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peng_weishi
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5楼2016-06-01 23:28:51
lzg020716
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Multi-objective particle optimization algorithm based on sharing-learning and dynamic crowding distance 作者  eng, G (Peng, Guang)[ 1 ] ; Fang, YW (Fang, Yang-Wang)[ 1 ] ; Peng, WS (Peng, Wei-Shi)[ 1 ] ; Chai, D (Chai, Dong)[ 1 ] ; Xu, Y (Xu, Yang)[ 1 ]OPTIK 卷: 127 期: 12 页: 5013-5020 DOI: 10.1016/j.ijleo.2016.02.045 出版年: 2016 查看期刊信息 摘要 A multi-objective particle swarm optimization algorithm, based on share-learning and dynamic crowding distance (MOPSO-SDCD), is proposed to improve the convergence accuracy and keep the diversity of the Pareto optimal solutions. First, the sharing-learning factor is applied to modify the velocity updating formulas, which improves both the global search ability and local search accuracy of the algorithm. Meanwhile, Gaussian mutation and greedy strategy are adopted to update personal best position and external archive, which make the algorithm approximate the Pareto front quickly and avoid premature convergence. Finally, MOPSO-SDCD maintains the external archive based on dynamic crowding distance sorting strategy, whose purpose is boosting the diversity and distribution of Pareto optimal solutions. The ZDT series test functions are used to test the performance of MOPSO-SDCD and compare with other three typical algorithms. Simulation results verify the superiority and effectiveness of the proposed algorithm. (C) 2016 Elsevier GmbH. All rights reserved. 关键词 作者关键词:Multi-objective optimization; Particle swarm optimization; Sharing-learning; Gaussian mutation; Dynamic crowding distance KeyWords Plus:SWARM OPTIMIZER 作者信息 通讯作者地址: Peng, G (通讯作者) Air Force Engn Univ, Aeronaut & Astronaut Engn Coll, Baling Rd 1, Xian, Peoples R China. 地址: [ 1 ] Air Force Engn Univ, Aeronaut & Astronaut Engn Coll, Baling Rd 1, Xian, Peoples R China 电子邮件地址:pg1445334307@163.com 出版商 ELSEVIER GMBH, URBAN & FISCHER VERLAG, OFFICE JENA, P O BOX 100537, 07705 JENA, GERMANY 类别 / 分类 研究方向:Optics Web of Science 类别:Optics 文献信息 文献类型:Article 语种:English 入藏号: WOS:000374618900015 ISSN: 0030-4026 期刊信息 目录： Current Contents Connect® Impact Factor (影响因子): Journal Citation Reports® 其他信息 IDS 号: DK0RC Web of Science 核心合集中的 "引用的参考文献": 18 Web of Science 核心合集中的 "被引频次": 0 |
2楼2016-06-01 22:09:30
peng_weishi
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3楼2016-06-01 22:50:18
心静_依然
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4楼2016-06-01 22:57:53
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