Opposition Based Comprehensive Learning Particle Swarm Optimization

摘要：

This paper proposes a novel scheme that we call theopposition based comprehensive learning panicleswarm optimizers (OCLPSO),which employsopposition based learning(OBL)for populationinitialization and also for exemplar selecting.Thisscheme enables the swarm to explore and exploit withthe more diversity and not to be prematureconvergence. Experiments were conducted onbenchmark functions and comparisons between theoriginal CLPSO and the OCLPSO are presented.Theresults are very promising,as the OCLPSO seems tofind better solutions in multimodal problems whencompared with the CLPSO.

作者: Zhangjun Wu Zhiwei Ni Chang Zhang Lichuan Gu

作者单位: Institute of Intelligent Management,Hefei University of Technology;Key Laboratory of Process Optimiz Institute of Intelligent Management,Hefei University of Technology Key Laboratory of Process Optimiz

会议类型: 国际会议

会议名称: 2008 3rd International Conference on Intelligent System and Knowledge Engineering(第三届智能系统与知识工程国际会议)(ISKE 2008)

会议地点: 厦门

会议语种:英文

页码: 1013-1019

在线出版日期: 2008-11-17（万方平台首次上网日期，不代表论文的发表时间）