文献类型：期刊文献

中文题名：基于模式优选思想改进的粒子群优化算法

英文题名：Improved Particle Swarm Optimization Algorithm by Schema

作者：李绍军[1];王惠[1];钱锋[1]

机构：[1]华东理工大学自动化研究所,上海200237

年份：2006

卷号：21

期号：10

起止页码：1193

中文期刊名：控制与决策

外文期刊名：Control and Decision

收录：CSTPCD;;EI(收录号：20070210351835);Scopus;北大核心:【北大核心2004】；CSCD:【CSCD2011_2012】；

基金：国家973计划项目(2002CB3122000);国家863计划项目(2003AA412010);上海科委科技攻关项目(04DZ11010);上海市优秀学科带头人计划项目

语种：中文

中文关键词：粒子群;模式;反应动力学;优化

外文关键词：Particle swarm ; Schema ; Kinetic ; Optimization

摘要：针对粒子群优化算法(PSO)容易陷入局部最优值的缺点,提出一种基于遗传算法模式定理思想改进的粒子群优化算法(IPSO).新算法改善了粒子群优化算法摆脱局部极小点的能力.对典型函数的测试表明,IPSO算法的全局搜索能力有了显著提高,特别是对多峰函数能有效地避免早熟收敛问题.将改进的粒子群优化算法用于氧化反应动力学参数的优化,计算结果表明,新算法优化结果明显优于文献报道.
An improved particle swarm optimization algorithm （IPSO） is proposed based on the idea of schema optimal choice, which is the basic character of genetic algorithm. The IPSO has merits of both PSO and genetic algorithm choice. Both IPSO and basic PSO are used to resolve several well-known benchmark functions optimation problems. Results show that IPSO has greater efficiency and better performance than PSO, especially to the highdimesional, multi-apices functions. The application of IPSO to estimate of the kinetic parameters of 2-chloropheol oxidation in supercritical water provides better parameters than those reported in the literature.