摘要
针对粒子群算法和克隆选择原理的特点,提出了基于克隆选择和粒子群思想的动态多群体优化算法.该算法将整个群体分为若干子群体,在子群体内部应用基本的粒子群算法,以子群体作为抗体设计了克隆、变异、选择和受体编辑算子.变异算子使子群体动态变化实现子群体间相互交换信息,具有良好的全局搜索能力.实验结果表明,该算法具有寻优能力强、搜索精度高的优点,可用于工程问题中具有各种特性的复杂函数优化.
Based on the promising fusion of the clonal selection and particle swarm principles, a dynamic multi-swarm optimization algorithm is proposed. In the approach, the whole swarm is divided into dynamic subpopulations, which are considered as the evolving antibodies. These subpopulations are further optimized by using the particle swarm method to increase the necessary antibody diversity. Moreover, they can exchange useful optimization information among themselves during the iteration procedure. The cloning, hypermutation, selection and receptor editing operators are also employed in the proposed hybrid scheme. Simulations demonstrate that the optimization algorithm can overcome the premature and slow convergence drawbacks of the standard particle swarm and clonal selection methods, and it is very effective in dealing with the challenging nonlinear function optimization problems.
出处
《控制与决策》
EI
CSCD
北大核心
2008年第9期1073-1076,共4页
Control and Decision
基金
国防预研项目(9140A17030207HT0150)
芬兰科学院研究项目(Grant214144)
关键词
克隆选择
粒子群
优化算法
多维函数优化
多群体
Clonal selection
Particle swarm
Optimization algorithm
Multi-dimension function optimization
Multiswarm
