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.

翻译：求解带约束多目标优化问题（CMOPs）是一项具有挑战性的任务。尽管已有许多实用算法被开发用于处理CMOPs，但实际场景中常出现约束函数未知或不可量化的情况，仅能获得二元判定结果（可行或不可行）。这一局限削弱了约束违反度引导的有效性，可能对依赖该方法的现有算法性能产生负面影响。此类挑战对采用基于ε方法的算法尤为不利，因其阻碍了可行域的有效松弛。为应对这些挑战，本文提出一种基于检测区域方法的新型算法DRMCMO。在DRMCMO中，检测区域动态监测可行解以增强收敛性，帮助种群逃离局部最优。此外，这些区域与邻域配对策略协同工作，以提升狭窄可行域内的种群多样性。我们修改了三个现有测试集，将其作为带二元约束的CMOPs（CMOP/BC）的基准测试问题，并在这些测试集和实际问题上与前沿算法进行了全面对比实验。结果表明，DRMCMO相较于前沿算法具有强劲竞争力。鉴于当前针对CMOP/BC的研究有限，本研究为该领域的推进提供了新的视角。