Maximizing Diversity for Multimodal Optimization

TL;DR

This paper proposes using the Line Distance measure as the main objective function to enhance the ability of algorithms to locate multiple optima simultaneously in multimodal optimization tasks.

Contribution

It introduces a novel approach that employs the Line Distance measure as the primary objective to improve diversity and solution discovery in multimodal optimization.

Findings

Effective in locating multiple optima

Improves diversity in solution populations

Potentially enhances multimodal optimization performance

Abstract

Most multimodal optimization algorithms use the so called \textit{niching methods}~\cite{mahfoud1995niching} in order to promote diversity during optimization, while others, like \textit{Artificial Immune Systems}~\cite{de2010conceptual} try to find multiple solutions as its main objective. One of such algorithms, called \textit{dopt-aiNet}~\cite{de2005artificial}, introduced the Line Distance that measures the distance between two solutions regarding their basis of attraction. In this short abstract I propose the use of the Line Distance measure as the main objective-function in order to locate multiple optima at once in a population.