文件名称:PSO
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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.
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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下载文件列表
| 文件名 | 大小 | 更新时间 |
|---|---|---|
| PSO | 0 | 2018-01-04 |
| PSO\.vs | 0 | 2018-01-04 |
| PSO\.vs\PSO | 0 | 2018-01-04 |
| PSO\.vs\PSO\v15 | 0 | 2018-01-04 |
| PSO\.vs\PSO\v15\.suo | 35328 | 2018-01-04 |
| PSO\.vs\PSO\v15\Browse.VC.db | 5808128 | 2018-01-04 |
| PSO\.vs\PSO\v15\ipch | 0 | 2018-01-04 |
| PSO\.vs\PSO\v15\ipch\AutoPCH | 0 | 2018-01-04 |
| PSO\.vs\PSO\v15\ipch\AutoPCH\80221537035d956 | 0 | 2018-01-04 |
| PSO\.vs\PSO\v15\ipch\AutoPCH\80221537035d956\MAIN_FUNCTION.ipch | 5505024 | 2018-01-04 |
| PSO\.vs\PSO\v15\ipch\AutoPCH\96e5375c1be709ba | 0 | 2018-01-04 |
| PSO\.vs\PSO\v15\ipch\AutoPCH\96e5375c1be709ba\PSO.ipch | 21430272 | 2018-01-04 |
| PSO\.vs\PSO\v15\ipch\AutoPCH\bef07b8961410244 | 0 | 2018-01-04 |
| PSO\.vs\PSO\v15\ipch\AutoPCH\bef07b8961410244\PSO.ipch | 25886720 | 2018-01-04 |
| PSO\.vs\PSO\v15\ipch\cc5c306eb8c2adab.ipch | 3866624 | 2018-01-04 |
| PSO\PSO | 0 | 2018-01-04 |
| PSO\PSO.sln | 1423 | 2018-01-04 |
| PSO\PSO\M.cpp | 170089 | 2018-01-04 |
| PSO\PSO\main_function.cpp | 11706 | 2018-01-04 |
| PSO\PSO\O.cpp | 4134 | 2018-01-04 |
| PSO\PSO\PSO.cpp | 7647 | 2018-01-04 |
| PSO\PSO\PSO.h | 2420 | 2018-01-04 |
| PSO\PSO\PSO.vcxproj | 8292 | 2018-01-04 |
| PSO\PSO\PSO.vcxproj.filters | 1327 | 2018-01-04 |