Chaotic Particle Swarm Optimization Algorithm Based on Adaptive Inertia Weight

摘要：

　　In order to overcome the disadvantages of premature and local convergence in the traditional particle swarm optimization(PSO),an improved chaotic PSO algorithm based on adaptive inertia weight(AIWCPSO)is proposed.The initial population is generated by using chaotic mapping appropriately,in order to improve both the diversity of population and the periodicity of particles.The value of the new inertia weight is adjusted adaptively by feedback parameters,which including iterative number,aggregation degree factor and the improved evolution speed parameter.We judge premature convergence by the relationship between the variance of the populations fitness and the set threshold,if it occurs,we add chaotic disturbance to make it jump out of the local optima.Experimental results on four well-known benchmark functions show that: the AIWCPSO algorithm improves the convergence accuracy and has the ability of suppressing premature convergence.

关键词： Particle Swarm Optimization Chaos Inertia Weight Premature Convergence Adaptability

作者: Jun-wei LI Yong-mei CHENG Ke-zhe CHEN

作者单位: College of Computer and Information Engineering,Henan University,Kaifeng 475004 College of Automation,Northwestern Polytechnical University,Xian 710072,China

会议类型: 国际会议

会议名称: 第26届中国控制与决策会议(2014 CCDC)

会议地点: 长沙

会议语种:英文

页码: 1310-1315

在线出版日期: 2014-05-31（万方平台首次上网日期，不代表论文的发表时间）