A New Repair Operator for Multi-objective Evolutionary Algorithm in Constrained Optimization Problems

TL;DR

This paper introduces a novel repair operator for multi-objective evolutionary algorithms in constrained optimization, improving solution quality and diversity by preventing local optima.

Contribution

It presents a new reversed correction repair operator and demonstrates its effectiveness when integrated into MOEA/D and NSGA-II.

Findings

Enhanced convergence on benchmark problems

Improved solution diversity

Outperforms existing repair methods

Abstract

In this paper, we design a set of multi-objective constrained optimization problems (MCOPs) and propose a new repair operator to address them. The proposed repair operator is used to fix the solutions that violate the box constraints. More specifically, it employs a reversed correction strategy that can effectively avoid the population falling into local optimum. In addition, we integrate the proposed repair operator into two classical multi-objective evolutionary algorithms MOEA/D and NSGA-II. The proposed repair operator is compared with other two kinds of commonly used repair operators on benchmark problems CTPs and MCOPs. The experiment results demonstrate that our proposed approach is very effective in terms of convergence and diversity.