A Review of Evolutionary Multi-modal Multi-objective Optimization

Abstract

Multi-modal multi-objective optimization aims to find all Pareto optimal solutions including overlapping solutions in the objective space. Multi-modal multi-objective optimization has been investigated in the evolutionary computation community since 2005. However, it is difficult to survey existing studies in this field because they have been independently conducted and do not explicitly use the term "multi-modal multi-objective optimization". To address this issue, this paper reviews existing studies of evolutionary multi-modal multi-objective optimization, including studies published under names that are different from "multi-modal multi-objective optimization". Our review also clarifies open issues in this research area.

This is an accepted version of a paper published in the IEEE Transactions on Evolutionary Computation

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• Runtime Analysis for the NSGA-II: Proving, Quantifying, and Explaining the Inefficiency For Many Objectives
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• Advancements in Multimodal Differential Evolution: A Comprehensive Review and Future Perspectives
• MOLE: Digging Tunnels Through Multimodal Multi-Objective Landscapes
• A Simple Evolutionary Algorithm for Multi-modal Multi-objective Optimization