This paper presents an improved ant system algorithm for path planning of the mobile robot under the complicated environment. To solve the drawback of the traditional ant colony system algorithm (ACS), which usually falls into the local optimum, we propose an improved ant colony system algorithm (IACS) based on chaos. Simulation experiments show that chaotic ant colony algorithm not only enhances the global search capability, but also has more effective than the traditional algorithm.
An improved ant colony algorithm based onIJCI JOURNAL
This paper presents an improved chaotic ant colony system algorithm (ICACS) for solving combinatorial
optimization problems. The existing algorithms still have some imperfections, we use a combination of two
different operators to improve the performance of algorithm in this work. First, 3-opt local search is used
as a framework for the implementation of the ACS to improve the solution quality; Furthermore, chaos is
proposed in the work to modify the method of pheromone update to avoid the algorithm from dropping into
local optimum, thereby finding the favorable solutions. From the experimental results, we can conclude
that ICACS has much higher quality solutions than the original ACS, and can jump over the region of the
local optimum, and escape from the trap of a local optimum successfully and achieve the best solutions.
Therefore, it’s better and more effective algorithm for TSP.
The document summarizes research on using ant colony optimization algorithms to solve a multi-objective military pathfinding problem. It describes an ant colony system algorithm called hCHAC that finds paths for a military unit from an origin to target point on a hexagonal grid map, minimizing costs in energy and resources. Experiments test hCHAC and variations that consider the objectives separately or together, and use a constant or variable parameter to balance objective importance. Results show the constant approach generally performs better, while the variable method works better when both objectives must be optimized.
Robot Three Dimensional Space Path-planning Applying the Improved Ant Colony ...Nooria Sukmaningtyas
This document describes an improved ant colony optimization (ACO) algorithm for 3D robot path planning to avoid obstacles. It proposes modifying the pheromone updating and transition probability functions of the ACO algorithm. Specifically, it introduces a distance factor to the transition probability function to encourage paths closer to a direct line between start and end points. It also uses fuzzy control to vary the pheromone amount based on iteration count and path length, rather than a fixed value. Simulation results show the improved ACO finds better paths with fewer iterations than the conventional ACO algorithm.
The document compares different heuristic algorithms for solving the traveling salesman problem (TSP), including greedy, 2-opt, 3-opt, genetic algorithm, simulated annealing, and neural networks. It implemented these algorithms and evaluated their computational efficiency on TSP problems of varying sizes (2-10,000 nodes). For small TSP problems (n<=50 nodes), the greedy 2-opt algorithm performed well with a high solution quality and short computation time. The neural network approach showed the best efficiency across all problem sizes. The algorithms were also improved using non-crossing methods, which always resulted in better solutions.
This document proposes a bacterial foraging algorithm-based polyclonal selection algorithm (BFA-PSA) to improve the global path planning abilities of coal mine rescue robots in complex environments. The BFA-PSA introduces bacterial foraging algorithms into traditional clonal selection algorithms to address issues like slow convergence and getting stuck in local minima. The BFA-PSA combines bacterial chemotaxis, reproduction, and elimination-dispersal operators with clonal selection operations. Test results on traveling salesman problems and grid-based path planning simulations for coal mine rescue robots show the BFA-PSA has better searching abilities, efficiency, robustness, and validity compared to genetic and clonal selection algorithms.
The document summarizes research on using ant colony optimization (ACO) supervised by particle swarm intelligence (PSI) to solve multi-objective vehicle routing problems. It proposes applying this approach to determine optimal routes on a linearly expanded network model. The ACO algorithm finds shortest paths between nodes while avoiding local optima, guided by PSI. Experimental results show the ACOLS-PSI algorithm improves average route distance by 8% compared to existing greedy algorithms. Future work could combine this approach with other shortest path methods into a memetic algorithm to better solve wide and sparse vehicle routing networks.
Quantum ant colony optimization algorithm based onBloch spherical searchIJRES Journal
In the existing quantum-behaved optimization algorithms, almost all of the individuals are encoded by qubits described on plane unit circle. As qubits contain only a variable parameter, quantum properties have not been fully embodied, which limits the optimization ability rise further. In order to solve this problem, this paper proposes a quantum ant colony optimization algorithm based on Bloch sphere search. In the proposed algorithm, the positions of ants are encoded by qubits described on Bloch sphere. First, the destination to move is determined according to the select probability constructed by the pheromone and heuristic information, then, the rotation axis is established with Pauli matrixes, and the evolution search is realized with the rotation of qubits on Bloch sphere. In order to avoid premature convergence, the mutation is performed with Hadamard gates. Finally, the pheromone and the heuristic information are updated in the new positions of ants. As the optimization process is performed in n-dimensional hypercube space [−1, 1]n, which has nothing to do with the specific issues, hence, the proposed method has good adaptability for a variety of optimization problems. The simulation results show that the proposed algorithm is superior to other quantum-behaved optimization algorithms in both search ability and optimization efficiency.
An improved ant colony algorithm based onIJCI JOURNAL
This paper presents an improved chaotic ant colony system algorithm (ICACS) for solving combinatorial
optimization problems. The existing algorithms still have some imperfections, we use a combination of two
different operators to improve the performance of algorithm in this work. First, 3-opt local search is used
as a framework for the implementation of the ACS to improve the solution quality; Furthermore, chaos is
proposed in the work to modify the method of pheromone update to avoid the algorithm from dropping into
local optimum, thereby finding the favorable solutions. From the experimental results, we can conclude
that ICACS has much higher quality solutions than the original ACS, and can jump over the region of the
local optimum, and escape from the trap of a local optimum successfully and achieve the best solutions.
Therefore, it’s better and more effective algorithm for TSP.
