针对有限评估预算的约束多目标优化，提出高效可行解搜索方法。
RDEx-CMOP: Feasibility-Aware Indicator-Guided Differential Evolution for Fixed-Budget Constrained Multiobjective Optimization
- 引入ε级可行性调度与指标驱动的适应度分配策略
- 在CEC 2025基准上获得最高总分和最优平均排名
- 适合需快速满足约束且保持解多样性的问题场景
约束多目标优化在严格评估预算下需兼顾快速可行性达成、稳定收敛与多样性保持。本文介绍用于IEEE CEC 2025数值优化竞赛（C06专题）约束多目标赛道的差分进化变体RDEx-CMOP。该方法融合ε级可行性调度、基于SPEA2风格的指标驱动适应度分配，以及面向适应度的current-to-pbest/1变异算子。在官方CEC 2025 CMOP基准上，采用中位数目标U得分框架与公开追踪数据进行评估。实验结果表明，RDEx-CMOP在所有已发布算法中总分最高，整体平均排名最优，展现出强目标达成能力，多数问题最终约束违反接近零。
原文摘要 · Abstract (English)
Constrained multiobjective optimisation requires fast feasibility attainment together with stable convergence and diversity preservation under strict evaluation budgets. This report documents RDEx-CMOP, the differential evolution variant used in the IEEE CEC 2025 numerical optimisation competition (C06 special session) constrained multiobjective track. RDEx-CMOP integrates an ε-level feasibility schedule, a SPEA2-style indicator-driven fitness assignment, and a fitness-oriented current-to-pbest/1 mutation operator. We evaluate RDEx-CMOP on the official CEC 2025 CMOP benchmark using the median-target U-score framework and the released trace data. Experimental results show that RDEx-CMOP achieves the highest total score and the best overall average rank among all released comparison algorithms, with strong target-attainment behaviour and near-zero final violation on most problems.
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