs because of EMI and sensors give misleading values which can prove fatal in case of automotives. In this paper, the authors have non exhaustively tried to review research work concerned with the investigation of EMI in ICs and prediction of this EMI using various modelling methodologies and measurement setups.
Smart monitoring system using NodeMCU for maintenance of production machinesnooriasukmaningtyas
Maintenance is an activity that helps to reduce risk, increase productivity, improve quality, and minimize production costs. The necessity for maintenance actions will increase efficiency and enhance the safety and quality of products and processes. On getting these conditions, it is necessary to implement a monitoring system used to observe machines' conditions from time to time, especially the machine parts that often experience problems. This paper presents a low-cost intelligent monitoring system using NodeMCU to continuously monitor machine conditions and provide warnings in the case of machine failure. Not only does it provide alerts, but this monitoring system also generates historical data on machine conditions to the Google Cloud (Google Sheet), includes which machines were down, downtime, issues occurred, repairs made, and technician handling. The results obtained are machine operators do not need to lose a relatively long time to call the technician. Likewise, the technicians assisted in carrying out machine maintenance activities and online reports so that errors that often occur due to human error do not happen again. The system succeeded in reducing the technician-calling time and maintenance workreporting time up to 50%. The availability of online and real-time maintenance historical data will support further maintenance strategy.
Design and simulation of a software defined networkingenabled smart switch, f...nooriasukmaningtyas
Using sustainable energy is the future of our planet earth, this became not only economically efficient but also a necessity for the preservation of life on earth. Because of such necessity, smart grids became a very important issue to be researched. Many literatures discussed this topic and with the development of internet of things (IoT) and smart sensors, smart grids are developed even further. On the other hand, software defined networking is a technology that separates the control plane from the data plan of the network. It centralizes the management and the orchestration of the network tasks by using a network controller. The network controller is the heart of the SDN-enabled network, and it can control other networking devices using software defined networking (SDN) protocols such as OpenFlow. A smart switching mechanism called (SDN-smgrid-sw) for the smart grid will be modeled and controlled using SDN. We modeled the environment that interact with the sensors, for the sun and the wind elements. The Algorithm is modeled and programmed for smart efficient power sharing that is managed centrally and monitored using SDN controller. Also, all if the smart grid elements (power sources) are connected to the IP network using IoT protocols.
Efficient wireless power transmission to remote the sensor in restenosis coro...nooriasukmaningtyas
In this study, the researchers have proposed an alternative technique for designing an asymmetric 4 coil-resonance coupling module based on the series-to-parallel topology at 27 MHz industrial scientific medical (ISM) band to avoid the tissue damage, for the constant monitoring of the in-stent restenosis coronary artery. This design consisted of 2 components, i.e., the external part that included 3 planar coils that were placed outside the body and an internal helical coil (stent) that was implanted into the coronary artery in the human tissue. This technique considered the output power and the transfer efficiency of the overall system, coil geometry like the number of coils per turn, and coil size. The results indicated that this design showed an 82% efficiency in the air if the transmission distance was maintained as 20 mm, which allowed the wireless power supply system to monitor the pressure within the coronary artery when the implanted load resistance was 400 Ω.
Grid reactive voltage regulation and cost optimization for electric vehicle p...nooriasukmaningtyas
Expecting large electric vehicle (EV) usage in the future due to environmental issues, state subsidies, and incentives, the impact of EV charging on the power grid is required to be closely analyzed and studied for power quality, stability, and planning of infrastructure. When a large number of energy storage batteries are connected to the grid as a capacitive load the power factor of the power grid is inevitably reduced, causing power losses and voltage instability. In this work large-scale 18K EV charging model is implemented on IEEE 33 network. Optimization methods are described to search for the location of nodes that are affected most due to EV charging in terms of power losses and voltage instability of the network. Followed by optimized reactive power injection magnitude and time duration of reactive power at the identified nodes. It is shown that power losses are reduced and voltage stability is improved in the grid, which also complements the reduction in EV charging cost. The result will be useful for EV charging stations infrastructure planning, grid stabilization, and reducing EV charging costs.
Topology network effects for double synchronized switch harvesting circuit on...nooriasukmaningtyas
Energy extraction takes place using several different technologies, depending on the type of energy and how it is used. The objective of this paper is to study topology influence for a smart network based on piezoelectric materials using the double synchronized switch harvesting (DSSH). In this work, has been presented network topology for circuit DSSH (DSSH Standard, Independent DSSH, DSSH in parallel, mono DSSH, and DSSH in series). Using simulation-based on a structure with embedded piezoelectric system harvesters, then compare different topology of circuit DSSH for knowledge is how to connect the circuit DSSH together and how to implement accurately this circuit strategy for maximizing the total output power. The network topology DSSH extracted power a technique allows again up to in terms of maximal power output compared with network topology standard extracted at the resonant frequency. The simulation results show that by using the same input parameters the maximum efficiency for topology DSSH in parallel produces 120% more energy than topology DSSH-series. In addition, the energy harvesting by mono-DSSH is more than DSSH-series by 650% and it has exceeded DSSHind by 240%.
Improving the design of super-lift Luo converter using hybrid switching capac...nooriasukmaningtyas
In this article, an improvement to the positive output super-lift Luo converter (POSLC) has been proposed to get high gain at a low duty cycle. Also, reduce the stress on the switch and diodes, reduce the current through the inductors to reduce loss, and increase efficiency. Using a hybrid switch unit composed of four inductors and two capacitors it is replaced by the main inductor in the elementary circuit. It’s charged in parallel with the same input voltage and discharged in series. The output voltage is increased according to the number of components. The gain equation is modeled. The boundary condition between continuous conduction mode (CCM) and discontinuous conduction mode (DCM) has been derived. Passive components are designed to get high output voltage (8 times at D=0.5) and low ripple about (0.004). The circuit is simulated and analyzed using MATLAB/Simulink. Maximum power point tracker (MPPT) controls the converter to provide the most interest from solar energy.
