@inproceedings{f408920f17544dca9c3c9880db4a7641,
title = "Generalized lehmer mean for success history based adaptive differential evolution",
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.",
keywords = "Differential evolution, Metaheuristic, Optimization, Parameter control",
author = "Vladimir Stanovov and Shakhnaz Akhmedova and Eugene Semenkin and Mariia Semenkina",
year = "2019",
doi = "10.5220/0008163600930100",
language = "English",
series = "IJCCI 2019 - Proceedings of the 11th International Joint Conference on Computational Intelligence",
publisher = "SciTePress",
pages = "93--100",
editor = "Merelo, {Juan Julian} and Jonathan Garibaldi and Alejandro Linares-Barranco and Kurosh Madani and Kevin Warwick and Kevin Warwick",
booktitle = "IJCCI 2019 - Proceedings of the 11th International Joint Conference on Computational Intelligence",
note = "11th International Joint Conference on Computational Intelligence, IJCCI 2019 ; Conference date: 17-09-2019 Through 19-09-2019",
}