The document summarizes research on using ant colony optimization algorithms to solve a multi-objective military pathfinding problem. It describes an ant colony system algorithm called hCHAC that finds paths for a military unit from an origin to target point on a hexagonal grid map, minimizing costs in energy and resources. Experiments test hCHAC and variations that consider the objectives separately or together, and use a constant or variable parameter to balance objective importance. Results show the constant approach generally performs better, while the variable method works better when both objectives must be optimized.
Robot Three Dimensional Space Path-planning Applying the Improved Ant Colony ...Nooria Sukmaningtyas
This document describes an improved ant colony optimization (ACO) algorithm for 3D robot path planning to avoid obstacles. It proposes modifying the pheromone updating and transition probability functions of the ACO algorithm. Specifically, it introduces a distance factor to the transition probability function to encourage paths closer to a direct line between start and end points. It also uses fuzzy control to vary the pheromone amount based on iteration count and path length, rather than a fixed value. Simulation results show the improved ACO finds better paths with fewer iterations than the conventional ACO algorithm.
The document compares different heuristic algorithms for solving the traveling salesman problem (TSP), including greedy, 2-opt, 3-opt, genetic algorithm, simulated annealing, and neural networks. It implemented these algorithms and evaluated their computational efficiency on TSP problems of varying sizes (2-10,000 nodes). For small TSP problems (n<=50 nodes), the greedy 2-opt algorithm performed well with a high solution quality and short computation time. The neural network approach showed the best efficiency across all problem sizes. The algorithms were also improved using non-crossing methods, which always resulted in better solutions.
This document proposes a bacterial foraging algorithm-based polyclonal selection algorithm (BFA-PSA) to improve the global path planning abilities of coal mine rescue robots in complex environments. The BFA-PSA introduces bacterial foraging algorithms into traditional clonal selection algorithms to address issues like slow convergence and getting stuck in local minima. The BFA-PSA combines bacterial chemotaxis, reproduction, and elimination-dispersal operators with clonal selection operations. Test results on traveling salesman problems and grid-based path planning simulations for coal mine rescue robots show the BFA-PSA has better searching abilities, efficiency, robustness, and validity compared to genetic and clonal selection algorithms.
The document summarizes research on using ant colony optimization (ACO) supervised by particle swarm intelligence (PSI) to solve multi-objective vehicle routing problems. It proposes applying this approach to determine optimal routes on a linearly expanded network model. The ACO algorithm finds shortest paths between nodes while avoiding local optima, guided by PSI. Experimental results show the ACOLS-PSI algorithm improves average route distance by 8% compared to existing greedy algorithms. Future work could combine this approach with other shortest path methods into a memetic algorithm to better solve wide and sparse vehicle routing networks.
Quantum ant colony optimization algorithm based onBloch spherical searchIJRES Journal
In the existing quantum-behaved optimization algorithms, almost all of the individuals are encoded by qubits described on plane unit circle. As qubits contain only a variable parameter, quantum properties have not been fully embodied, which limits the optimization ability rise further. In order to solve this problem, this paper proposes a quantum ant colony optimization algorithm based on Bloch sphere search. In the proposed algorithm, the positions of ants are encoded by qubits described on Bloch sphere. First, the destination to move is determined according to the select probability constructed by the pheromone and heuristic information, then, the rotation axis is established with Pauli matrixes, and the evolution search is realized with the rotation of qubits on Bloch sphere. In order to avoid premature convergence, the mutation is performed with Hadamard gates. Finally, the pheromone and the heuristic information are updated in the new positions of ants. As the optimization process is performed in n-dimensional hypercube space [−1, 1]n, which has nothing to do with the specific issues, hence, the proposed method has good adaptability for a variety of optimization problems. The simulation results show that the proposed algorithm is superior to other quantum-behaved optimization algorithms in both search ability and optimization efficiency.
In this project, the travelling salesman problem, its complexity, variations and its applications in various domains was studied. Here, we proposed GACO to solve the complex problem and compare the result with the nearest Neighbour method, metaheuristics such as Simulated Annealing, Tabu Search and Evolutionary Algorithms like Genetic Algorithm and Ant Colony Optimization. The experimental results demonstrated that the HYBRID GACO approach of finding the solution gives the best result in terms of the optimal route travelled by the salesman as compared to other heuristics used in this project. The minimum distance travelled by the salesman is the least for GACO.
A Study on Image Reconfiguration Algorithm of Compressed SensingTELKOMNIKA JOURNAL
Compressed sensing theory is a subversion of the traditional theory. The theory obtains data
sampling points while achieves data compression. The main content of this thesis is reconstruction
algorithm. It’s the key of the compressed sensing theory, which directly determines the quality of
reconstructed signal, reconstruction speed and application effect. In this paper, we have studied the theory
of compressed sensing and the existing reconstruction algorithms, then choosing three algorithms (OMP,
CoSaMP, and StOMP) as the research. On the basis of summarizing the existing algorithms and models,
we analyze the results such as PSNR, relative error, matching ratio and running time of them from image
signal respectively. In the three reconstruction algorithms, OMP algorithm has the best accuracy for image
reconstruction. The convergence speed of CoSaMP algorithm is faster than that of the OMP algorithms,
but it depends on sparsity K quietly. StOMP algorithm on image reconstruction effect is the best, and the
convergence speed is also the fastest.
This document presents a method for solving the combined passenger and freight operational timetable problem using mixed integer programming. The method accounts for fixed timetables of passenger trains. It allows delaying passenger trains to decrease overall costs by optimizing departure times from each station for all trains. The objective is to minimize total travel times for all trains while satisfying constraints like train speeds, minimum intervals between trains, and respecting fixed passenger train schedules. An example timetable output is shown that incorporates intervals for passenger boarding.
