A Hybrid Differential Evolution Algorithm Integrated with Particle Swarm Optimization  

作　　者：范勤勤 颜学峰 

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

出　　处：《Journal of Donghua University(English Edition)》2014年第2期197-200,共4页东华大学学报（英文版）

基　　金：National Key Basic Research Project of China(973 program)(No.2013CB733600);National Natural Science Foundation of China(No.21176073);Program for New Century Excellent Talents in University,China(No.NCET-09-0346);the Fundamental Research Funds for the Central Universities,China

摘　　要：To implement self-adaptive control parameters, a hybrid differential evolution algorithm integrated with particle swarm optimization （PSODE） is proposed. In the PSODE, control parameters are encoded to be a symbiotic individual of original individual, and each original individual has its own symbiotic individual. Differential evolution （ DE） operators are used to evolve the original population. And, particle swarm optimization （PSO） is applied to co-evolving the symbiotic population. Thus, with the evolution of the original population in PSODE, the symbiotic population is dynamically and self-adaptively adjusted and the realtime optimum control parameters are obtained. The proposed algorithm is compared with some DE variants on nine functious. The results show that the average performance of PSODE is the best.To implement self-adaptive control parameters,a hybrid differential evolution algorithm integrated with particle swarm optimization( PSODE) is proposed. In the PSODE, control parameters are encoded to be a symbiotic individual of original individual,and each original individual has its own symbiotic individual. Differential evolution( DE) operators are used to evolve the original population. And,particle swarm optimization( PSO) is applied to co-evolving the symbiotic population. Thus,with the evolution of the original population in PSODE, the symbiotic population is dynamically and self-adaptively adjusted and the realtime optimum control parameters are obtained. The proposed algorithm is compared with some DE variants on nine functions. The results show that the average performance of PSODE is the best.

关 键 词：differential evolution algorithm particle swann optimization SELF-ADAPTIVE CO-EVOLUTION 

分 类 号：TP273[自动化与计算机技术—检测技术与自动化装置]