Fractional Order Differential Evolution

Kaiyu Wang, Shangce Gao, Meng Chu Zhou, Zhi Hui Zhan, Jiujun Cheng

Research output: Contribution to journalArticlepeer-review


Differential evolution (DE) is a widely recognized method to solve complex optimization problems as shown by many researchers. Yet, non-adaptive versions of DE suffer from insufficient exploration ability and uses no historical information for its performance enhancement. This work proposes Fractional Order Differential Evolution (FODE) to enhance DE performance from two aspects. Firstly, a bi-strategy co-deployment framework is proposed. The population-based and parameter-based strategies are combined to leverage their respective advantages. Secondly, the fractional order calculus is first applied to the differential vector to enhance DE’s exploration ability by using the historical information of populations, and ensures the diversity of population in an evolutionary process. We use the 2017 IEEE Congress on Evolutionary Computation (CEC) test functions, and CEC2011 real-world problems to evaluate FODE’s performance. Its sensitivity to parameter changes is discussed and an ablation study of multi-strategies is systematically performed. Furthermore, the variations of exploration and exploitation in FODE are visualized and analyzed. Experimental results show that FODE is superior to other state-of-the-art DE variants, the winners of CEC competitions, other fractional order calculus-based algorithms, and some powerful variants of classic algorithms.

Original languageEnglish (US)
Pages (from-to)1
Number of pages1
JournalIEEE Transactions on Evolutionary Computation
StateAccepted/In press - 2024

All Science Journal Classification (ASJC) codes

  • Software
  • Theoretical Computer Science
  • Computational Theory and Mathematics


  • Bi-Strategy Co-Deployment Framework
  • Calculus
  • Differential Evolution
  • Fractional Order
  • History
  • Indexes
  • Optimization
  • Optimization
  • Particle swarm optimization
  • Sociology
  • Vectors


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