Third harmonic current minimization using third harmonic blocking transformernooriasukmaningtyas
Zero sequence blocking transformers (ZSBTs) are used to suppress third harmonic currents in 3-phase systems. Three-phase systems where singlephase loading is present, there is every chance that the load is not balanced. If there is zero-sequence current due to unequal load current, then the ZSBT will impose high impedance and the supply voltage at the load end will be varied which is not desired. This paper presents Third harmonic blocking transformer (THBT) which suppresses only higher harmonic zero sequences. The constructional features using all windings in single-core and construction using three single-phase transformers explained. The paper discusses the constructional features, full details of circuit usage, design considerations, and simulation results for different supply and load conditions. A comparison of THBT with ZSBT is made with simulation results by considering four different cases
Power quality improvement of distribution systems asymmetry caused by power d...nooriasukmaningtyas
With an increase of non-linear load in today’s electrical power systems, the rate of power quality drops and the voltage source and frequency deteriorate if not properly compensated with an appropriate device. Filters are most common techniques that employed to overcome this problem and improving power quality. In this paper an improved optimization technique of filter applies to the power system is based on a particle swarm optimization with using artificial neural network technique applied to the unified power flow quality conditioner (PSO-ANN UPQC). Design particle swarm optimization and artificial neural network together result in a very high performance of flexible AC transmission lines (FACTs) controller and it implements to the system to compensate all types of power quality disturbances. This technique is very powerful for minimization of total harmonic distortion of source voltages and currents as a limit permitted by IEEE-519. The work creates a power system model in MATLAB/Simulink program to investigate our proposed optimization technique for improving control circuit of filters. The work also has measured all power quality disturbances of the electrical arc furnace of steel factory and suggests this technique of filter to improve the power quality.
Studies enhancement of transient stability by single machine infinite bus sys...nooriasukmaningtyas
Maintaining network synchronization is important to customer service. Low fluctuations cause voltage instability, non-synchronization in the power system or the problems in the electrical system disturbances, harmonics current and voltages inflation and contraction voltage. Proper tunning of the parameters of stabilizer is prime for validation of stabilizer. To overcome instability issues and get reinforcement found a lot of the techniques are developed to overcome instability problems and improve performance of power system. Genetic algorithm was applied to optimize parameters and suppress oscillation. The simulation of the robust composite capacitance system of an infinite single-machine bus was studied using MATLAB was used for optimization purpose. The critical time is an indication of the maximum possible time during which the error can pass in the system to obtain stability through the simulation. The effectiveness improvement has been shown in the system
Renewable energy based dynamic tariff system for domestic load managementnooriasukmaningtyas
To deal with the present power-scenario, this paper proposes a model of an advanced energy management system, which tries to achieve peak clipping, peak to average ratio reduction and cost reduction based on effective utilization of distributed generations. This helps to manage conventional loads based on flexible tariff system. The main contribution of this work is the development of three-part dynamic tariff system on the basis of time of utilizing power, available renewable energy sources (RES) and consumers’ load profile. This incorporates consumers’ choice to suitably select for either consuming power from conventional energy sources and/or renewable energy sources during peak or off-peak hours. To validate the efficiency of the proposed model we have comparatively evaluated the model performance with existing optimization techniques using genetic algorithm and particle swarm optimization. A new optimization technique, hybrid greedy particle swarm optimization has been proposed which is based on the two aforementioned techniques. It is found that the proposed model is superior with the improved tariff scheme when subjected to load management and consumers’ financial benefit. This work leads to maintain a healthy relationship between the utility sectors and the consumers, thereby making the existing grid more reliable, robust, flexible yet cost effective.
Energy harvesting maximization by integration of distributed generation based...nooriasukmaningtyas
The purpose of distributed generation systems (DGS) is to enhance the distribution system (DS) performance to be better known with its benefits in the power sector as installing distributed generation (DG) units into the DS can introduce economic, environmental and technical benefits. Those benefits can be obtained if the DG units' site and size is properly determined. The aim of this paper is studying and reviewing the effect of connecting DG units in the DS on transmission efficiency, reactive power loss and voltage deviation in addition to the economical point of view and considering the interest and inflation rate. Whale optimization algorithm (WOA) is introduced to find the best solution to the distributed generation penetration problem in the DS. The result of WOA is compared with the genetic algorithm (GA), particle swarm optimization (PSO), and grey wolf optimizer (GWO). The proposed solutions methodologies have been tested using MATLAB software on IEEE 33 standard bus system
Intelligent fault diagnosis for power distribution systemcomparative studiesnooriasukmaningtyas
Short circuit is one of the most popular types of permanent fault in power distribution system. Thus, fast and accuracy diagnosis of short circuit failure is very important so that the power system works more effectively. In this paper, a newly enhanced support vector machine (SVM) classifier has been investigated to identify ten short-circuit fault types, including single line-toground faults (XG, YG, ZG), line-to-line faults (XY, XZ, YZ), double lineto-ground faults (XYG, XZG, YZG) and three-line faults (XYZ). The performance of this enhanced SVM model has been improved by using three different versions of particle swarm optimization (PSO), namely: classical PSO (C-PSO), time varying acceleration coefficients PSO (T-PSO) and constriction factor PSO (K-PSO). Further, utilizing pseudo-random binary sequence (PRBS)-based time domain reflectometry (TDR) method allows to obtain a reliable dataset for SVM classifier. The experimental results performed on a two-branch distribution line show the most optimal variant of PSO for short fault diagnosis.
