一种基于聚类分析的小生境微粒群优化算法

A niching PSO algorithm based on clustering

为了寻找多峰函数的多个极值点,在标准微粒群优化算法的基础上,提出一种基于聚类分析的小生境微粒群优化算法.采用基于密度的聚类分析方法辨识和构造小生境微粒子群,通过这种多子群方法,可以保持种群多样性,直接搜索到所有的局部/全局最优点.实验测试结果表明,该算法对一元函数优化和多元函数优化都有很好的效果.图6,参10.

For searching multi-maximum points of multi-modal functions, by analyzing standard particle swarm optimizer, a new niching method for particle swarm optimizer was proposed, which could identify and track global and local optima in a multi-modal search space. The sub-populations which represent the groups of particles specialized on niches were dynamically identified using density-based clustering algorithms. With this multi-population strategy,the diversity within the population was preserved and all the global/local optima were identified directly without further post-processing. Test solutions illustrate that the presented algorithm is efficient for both one-variable functions and multi-variable functions. 6figs.,10refs.