自适应扩散混合变异机制微粒群算法

为了避免微粒群算法(particle swarm optimization,简称PSO)在全局优化中陷入局部极值,分析了标准PSO算法早熟收敛的原因,提出了自适应扩散混合变异机制微粒群算法(InformPSO).结合生物群体信息扩散的习性,设计了一个考虑微粒分布和迭代次数的函数,自适应调整微粒的"社会认知"能力,提高种群的多样性;模拟了基因自组织和混沌进化规律,引入克隆选择使群体最佳微粒gBest实现遗传微变、局部增值,具有变异确定性;利用Logistic序列指导gBest随机漂移,进一步增强逃离局部极值能力.基于种群的随机状态转移过程,证明了新算法具有全局收敛性.与其他几种PSO变种相比,复杂基准函数仿真优化结果表明,新算法收敛速度快,求解精度高,稳定性好,能够有效抑制早熟收敛.

Particle Swarm Optimization Based on Adaptive Diffusion and Hybrid Mutation

Conventional algorithms of particle swarm optimization(PSO)are often trapped in local optima in global optimization.In this paper,following an analysis of the main causes of the premature convergence,it proposes a novel PSO algorithm,which is called InformPSO,based on the principles of adaptive diffusion and hybrid mutation.Inspired by the physics of information diffusion,a function is designed to achieve a better particle diversity,by both taking into account their distribution and the number of evolutionary generations and adjusting their"social cognitive"abilities.Based on genetic self-organization and chaos evolution,clonal selection is built into InformPSO to implement the local evolution of the best particle candidate,gBest,and make use of a Logistic sequence to control the random drift of gBest.These techniques greatly contribute to breaking away from local optima.The global convergence of the algorithm is proved using the theorem of Markov chain.Experiments on optimization of unimodal and multimodal benchmark functions show that,comparing with some other PSO variants, InformPSO converges faster,results in better optima,is more robust,and prevents more effectively the premature convergence.