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

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

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.