This document summarizes a research paper that proposes a new swarm intelligence algorithm called a Hybrid Bat Algorithm. The Hybrid Bat Algorithm combines the original Bat Algorithm with strategies from Differential Evolution. The Bat Algorithm is based on the echolocation behavior of bats and has been shown to effectively solve lower-dimensional optimization problems. However, it can struggle with higher-dimensional problems due to its tendency to converge quickly. The researchers propose hybridizing it with Differential Evolution strategies to improve its performance on higher-dimensional problems. They test the Hybrid Bat Algorithm on standard benchmark functions and find that it significantly outperforms the original Bat Algorithm.
an improver particle optmizacion plan de negociosCarlos Iza
This document proposes an improved particle swarm optimization (IPSO) algorithm to optimize trajectory paths for multiple robots in a cluttered environment. IPSO incorporates independent decision making, coordination, and cooperation between robots to accomplish a shared goal. A path planning scheme using IPSO was developed to determine optimal successive robot positions from initial to target locations. Simulation and experiment results show IPSO outperforms particle swarm optimization and differential evolution algorithms with respect to average total trajectory deviation and average uncovered target distance.
An agent based particle swarm optimization for papr reduction of ofdm systemsaliasghar1989
This document proposes an agent-based particle swarm optimization (APSO) algorithm to reduce the computational complexity of the original particle swarm optimization (OPSO) technique for partial transmit sequence (PTS) peak-to-average power ratio (PAPR) reduction in orthogonal frequency division multiplexing (OFDM) systems. Simulation results show that APSO achieves nearly the same PAPR reduction performance as OPSO but with significantly lower complexity, as the number of additions and multiplications is reduced by setting the velocity of all particles equal to the velocity of the agent particle in each iteration. APSO is thus an effective method to solve the phase optimization problem in PTS with lower complexity than OPSO.
This document summarizes a research project on process identification using relay feedback tests. The project aims to identify low-order models like FOPDT and SOPDT from relay feedback data to enable performance assessment and controller tuning. A new identification method is proposed that uses neural networks to estimate the apparent deadtime from steady-state cycles. This deadtime and other parameters allow classification of the process model and parameter estimation for assessment and auto-tuning.
This document summarizes the bat algorithm, which is a metaheuristic optimization algorithm inspired by the echolocation behavior of microbats. It describes how bats use echolocation to locate prey and obstacles. The basic steps of the bat algorithm are outlined, including how bats emit calls and adjust properties like frequency and loudness. Variants of the bat algorithm are mentioned for solving multi-objective, fuzzy logic, and other problems. Applications discussed include engineering design, scheduling, data clustering, and image processing. Advantages include quick convergence and flexibility, while disadvantages include possible stagnation if parameters are adjusted too rapidly.
Show ant-colony-optimization-for-solving-the-traveling-salesman-problemjayatra
The document describes using ant colony optimization to solve the traveling salesman problem. It outlines the traveling salesman problem and introduces ant colony optimization as a metaheuristic for solving optimization problems inspired by ant behavior. The document then provides an example of using ant colony optimization to iteratively find the shortest route between 5 cities, with ants probabilistically choosing paths based on pheromone levels and distance.
Firefly Algorithm: Recent Advances and ApplicationsXin-She Yang
This document summarizes a research paper on the firefly algorithm, a nature-inspired metaheuristic optimization algorithm. It briefly reviews the fundamentals and development of the firefly algorithm, discussing how it balances exploration and exploitation. The firefly algorithm is shown to be more efficient than intermittent search strategies through numerical experiments. Its automatic subdivision ability and ability to handle multimodality make it well-suited for complex optimization problems.
Firefly Algorithm is a nature-inspired metaheuristic algorithm based on the flashing patterns of fireflies. The paper reviews recent developments in Firefly Algorithm and its applications. Firefly Algorithm uses three rules: all fireflies are attracted to other fireflies regardless of sex; attractiveness depends on brightness which decreases with distance; and brightness depends on the landscape of the objective function. The algorithm balances exploration and exploitation through parameters that control randomness and attractiveness. It has been shown to efficiently solve multimodal optimization problems and outperform other algorithms in applications such as engineering design, antenna design, scheduling, and clustering.
The document discusses the traveling salesman problem (TSP) and methods for solving it. It begins with an introduction to the TSP and provides an example on Madiera Island. It then covers building tours from scratch using heuristics like nearest neighbor and savings. Local search methods like 2-opt and 3-opt are presented for improving tours. The document also discusses metaheuristics such as tabu search, simulated annealing, and variable neighborhood search. Exact methods and benchmarks for solving larger TSP instances are presented at the end.
A new Evolutionary Reinforcement Scheme for Stochastic Learning Automatainfopapers
F. Stoica, E. M. Popa, A new Evolutionary Reinforcement Scheme for Stochastic Learning Automata, Proceedings of the 12th WSEAS International Conference on COMPUTERS, Heraklion, Greece, July 23-25, ISBN: 978-960-6766-85-5, ISSN: 1790-5109, pp. 268-273, 2008
Application Of The Three-In-One Control Platform Based On OPC In The Lifting-...IJRES Journal
The document describes a three-in-one control platform that uses MATLAB, WINCC, and S7 300 PLC to optimize scheduling for a lifting-sliding stereo garage. Genetic algorithms in MATLAB are used to find the optimal scheduling scheme, which is passed to WINCC via OPC. WINCC then sends the scheme to the PLC to control the garage movements. The platform aims to minimize vehicle loading/unloading times. It includes details on encoding schemes, fitness functions, parameter selection, and the monitoring interface design in WINCC.