A deep learning approach based on stochastic gradient descent and least absol...nooriasukmaningtyas
More than eighty-five to ninety percentage of the diabetic patients are affected with diabetic retinopathy (DR) which is an eye disorder that leads to blindness. The computational techniques can support to detect the DR by using the retinal images. However, it is hard to measure the DR with the raw retinal image. This paper proposes an effective method for identification of DR from the retinal images. In this research work, initially the Weiner filter is used for preprocessing the raw retinal image. Then the preprocessed image is segmented using fuzzy c-mean technique. Then from the segmented image, the features are extracted using grey level co-occurrence matrix (GLCM). After extracting the fundus image, the feature selection is performed stochastic gradient descent, and least absolute shrinkage and selection operator (LASSO) for accurate identification during the classification process. Then the inception v3-convolutional neural network (IV3-CNN) model is used in the classification process to classify the image as DR image or non-DR image. By applying the proposed method, the classification performance of IV3-CNN model in identifying DR is studied. Using the proposed method, the DR is identified with the accuracy of about 95%, and the processed retinal image is identified as mild DR.
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.
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.
Introduction- e - waste – definition - sources of e-waste– hazardous substances in e-waste - effects of e-waste on environment and human health- need for e-waste management– e-waste handling rules - waste minimization techniques for managing e-waste – recycling of e-waste - disposal treatment methods of e- waste – mechanism of extraction of precious metal from leaching solution-global Scenario of E-waste – E-waste in India- case studies.
6th International Conference on Machine Learning & Applications (CMLA 2024)ClaraZara1
6th International Conference on Machine Learning & Applications (CMLA 2024) will provide an excellent international forum for sharing knowledge and results in theory, methodology and applications of on Machine Learning & Applications.
DEEP LEARNING FOR SMART GRID INTRUSION DETECTION: A HYBRID CNN-LSTM-BASED MODELgerogepatton
As digital technology becomes more deeply embedded in power systems, protecting the communication
networks of Smart Grids (SG) has emerged as a critical concern. Distributed Network Protocol 3 (DNP3)
represents a multi-tiered application layer protocol extensively utilized in Supervisory Control and Data
Acquisition (SCADA)-based smart grids to facilitate real-time data gathering and control functionalities.
Robust Intrusion Detection Systems (IDS) are necessary for early threat detection and mitigation because
of the interconnection of these networks, which makes them vulnerable to a variety of cyberattacks. To
solve this issue, this paper develops a hybrid Deep Learning (DL) model specifically designed for intrusion
detection in smart grids. The proposed approach is a combination of the Convolutional Neural Network
(CNN) and the Long-Short-Term Memory algorithms (LSTM). We employed a recent intrusion detection
dataset (DNP3), which focuses on unauthorized commands and Denial of Service (DoS) cyberattacks, to
train and test our model. The results of our experiments show that our CNN-LSTM method is much better
at finding smart grid intrusions than other deep learning algorithms used for classification. In addition,
our proposed approach improves accuracy, precision, recall, and F1 score, achieving a high detection
accuracy rate of 99.50%.
We have compiled the most important slides from each speaker's presentation. This year’s compilation, available for free, captures the key insights and contributions shared during the DfMAy 2024 conference.
CHINA’S GEO-ECONOMIC OUTREACH IN CENTRAL ASIAN COUNTRIES AND FUTURE PROSPECTjpsjournal1
The rivalry between prominent international actors for dominance over Central Asia's hydrocarbon
reserves and the ancient silk trade route, along with China's diplomatic endeavours in the area, has been
referred to as the "New Great Game." This research centres on the power struggle, considering
geopolitical, geostrategic, and geoeconomic variables. Topics including trade, political hegemony, oil
politics, and conventional and nontraditional security are all explored and explained by the researcher.
Using Mackinder's Heartland, Spykman Rimland, and Hegemonic Stability theories, examines China's role
in Central Asia. This study adheres to the empirical epistemological method and has taken care of
objectivity. This study analyze primary and secondary research documents critically to elaborate role of
china’s geo economic outreach in central Asian countries and its future prospect. China is thriving in trade,
pipeline politics, and winning states, according to this study, thanks to important instruments like the
Shanghai Cooperation Organisation and the Belt and Road Economic Initiative. According to this study,
China is seeing significant success in commerce, pipeline politics, and gaining influence on other
governments. This success may be attributed to the effective utilisation of key tools such as the Shanghai
Cooperation Organisation and the Belt and Road Economic Initiative.
Literature Review Basics and Understanding Reference Management.pptxDr Ramhari Poudyal
Three-day training on academic research focuses on analytical tools at United Technical College, supported by the University Grant Commission, Nepal. 24-26 May 2024
bank management system in java and mysql report1.pdf
Lion optimization algorithm for team orienteering problem with time window
1. Indonesian Journal of Electrical Engineering and Computer Science
Vol. 21, No. 1, January 2021, pp. 538~545
ISSN: 2502-4752, DOI: 10.11591/ijeecs.v21.i1. pp538-545 538
Journal homepage: http://ijeecs.iaescore.com
Lion optimization algorithm for team orienteering problem
with time window
Esam Taha Yassen, Alaa Abdulkhar Jihad, Sudad H. Abed
Computer Center, University of Anbar, Al_Anbar, Iraq
Article Info ABSTRACT
Article history:
Received May 20, 2020
Revised Jul 22, 2020
Accepted Aug 2, 2020
Over the last decade, many nature-inspired algorithms have been received
considerable attention among practitioners and researchers to handle several
optimization problems. Lion optimization algorithm (LA) is inspired by a
distinctive lifestyle of lions and their collective behavior in their social
groups. LA has been presented as a powerful optimization algorithm to solve
various optimization problems. In this paper, the LA is proposed to
investigate its performance in solving one of the most popular and
widespread real-life optimization problems called team orienteering problem
with time windows (TOPTW). However, as any population-based
metaheuristic, the LA is very efficient in exploring the search space, but
inefficient in exploiting it. So, this paper proposes enhancing LA to tackle
the TOPTW by utilizing its strong ability to explore the search space and
improving its exploitation ability. This enhancement is achieved via
improving a process of territorial defense to generate a trespass strong
nomadic lion to prevail a pride by fighting its males. As a result of this
improving process, an enhanced LA (ILA) emerged. The obtained solutions
have been compared with the best known and standard results obtained in the
former studies. The conducted experimental test verifies the effectiveness of
the ILA in solving the TOPTW as it obtained a very competitive results
compared to the LA and the state-of-the-art methods across all tested
instances.
