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arXiv · 2410.19203

An Inverse Modeling Constrained Multi-Objective Evolutionary Algorithm Based on Decomposition

Lucas R. C. Farias·Aluizio F. R. Araújo
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Abstract

This paper introduces the inverse modeling constrained multi-objective evolutionary algorithm based on decomposition (IM-C-MOEA/D) for addressing constrained real-world optimization problems. Our research builds upon the advancements made in evolutionary computing-based inverse modeling, and it strategically bridges the gaps in applying inverse models based on decomposition to problem domains with constraints. The proposed approach is experimentally evaluated on diverse real-world problems (RWMOP1-35), showing superior performance to state-of-the-art constrained multi-objective evolutionary algorithms (CMOEAs). The experimental results highlight the robustness of the algorithm and its applicability in real-world constrained optimization scenarios.

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Lucas R. C. Farias, Aluizio F. R. Araújo. 2024-10-24. An Inverse Modeling Constrained Multi-Objective Evolutionary Algorithm Based on Decomposition. https://arxiv.org/abs/2410.19203

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