Evolutionary population dynamics and grey wolf optimizer

Shahrzad Saremi, Seyedeh Zahra Mirjalili, Seyed Mohammad Mirjalili

Research output: Contribution to journalArticlepeer-review

165 Citations (Scopus)

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.

Original languageEnglish
Pages (from-to)1257-1263
Number of pages7
JournalNeural Computing and Applications
Volume26
Issue number5
DOIs
Publication statusPublished - 8 Jul 2015
Externally publishedYes

Keywords

  • Evolutionary algorithms
  • Grey wolf optimizer
  • Heuristic
  • Optimization

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