Generalized lehmer mean for success history based adaptive differential evolution

Vladimir Stanovov, Shakhnaz Akhmedova, Eugene Semenkin, Mariia Semenkina

Research output: Chapter in Book/Report/Conference proceedingsConference contributionpeer-review

2 Citations (Scopus)

Abstract

The Differential Evolution (DE) is a highly competitive numerical optimization algorithm, with a small number of control parameters. However, it is highly sensitive to the setting of these parameters, which inspired many researchers to develop adaptation strategies. One of them is the popular Success-History based Adaptation (SHA) mechanism, which significantly improves the DE performance. In this study, the focus is on the choice of the metaparameters of the SHA, namely the settings of the Lehmer mean coefficients for scaling factor and crossover rate memory cells update. The experiments are performed on the LSHADE algorithm and the Congress on Evolutionary Computation competition on numerical optimization functions set. The results demonstrate that for larger dimensions the SHA mechanism with modified Lehmer mean allows a significant improvement of the algorithm efficiency. The theoretical considerations of the generalized Lehmer mean could be also applied to other adaptive mechanisms.

Original languageEnglish
Title of host publicationIJCCI 2019 - Proceedings of the 11th International Joint Conference on Computational Intelligence
EditorsJuan Julian Merelo, Jonathan Garibaldi, Alejandro Linares-Barranco, Kurosh Madani, Kevin Warwick, Kevin Warwick
PublisherSciTePress
Pages93-100
Number of pages8
ISBN (Electronic)9789897583841
DOIs
Publication statusPublished - 2019
Event11th International Joint Conference on Computational Intelligence, IJCCI 2019 - Vienna, Austria
Duration: 17 Sep 201919 Sep 2019

Publication series

NameIJCCI 2019 - Proceedings of the 11th International Joint Conference on Computational Intelligence

Conference

Conference11th International Joint Conference on Computational Intelligence, IJCCI 2019
CountryAustria
CityVienna
Period17.09.201919.09.2019

Keywords

  • Differential evolution
  • Metaheuristic
  • Optimization
  • Parameter control

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