Micro-differential evolution with vectorized random mutation factor

Hojjat Salehinejad, Shahryar Rahnamayan, Hamid R. Tizhoosh, Stephen Y. Chen

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

Abstract

One of the main disadvantages of population-based evolutionary algorithms (EAs) is their high computational cost due to the nature of evaluation, specially when the population size is large. The micro-algorithms employ a very small number of individuals, which can accelerate the convergence speed of algorithms dramatically, while it highly increases the stagnation risk. One approach to overcome the stagnation problem can be increasing the diversity of the population. To do so, a microdifferential evolution with vectorized random mutation factor (MDEVM) algorithm is proposed in this paper, which utilizes the small size population benefit while preventing stagnation through diversification of the population. The proposed algorithm is tested on the 28 benchmark functions provided at the IEEE congress on evolutionary computation 2013 (CEC-2013). Simulation results on the benchmark functions demonstrate that the proposed algorithm improves the convergence speed of its parent algorithm.

Original languageEnglish (US)
Title of host publicationProceedings of the 2014 IEEE Congress on Evolutionary Computation, CEC 2014
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages2055-2062
Number of pages8
ISBN (Electronic)9781479914883
DOIs
StatePublished - Sep 16 2014
Event2014 IEEE Congress on Evolutionary Computation, CEC 2014 - Beijing, China
Duration: Jul 6 2014Jul 11 2014

Publication series

NameProceedings of the 2014 IEEE Congress on Evolutionary Computation, CEC 2014

Conference

Conference2014 IEEE Congress on Evolutionary Computation, CEC 2014
Country/TerritoryChina
CityBeijing
Period7/6/147/11/14

ASJC Scopus subject areas

  • Artificial Intelligence
  • Computational Theory and Mathematics
  • Theoretical Computer Science

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