The Bat Algorithm with Dynamic Niche Radius for Multimodal Optimization

Abstract

In this paper, we proposed Bat Algorithm extending with Dynamic Niche Radius (DNRBA) which enables solutions to locate multiple local and global optima for solving multimodal optimization problems. This proposed algorithm is designed Bat Algorithm (BA) dealing with the exploration and the exploitation search with Niche Radius which is calculated by the fitness landscape and the number of local and global optima to avoid converging solutions at the same optimum. Although the Niche Radius is an effective niching method for locating solutions at the peaks in the fitness landscape, it is not applicable for uneven multimodal functions and easily fails to keep multiple optima. To overcome this problem, we introduce a dynamic niche sharing scheme which is able to adjust the distance of the niche radius in the search process dynamically for the BA. In the experiment, we employ several multimodal functions and compare DNRBA with the conventional BA to evaluate the performance of DNRBA.