Population Diversity Analysis of The Adaptive Partly Informed PSO Algorithm

摘要：

Particle Swarm Optimization (FSO)is one of the newly developed intelligence optimization algorithms. With its simple concept, few parameters and scalable performance, PSO has become a very promising optimization tool and attracted extensive attention. In this paper, probabilistic analysis on swarm diversity in Adaptive Partly Informed Particle Swarm Optimization algorithm (API-PS O) was conducted. From the analysis results we can learn that the expectation of population diversity in the next moment are affected by the population size, problemsolving dimensions, the topology and the specific optimization function to be solved, ect. We also get the relationship between the current population diversity and the state of next time. This information could serve as the theoretic basis to solve swarm diversity lack, facilitate swarm evolution development and improve algorithm performance.

关键词： Particle Swarm Optmization Adaptive Partly Informed Particle Swarm Optimization (API-PSO) Premature Convergence Population Diversity

作者: Tao Wu Yusong Yan Xi Chen

作者单位: School of Information Science & Technology Southwest Jiaotong University Chengdu, P.R. China

会议类型: 国际会议

会议名称: 2011 International Conference on Computer Science and Network Technology(2011计算机科学与网络技术国际会议 ICCSNT 2011)

会议地点: 哈尔滨

会议语种:英文

页码: 2154-2158

在线出版日期: 2011-12-24（万方平台首次上网日期，不代表论文的发表时间）