Introducing Competitive Mechanism to Differential Evolution for Numerical Optimization

TL;DR

This paper proposes a new competitive mechanism integrated into differential evolution, creating a variant called CDE that enhances optimization performance through a novel mutation strategy and stochastic parameter adaptation.

Contribution

The paper introduces a competitive mechanism and a new mutation strategy for differential evolution, demonstrating improved optimization capabilities on benchmark and real-world problems.

Findings

CDE outperforms several state-of-the-art optimizers in experiments.

The new mutation strategy improves convergence speed.

Stochastic parameter setting enhances algorithm robustness.

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.