Multimodal Optimization using Niching Methods

Abstract

Abstract This article introduces multimodal optimization (MMO) methods aiming to locate multiple optimal (or close to optimal) solutions for an optimization problem. MMO is an important topic that has practical relevance in problem solving across many fields. Many real‐world optimization problems are multimodal by nature –in other words, processing in more than one mode. There often exist multiple satisfactory solutions. For such an optimization problem, it may be desirable to locate all global optima and/or some local optima that are considered as being satisfactory. MMO has practical relevance to many engineering problems. Optimization methods specifically designed for solving MMO problems, often called niching methods, are predominantly developed from the field of evolutionary computation that belongs to a family of stochastic optimization algorithms (or metaheuristic algorithms), including genetic algorithms, evolutionary strategies, particle swarm optimization, differential evolution, and so on. This article covers selected classic niching methods, along with performance measures and benchmark test function suites developed for evaluating niching methods. The article also presents a list of niching application examples and suggestions on further readings of niching methods.