Evolutionary Multimodal Optimization: A Short Survey

TL;DR

This survey reviews evolutionary algorithms designed for multimodal optimization, highlighting their ability to find multiple solutions simultaneously and maintain diversity, with applications across various complex problems.

Contribution

It provides a comprehensive overview of the latest evolutionary algorithms for multimodal optimization, focusing on methodology, benchmarking, and practical applications.

Findings

Evolutionary algorithms effectively locate multiple optima in a single run.

They maintain population diversity to enhance global search capabilities.

Techniques for multimodal optimization are used as diversity methods in other problems.

Abstract

Real world problems always have different multiple solutions. For instance, optical engineers need to tune the recording parameters to get as many optimal solutions as possible for multiple trials in the varied-line-spacing holographic grating design problem. Unfortunately, most traditional optimization techniques focus on solving for a single optimal solution. They need to be applied several times; yet all solutions are not guaranteed to be found. Thus the multimodal optimization problem was proposed. In that problem, we are interested in not only a single optimal point, but also the others. With strong parallel search capability, evolutionary algorithms are shown to be particularly effective in solving this type of problem. In particular, the evolutionary algorithms for multimodal optimization usually not only locate multiple optima in a single run, but also preserve their population diversity throughout a run, resulting in their global optimization ability on multimodal functions. In addition, the techniques for multimodal optimization are borrowed as diversity maintenance techniques to other problems. In this chapter, we describe and review the state-of-the-arts evolutionary algorithms for multimodal optimization in terms of methodology, benchmarking, and application.