Keywords:
Lion optimization algorithm
Nature-inspired algorithms
Population-based metaheuristic
Team orienteering problem
This is an open access article under the CC BY-SA license.
Corresponding Author:
Esam Taha Yassen
Computer Center
University of Anbar
Al_Anbar, Iraq
Email: co.esamtaha@uoanbar.edu.iq
1. INTRODUCTION
It is often very difficult for tourists who plan to visit a place for few days to visit all the sites they
are fascinated with. Consequently, tourists need to determine what they consider to be the most valued
attractions. The task of making a practicable plan to visit the most exciting places within the limited available
time is surely difficult [1].
As described above, the problem was first introduced as the Orienteering Problem (OP) by
Tsiligrides [2] (also, the problem is sometimes named as the Selective Traveling Salesman Problem, see
Laporte and Martello [3]). Orienteering is a sport in which a player (competitor) has to select a path from a
starting point (all competitors start out form the same point) to a final destination, going through control
points alongside the path. A score is associated with every control point. A travel cost is associated with each
pair of control points. The competitor has to select a group of control points to visit; hence the overall score
is maximized, while the total travel cost does not exceed a given threshold [4].
2. Indonesian J Elec Eng & Comp Sci ISSN: 2502-4752
Lion optimization algorithm for team orienteering problem with time window (Esam Taha Yassen)
539
The Team Orienteering Problem (TOP) describes as a generalization of the OP in which the
competitors are organized in teams [5]. The TOP is known as a model of numerous real life applications, like
the multi-vehicle version of the home fuel delivery problem [6, 7], selecting college football players [8], the
sport game of team orienteering [9], several applications related to pick up and delivery services including
public carriers and private fleets [10], and the routing technicians scheduling service [11].
Several variants of the TOP exist, and of them is the one that considers Time Window constraints
(TOPTW). These constraints are inspired by several practical conditions and usually appear in routing
problems in which every location must be visited within a previously identified time interval (the earliest and
the latest time) at which the service should begin [5]. A list of places (points) is assigned in the TOPTW and
every place is given a score, a service time and a time window (open and close). The aim is to maximize the
total number of gained scores by with fixed number. These routes should be with a limited length and should
also permit visiting locations at the precise time allocating a group of routes [5].
For TOPTW, an Ant Colony System is suggested by Montemanni and Gambardella [5]. Further
improve Ant Colony System is proposed by Montemanni et. al. [4]. The algorithm includes two extra operations
to enhance the ability of Ant Colony System. These operations focus on utilizing the best obtained solutions
during the construction phase and applying the local search procedure on those solutions [4]. Two different
versions of Simulated Annealing are introduced by Lin and Yu [12]; FastSA and SlowSA. FastSA is mainly
applied when the quick responses are needed, while SlowSA is concerned about the solutions’ qualities in
exchange for more computational time [12]. The hybridization of the Greedy Randomized Adaptive Search
Procedure (GRASP) and Evolutionary Local Search is proposed by Labadie et al [13] for the TOPTW. Hu and
Lim [14] introduced a hybrid algorithm based on local search, Simulated Annealing and Route Combination.
Within a particular number of iterations, this hybridization is iteratively incorporated those three components
[14]. Cura [15] proposes hybridization based on an Artificial Bee Colony and simulated annealing in order to
improve the TOPTW solution quality [15]. In order to achieve the balance between diversification and
intensification, Gunawan et. al. [16] suggest an Iterated Local Search based on swap, 2-opt, insert and replace
neighborhood structures. Additionally, they implemented the combination between acceptance criterion and
perturbation mechanisms. A hybridization between Simulated Annealing and Iterated Local Search is proposed
by Gunawan et. al. [17, 18] to solve the TOPTW. A hybrid artificial bee colony algorithm is introduced by Yu
et. al. [19] to obtain an optimal solutions for small instances and near-optimal solutions for larger instances.
Based on the above and to the best knowledge of the researchers, the need to utilize a new algorithm is
still necessary. Lion Optimization Algorithm (LA) which is inspired by special lifestyle of lions and their
collective behavior in their social groups [20]. LA has been presented as a powerful optimization algorithm to
solve various optimization problems. This stimulates the current study to propose LA for solving the TOPTW.
2. LION ALGORITHM
Natural computation is an effective computing area that aims at developing search, optimization and
machine learning algorithms by analyzing natural phenomena and model it to problem solving conditions
[21]. Lion’s algorithm is proposed based on the lions’ social behavior, which is to be the strongest in every
generation. LA searches an optimal solution based on two unique lions’ behaviors, namely territorial defense
and territorial takeover. Territorial defense happens between the occupant males and wandering males while
the territorial takeover happens between the old resident male and new mature resident male [20, 22].
Definition 1: A Lion represents a solution to be identified, while the cubs are solutions produced from
current solutions [20].
Definition 2: Territorial Defense is meant to be the procedure of assessing the current solution (territorial
lion) and produced solution (nomadic lion). More specifically; the current solution is replaced by the new
solution whenever the new solution sounds better than the current one. Hence, all the obtained solutions of
old solution are vanished [20].
Definition 3: Territorial Takeover is defined as the act of retaining the best derived male and female
solutions. These solutions are rather more efficient than new solutions. Thus, current solutions are vanished
in the pride [20].
