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Description: 此程序包是用双种群蚁群算法来求中国75个城市的最短路径问题，即典型的TSP问题，把包解压运行main.m文件即可-This package is a dual population of ant colony algorithm to seek China s 75 cities, the shortest path problem, that is typical TSP problem, the package can extract the files to run main.m
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Size: 8192 |
Author: 梁锦兆 |
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Description: This package is a dual population of ant colony algorithm to seek, the shortest path problem, that is typical TSP problem, the packa
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Size: 286720 |
Author: prakashmhp |
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Description: 使用matlab实现的双种群蚁群算法，输出最优路径-Matlab implementation of the Dual Population Ant Colony Algorithm, the output of the optimal path
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Size: 4096 |
Author: |
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Description: 改进的双种群遗传算法，适合大家修改使用，会对大家有很大帮助-Improved dual population genetic algorithm, suitable for everyone to modify the use of
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Size: 4096 |
Author: 陆涛 |
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Description: Abstract: With the development of engineering technology and the improvement of mathematical model, a large number of optimization
problems were developed from low dimensional optimization to large-scale complex optimization. Large scale global optimization is an
active research topic in the real-parameter optimization. Based on the analysis of the characteristics of large scale problems, a stochastic
dynamic cooperative coevolution strategy was proposed. The strategy was added to the dynamic multi-swarm particle swarm optimization
algorithm. And the dual grouping of population and decision variables was realized. Next, the performance of the novel optimization on
the set of benchmark functions provided for the CEC2013 Special Session on Large Scale optimization is reported. Finally the validity of
the algorithm was verified by comparing with other algorithms
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Size: 78848 |
Author: DirtyMind |
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Description: Abstract: With the development of engineering technology and the improvement of mathematical model, a large number of optimization
problems were developed from low dimensional optimization to large-scale complex optimization. Large scale global optimization is an
active research topic in the real-parameter optimization. Based on the analysis of the characteristics of large scale problems, a stochastic
dynamic cooperative coevolution strategy was proposed. The strategy was added to the dynamic multi-swarm particle swarm optimization
algorithm. And the dual grouping of population and decision variables was realized. Next, the performance of the novel optimization on
the set of benchmark functions provided for the CEC2013 Special Session on Large Scale optimization is reported. Finally the validity of
the algorithm was verified by comparing with other algorithms.
Platform: |
Size: 12879872 |
Author: DirtyMind |
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Description: Abstract: With the development of engineering technology and the improvement of mathematical model, a large number of optimization
problems were developed from low dimensional optimization to large-scale complex optimization. Large scale global optimization is an
active research topic in the real-parameter optimization. Based on the analysis of the characteristics of large scale problems, a stochastic
dynamic cooperative coevolution strategy was proposed. The strategy was added to the dynamic multi-swarm particle swarm optimization
algorithm. And the dual grouping of population and decision variables was realized. Next, the performance of the novel optimization on
the set of benchmark functions provided for the CEC2013 Special Session on Large Scale optimization is reported. Finally the validity of
the algorithm was verified by comparing with other algorithms
Platform: |
Size: 553984 |
Author: DirtyMind |
Hits:
Description: Abstract: With the development of engineering technology and the improvement of mathematical model, a large number of optimization
problems were developed from low dimensional optimization to large-scale complex optimization. Large scale global optimization is an
active research topic in the real-parameter optimization. Based on the analysis of the characteristics of large scale problems, a stochastic
dynamic cooperative coevolution strategy was proposed. The strategy was added to the dynamic multi-swarm particle swarm optimization
algorithm. And the dual grouping of population and decision variables was realized. Next, the performance of the novel optimization on
the set of benchmark functions provided for the CEC2013 Special Session on Large Scale optimization is reported. Finally the validity of
the algorithm was verified by comparing with other algorithm
Platform: |
Size: 83968 |
Author: DirtyMind |
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