Fuzzy clustering algorithm can not obtain good clustering effect when the sample characteristic is not
obvious and need to determine the number of clusters firstly. For thi0s reason, this paper proposes an
adaptive fuzzy kernel clustering algorithm. The algorithm firstly use the adaptive function of clustering
number to calculate the optimal clustering number, then the samples of input space is mapped to highdimensional
feature space using gaussian kernel and clustering in the feature space. The Matlab simulation
results confirmed that the algorithm's performance has greatly improvement than classical clustering algorithm and has faster convergence speed and more accurate clustering results
The Chaos and Stability of Firefly Algorithm Adjacent IndividualTELKOMNIKA JOURNAL
In this paper, in order to overcome the defect of the firefly algorithm, for example, the slow
convergence rate, low accuracy and easily falling into the local optima in the global optimization search,
we propose a dynamic population firefly algorithm based on chaos. The stability between the fireflies is
proved, and the similar chaotic phenomenon in firefly algorithm can be simulated.
IJRET : International Journal of Research in Engineering and Technology is an international peer reviewed, online journal published by eSAT Publishing House for the enhancement of research in various disciplines of Engineering and Technology. The aim and scope of the journal is to provide an academic medium and an important reference for the advancement and dissemination of research results that support high-level learning, teaching and research in the fields of Engineering and Technology. We bring together Scientists, Academician, Field Engineers, Scholars and Students of related fields of Engineering and Technology
Ant Colony Optimization for Optimal Low-Pass State Variable Filter Sizing IJECEIAES
In analog filter design, discrete components values such as resistors (R) and capacitors (C) are selected from the series following constant values chosen. Exhaustive search on all possible combinations for an optimized design is not feasible. In this paper, we present an application of the Ant Colony Optimization technique (ACO) in order to selected optimal values of resistors and capacitors from different manufactured series to satisfy the filter design criteria. Three variants of the Ant Colony Optimization are applied, namely, the AS (Ant System), the MMAS (Min-Max AS) and the ACS (Ant Colony System), for the optimal sizing of the Low-Pass State Variable Filter. SPICE simulations are used to validate the obtained results/performances which are compared with already published works.
The document discusses a new approach for mobile robot path planning in complex dynamic environments using fuzzy logic and ant colony optimization algorithms. It proposes combining these methods to improve path finding. The ant colony algorithm is used to evolve and optimize the fuzzy rule table to help the robot select optimal paths without collisions amidst moving obstacles of varying speeds, directions and shapes. Simulation results show the combined approach finds shorter paths in less time compared to using just fuzzy logic.
Path Planning of Mobile aco fuzzy-presentation.pptxssuserf6b378
The document describes a study on path planning for mobile robots using fuzzy logic and ant colony algorithms in complex dynamic environments. It proposes a new approach that combines these methods. The study models the robot's workspace as a grid with fixed and moving obstacles. It explains how the ant colony algorithm and fuzzy logic are applied to determine optimal paths between start and end points while avoiding obstacles. Simulation results show the combined approach finds shorter paths in less time compared to using each method individually.
The document discusses ant colony optimization (ACO), which is an algorithm inspired by the behavior of ants seeking paths between their colony and food sources. It was originally applied to solve the traveling salesman problem. The algorithm works by "ants" probabilistically constructing solutions and adjusting pheromone trails that guide future ants towards better solutions. Over time, the pheromone trails reinforce shorter solution paths through positive feedback. The document provides examples of how ACO can be applied to problems like routing in networks and scheduling. It also discusses extensions of the basic ACO approach.
In this project, the travelling salesman problem, its complexity, variations and its applications in various domains was studied. Here, we proposed GACO to solve the complex problem and compare the result with the nearest Neighbour method, metaheuristics such as Simulated Annealing, Tabu Search and Evolutionary Algorithms like Genetic Algorithm and Ant Colony Optimization. The experimental results demonstrated that the HYBRID GACO approach of finding the solution gives the best result in terms of the optimal route travelled by the salesman as compared to other heuristics used in this project. The minimum distance travelled by the salesman is the least for GACO.
A Study on Image Reconfiguration Algorithm of Compressed SensingTELKOMNIKA JOURNAL
Compressed sensing theory is a subversion of the traditional theory. The theory obtains data
sampling points while achieves data compression. The main content of this thesis is reconstruction
algorithm. It’s the key of the compressed sensing theory, which directly determines the quality of
reconstructed signal, reconstruction speed and application effect. In this paper, we have studied the theory
of compressed sensing and the existing reconstruction algorithms, then choosing three algorithms (OMP,
CoSaMP, and StOMP) as the research. On the basis of summarizing the existing algorithms and models,
we analyze the results such as PSNR, relative error, matching ratio and running time of them from image
signal respectively. In the three reconstruction algorithms, OMP algorithm has the best accuracy for image
reconstruction. The convergence speed of CoSaMP algorithm is faster than that of the OMP algorithms,
but it depends on sparsity K quietly. StOMP algorithm on image reconstruction effect is the best, and the
convergence speed is also the fastest.
This document presents a method for solving the combined passenger and freight operational timetable problem using mixed integer programming. The method accounts for fixed timetables of passenger trains. It allows delaying passenger trains to decrease overall costs by optimizing departure times from each station for all trains. The objective is to minimize total travel times for all trains while satisfying constraints like train speeds, minimum intervals between trains, and respecting fixed passenger train schedules. An example timetable output is shown that incorporates intervals for passenger boarding.
