文献类型：期刊文献

英文题名：An improved particle swarm algorithm based on cultural algorithm for constrained optimization

作者：Wang, Lina[1]; Cao, Cuiwen[1]; Xu, Zhenhao[1]; Gu, Xingsheng[1]

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

年份：2012

卷号：135

起止页码：453

外文期刊名：Advances in Intelligent and Soft Computing

收录：EI(收录号：20121714971659)

语种：英文

外文关键词：Particle swarm optimization (PSO)

摘要：This paper develops an improved particle swarm optimization algorithm based on cultural algorithm for constrained optimization problems. Firstly, chaos method is utilized in the initialization process of single swarm in population space to assure the searching breadth, and evolves with standard particle swarm optimization (PSO). Secondly, fixed proportion elites are selected from population space to construct the swarm of belief space through acceptance function. Then, the belief space updates its normative knowledge and situational knowledge according to the elite particles, and the elite-swarm in the belief space performs PSO operation according to the update knowledge and generates new particles. After that, the belief space renews the knowledge again, and passes down the new knowledge which has been updated twice to give better guidance to all the particles in the population space. The efficiency of the initialization strategy and the double evolving knowledge strategy are verified in six constrained optimization problems. ? 2012 Springer-Verlag GmbH Berlin Heidelberg.