文献类型：会议论文

英文题名：Co-Evolutionary Cultural Based Particle Swarm Optimization Algorithm

作者：Sun, Yang[1];Zhang, Lingbo[1];Gu, Xingsheng[1]

机构：[1]E China Univ Sci & Technol, Res Inst Automat, Shanghai 200237, Peoples R China

会议论文集：International Conference on Life System Modeling and Simulation / International Conference on Intelligent Computing for Sustainable Energy and Environment (LSMS-ICSEE)

会议日期：SEP 17-20, 2010

会议地点：Wuxi, PEOPLES R CHINA

语种：英文

外文关键词：Global optimization; particle swarm optimization; cultural algorithm; co-evolutionary algorithm

摘要：Particle swarm optimization (PSO), cultural algorithm (CA) and co-evolutionary algorithm (CEA) are all research hotspots in the field of intelligent computing. In order to apply their advantages, a hybrid algorithm CECBPSO is proposed in this paper. In the hybridization, PSO is introduced into the framework of CA, and then a co-evolutionary mechanism between two cultural based PSO algorithms is established. In this way, useful experiences can be exchanged among the populations, and randomly reinitialized particles are introduced into the algorithm. Both of them can help the algorithm improving the efficiency and escape the local optima when the particles get premature. The performance is evaluated on five test functions. Simulation results show that the hybridizing of the three algorithms greatly improves the performance.