Whale swarm algorithm with the mechanism of identifying and escaping from extreme points for multimodal function optimization

TL;DR

This paper introduces WSA-IC, an improved whale swarm algorithm that efficiently finds multiple optima in multimodal functions without needing problem-specific parameters, and outperforms existing methods in benchmark tests.

Contribution

WSA-IC enhances the original whale swarm algorithm by removing the need for niching parameters and adding a mechanism to escape local optima, with proven convergence and superior performance.

Findings

WSA-IC outperforms other niching algorithms on most benchmark functions.

The new method does not require problem-specific niching parameters.

WSA-IC effectively identifies and escapes from extreme points.

Abstract

Most real-world optimization problems often come with multiple global optima or local optima. Therefore, increasing niching metaheuristic algorithms, which devote to finding multiple optima in a single run, are developed to solve these multimodal optimization problems. However, there are two difficulties urgently to be solved for most existing niching metaheuristic algorithms: how to set the optimal values of niching parameters for different optimization problems, and how to jump out of the local optima efficiently. These two difficulties limited their practicality largely. Based on Whale Swarm Algorithm (WSA) we proposed previously, this paper presents a new multimodal optimizer named WSA with Iterative Counter (WSA-IC) to address these two difficulties. In the one hand, WSA-IC improves the iteration rule of the original WSA for multimodal optimization, which removes the need of specifying different values of attenuation coefficient for different problems to form multiple subpopulations, without introducing any niching parameter. In the other hand, WSA-IC enables the identification of extreme point during iterations relying on two new parameters (i.e., stability threshold Ts and fitness threshold Tf), to jump out of the located extreme point. Moreover, the convergence of WSA-IC is proved. Finally, the proposed WSA-IC is compared with several niching metaheuristic algorithms on CEC2015 niching benchmark test functions and five additional classical multimodal functions with high dimensions. The experimental results demonstrate that WSA-IC statistically outperforms other niching metaheuristic algorithms on most test functions.