This document summarizes a research paper that proposes a new swarm intelligence algorithm called a Hybrid Bat Algorithm. The Hybrid Bat Algorithm combines the original Bat Algorithm with strategies from Differential Evolution. The Bat Algorithm is based on the echolocation behavior of bats and has been shown to effectively solve lower-dimensional optimization problems. However, it can struggle with higher-dimensional problems due to its tendency to converge quickly. The researchers propose hybridizing it with Differential Evolution strategies to improve its performance on higher-dimensional problems. They test the Hybrid Bat Algorithm on standard benchmark functions and find that it significantly outperforms the original Bat Algorithm.
an improver particle optmizacion plan de negociosCarlos Iza
This document proposes an improved particle swarm optimization (IPSO) algorithm to optimize trajectory paths for multiple robots in a cluttered environment. IPSO incorporates independent decision making, coordination, and cooperation between robots to accomplish a shared goal. A path planning scheme using IPSO was developed to determine optimal successive robot positions from initial to target locations. Simulation and experiment results show IPSO outperforms particle swarm optimization and differential evolution algorithms with respect to average total trajectory deviation and average uncovered target distance.
An agent based particle swarm optimization for papr reduction of ofdm systemsaliasghar1989
This document proposes an agent-based particle swarm optimization (APSO) algorithm to reduce the computational complexity of the original particle swarm optimization (OPSO) technique for partial transmit sequence (PTS) peak-to-average power ratio (PAPR) reduction in orthogonal frequency division multiplexing (OFDM) systems. Simulation results show that APSO achieves nearly the same PAPR reduction performance as OPSO but with significantly lower complexity, as the number of additions and multiplications is reduced by setting the velocity of all particles equal to the velocity of the agent particle in each iteration. APSO is thus an effective method to solve the phase optimization problem in PTS with lower complexity than OPSO.
This document summarizes a research project on process identification using relay feedback tests. The project aims to identify low-order models like FOPDT and SOPDT from relay feedback data to enable performance assessment and controller tuning. A new identification method is proposed that uses neural networks to estimate the apparent deadtime from steady-state cycles. This deadtime and other parameters allow classification of the process model and parameter estimation for assessment and auto-tuning.
This document summarizes the bat algorithm, which is a metaheuristic optimization algorithm inspired by the echolocation behavior of microbats. It describes how bats use echolocation to locate prey and obstacles. The basic steps of the bat algorithm are outlined, including how bats emit calls and adjust properties like frequency and loudness. Variants of the bat algorithm are mentioned for solving multi-objective, fuzzy logic, and other problems. Applications discussed include engineering design, scheduling, data clustering, and image processing. Advantages include quick convergence and flexibility, while disadvantages include possible stagnation if parameters are adjusted too rapidly.
Show ant-colony-optimization-for-solving-the-traveling-salesman-problemjayatra
The document describes using ant colony optimization to solve the traveling salesman problem. It outlines the traveling salesman problem and introduces ant colony optimization as a metaheuristic for solving optimization problems inspired by ant behavior. The document then provides an example of using ant colony optimization to iteratively find the shortest route between 5 cities, with ants probabilistically choosing paths based on pheromone levels and distance.
Firefly Algorithm: Recent Advances and ApplicationsXin-She Yang
This document summarizes a research paper on the firefly algorithm, a nature-inspired metaheuristic optimization algorithm. It briefly reviews the fundamentals and development of the firefly algorithm, discussing how it balances exploration and exploitation. The firefly algorithm is shown to be more efficient than intermittent search strategies through numerical experiments. Its automatic subdivision ability and ability to handle multimodality make it well-suited for complex optimization problems.
Firefly Algorithm is a nature-inspired metaheuristic algorithm based on the flashing patterns of fireflies. The paper reviews recent developments in Firefly Algorithm and its applications. Firefly Algorithm uses three rules: all fireflies are attracted to other fireflies regardless of sex; attractiveness depends on brightness which decreases with distance; and brightness depends on the landscape of the objective function. The algorithm balances exploration and exploitation through parameters that control randomness and attractiveness. It has been shown to efficiently solve multimodal optimization problems and outperform other algorithms in applications such as engineering design, antenna design, scheduling, and clustering.
The document discusses the traveling salesman problem (TSP) and methods for solving it. It begins with an introduction to the TSP and provides an example on Madiera Island. It then covers building tours from scratch using heuristics like nearest neighbor and savings. Local search methods like 2-opt and 3-opt are presented for improving tours. The document also discusses metaheuristics such as tabu search, simulated annealing, and variable neighborhood search. Exact methods and benchmarks for solving larger TSP instances are presented at the end.
A new Evolutionary Reinforcement Scheme for Stochastic Learning Automatainfopapers
F. Stoica, E. M. Popa, A new Evolutionary Reinforcement Scheme for Stochastic Learning Automata, Proceedings of the 12th WSEAS International Conference on COMPUTERS, Heraklion, Greece, July 23-25, ISBN: 978-960-6766-85-5, ISSN: 1790-5109, pp. 268-273, 2008
Application Of The Three-In-One Control Platform Based On OPC In The Lifting-...IJRES Journal
The document describes a three-in-one control platform that uses MATLAB, WINCC, and S7 300 PLC to optimize scheduling for a lifting-sliding stereo garage. Genetic algorithms in MATLAB are used to find the optimal scheduling scheme, which is passed to WINCC via OPC. WINCC then sends the scheme to the PLC to control the garage movements. The platform aims to minimize vehicle loading/unloading times. It includes details on encoding schemes, fitness functions, parameter selection, and the monitoring interface design in WINCC.
