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...

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Bibliographic Details
Published in:arXiv.org 2024-06
Main Authors: Zhong, Rui, Cao, Yang, Zhang, Enzhi, Munetomo, Masaharu
Format: Article
Language:English
Subjects:
Algorithms
Business competition
Evolutionary computation
Mutation
Normal distribution
Optimization
Random numbers
Scaling factors
Statistical analysis
Strategy
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Summary: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.
ISSN:2331-8422