In recent years, multi-operator and multi-method algorithms have succeeded, encouraging their combination within single frameworks. Despite promising results, there remains room for improvement as only some evolutionary algorithms (EAs) consistently excel across all optimization problems. This paper proposes mLSHADE-RL, an enhanced version of LSHADE-cnEpSin, which is one of the winners of the CEC 2017 competition in real-parameter single-objective optimization. mLSHADE-RL integrates multiple EAs and search operators to improve performance further. Three mutation strategies such as DE/current-to-pbest-weight/1 with archive, DE/current-to-pbest/1 without archive, and DE/current-to-ordpbest-weight/1 are integrated in the original LSHADE-cnEpSin. A restart mechanism is also proposed to overcome the local optima tendency. Additionally, a local search method is applied in the later phase of the evolutionary procedure to enhance the exploitation capability of mLSHADE-RL. mLSHADE-RL is tested on 30 dimensions in the CEC 2024 competition on single objective bound constrained optimization, demonstrating superior performance over other state-of-the-art algorithms in producing high-quality solutions across various optimization scenarios.

翻译：近年来，多算子与多方法算法取得了成功，这促进了它们在单一框架内的融合。尽管成果令人鼓舞，但由于仅有部分进化算法（EAs）能在所有优化问题中持续表现优异，因此仍有改进空间。本文提出mLSHADE-RL，它是LSHADE-cnEpSin的增强版本，而后者是CEC 2017实参数单目标优化竞赛的优胜算法之一。mLSHADE-RL通过集成多种进化算法与搜索算子来进一步提升性能。在原始LSHADE-cnEpSin中整合了三种变异策略：带存档的DE/current-to-pbest-weight/1、不带存档的DE/current-to-pbest/1以及DE/current-to-ordpbest-weight/1。同时提出了一种重启机制以克服局部最优倾向。此外，在进化过程的后期阶段应用了局部搜索方法，以增强mLSHADE-RL的局部开发能力。mLSHADE-RL在CEC 2024单目标边界约束优化竞赛的30维问题上进行了测试，结果表明其在多种优化场景下生成高质量解的性能优于其他先进算法。