Fuzzy clustering algorithm can not obtain good clustering effect when the sample characteristic is not
obvious and need to determine the number of clusters firstly. For thi0s reason, this paper proposes an
adaptive fuzzy kernel clustering algorithm. The algorithm firstly use the adaptive function of clustering
number to calculate the optimal clustering number, then the samples of input space is mapped to highdimensional
feature space using gaussian kernel and clustering in the feature space. The Matlab simulation
results confirmed that the algorithm's performance has greatly improvement than classical clustering algorithm and has faster convergence speed and more accurate clustering results
The Chaos and Stability of Firefly Algorithm Adjacent IndividualTELKOMNIKA JOURNAL
In this paper, in order to overcome the defect of the firefly algorithm, for example, the slow
convergence rate, low accuracy and easily falling into the local optima in the global optimization search,
we propose a dynamic population firefly algorithm based on chaos. The stability between the fireflies is
proved, and the similar chaotic phenomenon in firefly algorithm can be simulated.
IJRET : International Journal of Research in Engineering and Technology is an international peer reviewed, online journal published by eSAT Publishing House for the enhancement of research in various disciplines of Engineering and Technology. The aim and scope of the journal is to provide an academic medium and an important reference for the advancement and dissemination of research results that support high-level learning, teaching and research in the fields of Engineering and Technology. We bring together Scientists, Academician, Field Engineers, Scholars and Students of related fields of Engineering and Technology
Ant Colony Optimization for Optimal Low-Pass State Variable Filter Sizing IJECEIAES
In analog filter design, discrete components values such as resistors (R) and capacitors (C) are selected from the series following constant values chosen. Exhaustive search on all possible combinations for an optimized design is not feasible. In this paper, we present an application of the Ant Colony Optimization technique (ACO) in order to selected optimal values of resistors and capacitors from different manufactured series to satisfy the filter design criteria. Three variants of the Ant Colony Optimization are applied, namely, the AS (Ant System), the MMAS (Min-Max AS) and the ACS (Ant Colony System), for the optimal sizing of the Low-Pass State Variable Filter. SPICE simulations are used to validate the obtained results/performances which are compared with already published works.
The document discusses a new approach for mobile robot path planning in complex dynamic environments using fuzzy logic and ant colony optimization algorithms. It proposes combining these methods to improve path finding. The ant colony algorithm is used to evolve and optimize the fuzzy rule table to help the robot select optimal paths without collisions amidst moving obstacles of varying speeds, directions and shapes. Simulation results show the combined approach finds shorter paths in less time compared to using just fuzzy logic.
Path Planning of Mobile aco fuzzy-presentation.pptxssuserf6b378
The document describes a study on path planning for mobile robots using fuzzy logic and ant colony algorithms in complex dynamic environments. It proposes a new approach that combines these methods. The study models the robot's workspace as a grid with fixed and moving obstacles. It explains how the ant colony algorithm and fuzzy logic are applied to determine optimal paths between start and end points while avoiding obstacles. Simulation results show the combined approach finds shorter paths in less time compared to using each method individually.
The document discusses ant colony optimization (ACO), which is an algorithm inspired by the behavior of ants seeking paths between their colony and food sources. It was originally applied to solve the traveling salesman problem. The algorithm works by "ants" probabilistically constructing solutions and adjusting pheromone trails that guide future ants towards better solutions. Over time, the pheromone trails reinforce shorter solution paths through positive feedback. The document provides examples of how ACO can be applied to problems like routing in networks and scheduling. It also discusses extensions of the basic ACO approach.
Iaetsd modified artificial potential fields algorithm for mobile robot path ...Iaetsd Iaetsd
This document presents a modified artificial potential fields algorithm for mobile robot path planning in unknown and dynamic environments. The algorithm uses artificial potential fields to iteratively find optimal points to form a collision-free path from the start to destination. For static obstacles, potential values are used to identify clusters of points around the start and goal, and find a connecting midpoint. This process is repeated iteratively. For dynamic obstacles, Markov models are used to analyze obstacle behavior from sensor data and predict collision points. The robot's path is replanned as needed to avoid collisions based on feedback from sensors and odometry. Simulation results show the algorithm can efficiently plan paths in unknown environments and avoid both static and dynamic obstacles.
IJERA (International journal of Engineering Research and Applications) is International online, ... peer reviewed journal. For more detail or submit your article, please visit www.ijera.com
This document presents a path planning algorithm called Ant Colony Robot Path Planning (ARPP) that is based on Ant Colony Optimization (ACO) techniques. The ARPP algorithm uses artificial ants to find optimal paths for a warehouse material handling robot to navigate between locations, avoiding obstacles. The algorithm models the environment as a visibility graph and applies ACO concepts like pheromone deposition and evaporation to guide the ants toward shorter paths. Simulation results on a sample visibility graph show that after 100 iterations, the ARPP approach consistently finds the shortest path of 33 units in length. The algorithm provides an effective method for mobile robot path planning in complex warehouse environments.
The document discusses ant colony optimization (ACO), which is a metaheuristic algorithm inspired by the behavior of real ant colonies. It describes how real ants deposit pheromone trails to communicate indirectly and find the shortest path between their colony and food sources. The algorithm works by "artificial ants" probabilistically building solutions to optimization problems and adjusting pheromone levels based on solution quality, similar to how real ants reinforce shorter paths. It provides examples of how ACO has been applied to problems like the traveling salesman problem and discusses some extensions to the basic ACO algorithm.
Swarm Intelligence Technique ACO and Traveling Salesman ProblemIRJET Journal
The document discusses the ant colony optimization (ACO) algorithm, a swarm intelligence technique inspired by ant behavior, and its application to the traveling salesman problem (TSP). ACO mimics how ants deposit and follow pheromone trails to probabilistically determine paths, and it has been shown to find good solutions to TSP. The paper also reviews the ACO algorithm and describes how it can be applied to find the shortest tour between cities in TSP.
