Generalized lehmer mean for success history based adaptive differential evolution

Описание

Тип публикации: доклад, тезисы доклада, статья из сборника материалов конференций

Конференция: 11th International Joint Conference on Computational Intelligence, IJCCI 2019

Год издания: 2019

Ключевые слова: Differential evolution, Metaheuristic, Optimization, Parameter control

Аннотация: 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) Показать полностьюmechanism, 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. Copyright © 2019 by SCITEPRESS - Science and Technology Publications, Lda. All rights reserved.

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Издание

Журнал: IJCCI 2019 - Proceedings of the 11th International Joint Conference on Computational Intelligence

Номера страниц: 93-100

Издатель: SciTePress

Авторы

  • Stanovov V. (Reshetnev Siberian State University, Krasnoyarskii Rabochii Ave. 31, Krasnoyarsk, 660037, Russian Federation, Siberian Federal University, Institute of Mathematics and Computer Science, 79 Svobodny Pr, Krasnoyarsk, 660041, Russian Federation)
  • Akhmedova S. (Reshetnev Siberian State University, Krasnoyarskii Rabochii Ave. 31, Krasnoyarsk, 660037, Russian Federation)
  • Semenkin E. (Siberian Federal University, Institute of Mathematics and Computer Science, 79 Svobodny Pr, Krasnoyarsk, 660041, Russian Federation)
  • Semenkina M. (Heuristic and Evolutionary Algorithms Laboratory (HEAL), University of Applied Sciences Upper Austria, Softwarepark 11, Hagenberg, 4232, Austria)

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