Multi-strategy Differential Evolution

Anil Yaman, Giovanni Iacca, Matthew Coler, George Fletcher, Mykola Pechenizkiy

Research output: Chapter in Book/Report/Conference proceedingConference contributionAcademicpeer-review

7 Citations (Scopus)
286 Downloads (Pure)


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.
Original languageEnglish
Title of host publicationApplications of Evolutionary Computation
Number of pages17
ISBN (Electronic)978-3-319-77538-8
ISBN (Print)978-3-319-77537-1
Publication statusPublished - 8-Mar-2018
EventEvoStar - Amsterdam, Netherlands
Duration: 1-Apr-2017 → …


Abbreviated titleevo*2017
Period01/04/2017 → …
Internet address


  • Evolutionary Computation
  • Differential Evolution
  • optimization problem

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