文献类型：会议论文

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]E China Univ Sci & Technol, Key Lab Adv Control & Optimizat Chem Proc, Minist Educ, Shanghai 200237, Peoples R China

会议论文集：4th International Conference on Knowledge Discovery and Data Mining

会议日期：MAR 01-02, 2012

会议地点：Macau, PEOPLES R CHINA

语种：英文

外文关键词：particle swarm optimization; cultural algorithm; constrained optimization

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.