Heuristic algorithms for solving TSP.doc.pptxlwz614595250
For NP problems such as TSP, when the task size is small, traditional methods can obtain the exact optimal solution; however, in real life, the problems are generally more complex and larger in size, and traditional methods require huge resources for computation and the results will not be ideal. Traditional methods are challenged. Therefore, heuristic methods are derived. Some heuristics can help us to find approximate optimal solutions. Usually, this is enough to be applied in practical situations.
In this presentation, we present the application of the basic ant colony algorithm to the tsp problem and implement it using matlab; and, conduct comparative experiments with the application of other other heuristics (particle swarm algorithm, genetic algorithm).
This is useful for beginners to understand heuristic algorithms and NP problems.
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with the TSP nodes representing the train arrival /departure events and the TSP total cost
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Chaotic ANT System Optimization for Path Planning of the Mobile Robots
1. Computer Science & Engineering: An International Journal (CSEIJ), Vol.6, No.2/3, June 2016
DOI:10.5121/cseij.2016.6301 1
CHAOTIC ANT SYSTEM OPTIMIZATION FOR PATH
PLANNING OF THE MOBILE ROBOTS
Xu Mingle and You Xiaoming
Shanghai University of Engineering Science, Shanghai, China
ABSTRACT
This paper presents an improved ant system algorithm for path planning of the mobile robot under the
complicated environment. To solve the drawback of the traditional ant colony system algorithm (ACS),
which usually falls into the local optimum, we propose an improved ant colony system algorithm (IACS)
based on chaos. Simulation experiments show that chaotic ant colony algorithm not only enhances the
global search capability, but also has more effective than the traditional algorithm.
KEYWORDS
Ant colony system, Chaotic, path planning of the mobile robot
1. INTRODUCTION
The swarm intelligence algorithm (SIA), which is a new evolution algorithm, has increasingly
attracted more and more researchers [1-4]. Among the SIA, ant colony system (ACS), which was
created from the enlightenment from the nature world, in fact, is a positive feedback method of
studying the real swarm ants which look for foods in the nature: the more heuristic is in the road
when more ants have got through the road, so the latter ants are more impossible to select the
road. The ant can cooperate and communicate with other ants, and in this way the ant system can
find the shortest road from their nest to the foods.
ACS, which has the more quick convergence speed and more accurate solution than the simple
ant colony optimization algorithm, is one of the best ant colony optimization algorithms [1]. So,
this paper is based on the ACS to solve the path planning of the robot.
The rest of the paper is outlined as follows. Sect.2 briefly addresses the concept of ACS and
briefly reviews the work of ACS about robot. Sect.3 describes the improved ACS based on
chaotic (IACS) for the problems of path planning of robots. Sect.3 presents the experimental
results and it shows that IACS is beneficial for ACS algorithm when applied on the path planning
of the robot.
2. ACS
In the way of the real ant system in the nature world to look for the food, ant colony system
algorithm (ACS) is being used to find the optimum. Artificial ant will not only find the way to the
food in oneself, but accumulate with other ants by their means also [5].
Path planning is one of the most important techniques for the mobile robot. As mobile robot is in
the way with barriers, we want to find the best path from the start point to the object point
according some evaluation criterions, such as the shortest road and the safest road [6]. The
traditional ACS algorithm includes state transition and updating pheromones.
When the ant select the next city in the path planning of the ACS algorithm, the ant will select
according to the amount of the road’s pheromones and generally there are more possibilities to
2. Computer Science & Engineering: An International Journal (CSEIJ), Vol.6, No.2/3, June 2016
2
select the road of having more pheromones. In particular, the norm with which ant k, currently at
city i, chooses to go to city j is
{ }
( )
0
0
(t)arg ( ,t)
,
ijij
k
ij
max q q
j
J p q q
β
τ η ≤
=
>
(1)
Where the ( )ij tτ is the existing pheromone trail between the city i and city j in the time t, ( )ij tη
is the heuristic information on the path from city i to city j, (), (0,1)q rand q= ∈ , 0q is a
parameter available a prior ( 00 1q≤ ≤ ). While ant k select the next city, there is a random figure
q , and if 0q q≤ , the ant will select the next node according the equation (1), otherwise
according the equation (2).
[ ]k
i
ij ijk
ij
ij ijl N
p
α β
α β
τ η
τ η∈
=
∑
(2)
Where ijη is the heuristic information on the path from city i to city j, which is defined as 1/ ijd ,
where ijd is the distance between city i and city j. k
iN denotes the neighborhood of cities of ant k
when being on city i. Parameter α determine the relative influence of the pheromone trail and
the parameter β determine the relative influence of the heuristic information. In the way of the
ACS, the pheromone on the path city i to city j will update after the ant k march up from the city i
to city j according above the rule and the process conform to the equation(3).
( )(t) (t) (t)1ij ij ijτ ϕ τ ϕ τ= − + ∆ (3)
1
n
ij
k
ij
k
τ τ
=
∆ = ∆∑ (4)
Where 0 1ρ< ≤ is the rate of the evaporation. In general, lowering the pheromone values enables
the algorithm to forget bad decisions made in previous iterations, ( )ij tτ∆ is the pheromone
produced by the ant k after passing the road from the time t to time t+1. After all of the ant get to
the end from the start, only the road (may be not only one) of the best ant will update the
pheromone according to the rule, and in one point, the rule is accelerating the convergence speed
of algorithm. This process is subjected to rule as follows:
1
(i, j)belong to best loo,
0, otherwise
pk
kij
arc
Lτ
= ∆
(5)
Where Q denotes the intensity of the pheromone, and kL denotes the length of the best ant in
this iteration.
