Complex single-objective bounded problems are often difficult to solve. In evolutionary computation methods, since the proposal of differential evolution algorithm in 1997, it has been widely studied and developed due to its simplicity and efficiency. These developments include various adaptive strategies, operator improvements, and the introduction of other search methods. After 2014, research based on LSHADE has also been widely studied by researchers. However, although recently proposed improvement strategies have shown superiority over their previous generation's first performance, adding all new strategies may not necessarily bring the strongest performance. Therefore, we recombine some effective advances based on advanced differential evolution variants in recent years and finally determine an effective combination scheme to further promote the performance of differential evolution. In this paper, we propose a strategy recombination and reconstruction differential evolution algorithm called reconstructed differential evolution (RDE) to solve single-objective bounded optimization problems. Based on the benchmark suite of the 2024 IEEE Congress on Evolutionary Computation (CEC2024), we tested RDE and several other advanced differential evolution variants. The experimental results show that RDE has superior performance in solving complex optimization problems.

翻译：复杂单目标有界问题通常难以求解。在进化计算方法中，自1997年差分进化算法提出以来，因其简洁高效而被广泛研究和发展。这些发展包括各种自适应策略、算子改进以及其他搜索方法的引入。2014年后，基于LSHADE的研究也受到研究人员的广泛关注。然而，尽管近期提出的改进策略在性能上优于前一代方法，但将所有新策略简单叠加未必能带来最强性能。因此，我们基于近年来先进差分进化变体中的若干有效进展进行重组，最终确定了一种有效的组合方案，以进一步提升差分进化算法的性能。本文提出一种策略重组与重构的差分进化算法——重构差分进化（RDE），用于求解单目标有界优化问题。基于2024年IEEE进化计算大会（CEC2024）基准测试集，我们对RDE及其他多种先进差分进化变体进行了测试。实验结果表明，RDE在求解复杂优化问题时具有优越性能。