Multiobjective optimisation in the CEC 2025 MOP track is evaluated not only by final IGD values but also by how quickly an algorithm reaches the target region under a fixed evaluation budget. This report documents RDEx-MOP, the reconstructed differential evolution variant used in the IEEE CEC 2025 numerical optimisation competition (C06 special session) bound-constrained multiobjective track. RDEx-MOP integrates indicator-based environmental selection, a niche-maintained Pareto-candidate set, and complementary differential evolution operators for exploration and exploitation. We evaluate RDEx-MOP on the official CEC 2025 MOP benchmark using the released checkpoint traces and the median-target U-score framework. Experimental results show that RDEx-MOP achieves the highest total score and the best average rank among all released comparison algorithms, including the earlier RDEx baseline.

翻译：CEC 2025多目标优化问题(MOP)赛道不仅通过最终IGD值评估算法性能，还衡量算法在固定评估预算内到达目标区域的速度。本报告记录了RDEx-MOP——用于IEEE CEC 2025数值优化竞赛（C06特别会议）有界约束多目标赛道的重构差分进化变体。RDEx-MOP融合了基于指标的环境选择、维持生态位的帕累托候选解集以及用于勘探与开发的互补型差分进化算子。我们利用官方发布的检查点追踪数据和中位数目标U-score框架，在CEC 2025 MOP基准测试上评估RDEx-MOP。实验结果表明，在所有已发布的对比算法（包括早期RDEx基线）中，RDEx-MOP取得了最高总得分和最佳平均排名。