Multispecies Coevolution Particle Swarm Optimization Based on Previous Search History
A hybrid coevolution particle swarm optimization algorithm with dynamic multispecies strategy based on K-means clustering and nonrevisit strategy based on Binary Space Partitioning fitness tree (called MCPSO-PSH) is proposed. Previous search history memorized into the Binary Space Partitioning fitne...
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| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Wiley
2017-01-01
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| Series: | Discrete Dynamics in Nature and Society |
| Online Access: | http://dx.doi.org/10.1155/2017/5193013 |
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| _version_ | 1850208309212086272 |
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| author | Danping Wang Kunyuan Hu Lianbo Ma Maowei He Hanning Chen |
| author_facet | Danping Wang Kunyuan Hu Lianbo Ma Maowei He Hanning Chen |
| author_sort | Danping Wang |
| collection | DOAJ |
| description | A hybrid coevolution particle swarm optimization algorithm with dynamic multispecies strategy based on K-means clustering and nonrevisit strategy based on Binary Space Partitioning fitness tree (called MCPSO-PSH) is proposed. Previous search history memorized into the Binary Space Partitioning fitness tree can effectively restrain the individuals’ revisit phenomenon. The whole population is partitioned into several subspecies and cooperative coevolution is realized by an information communication mechanism between subspecies, which can enhance the global search ability of particles and avoid premature convergence to local optimum. To demonstrate the power of the method, comparisons between the proposed algorithm and state-of-the-art algorithms are grouped into two categories: 10 basic benchmark functions (10-dimensional and 30-dimensional), 10 CEC2005 benchmark functions (30-dimensional), and a real-world problem (multilevel image segmentation problems). Experimental results show that MCPSO-PSH displays a competitive performance compared to the other swarm-based or evolutionary algorithms in terms of solution accuracy and statistical tests. |
| format | Article |
| id | doaj-art-963a4527a9b74ca4bfcd7b86b5959778 |
| institution | OA Journals |
| issn | 1026-0226 1607-887X |
| language | English |
| publishDate | 2017-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Discrete Dynamics in Nature and Society |
| spelling | doaj-art-963a4527a9b74ca4bfcd7b86b59597782025-08-20T02:10:16ZengWileyDiscrete Dynamics in Nature and Society1026-02261607-887X2017-01-01201710.1155/2017/51930135193013Multispecies Coevolution Particle Swarm Optimization Based on Previous Search HistoryDanping Wang0Kunyuan Hu1Lianbo Ma2Maowei He3Hanning Chen4Department of Information Service & Intelligent Control, Shenyang Institute of Automation, Chinese Academy of Sciences, Shenyang 110016, ChinaDepartment of Information Service & Intelligent Control, Shenyang Institute of Automation, Chinese Academy of Sciences, Shenyang 110016, ChinaDepartment of Information Service & Intelligent Control, Shenyang Institute of Automation, Chinese Academy of Sciences, Shenyang 110016, ChinaDepartment of Information Service & Intelligent Control, Shenyang Institute of Automation, Chinese Academy of Sciences, Shenyang 110016, ChinaSchool of Computer Science and Software, Tianjin Polytechnic University, Tianjin 300387, ChinaA hybrid coevolution particle swarm optimization algorithm with dynamic multispecies strategy based on K-means clustering and nonrevisit strategy based on Binary Space Partitioning fitness tree (called MCPSO-PSH) is proposed. Previous search history memorized into the Binary Space Partitioning fitness tree can effectively restrain the individuals’ revisit phenomenon. The whole population is partitioned into several subspecies and cooperative coevolution is realized by an information communication mechanism between subspecies, which can enhance the global search ability of particles and avoid premature convergence to local optimum. To demonstrate the power of the method, comparisons between the proposed algorithm and state-of-the-art algorithms are grouped into two categories: 10 basic benchmark functions (10-dimensional and 30-dimensional), 10 CEC2005 benchmark functions (30-dimensional), and a real-world problem (multilevel image segmentation problems). Experimental results show that MCPSO-PSH displays a competitive performance compared to the other swarm-based or evolutionary algorithms in terms of solution accuracy and statistical tests.http://dx.doi.org/10.1155/2017/5193013 |
| spellingShingle | Danping Wang Kunyuan Hu Lianbo Ma Maowei He Hanning Chen Multispecies Coevolution Particle Swarm Optimization Based on Previous Search History Discrete Dynamics in Nature and Society |
| title | Multispecies Coevolution Particle Swarm Optimization Based on Previous Search History |
| title_full | Multispecies Coevolution Particle Swarm Optimization Based on Previous Search History |
| title_fullStr | Multispecies Coevolution Particle Swarm Optimization Based on Previous Search History |
| title_full_unstemmed | Multispecies Coevolution Particle Swarm Optimization Based on Previous Search History |
| title_short | Multispecies Coevolution Particle Swarm Optimization Based on Previous Search History |
| title_sort | multispecies coevolution particle swarm optimization based on previous search history |
| url | http://dx.doi.org/10.1155/2017/5193013 |
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