Introducing Competitive Mechanism to Differential Evolution for Numerical Optimization
<|reference_start|>Introducing Competitive Mechanism to Differential Evolution for Numerical Optimization: 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.<|reference_end|>
@article{zhong2024introducing,
    title={Introducing Competitive Mechanism to Differential Evolution for
  Numerical Optimization},
    author={Rui Zhong, Yang Cao, Enzhi Zhang, Masaharu Munetomo},
    journal={arXiv preprint arXiv:2406.05436},
    year={2024},
    archivePrefix={arXiv},
    eprint={2406.05436},
    primaryClass={cs.NE}
}