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Neural Computing and Applications

, Volume 26, Issue 5, pp 1257–1263 | Cite as

Evolutionary population dynamics and grey wolf optimizer

  • Shahrzad SaremiEmail author
  • Seyedeh Zahra Mirjalili
  • Seyed Mohammad Mirjalili
Original Article

Abstract

Evolutionary population dynamics (EPD) deal with the removal of poor individuals in nature. It has been proven that this operator is able to improve the median fitness of the whole population, a very effective and cheap method for improving the performance of meta-heuristics. This paper proposes the use of EPD in the grey wolf optimizer (GWO). In fact, EPD removes the poor search agents of GWO and repositions them around alpha, beta, or delta wolves to enhance exploitation. The GWO is also required to randomly reinitialize its worst search agents around the search space by EPD to promote exploration. The proposed GWO–EPD algorithm is benchmarked on six unimodal and seven multi-modal test functions. The results are compared to the original GWO algorithm for verification. It is demonstrated that the proposed operator is able to significantly improve the performance of the GWO algorithm in terms of exploration, local optima avoidance, exploitation, local search, and convergence rate.

Keywords

Grey wolf optimizer Optimization Evolutionary algorithms Heuristic 

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Copyright information

© The Natural Computing Applications Forum 2014

Authors and Affiliations

  • Shahrzad Saremi
    • 1
    • 3
    Email author
  • Seyedeh Zahra Mirjalili
    • 2
  • Seyed Mohammad Mirjalili
    • 2
  1. 1.School of Information and Communication TechnologyNathan Campus, Griffith UniversityBrisbaneAustralia
  2. 2.Zharfa Pajohesh System (ZPS) Co.TehranIran
  3. 3.Queensland Institute of Business and TechnologyBrisbaneAustralia

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