Bound-constrained single-objective numerical optimisation remains a key benchmark for assessing the robustness and efficiency of evolutionary algorithms. This report documents RDEx-SOP, an exploitation-biased success-history differential evolution variant used in the IEEE CEC 2025 numerical optimisation competition (C06 special session). RDEx-SOP combines success-history parameter adaptation, an exploitation-biased hybrid branch, and lightweight local perturbations to balance fast convergence and final solution quality under a strict evaluation budget. We evaluate RDEx-SOP on the official CEC 2025 SOP benchmark with the U-score framework (Speed and Accuracy categories). Experimental results show that RDEx-SOP achieves strong overall performance and statistically competitive final outcomes across the 29 benchmark functions.

翻译：有界约束单目标数值优化仍是评估进化算法鲁棒性与效率的关键基准。本文记录了RDEx-SOP——一种用于IEEE CEC 2025数值优化竞赛（C06特别会议）的偏向开发的成功历史差分进化变体。RDEx-SOP将成功历史参数自适应、偏向开发的混合分支与轻量级局部扰动相结合，在严格评估预算下兼顾快速收敛与最终解质量。我们采用U-score框架（速度与准确度类别）在官方CEC 2025 SOP基准上评估RDEx-SOP。实验结果表明，RDEx-SOP在全部29个基准函数上展现出强劲的整体性能与统计上具有竞争力的最终结果。