3. Computer Science & Engineering: An International Journal (CSEIJ), Vol.6, No.2/3, June 2016
3
3. IACS FOR PATH PLANNING OF ROBOTS
Recently, the path planning of the mobile robot has attracted a great deal of researchers. Paper 6
has been proposed an ant colony algorithm about rolling planning. An improved ant colony
algorithm of differential evolution based on chaotic has been improved in paper 7. The results of
their experiments have revealed that the improved algorithm can still find the safe better path
even in a very complex environment. With the help of the map created under the start point and
the end point, the algorithm add the local path information of the environment to the step of
initializing pheromone trails and the step of selecting the next city, and have a operation to select
and crossover with adapt the values of ,α β and ϕ ,which make the algorithm have more
capabilities to flee the local optimum [8]. To improve the performance of the results of the
traditional ACS applied in the mobile robots, we investigate improved ACS called IACS
algorithm, which is based on the conception of logistic mapping to modify the rule of updating
pheromones that it will escape from the local optimum in the early.
3.1 Improvement of the Local Updating Pheromone
The main characteristic of the chaotic is pseudo randomness, ergodic property, sensibility to the
condition of the start [9]. In consideration of chaotic characteristic, change the rule of local
updating pheromone, which may be improving diversity of the algorithm so as to avoid fall into
the local optimum in the early time. We have using the mapping of logistic in this paper, and the
rule as follows:
[ ] ( ]1 0(1 ),k 0,1,2,...; 0,1 3,4k kx x x µµ+ = − = ∈∈ (6)
We will get a series of values of 1 2, ,..., kx x x while the value k changes. Through testing the
value µ , it concludes that the dynamic system is simple displaying a litter change or complicated
displaying periodicity chaotic while0 1µ< ≤ and3 4µ≤ ≤ respectively. Then we design two
simulation experiments by means of mapping logistic on Matlab, and initialize value m as 0.5, the
number of iteration as 500, and the value µ as 2,3,3.3 , 3.6. The result of different value kµ is
given in the Fig.1 - Fig.4.
Fig.1 Chaos While 2=µ
4. Computer Science & Engineering: An International Journal (CSEIJ), Vol.6, No.2/3, June 2016
4
Fig.2 Chaos While 3=µ
Fig.3 Chaos While 3.3u =
Fig.4 Chaos While 3.6µ =
5. Computer Science & Engineering: An International Journal (CSEIJ), Vol.6, No.2/3, June 2016
5
The parameter µ is changeable. There is the situation of complete chaotic for the mapping logistic
when 04,0 1xµ = ≤ ≤ . To avoid the algorithm drop early into a local premium and improve its
diversity, this paper propose a algorithm called IACS which change the rule of updating the local
pheromone based on the chaotic performance and different from the formula (3) it do as follows:
( ) ( ) ( ) ( )1 1ij ij ij ijt t t qxτ ϕ τ ϕ τ+ = − + ∆ + (7)
Where chaotic variable ijx is produced from the formula (6) and qis a rate.
3.2 The Framework of the IACS
The IACS algorithm and its path planning experiment of the robot as follows:
Step1: initialize parameters and set the data structure for saving the results, generate chaotic
values from formula (6) and put it to *( )ij N Nτ , initialize the matrix of pheromone;
Step2: ant k 1,2,...,k m= set out from the point startg , and write startg into the ant memory ktabu
which contains the cities already visited, and find the available next cities;
Step3: select a path from the start to the end according to the pheromones of the available road
and heuristic information;
Step4: after every period of searching process, count and compare every ant road’s path. Create a
series of chaotic values according to the formula (6) and update the pheromone according to the
formula (7). 1n n= + and continues the next loop;
Step5: clear up the ant memory when maxn N> and back to the Step2.
4. SIMULATION EXPERIMENTS
In order to investigate the performance of improved ant colony system algorithms for the path
planning of the robot, we have had several simulation experiments. In this paper, the simulations
are based on a map of 20*20. The parameter 1α = , 0.1ϕ = , 3Q = , 0 0.15q = .
Fig.5 Diversity of Results and the Convergence of the Algorithm
6. Computer Science & Engineering: An International Journal (CSEIJ), Vol.6, No.2/3, June 2016
6
Fig.6 The Best Road of our Algorithm Under the Complicated Environment1
Fig.7 The Best Road of Our Algorithm Under the Complicated Environment2
Fig.8 The Best Road of Our Algorithm Under the Complicated Environment3
7. Computer Science & Engineering: An International Journal (CSEIJ), Vol.6, No.2/3, June 2016
7
Fig.9 The Best Road of Our Algorithm Under the Complicated Environment4
Fig.10 The Best Road of Our Algorithm Under the Complicated Environment5
Fig.5 shows the diversity of the results and the convergence of the algorithm. The line in Fig.6-
Fig.10 is the best road of our IACS algorithm under the complicated environment. The
experiment has proved that our IACS algorithm can find the best path with a quicker convergence
speed.
5. CONCLUSIONS AND FUTURE WORK
This paper has proposed an improved ant colony algorithm based on chaotic which improve the
algorithm diversity by changing the updating local pheromone for path planning of robots, so it
has improved the accuracy of the best road. Simulation experiments show that the IACS is better
to solve the path planning of the robot under the complicated environment.
In future, we should research the impact of the parameter in our algorithm to improve the
algorithm performance.
8. Computer Science & Engineering: An International Journal (CSEIJ), Vol.6, No.2/3, June 2016
8
ACKNOWLEDGEMENTS
The authors gratefully acknowledge the support of Innovation Program of Shanghai
Municipal Education Commission (Grant No.12ZZ185), Natural Science Foundation of
China (Grant No.61075115,No.61403249), Foundation of No. XKCZ1212. Xiao-
Ming You is corresponding author.
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