  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.