Algirithm 1. Lion Algorithm [20]
Step 1: Pride Generation
Step 1.1: Generate territorial male and female subject to solution constraints
Step 2: Mating
Step 2.1: Crossover
Step 2.2: Mutation
Step 2.3: Gender grouping
Step 2.4: Kill sick/ weak cubs
Step 2.5: Update Pride
3. ISSN: 2502-4752
Indonesian J Elec Eng & Comp Sci, Vol. 21, No. 1, January 2021 : 538 - 545
540
Step 3: Territorial Defense
Step 3.1: Keep the record of cubs’ age
Step 3.2: Do
Step 3.2.1: Generate and Trespass Nomadic lion
Until stronger Nomadic lion trespasses
Until Cubs get matured
Step 4: Territorial Takeover
Step 4.1: Selection of best lion and lionesses
Step 4.2: Go to Step 2 until termination criteria is met
LA can be commonly classified into four major steps on the basis of the nature of the functions they
perform. These steps are, (i) Pride Generation, which is in charge of producing solutions, (ii) Mating, which
denotes the process of generating new solutions, (iii) Territorial Defense and (iv) Territorial Takeover, which
both aim to substitute the new most excellent quality solution with the identified poorest quality solution.
This process keeps iterating to allow heuristic search meeting the nearer solution to the preferable one [20].
Definition 4: A constantly altering solution pool and as well as altering size is representing pride. It starts
with a couple of random solutions, one of these solutions refers to the male while the other one refers to the
female. In that pool, the action of updating the generated solutions and the vanishing of undesirable ones take
place [20].
Definition 5: Mating refers to the process of generating the new finest solutions from the existing solutions
that involve crossover and mutation for generating new solutions, gathering genders that help to discover
divergence within the solutions. Assuring that the newly generated solutions are the best by eliminating sick/
weak cubs [20].
The LA’s search procedure primarily concentrates on detecting the best solutions, which solve the objective
function i.e. minimize or maximize the objective function.
Definition 6: Gender grouping is the action that splits the obtained solution pool into a pair of collections,
where there is a collection that includes the male cubs and the other includes the females [20].
3. IMPROVED LION ALGORITHM
Despite the positive characteristics of LA and its successful application to address various problems,
it still suffers from slow convergence problem when addressing constraint problems especially TOPTW. As
any population-based metaheuristic [23-25], this problem is attributed to the fact that LA is effective in
exploring the search space but ineffective in exploiting it. In order to improve this issue, an Improved Lion
Algorithm (ILA) is proposed. In ILA, a territorial defense (algorithm 1, step 3) is improved through replacing
the traditional process of generate and trespass nomadic lion with a new one. More precisely, instead of
randomly generate a nomadic lion; a new process aims to introduce a strong nomadic lion to take over a pride
by fighting its males. This aim is achieved via utilizing different criteria during the search to select which
solution is the best to handle the pride. According to TOPTW, four criteria have a significant effect on the
performance of the generated solution; closing time window (C_time), opening time window (O_time), profit
of the location (Prof) and distance between two vertexes (Dist). As a result of these criteria, five generating
heuristics are emerged to select the most appropriate one which is adopted to generate the strong nomadic
lion. These heuristics are: Heu1, Heu2, Heu3, Heu4 and Heu5. The generation process is the same for all
heuristics, only the adopting criteria change from one heuristic to another.
a) Heu1: This generating heuristic deals with two criteria out of four which are the closing time window
C_time and distance Dist. In this method, the goal is to search for a location with the nearest distance
Dist and close time C_time to be adopted in the generated solution.
b) Heu2: In this method three criteria have been taking into consideration. Those three criteria are open
time window O_time, close time window C_time and distance Dist. this method tries to select a location
with the largest open time window O_time and the nearest distance Dist and close time C_time.
c) Heu3: This method deals with three criteria which are Prof, C_time, and Dist. This method tries to
select a location based on a threshold number that can be obtained by the multiply the Dist by the result
of dividing the Prof on C_time.
d) Heu4: This method deals with two criteria which are Prof and Dist. trying to find a threshold number
by dividing the Prof by Dist to decide whether to select a location or not.
e) Heu5: This method deals with only two criteria which are the Prof and Dist. In this method, the aim is
to select a location with the largest profit and nearest distance.
The following algorithm illustrates the process of generating a nomadic lion (Solution) (Algorithm 2).
4. Indonesian J Elec Eng & Comp Sci ISSN: 2502-4752
Lion optimization algorithm for team orienteering problem with time window (Esam Taha Yassen)
541
Algoritm 2. Generating a nomadic lion
Repate
for each player (i)
select_vertex = null
thresholdselect_vertex = 0
for each unvisited Vertex (uver)
get the criteria values (C_timeuver, O_timeuver, Distuver, Profuver)
Normalize these values
calculate the thresholduver according to the selected heuristic (Heu1,
Heu2, Heu3, Heu4 and Heu5)
randomly generate ratio number
if ratio < thresholduver and thresholduver better than
thresholdselect_vertex
select_vertex = uver
Add this select_vertex to the path of playeri :
Path=Path ∪ select_vertex
Until no vertex can be visited
Return Solution
4. EXPERIMENTAL SETUP
The proposed algorithms (LA and ILA) are programmed and executed with C# which was running
on a PC equipped with windows7, Intel processor core i7 CPU 2.10GHz, RAM 8.00GB. In this work, the
appropriate values for the parameters of the proposed algorithms are determined based on a preliminary test
or as suggested by previous studies. The proposed algorithms, during the preliminary test, are executed 10
runs on some instances. Finally, the parameter settings that have been adopted in this work are summarized
in Table 1.
