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Authors: Vladimir Stanovov 1 ; Shakhnaz Akhmedova 2 ; Eugene Semenkin 3 and Mariia Semenkina 4

Affiliations: 1 Reshetnev Siberian State University, Krasnoyarskii rabochii ave. 31, 660037, Krasnoyarsk, Russian Federation, Siberian Federal University, Institute of Mathematics and Computer Science, 79 Svobodny pr., 660041, Krasnoyarsk and Russian Federation ; 2 Reshetnev Siberian State University, Krasnoyarskii rabochii ave. 31, 660037, Krasnoyarsk and Russian Federation ; 3 Siberian Federal University, Institute of Mathematics and Computer Science, 79 Svobodny pr., 660041, Krasnoyarsk and Russian Federation ; 4 Heuristic and Evolutionary Algorithms Laboratory (HEAL), University of Applied Sciences Upper Austria and Softwarepark

Keyword(s): Differential Evolution, Optimization, Parameter Control, Metaheuristic.

Related Ontology Subjects/Areas/Topics: Artificial Intelligence ; Computational Intelligence ; Evolutionary Computing ; Memetic Algorithms ; Soft Computing

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 mechani sms. (More)

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Paper citation in several formats:
Stanovov, V.; Akhmedova, S.; Semenkin, E. and Semenkina, M. (2019). Generalized Lehmer Mean for Success History based Adaptive Differential Evolution. In Proceedings of the 11th International Joint Conference on Computational Intelligence (IJCCI 2019) - ECTA; ISBN 978-989-758-384-1; ISSN 2184-3236, SciTePress, pages 93-100. DOI: 10.5220/0008163600930100

@conference{ecta19,
author={Vladimir Stanovov. and Shakhnaz Akhmedova. and Eugene Semenkin. and Mariia Semenkina.},
title={Generalized Lehmer Mean for Success History based Adaptive Differential Evolution},
booktitle={Proceedings of the 11th International Joint Conference on Computational Intelligence (IJCCI 2019) - ECTA},
year={2019},
pages={93-100},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0008163600930100},
isbn={978-989-758-384-1},
issn={2184-3236},
}

TY - CONF

JO - Proceedings of the 11th International Joint Conference on Computational Intelligence (IJCCI 2019) - ECTA
TI - Generalized Lehmer Mean for Success History based Adaptive Differential Evolution
SN - 978-989-758-384-1
IS - 2184-3236
AU - Stanovov, V.
AU - Akhmedova, S.
AU - Semenkin, E.
AU - Semenkina, M.
PY - 2019
SP - 93
EP - 100
DO - 10.5220/0008163600930100
PB - SciTePress