Introducing Competitive Mechanism to Differential Evolution for Numerical Optimization

• URL: http://arxiv.org/abs/2406.05436v1
• Date: Sat, 8 Jun 2024 10:48:39 GMT
• Title: Introducing Competitive Mechanism to Differential Evolution for Numerical Optimization
• Abstract summary: This paper introduces a novel competitive mechanism into differential evolution (DE) It features a mutation strategy: DE/winner-to-best/1. The incorporation of DE/winner-to-best/1 and the competitive mechanism provide new avenues for advancing DE techniques.
• Score: 6.464980828114218
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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.

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