A Decomposition-based Large-scale Multi-modal Multi-objective Optimization Algorithm

TL;DR

This paper introduces a novel large-scale multi-modal multi-objective optimization algorithm based on MOEA/D, which effectively preserves diverse Pareto solutions using a sub-population and clearing mechanism.

Contribution

It presents a new algorithm that enhances diversity preservation in large-scale multi-modal multi-objective problems by integrating sub-populations with a clearing mechanism.

Findings

Effectively preserves diverse Pareto solutions.

Improves solution diversity in decision space.

Performs well on large-scale problems.

Abstract

A multi-modal multi-objective optimization problem is a special kind of multi-objective optimization problem with multiple Pareto subsets. In this paper, we propose an efficient multi-modal multi-objective optimization algorithm based on the widely used MOEA/D algorithm. In our proposed algorithm, each weight vector has its own sub-population. With a clearing mechanism and a greedy removal strategy, our proposed algorithm can effectively preserve equivalent Pareto optimal solutions (i.e., different Pareto optimal solutions with same objective values). Experimental results show that our proposed algorithm can effectively preserve the diversity of solutions in the decision space when handling large-scale multi-modal multi-objective optimization problems.