Table 1. The parameter settings
Parameter Value
gen_max 300
k 2
Agemat 5
4.1. The toptw benchmark
In order to evaluate the performance of LA and ILA, a larger benchmark is adopted. This
benchmark is provided by Vansteenwegen et. al. [1] based on Solomon’s and Cordeau et al.’s benchmarks;
the instances of these benchmarks can be publicly found at (https://www.mech.kuleuven.be/en/cib/op). It
should be taken into consideration that the benchmarks used in this work contain instances with different
situations related to nodes’ positions, scores, service times and the density of time windows. Those
benchmarks were designed for the Vehicle Routing Problem with Time Windows (VRPTW) and the Multi
Depot Periodic VRPTW respectively. Hence, these benchmarks are very realistic. Additionally, these
benchmarks have been adopted by most studies to evaluate the effectiveness of their heuristics [26].
The Solomon’s benchmark involves 56 instances each of them has 100 nodes. According to the
distribution of nodes, the Solomon’s instances are categorized into three classes (R, C and RC). Class C
contains clustered nodes in which the distances between nodes are short. Class R contains randomly
distributed customers in which the distances between nodes are comparatively longer than class C. Class RC
contains mixed distribution nodes (clustered and randomized). The Cordeau et al.’s benchmark involves 20
instances each of them has different number of nodes; varying from 48 to 288 nodes (pr01–pr20) [1].
4.2. Results and discussion
In order to investigate the performance of the LA and ILA for TOPTW, we conduct three sets of
experiments. In the first experiments, the efficiency of LA in solving VRPTW is evaluated. In the second
experimental test, the effect of the process that generates a nomadic lion on the LA’s performance is
analyzed. Finally, the results of ILA are compared with those of standard LA. The proposed algorithms were
executed for 11 independent runs. Then, the best (Max), average (Avr) and minimum (Min) are reported. The
proposed algorithms are tested on Solomon’s and Cordeau et al.’s benchmarks.
4.2.1. The Standard LA results
In this experiment, the effectiveness of LA in solving the TOPTW is investigated. Table 2 presents
the Max, Avr and Min for LA over 11 runs.
5. ISSN: 2502-4752
Indonesian J Elec Eng & Comp Sci, Vol. 21, No. 1, January 2021 : 538 - 545
542
Table 2. Results of standard LA
Instance Max Avr Min Instance Max Avr Min
50_c101 850 750 650 pr01 297 252.5 208
50_c102 800 755 710 pr02 620 547.5 475
50_r101 146 115 84 r101 371 345 319
50_r102 655 594.5 534 r102 1331 1185.5 1040
50_rc101 640 485 330 r201 1309 1249.5 1190
50_rc102 960 850 740 r202 1202 1138 1074
c101 1570 1520 1470 rc101 916 845.5 775
c102 1570 1500 1430 rc102 1506 1224.5 943
c201 1580 1545 1510 rc201 1378 1345.5 1313
c202 1640 1585 1530 rc202 1222 1155 1088
4.2.2. The effect of generating a nomadic lion process
In this experiment, the effect of the process to generate a nomadic lion on the LA’s performance is
investigated. So, the effectiveness of different suggested heuristics based on different criteria are verified to
select the most appropriate one which is adopted to generate a strong nomadic lion. The results of these
methods are presented in Table 3. These results show that the Heu1 gained the best results on 16 instances
out of 20 tested instances. Consequently, due to the fact that the Heu1 is more efficient than others as it
obtained the based results, it will be adopted in ILA.
The results of the ILA are compared with these of the standard LA. Twenty instances are selected in
this experiment. For each tested instance, the Max, Avr, Min values are reported in Table 4. In terms of the
Max values, the ILA achieved better results than LA in 18 out of 20 instances. Based on the Avr and Min
values, the ILA achieved the best Avr results on all tested instances.
Table 3. results of different suggested heuristics
based on different criteria
Instance Heu1 Heu2 Heu3 Heu4 Heu5
50_c101 860 860 860 860 860
50_c102 860 860 860 850 860
50_r101 721 577 686 665 685
50_r102 721 721 721 721 721
50_rc101 970 820 960 970 870
50_rc102 950 920 930 920 950
c101 1810 1810 1810 1810 1640
c102 1800 1800 1740 1560 1560
c201 1810 1810 1800 1810 1730
c202 1810 1810 1790 1740 1650
pr01 299 280 298 267 286
pr02 620 620 616 604 604
r101 1293 877 1258 1119 1171
r102 1397 1369 1369 1285 1281
r201 1445 1342 1419 1418 1357
r202 1387 1401 1370 1331 1267
rc101 1510 1324 1529 1409 1221
rc102 1480 1292 1512 1506 1275
rc201 1705 1564 1616 1621 1531
rc202 1569 1453 1572 1532 1346
Table 4. Comparison between standard
LA and ILA
Instance
LA ILA
Max Avr Min Max Avr Min
50_c101 850 750 650 860 860 860
50_c102 800 755 710 860 855 850
50_r101 146 115 84 721 649 577
50_r102 655 594.5 534 721 721 721
50_rc101 640 485 330 970 895 820
50_rc102 960 850 740 950 935 920
c101 1570 1520 1470 1810 1725 1640
c102 1570 1500 1430 1800 1680 1560
c201 1580 1545 1510 1810 1770 1730
c202 1640 1585 1530 1810 1730 1650
pr01 297 252.5 208 299 283 267
pr02 620 547.5 475 620 612 604
r101 371 345 319 1293 1085 877
r102 1331 1185.5 1040 1397 1339 1281
r201 1309 1249.5 1190 1445 1393.5 1342
r202 1202 1138 1074 1387 1334 1267
rc101 916 845.5 775 1510 1375 1221
rc102 1506 1224.5 943 1480 1393.5 1275
rc201 1378 1345.5 1313 1705 1618 1531
rc202 1222 1155 1088 1569 1459 1346
These results demonstrated that giving a better chance for points with the nearest distance (Dist) and
close time (C_time) to be selected and added into the new solution can effectively raise the performance of
ILA. So, Heu1 method has given the best result among the other four proposed methods. On the other hand,
the results achieved using Heu2 is competitively close to these achieved using Heu1 but with the cost of
dealing three criteria instead of two. As a result, we adopted Heu1 to select the nomadic lion since it has
given the best result with lost cost.
