A Niching Indicator-Based Multi-modal Many-objective Optimizer

TL;DR

This paper introduces a niching indicator-based algorithm designed for multi-modal many-objective optimization, effectively handling problems with over three objectives and locating multiple equivalent Pareto solutions.

Contribution

It proposes a novel niching indicator-based method that improves diversity and performance in high-dimensional multi-modal many-objective optimization tasks.

Findings

Successfully handles problems with up to 15 objectives

Outperforms eight existing multi-objective evolutionary algorithms

Effectively finds multiple equivalent Pareto optimal solutions

Abstract

Multi-modal multi-objective optimization is to locate (almost) equivalent Pareto optimal solutions as many as possible. Some evolutionary algorithms for multi-modal multi-objective optimization have been proposed in the literature. However, there is no efficient method for multi-modal many-objective optimization, where the number of objectives is more than three. To address this issue, this paper proposes a niching indicator-based multi-modal multi- and many-objective optimization algorithm. In the proposed method, the fitness calculation is performed among a child and its closest individuals in the solution space to maintain the diversity. The performance of the proposed method is evaluated on multi-modal multi-objective test problems with up to 15 objectives. Results show that the proposed method can handle a large number of objectives and find a good approximation of multiple equivalent Pareto optimal solutions. The results also show that the proposed method performs significantly better than eight multi-objective evolutionary algorithms.