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Soft Computing
Erschienen in: Soft Computing 17/2019

02.08.2018 | Methodologies and Application

Hybrid evolutionary programming using adaptive Lévy mutation and modified Nelder–Mead method

verfasst von: Jinwei Pang, Jun He, Hongbin Dong

Erschienen in: Soft Computing | Ausgabe 17/2019

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Abstract

Evolutionary programming has been widely applied to solve global optimization problems. Its performance is related to both mutation operators and fitness landscapes. In order to make evolutionary programming more efficient, its mutation operator should adapt to fitness landscapes. The paper presents novel hybrid evolutionary programming with adaptive Lévy mutation, in which the shape parameter of Lévy probability distribution adapts to the roughness of local fitness landscapes. Furthermore, a modified Nelder–Mead method is added to evolutionary programming for enhancing its exploitation ability. The proposed algorithm is tested on 39 selected benchmark functions and also benchmark functions in CEC2005 and CEC2017. The experimental results demonstrate that the overall performance of the proposed algorithm is better than other algorithms in terms of the solution accuracy.
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Metadaten
Titel
Hybrid evolutionary programming using adaptive Lévy mutation and modified Nelder–Mead method
verfasst von
Jinwei Pang
Jun He
Hongbin Dong
Publikationsdatum
02.08.2018
Verlag
Springer Berlin Heidelberg
Erschienen in
Soft Computing / Ausgabe 17/2019
Print ISSN: 1432-7643
Elektronische ISSN: 1433-7479
DOI
https://doi.org/10.1007/s00500-018-3422-4

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