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.
@article{arxiv.2410.19203,
title = {An Inverse Modeling Constrained Multi-Objective Evolutionary Algorithm Based on Decomposition},
author = {Lucas R. C. Farias and Aluizio F. R. Araújo},
journal= {arXiv preprint arXiv:2410.19203},
year = {2024}
}