Multi-strategy Differential Evolution

  • Anil YamanEmail author
  • Giovanni Iacca
  • Matt Coler
  • George Fletcher
  • Mykola Pechenizkiy
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10784)


We propose the Multi-strategy Differential Evolution (MsDE) algorithm to construct and maintain a self-adaptive ensemble of search strategies while solving an optimization problem. The ensemble of strategies is represented as agents that interact with the candidate solutions to improve their fitness. In the proposed algorithm, the performance of each agent is measured so that successful strategies are promoted within the ensemble. We propose two performance measures, and show their effectiveness in selecting successful strategies. We then present three population adaptation mechanisms, based on sampling, clone-best and clone-multiple adaptation schemes. The MsDE with different performance measures and population adaptation schemes is tested on the CEC2013 benchmark functions and compared with basic DE and with Self-Adaptive DE (SaDE). Our results show that MsDE is capable of efficiently adapting the strategies and parameters of DE and providing competitive results with respect to the state-of-the-art.


Continuous optimization Differential evolution Parameter control Strategy adaptation 



Open image in new window We would like to thank Dr. Samaneh Khoshrou from Eindhoven University of Technology for the informative discussion. This project has received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement No 665347.


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Copyright information

© Springer International Publishing AG, part of Springer Nature 2018

Authors and Affiliations

  1. 1.Eindhoven University of TechnologyEindhovenThe Netherlands
  2. 2.RWTH Aachen UniversityAachenGermany
  3. 3.University of Groningen/Campus FryslânLeeuwardenThe Netherlands

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