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一种改进粒子群优化算法 被引量：39

A Modified Particle Swarm Optimization Algorithm
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摘要 作为群集智能的代表性方法之一,粒子群优化(PSO)算法通过粒子之间的合作与竞争以实现对多维复杂空间的高效搜索。提出了一种改进粒子群优化(MPSO)算法。MPSO同时采用局部模式压缩因子方法和全局模式惯性权重方法以获得相对较高的性能。针对PSO算法可能出现的停滞现象,MPSO引入了基于全局信息反馈的重新初始化机制。数值仿真结果显示了该算法的有效性。 As a representative method of swarm intelligence, particle swarm optimization (PSO) is an algorithm for searching the multidimensional complex space efficiently through cooperation and competition among the individuals in a population of particles. A modified PSO (MPSO) algorithm is proposed. The MPSO employs local version constriction factor method and global version inertia weight method simultaneously to achieve relatively high performance. To avoid the possible occurring of stagnation phenomenon in the PSO algorithm, the re-initialization mechanism based on the global information feedback is introduced in the MPSO. Numerical examples show the effectiveness of the proposed algorithm.
作者 柯晶 钱积新 乔谊正
机构地区 山东大学控制科学与工程学院 浙江大学系统工程研究所
出处 《电路与系统学报》 CSCD 2003年第5期87-91,共5页 Journal of Circuits and Systems
关键词 进化计算 群集智能 粒子群优化 evolutionary computation swarm intelligence particle swarm optimization
分类号 TP18 [自动化与计算机技术—控制理论与控制工程]
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参考文献14

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电路与系统学报
2003年 第5期

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