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arXiv · 2406.05436

Introducing Competitive Mechanism to Differential Evolution for Numerical Optimization

Rui Zhong·Yang Cao·Enzhi Zhang·Masaharu Munetomo
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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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Rui Zhong, Yang Cao, Enzhi Zhang, Masaharu Munetomo. 2024-06-08. Introducing Competitive Mechanism to Differential Evolution for Numerical Optimization. https://arxiv.org/abs/2406.05436

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