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Dynamic competitive constraint handling for constrained multi-objective optimization
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DOI：10.1016/j.swevo.2026.102506.png)
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Abstract
En 中文
When solving constrained multi-objective optimization problems (CMOPs), traditional methods often utilize a holistic constraint violation, which obscures the unique effect of individual constraints. Recently, methods based on constraint decomposition or prioritization have been proposed to consider constraints individually. However, those methods typically rely on pre-defined evaluation rules, which lack the ability to dynamically exploit the effect of an independent constraint for enhancing the exploration of the constrained Pareto front (CPF). To address this limitation, a Dynamically Competitive Constraint Handling algorithm (DCCHT) is proposed for Constrained Multi-objective Optimization. Specifically, DCCHT decomposes a CMOP with <span class="math">
<math>
<mi is="true">K</mi>
</math></span> constraints into <span class="math">
<math>
<mrow is="true">
<mi is="true">K</mi>
<mo linebreak="goodbreak" linebreakstyle="after" is="true">+</mo>
<mn is="true">1</mn>
</mrow>
</math></span> distinct optimization pools, where one optimization pool considers all constraints and another <span class="math">
<math>
<mi is="true">K</mi>
</math></span> optimization pools respectively involve each constraint among the overall <span class="math">
<math>
<mi is="true">K</mi>
</math></span> constraints. The information exchange among distinct optimization pools is facilitated by a proposed bidirectional knowledge transfer strategy, which shares valuable landscape information and accelerates the overall convergence to the global feasible region. The ongoing progress of each optimization pool is dynamically controlled based on the competitiveness of the constraint. Computational resources are adaptively allocated to the optimization pool by a feedback-driven resource allocation method. More computational resources will be allocated to the optimization pool in which the considered constraint is more competitive with the exploration of the true CPF. Extensive experiments on three benchmark suites and various real-world engineering problems demonstrate the significantly better performance of DCCHT against some state-of-the-art algorithms.
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