This paper introduces a novel competitive mechanism into differential evolution (DE), presenting an effective DE variant named competitive DE (CDE). CDE features a simple yet efficient mutation strategy: DE/winner-to-best/1. Essentially, the proposed DE/winner-to-best/1 strategy can be recognized as an intelligent integration of the existing mutation strategies of DE/rand-to-best/1 and DE/cur-to-best/1. The incorporation of DE/winner-to-best/1 and the competitive mechanism provide new avenues for advancing DE techniques. Moreover, in CDE, the scaling factor $F$ and mutation rate $Cr$ are determined by a random number generator following a normal distribution, as suggested by previous research. To investigate the performance of the proposed CDE, comprehensive numerical experiments are conducted on CEC2017 and engineering simulation optimization tasks, with CMA-ES, JADE, and other state-of-the-art optimizers and DE variants employed as competitor algorithms. The experimental results and statistical analyses highlight the promising potential of CDE as an alternative optimizer for addressing diverse optimization challenges.

翻译：本文在差分进化（DE）中引入了一种新颖的竞争机制，提出了一种名为竞争差分进化（CDE）的高效DE变体。CDE采用了一种简单而高效的变异策略：DE/winner-to-best/1。本质上，所提出的DE/winner-to-best/1策略可视为现有变异策略DE/rand-to-best/1与DE/cur-to-best/1的智能融合。DE/winner-to-best/1策略与竞争机制的融合为推进DE技术提供了新途径。此外，在CDE中，缩放因子$F$和变异率$Cr$由遵循正态分布的随机数生成器确定，这与先前的研究建议一致。为评估所提出的CDE性能，我们在CEC2017基准测试及工程仿真优化任务上开展了全面的数值实验，并与CMA-ES、JADE及其他先进优化器和DE变体作为对比算法。实验结果与统计分析表明，CDE在解决多样化优化挑战方面展现出作为替代优化器的潜力。