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.

翻译：约束多目标优化要求在严格的评估预算下快速实现可行性，同时保持稳定的收敛性与多样性保持。本报告记录了RDEx-CMOP——用于IEEE CEC 2025数值优化竞赛（C06特别分会）约束多目标赛道的差分进化变体。RDEx-CMOP集成了ε-水平可行性调度策略、SPEA2风格指标驱动的适应度分配机制，以及面向适应度的current-to-pbest/1变异算子。我们采用中位数目标U分数框架及发布的轨迹数据，在官方CEC 2025 CMOP基准集上评估RDEx-CMOP。实验结果表明，在所有已发布的对比算法中，RDEx-CMOP获得了最高总分及最佳总体平均排名，在多数问题上展现出强目标达成性能与近乎零的最终违背度。