Differential evolution algorithms under multi-population strategy

Ishani Chatterjee, Mengchu Zhou

Research output: Chapter in Book/Report/Conference proceedingConference contribution

8 Scopus citations


A differential evolution (DE) algorithm is an evolutionary algorithm for optimization problems over a continuous domain. To solve high dimensional global optimization problems, this work investigates the performance of differential evolution algorithms under a multi-population strategy. The original DE algorithm generates an initial set of suitable solutions. The multi population strategy divides the set into several subsets. These subsets evolve independently and connect with each other according to the DE algorithm. This helps in preserving the diversity of the initial set. Furthermore, a comparison of combination of different mutation techniques on several optimization algorithms is studied to verify their performance. Finally the computational results on eleven well-know benchmark optimization functions, reveal some interesting relationship between the number of subpopulations and performance of the DE.

Original languageEnglish (US)
Title of host publication2017 26th Wireless and Optical Communication Conference, WOCC 2017
PublisherInstitute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)9781509049097
StatePublished - May 15 2017
Event26th Wireless and Optical Communication Conference, WOCC 2017 - Newark, United States
Duration: Apr 7 2017Apr 8 2017

Publication series

Name2017 26th Wireless and Optical Communication Conference, WOCC 2017


Other26th Wireless and Optical Communication Conference, WOCC 2017
Country/TerritoryUnited States

All Science Journal Classification (ASJC) codes

  • Computer Networks and Communications
  • Information Systems and Management
  • Information Systems
  • Electronic, Optical and Magnetic Materials


  • Differential evolution
  • crossover
  • fitness value
  • mutation
  • optimization
  • population


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