提出一种基于分解的约束多目标进化算法，有效解决实际优化问题。
An Inverse Modeling Constrained Multi-Objective Evolutionary Algorithm Based on Decomposition
- 将逆建模与分解思想结合，处理带约束的真实世界优化问题。
- 在RWMOP1-35测试集上性能优于现有先进算法。
- 适合工程设计、资源分配等实际约束优化场景使用。
本文提出基于分解的逆建模约束多目标进化算法（IM-C-MOEA/D），用于求解带有约束的真实世界优化问题。研究在进化计算驱动的逆建模基础上，系统性地填补了基于分解的逆模型在约束问题领域中的应用空白。该方法在35个真实世界多目标问题（RWMOP1-35）上进行了实验验证，结果表明其在求解性能和鲁棒性方面均优于当前最先进的约束多目标进化算法（CMOEA）。实验充分展示了该算法在复杂实际约束优化场景中的有效性与适用性。
原文摘要 · Abstract (English)
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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