Niche Radius Adaptation in Bat Algorithm for Locating Multiple Optima in Multimodal Functions

Evolutionary algorithms (EAs) are often used for multimodal optimization which is modeled as real-world problem. However, most EAs still not enough to find multiple local optima because of the concept of the solution movement between nearest neighbor solutions. This paper proposes the niche radius-based bat algorithm (NRBA), which is designed to find multiple local optima in multimodal optimization. We focus on bat algorithm (BA) which deals with the trade-off between exploration and exploitation in the evolutionary process and extend it with niche radius which can control and modify the search space of solutions to avoid overlapping the found optima. In detail, the proposed BA consists of three search phases: (i) the movement from neighbors for avoiding overlapping the same found optima; (ii) the exploitation for searching nearby the best solution of its domain with Niche Radius; (iii) the exploration for searching randomly in all domain of the radius. In order to evaluate the performance of NRBA, this paper employs some test-bed multimodal functions and compare NRBA with BA and NSBA. The experimental results suggest that NRBA is able to provide the better search performance than BA and NSBA to find multiple global optima in most of benchmark functions.