摘要
通过分析已有的几种微粒群算法,提出了一种统一模型,并通过线性控制理论分析了其收敛性能·为了进一步提高算法效率,提出了两种增强全局搜索性能的参数自适应算法:单群体参数自适应微粒群算法及双群体参数自适应微粒群算法·其中单群体参数自适应微粒群算法在进化初期使用算法发散的参数设置,从而能更大程度地提高算法全局收敛能力·双群体参数自适应微粒群算法使用两个种群,一个执行全局搜索,另一个执行局部搜索,通过信息交流以提高算法性能·仿真实例证明了算法的有效性·
Through mechanism analysis of several modified particle swarm optimizations （PSO）, a new uniform model of PSO is described, and the convergence is analysed with linear control theory. To improve the calculation efficiency, two enhanced global search capability self-adaptive PSOs, one-population selfadaptive PSO and two-population self-adaptive PSO, are proposed. The one-population self-adaptive PSO uses the diverse coefficients in the first evolutionary strategy. The two-population self-adaptive PSO uses two different populations： one owns global search capability, and the other owns local search, and through exchanging information the algorithm efficiency is improved. The simulation results show the correctness and efficiency of the presented methods.
出处
《计算机研究与发展》
EI
CSCD
北大核心
2006年第1期96-100,共5页
Journal of Computer Research and Development
基金
教育部科学技术研究重点基金项目(204018)
关键词
统一模型
收敛性
自适应
微粒群算法
unified model
convergence
self-adaptive
particle swarm optimization