4.2.3. Comparison of ILA with the state of the art methods
To demonstrate the performance of the ILA in solving TOPTW, this experiment compares the
results obtained by ILA with those found and presented by the other algorithms in literature and the best-
known results obtained in the literature. Five of the recent studies which achieved the best known results
were selected for comparison with the proposed method. These studies are as follows:
6. Indonesian J Elec Eng & Comp Sci ISSN: 2502-4752
Lion optimization algorithm for team orienteering problem with time window (Esam Taha Yassen)
543
a) I3CH: iterative three-component heuristic [27].
b) GVNS: granular variable neighborhood search based on linear programming [28].
c) SSA: slow version of the simulated annealing heuristic [29].
d) ABC: artificial bee colony algorithm [30].
e) ILS: iterated local search algorithm [31].
In terms of Max, Table 5 reveals that ILA has attained competitive results over all instances and better
results in some others. Finally, the obtained results have shown that the ILA outperformed the standard LA.
Table 5. Comparison among different heuristics
Instance I3CH GVNS SSA ABC ILS ILA
50_c101 - - - - - 860
50_c102 - - - - - 860
50_r101 - - - - - 721
50_r102 - - - - - 721
50_rc101 - - - - - 970
50_rc102 - - - - - 950
c101 1810 1754 1770 1786 1720 1810
c102 1810 1794 1810 1810 1790 1800
c201 1810 1810 1810 1810 1810 1810
c202 1810 1810 1810 1810 1810 1810
pr01 - - - - 608 299
pr02 - - - - 1180 620
r101 1458 1432.2 1455 1457.4 1441 1293
r102 1458 1441.2 1458 1455.4 1450 1397
r201 1458 1458 1458 1458 1458 1445
r202 1458 1450.2 1458 1458 1443 1387
rc101 1724 1690.2 1712 1713.6 1686 1510
rc102 1724 1685 1718 1705.2 1659 1480
rc201 1724 1724 1724 1451.2 1724 1705
rc202 1719 1702.8 1714 1724 1686 1569
“−” indicates that the results have not been reported.
5. CONCLUSION
In this paper, the LA was adopted to tackle the TOPTW. Through analyzing the LA results, it was
observed that it succeeded in tackling the TOPTW via improving the pride quality. But, the ability of LA is
gradually decreased. This is due to the fact that LA suffers from slow convergence which prevents it from
improving the quality of obtained solutions. This problem often occurred because of the LA is satisfying at
exploring the search space but not at exploiting it. In order to enhance the exploitation ability of LA, a new
process for generating a trespassing nomadic lion was introduced and resulted in the improved LA denoted as
(ILA). Instead of randomly generating a nomadic lion, this process works to generate a strong nomadic lion
to take over a pride by fighting its males. The obtained results have shown that the performance of ILA is
much better than the standard LA due to the fact that generating a strong nomadic lion can assist the
exploitation ability of ILA. Thus, this work concludes that the proposed ILA successes in addressing the
TOPTW, and it can be applied to solve other combinatorial optimization problems.
REFERENCES
[1] P. Vansteenwegen, W. Souffriau, and D. Van Oudheusden, "The orienteering problem: A survey," European
Journal of Operational Research, vol. 209, no. 1, pp. 1-10, 2011.
[2] T. Tsiligirides, "Heuristic methods applied to orienteering," Journal of the Operational Research Society, vol. 35,
no. 9, pp. 797-809, 1984.
[3] G. Laporte and S. Martello, "The selective travelling salesman problem," Discrete applied mathematics, vol. 26,
no. 2-3, pp. 193-207, 1990.
[4] R. Montemanni, D. Weyland, and L. Gambardella, "An enhanced ant colony system for the team orienteering
problem with time windows," 2011 International Symposium on Computer Science and Society, pp. 381-384, 2011.
[5] R. Montemanni and L. M. Gambardella, "An ant colony system for team orienteering problems with time
windows," Foundation Of Computing And Decision Sciences, vol. 34, no. 4, pp. 287-306, 2009.
[6] B. L. Golden, L. Levy, and R. Vohra, "The orienteering problem," Naval Research Logistics (NRL), vol. 34, no. 3,
pp. 307-318, 1987.
[7] B. L. Golden, Q. Wang, and L. Liu, "A multifaceted heuristic for the orienteering problem," Naval Research
Logistics (NRL), vol. 35, no. 3, pp. 359-366, 1988.
7. ISSN: 2502-4752
Indonesian J Elec Eng & Comp Sci, Vol. 21, No. 1, January 2021 : 538 - 545
544
[8] S. Butt and D. Ryan, "A heuristic for the multiple path maximum collection problem," Computers and Operations
Research, vol. 26, pp. 427-441, 1999.
[9] I.-M. Chao, B. L. Golden, and E. A. Wasil, "The team orienteering problem," European journal of operational
research, vol. 88, no. 3, pp. 464-474, 1996.
[10] R. W. Hall and M. Racer, "Transportation with common carrier and private fleets: system assignment and shipment
frequency optimization," IIE transactions, vol. 27, no. 2, pp. 217-225, 1995.
[11] H. Tang and E. Miller-Hooks, "A tabu search heuristic for the team orienteering problem," Computers &
Operations Research, vol. 32, no. 6, pp. 1379-1407, 2005.
