Constrained single-objective numerical optimisation requires both feasibility maintenance and strong objective-value convergence under limited evaluation budgets. This report documents RDEx-CSOP, a constrained differential evolution variant used in the IEEE CEC 2025 numerical optimisation competition (C06 special session). RDEx-CSOP combines success-history parameter adaptation with an exploitation-biased hybrid search and an ε-constraint handling mechanism with a time-varying threshold. We evaluate RDEx-CSOP on the official CEC 2025 CSOP benchmark using the U-score framework (Speed, Accuracy, and Constraint categories). The results show that RDEx-CSOP achieves the highest total score and the best average rank among all released comparison algorithms, mainly through strong speed and competitive constraint-handling performance across the 28 benchmark functions.

翻译：约束单目标数值优化需要在有限评估预算下同时维护可行性并实现强目标值收敛。本报告记录了RDEx-CSOP，一种用于IEEE CEC 2025数值优化竞赛（C06特别会议）的约束差分进化变体。RDEx-CSOP结合了成功历史参数自适应、偏向利用的混合搜索以及具有时变阈值的ε约束处理机制。我们使用U-score框架（包括速度、准确性和约束类别）在官方CEC 2025 CSOP基准上评估RDEx-CSOP。结果表明，在所有已发布的比较算法中，RDEx-CSOP取得了最高总分和最佳平均排名，这主要归功于其在28个基准函数上表现出的强劲速度与具有竞争力的约束处理性能。