自适应模糊聚类小生境遗传算法

Niching genetic algorithm based on self-adaptive controlling and fuzzy similarity clustering

提出了基于峰半径自适应调整和模糊相似聚类的小生境遗传算法。其基本思想是:在演化过程中,将峰半径作为决策变量的一部分参与染色体的编码,在对问题进行优化的同时对个体的峰半径进行自适应调整;在聚类过程中,通过对模糊相似度的调节来控制小生境的数目,以避免找到无效的极值点。理论分析和数值实验表明,该算法无需事先确定小生境的数目和半径,对于各类多峰函数具有较强的搜索能力。

Determining the count of niche and the value of niche radius is a hard problem for multiple hump functions,so the niche genetic algorithm based on fuzzy similarity clustering and self-adaptive controlling of peaks radii is proposed.The basic idea of the method is that,in the process of genetic evolvement,it takes the radii of peaks as a part of optimization variables,the radii of peaks are coded,put in the chromosomes and optimized with the variables of the problem by fitness sharing genetic algorithm without a prior knowledge of the above parameters;In the process of clustering,it controls the number of converged niches through adjusting the fuzzy similarity degree,avoiding finding the invalid extreme points as well.Theoretical analysis and numerical experiments indicate that the algorithm takes no need to know the concrete number of niches and the value of the niche radium in advance,having a good searching ability on various multiple hump functions.