Two-Archive Evolutionary Algorithm for Constrained Multi-Objective Optimization

TL;DR

This paper introduces a parameter-free two-archive evolutionary algorithm that balances convergence, diversity, and feasibility in constrained multi-objective optimization by maintaining two co-evolving populations with a restricted mating mechanism.

Contribution

It proposes a novel two-archive approach with a restricted mating selection to improve constrained multi-objective optimization performance.

Findings

Outperforms five state-of-the-art algorithms on benchmark problems.

Effectively balances convergence, diversity, and feasibility.

Demonstrates robustness in a real-world case study.

Abstract

When solving constrained multi-objective optimization problems, an important issue is how to balance convergence, diversity and feasibility simultaneously. To address this issue, this paper proposes a parameter-free constraint handling technique, two-archive evolutionary algorithm, for constrained multi-objective optimization. It maintains two co-evolving populations simultaneously: one, denoted as convergence archive, is the driving force to push the population toward the Pareto front; the other one, denoted as diversity archive, mainly tends to maintain the population diversity. In particular, to complement the behavior of the convergence archive and provide as much diversified information as possible, the diversity archive aims at exploring areas under-exploited by the convergence archive including the infeasible regions. To leverage the complementary effects of both archives, we develop a restricted mating selection mechanism that adaptively chooses appropriate mating parents from them according to their evolution status. Comprehensive experiments on a series of benchmark problems and a real-world case study fully demonstrate the competitiveness of our proposed algorithm, comparing to five state-of-the-art constrained evolutionary multi-objective optimizers.