Extended Social Learning Guided Particle Swarm Optimization

摘要：

In this paper social learning in particle swarm optimization is extended. A particle not only exchanges information with the best in its group, but also learns from an ensemble guide which combines some previous best positions of the particles using ensemble learning technique. In addition, a whole swarm is divided into several parts and in each sub swarm, a particle also learns from another sub swarms best particle. Based on these, an improved algorithm, named extended social learning guided particle swarm optimization (ECPSO), is proposed. Ensemble learning can help providing a more accurate global guide and learning from other groups can help increasing diversity. This algorithm is compared with standard PSO and some other improved PSO algorithms to illustrate how EGPSO can benefit from these strategies.

关键词： particle swarm optimization (PSO) ensemble learning sub swarms social learning

作者: Shi Yan Wang Qin

作者单位: School of Computer & Information Engineering Beijing Technology and Business University Beijing 1000 Information & Technology Department Bank of China Beijing 100818, China

会议类型: 国际会议

会议名称: The 2010 International Conference on Computer Application and System Modeling(2010计算机应用与系统建模国际会议 ICCASM 2010)

会议地点: 太原

会议语种:英文

页码: 567-571

在线出版日期: 2010-10-22（万方平台首次上网日期，不代表论文的发表时间）