Opposition-based particle swarm optimization with adaptive elite mutation and nonlinear inertia weight
An opposition-based particle swarm optimization with adaptive elite mutation and nonlinear inertia weight (OPSO-AEM＆NIW) was proposed to overcome the drawbacks, such as falling into local optimization, slow convergence speed of opposition-based particle swarm optimization. Two strategies were introd...
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| Format: | Article |
| Language: | zho |
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Editorial Department of Journal on Communications
2016-12-01
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| Series: | Tongxin xuebao |
| Subjects: | |
| Online Access: | http://www.joconline.com.cn/zh/article/doi/10.11959/j.issn.1000-436x.2016224/ |
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| _version_ | 1841539590903562240 |
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| author | Wen-yong DONG Lan-lan KANG Yu-hang LIU Kang-shun LI |
| author_facet | Wen-yong DONG Lan-lan KANG Yu-hang LIU Kang-shun LI |
| author_sort | Wen-yong DONG |
| collection | DOAJ |
| description | An opposition-based particle swarm optimization with adaptive elite mutation and nonlinear inertia weight (OPSO-AEM＆NIW) was proposed to overcome the drawbacks, such as falling into local optimization, slow convergence speed of opposition-based particle swarm optimization. Two strategies were introduced to balance the contradiction be-tween exploration and exploitation during its iterations process. The first one was nonlinear adaptive inertia weight (NIW), which aim to accelerate the process of convergence of the algorithm by adjusting the active degree of each parti-cle using relative information such as particle fitness proportion. The second one was adaptive elite mutation strategy (AEM), which aim to avoid algorithm trap into local optimum by trigging particle's activity. Experimental results show OPSO-AEM＆NIW algorithm has stronger competitive ability compared with opposition-based particle swarm optimiza-tions and its varieties in both calculation accuracy and computation cost. |
| format | Article |
| id | doaj-art-877ebebbc4a84c7b8251f29b8f61be48 |
| institution | Kabale University |
| issn | 1000-436X |
| language | zho |
| publishDate | 2016-12-01 |
| publisher | Editorial Department of Journal on Communications |
| record_format | Article |
| series | Tongxin xuebao |
| spelling | doaj-art-877ebebbc4a84c7b8251f29b8f61be482025-01-14T07:10:55ZzhoEditorial Department of Journal on CommunicationsTongxin xuebao1000-436X2016-12-013711059705076Opposition-based particle swarm optimization with adaptive elite mutation and nonlinear inertia weightWen-yong DONGLan-lan KANGYu-hang LIUKang-shun LIAn opposition-based particle swarm optimization with adaptive elite mutation and nonlinear inertia weight (OPSO-AEM＆NIW) was proposed to overcome the drawbacks, such as falling into local optimization, slow convergence speed of opposition-based particle swarm optimization. Two strategies were introduced to balance the contradiction be-tween exploration and exploitation during its iterations process. The first one was nonlinear adaptive inertia weight (NIW), which aim to accelerate the process of convergence of the algorithm by adjusting the active degree of each parti-cle using relative information such as particle fitness proportion. The second one was adaptive elite mutation strategy (AEM), which aim to avoid algorithm trap into local optimum by trigging particle's activity. Experimental results show OPSO-AEM＆NIW algorithm has stronger competitive ability compared with opposition-based particle swarm optimiza-tions and its varieties in both calculation accuracy and computation cost.http://www.joconline.com.cn/zh/article/doi/10.11959/j.issn.1000-436x.2016224/generalized opposition-based learningparticle swarm optimizationadaptive elite mutationnonlinear inertia weight |
| spellingShingle | Wen-yong DONG Lan-lan KANG Yu-hang LIU Kang-shun LI Opposition-based particle swarm optimization with adaptive elite mutation and nonlinear inertia weight Tongxin xuebao generalized opposition-based learning particle swarm optimization adaptive elite mutation nonlinear inertia weight |
| title | Opposition-based particle swarm optimization with adaptive elite mutation and nonlinear inertia weight |
| title_full | Opposition-based particle swarm optimization with adaptive elite mutation and nonlinear inertia weight |
| title_fullStr | Opposition-based particle swarm optimization with adaptive elite mutation and nonlinear inertia weight |
| title_full_unstemmed | Opposition-based particle swarm optimization with adaptive elite mutation and nonlinear inertia weight |
| title_short | Opposition-based particle swarm optimization with adaptive elite mutation and nonlinear inertia weight |
| title_sort | opposition based particle swarm optimization with adaptive elite mutation and nonlinear inertia weight |
| topic | generalized opposition-based learning particle swarm optimization adaptive elite mutation nonlinear inertia weight |
| url | http://www.joconline.com.cn/zh/article/doi/10.11959/j.issn.1000-436x.2016224/ |
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