[12] S.-W. Lin and F. Y. Vincent, "A simulated annealing heuristic for the team orienteering problem with time
windows," European Journal of Operational Research, vol. 217, no. 1, pp. 94-107, 2012.
[13] N. Labadie, J. Melechovský, and R. W. Calvo, "Hybridized evolutionary local search algorithm for the team
orienteering problem with time windows," Journal of Heuristics, vol. 17, no. 6, pp. 729-753, 2011.
[14] Q. Hu and A. Lim, "An iterative three-component heuristic for the team orienteering problem with time windows,"
European Journal of Operational Research, vol. 232, no. 2, pp. 276-286, 2014.
[15] T. Cura, "An artificial bee colony algorithm approach for the team orienteering problem with time windows,"
Computers & Industrial Engineering, vol. 74, pp. 270-290, 2014.
[16] A. Gunawan, H. C. Lau, and K. Lu, "An iterated local search algorithm for solving the orienteering problem with
time windows," European conference on evolutionary computation in combinatorial optimization, pp. 61-73, 2015.
[17] A. Gunawan, et al, "SAILS: hybrid algorithm for the team orienteering problem with time windows," MISTA 2015:
Proceedings of the 7th Multidisciplinary International Scheduling Conference, pp. 276-295, 2015.
[18] A. Gunawan, H. C. Lau, P. Vansteenwegen, and K. Lu, "Well-tuned algorithms for the team orienteering problem
with time windows," Journal of the Operational Research Society, vol. 68, no. 8, pp. 861-876, 2017.
[19] F. Y. Vincent, P. Jewpanya, S.-W. Lin, and A. P. Redi, "Team orienteering problem with time windows and time-
dependent scores," Computers & Industrial Engineering, vol. 127, pp. 213-224, 2019.
[20] B. Rajakumar, "The Lion's Algorithm: a new nature-inspired search algorithm," Procedia Technology, vol. 6,
pp. 126-135, 2012.
[21] J. Liu and K. C. Tsui, "Toward nature-inspired computing," Communications of the ACM, vol. 49, no. 10,
pp. 59-64, 2006.
[22] M. Yazdani and F. Jolai, "Lion optimization algorithm (LOA): a nature-inspired metaheuristic algorithm," Journal
of computational design and engineering, vol. 3, no. 1, pp. 24-36, 2016.
[23] Harikishore, S., & Sumalatha, V., "Ant colony optimization based energy efficiency for improving opportunistic
routing in multimedia wireless mesh network," Indonesian Journal of Electrical Engineering and Computer
Science, vol. 16, no. 3, pp. 1371-1378, 2019.
[24] Rasheed, M., Omar, R., Sulaiman, M., & Halim, W. A., "Particle swarm optimisation (PSO) algorithm with
reduced numberof switches in multilevel inverter (MLI)," Indonesian Journal of Electrical Engineering and
Computer Science, vol. 14, no. 3, pp. 1114-1124, 2019.
[25] Alhamdani, I. M., & Ibrahim, Y. I., "Swarm intelligent hyperdization biometric," Indonesian Journal of Electrical
Engineering and Computer Science, vol. 18, no. 1, pp. 385-395, 2020.
[26] E. T. Yassen, M. Ayob, M. Z. Ahmad Nazri, and N. R. Sabar, "A hybrid meta-heuristic algorithm for vehicle
routing problem with time windows," International Journal on Artificial Intelligence Tools, vol. 24, no. 6,
pp. 1550021, 2015.
[27] H. Qian and L. Andrew, “An iterative three-component heuristic for the team orienteering problem with time
windows,” European Journal of Operational Research, vol. 232, pp. 276–286, 2014.
[28] N. Labadie, et al., “The team orienteering problem with time windows: An lp-based granular variable neighborhood
search,” European Journal of Operational Research, vol. 220, no. 1, pp. 15–27, 2012.
[29] S.-W. Lin and V. F. Yu, “A simulated annealing heuristic for the team orienteering problem with time windows,”
European Journal of Operational Research, vol. 217, no. 1, pp. 94–107, 2012.
[30] T. Cura, “An artificial bee colony algorithm approach for the team orienteering problem with time windows,”
Computers & Industrial Engineering, vol. 74, pp. 270–290, 2014.
[31] P. Vansteenwegen, et al, “Iterated local search for the team orienteering problem with time windows,” Computers
& Operations Research, vol. 36, no. 12, pp. 3281–3290, 2009.
BIOGRAPHIES OF AUTHORS
Esam Taha Yassen is a lecturer in the college of Computer and Information Technology at the
University of Anbar, Iraq since 2002. He has obtained his PhD in Computer Science at The
University Kebangsaan Malaysia (UKM) in 2015. His main research areas include meta-
heuristics, hyper-heuristics and combinatorial Optimization problems especially, routing and
scheduling. He has been served as a programme committee for four international conferences
and reviewers for high impact journals. e is a researcher in ata ining and ptimi ation
esearch roup , entre for Artificial Intelligent AIT , U . urrentl , he is the
manager of omputers entre in Universit of An ar.
8. Indonesian J Elec Eng & Comp Sci ISSN: 2502-4752
Lion optimization algorithm for team orienteering problem with time window (Esam Taha Yassen)
545
Alaa Abdalqahar Jihad was born in Anbar-Iraq in 1985. He received his B.Sc. from Faculty of
Computer Science at Anbar University, Iraq in 2009. The MSc. degree Faculty of Computer
Science at Anbar University, Iraq 2012. His research interests are, Data Warehouse, Data
Mining, Artificial Intelligent, Machine Learning and Natural Language Processing.
Sudad H. Abed is a full-time instructor in the University of Anbar. He received his master
degree in computer science from Ball State University in 2016. His research area of interest is
machine learning, neural networks, deep learning, and data analytics. He has a solid experience
in web application development and currently servers as the director of the software
development department in the University of Anbar.