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Promising Boundaries Explore and Resource Allocation Evolutionary Algorithm for Constrained Multiobjective Optimization

Yuelin Qu, Yuhang Hu,Wei Li,Ying Huang

Swarm and Evolutionary Computation(2025)SCI 1区SCI 2区

JiangXi Univ Sci & Technol

Cited 0|Views5
Abstract
Constrained multiobjective optimization problems (CMOPs) typically present numerous local optima, which can be deceptive. Current constrained multiobjective algorithms (CMOEAs) encounter challenges in maintaining diversity and escaping these local optima because of the single function of the population in the same spacetime. Because they cannot keep exploring diversity and cannot balance their exploration focus. To this end, a dual-stage and dual-population algorithm named BPRRA is proposed in this article. Specifically, BPRRA utilizes new techniques to explore promising boundaries and allocate computing resources. In the first stage, one of the populations evolves to explore one promising boundary by ignoring constraints, and the other population explores another promising boundary by considering constraints. In the second stage, the two populations explore different regions from different promising boundaries using the diversity archiving strategy. Moreover, a novel resource allocation strategy is designed to dynamically allocate limited computational resources based on the ratio of potential offspring. The experiments involve five test suites and nine real-world problems to validate the performance of the proposed method. The results demonstrate that BPRRA has superior performance and can better solve CMOPs.
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Key words
Constrained multiobjective optimization,Constraint handling,Evolutionary algorithm,Resource allocation technology
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要点】:本文提出了一种新型的双阶段双种群算法BPRRA,通过探索潜在的边界并动态分配计算资源,有效解决了约束多目标优化问题(CMOPs)中的局部最优陷阱和多样性保持问题。

方法】:BPRRA算法采用双阶段策略,第一阶段两个种群分别探索忽略约束和考虑约束的潜在边界,第二阶段则利用多样性存档策略从不同的潜在边界探索不同区域,并设计了一种基于后代潜力比例的新型资源分配策略。

实验】:通过五个测试套件和九个真实世界问题验证了BPRRA的性能,实验结果表明BPRRA在解决CMOPs问题上具有优越性能。