| 引用本文: | 刘黎黎,李国家,汪定伟.动态环境下带有非线性效应的复合粒子群优化算法[J].控制理论与应用,2012,29(10):1253~1262.[点击复制] |
| LIU Li-li,LI Guo-jia,WANG Ding-wei.Composite particle swarm optimization with nonlinear effect in dynamic environment[J].Control Theory and Technology,2012,29(10):1253~1262.[点击复制] |
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| 动态环境下带有非线性效应的复合粒子群优化算法 |
| Composite particle swarm optimization with nonlinear effect in dynamic environment |
| 摘要点击 2275 全文点击 1354 投稿时间：2011-09-29 修订日期：2012-04-09 |
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| DOI编号 |
| 2012,29(10):1253-1262 |
| 中文关键词 粒子群优化 复合粒子 异速度映射 自适应步长调整 动态优化问题 |
| 英文关键词 particle swarm optimization (PSO) composite particle velocity-anisotropic reflection self-adaptive stepsize adjustment dynamic optimization problem |
| 基金项目 国家自然科学基金资助项目(70931001, 70771021, 70721001); 国家自然科学基金创新研究群体科学基金资助项目(60521003, 60821063); 国家自然科学基金青年基金资助项目(61004121, 71001018). |
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| 中文摘要 |
| 针对粒子群优化算法在求解动态优化问题存在多样性缺失, 寻优速度慢等缺陷, 借鉴物理学中的非线性复合效应, 本文提出带有非线性效应的复合粒子群优化算法, 该算法利用复合材料的相乘效应根据粒子的相似性, 基于“最坏优先”规则将种群划分成若干复合粒子. 为使种群迅速地在动态环境中找到最优解, 利用复合材料的共振效应, 成员粒子通过自适应异速度映射机制整合有价值信息. 为提高种群的多样性, 利用复合材料的诱导效应, 引入复合粒子的整体运动策略. 最后通过动态标准测试问题实验对相关参数设置进行了分析, 并与其他几种粒子群算法相比较, 验证了该算法在动态环境中的有效性. |
| 英文摘要 |
| This paper presents a new particle swarm optimization model, called composite particle swarm optimization with nonlinear effect (CPSO–NE), to deal with dynamic optimization problems. CPSO–NE partitions the swarm into a set of composite particles based on their similarity using a “worst-first” principle. Inspired by the notion of the composite particle phenomenon in physics, the elementary members in each composite particle interact via a velocity-anisotropic reflection scheme to integrate valuable information for effectively and rapidly finding the promising optima in the search space. Each composite particle maintains the diversity by a scattering operator. In addition, an integral movement strategy is introduced to promote the swarm diversity. Experiments on a typical dynamic test benchmark problem provide a guideline for setting the involved parameters and show that CPSO–NE is efficient in comparison with several state-of-the-art PSO algorithms for dynamic optimization problems. |
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