A Detection Region Method-Based Evolutionary Algorithm for Binary Constrained Multiobjective Optimization

TL;DR

This paper introduces DRMCMO, a novel evolutionary algorithm based on detection regions, to effectively solve binary constrained multi-objective optimization problems, especially when constraints are unknown or binary in nature.

Contribution

The paper proposes DRMCMO, a new algorithm utilizing detection regions to improve convergence and diversity in binary constrained multi-objective optimization, addressing limitations of existing methods.

Findings

DRMCMO outperforms state-of-the-art algorithms on benchmark problems.

Detection regions enhance convergence and escape from local optima.

The algorithm shows strong competitiveness on real-world CMOP/BC problems.

Abstract

Solving constrained multi-objective optimization problems (CMOPs) is a challenging task. While many practical algorithms have been developed to tackle CMOPs, real-world scenarios often present cases where the constraint functions are unknown or unquantifiable, resulting in only binary outcomes (feasible or infeasible). This limitation reduces the effectiveness of constraint violation guidance, which can negatively impact the performance of existing algorithms that rely on this approach. Such challenges are particularly detrimental for algorithms employing the epsilon-based method, as they hinder effective relaxation of the feasible region. To address these challenges, this paper proposes a novel algorithm called DRMCMO based on the detection region method. In DRMCMO, detection regions dynamic monitor feasible solutions to enhance convergence, helping the population escape local optima. Additionally, these regions collaborate with the neighbor pairing strategy to improve population diversity within narrow feasible areas. We have modified three existing test suites to serve as benchmark test problems for CMOPs with binary constraints(CMOP/BC) and conducted comprehensive comparative experiments with state-of-the-art algorithms on these test suites and real-world problems. The results demonstrate the strong competitiveness of DRMCMO against state-of-the-art algorithms. Given the limited research on CMOP/BC, our study offers a new perspective for advancing this field.