A novel hybrid adaptive differential evolution for global optimization

Zhiyong Zhang, Jianyong Zhu, Feiping Nie

Research output: Contribution to journalArticlepeer-review

2 Scopus citations

Abstract

Differential Evolution (DE) stands as a potent global optimization algorithm, renowned for its application in addressing a myriad of practical engineering issues. The efficacy of DE is profoundly influenced by its control parameters and mutation strategies. In light of this, we introduce a refined DE algorithm characterized by adaptive parameters and dual mutation strategies (APDSDE). APDSDE inaugurates an adaptive switching mechanism that alternates between two innovative mutation strategies: DE/current-to-pBest-w/1 and DE/current-to-Amean-w/1. Furthermore, a novel parameter adaptation technique rooted in cosine similarity is established, with the derivation of explicit calculation formulas for both the scaling factor weight and crossover rate weight. In pursuit of optimizing convergence speed whilst preserving population diversity, a sophisticated nonlinear population size reduction method is proposed. The robustness of each algorithm is rigorously evaluated against the CEC2017 benchmark functions, with empirical evidence underscoring the superior performance of APDSDE in comparison to a host of advanced DE variants.

Original languageEnglish
Article number19697
JournalScientific Reports
Volume14
Issue number1
DOIs
StatePublished - Dec 2024

Keywords

  • Adaptive parameters
  • Cosine similarity
  • Differential evolution
  • Dual mutation strategies
  • Population size

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