Immune particle swarm optimization algorithm based on the adaptive search strategy
The particle swarm algorithm is often trapped in a local optimum due to poor diversity, resulting in a premature stagnation phenomenon. In order to overcome this shortcoming, an immune particle swarm optimization algorithm based on the adaptive search strategy was proposed in this paper. Firstly, th...
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| Format: | Article |
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Science Press
2017-01-01
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| Series: | 工程科学学报 |
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| Online Access: | http://cje.ustb.edu.cn/article/doi/10.13374/j.issn2095-9389.2017.01.016 |
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| author | ZHANG Chao LI Qing WANG Wei-qian CHEN Peng FENG Yi-nan |
| author_facet | ZHANG Chao LI Qing WANG Wei-qian CHEN Peng FENG Yi-nan |
| author_sort | ZHANG Chao |
| collection | DOAJ |
| description | The particle swarm algorithm is often trapped in a local optimum due to poor diversity, resulting in a premature stagnation phenomenon. In order to overcome this shortcoming, an immune particle swarm optimization algorithm based on the adaptive search strategy was proposed in this paper. Firstly, the concentration mechanism was improved. Secondly, in order to make full use of the resources of the particle population, the number of particles of sub-populations was controlled by the maximum concentration of particles. Finally, the inferior sub-populations were vaccinated, and the maximum concentration of particles was used to control the search range of the vaccine, so the population degradation was avoided, and the convergence accuracy and the global search ability of the algorithm were improved. Simulation results show the effectiveness and superiority of the proposed algorithm in solving the complex function optimization problems. |
| format | Article |
| id | doaj-art-ae8e442e9b7740f7bdbbda57ad18315f |
| institution | OA Journals |
| issn | 2095-9389 |
| language | zho |
| publishDate | 2017-01-01 |
| publisher | Science Press |
| record_format | Article |
| series | 工程科学学报 |
| spelling | doaj-art-ae8e442e9b7740f7bdbbda57ad18315f2025-08-20T01:47:51ZzhoScience Press工程科学学报2095-93892017-01-0139112513210.13374/j.issn2095-9389.2017.01.016Immune particle swarm optimization algorithm based on the adaptive search strategyZHANG Chao0LI Qing1WANG Wei-qian2CHEN Peng3FENG Yi-nan41) School of Automation and Electrical Engineering, University of Science and Technology Beijing, Beijing 100083, China1) School of Automation and Electrical Engineering, University of Science and Technology Beijing, Beijing 100083, China2) The Second Research Institute of China Electronics Technology Group Corporation, Taiyuan 030024, China1) School of Automation and Electrical Engineering, University of Science and Technology Beijing, Beijing 100083, China1) School of Automation and Electrical Engineering, University of Science and Technology Beijing, Beijing 100083, ChinaThe particle swarm algorithm is often trapped in a local optimum due to poor diversity, resulting in a premature stagnation phenomenon. In order to overcome this shortcoming, an immune particle swarm optimization algorithm based on the adaptive search strategy was proposed in this paper. Firstly, the concentration mechanism was improved. Secondly, in order to make full use of the resources of the particle population, the number of particles of sub-populations was controlled by the maximum concentration of particles. Finally, the inferior sub-populations were vaccinated, and the maximum concentration of particles was used to control the search range of the vaccine, so the population degradation was avoided, and the convergence accuracy and the global search ability of the algorithm were improved. Simulation results show the effectiveness and superiority of the proposed algorithm in solving the complex function optimization problems.http://cje.ustb.edu.cn/article/doi/10.13374/j.issn2095-9389.2017.01.016particle swarm optimizationartificial immune algorithmadaptive searchhamming distance |
| spellingShingle | ZHANG Chao LI Qing WANG Wei-qian CHEN Peng FENG Yi-nan Immune particle swarm optimization algorithm based on the adaptive search strategy 工程科学学报 particle swarm optimization artificial immune algorithm adaptive search hamming distance |
| title | Immune particle swarm optimization algorithm based on the adaptive search strategy |
| title_full | Immune particle swarm optimization algorithm based on the adaptive search strategy |
| title_fullStr | Immune particle swarm optimization algorithm based on the adaptive search strategy |
| title_full_unstemmed | Immune particle swarm optimization algorithm based on the adaptive search strategy |
| title_short | Immune particle swarm optimization algorithm based on the adaptive search strategy |
| title_sort | immune particle swarm optimization algorithm based on the adaptive search strategy |
| topic | particle swarm optimization artificial immune algorithm adaptive search hamming distance |
| url | http://cje.ustb.edu.cn/article/doi/10.13374/j.issn2095-9389.2017.01.016 |
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