三种混合粒子群算法比较

Comparison Among Three Kinds of Hybrid Particle Swarm Optimization Algorithms

混合粒子群算法是融合其它算法或技术特性来针对性地对基本粒子群算法进行改进的一类算法。文中对其中有代表性的三种:交叉粒子群、免疫粒子群、混沌粒子群展开了比较研究,分别从混合目的、混合基本方式、混合算法实现的关键步骤、混合算法的优化性能等多个方面对这三种混合算法进行了比较。通过这些比较,总结出了三种混合算法基本的混合方式及实现步骤、算法各自的特点及适用范围,特别对三种混合算法的优化性能做了较深入的比较和归纳。

Hybrid particle swarm optimization（PSO） is such a kind of algorithms,which combine other algorithms or technologies to enhance the basic PSO.This paper compares three representative hybrid PSOs： breeding PSO,immune PSO,and chaos PSO.These comparisons are carried out in following aspects： the goals for hybrid,basic hybrid modes,the key implementation steps,hybrid algorithm optimization performances and so on.Through these comparisons and analysis,three hybrid algorithms＇construction,implementationand respective application scope are summarized,especially,the optimization performances ofthree hybrid PSOs have been researched carefully.