文献类型：期刊文献

英文题名：A novel hybrid particle swarm optimization algorithm merging crossover mutation and chaos

作者：Liu, Zhao[1]; Qi, Rongbin[1]; Qian, Feng[1]

机构：[1] Key Laboratory of Advanced Control and Optimization for Chemical Processes, Ministry of Education, East China University of Science and Technology, Shanghai 200237, China

年份：2010

卷号：61

期号：11

起止页码：2861

外文期刊名：Huagong Xuebao/CIESC Journal

收录：EI(收录号：20105013479820)

语种：英文

外文关键词：Iterative methods - Global optimization - Particle swarm optimization (PSO)

摘要：To solve the premature convergence problem of particle swarm optimization(PSO) in dealing with complex high dimensional function optimization, a novel hybrid particle swarm optimization algorithm merging crossover mutation and chaos(CMCPSO) was proposed. The main approaches included using chaos strategy to initiate positions and velocities of all particles in the design space, introducing crossover operation in each iteration to increase the diversity of particles, breaching the restrictions of local optimization points with a new chaotic disturbance mechanism and mutation operation during the later computation period. Four standard test functions were selected to have a simulation study on the proposed algorithm. The results showed that CMCPSO had a fast convergence rate and effective global optimization ability. ? All Rights Reserved.