A Modified Dynamic Particle Swarm Optimization Algorithm

摘要：

Inspired from social behavior of organisms such as bird flocking, particle swarm optimization(PSO) has rapid convergence speed and has been successfully applied in many optimization problems. In this paper, we present a dynamic particle swarm optimization algorithm to enhance the performance of standard PSO. We design a novel function to compute the initial dynamic inertia weight, and then calculate inertia weight through a nonlinear function. Afterwards, searching process is repeated until the max iteration number is reached or the minimum error condition is satisfied. To testify the effectiveness of the proposed algorithm, we conduct two experiments. Experimental results show that our algorithm performs better than FPSO and standard PSO in best fitness and convergence speed.

关键词： particle swarm optimization global solution local solution inertia weight

作者: LIU Wen

作者单位: The School of Computer Science and Technology Dalian University of Technology,Dalian, China Dept. of Electrical Engineeringjnstitute of XinJinag Mechano-Electrical Vocational and Technical, Urumqi China

会议类型: 国际会议

会议名称: 2012 Fifth International Symposium on Computational Intelligence and Design 第五届计算智能与设计国际会议 ISCID 2012

会议地点: 杭州

会议语种:英文

页码: 432-435

在线出版日期: 2012-10-28（万方平台首次上网日期，不代表论文的发表时间）