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Neural Computing and Applications
Erschienen in: Neural Computing and Applications 8/2020

26.10.2018 | Original Article

Weighted differential evolution algorithm for numerical function optimization: a comparative study with cuckoo search, artificial bee colony, adaptive differential evolution, and backtracking search optimization algorithms

verfasst von: Pinar Civicioglu, Erkan Besdok, Mehmet Akif Gunen, Umit Haluk Atasever

Erschienen in: Neural Computing and Applications | Ausgabe 8/2020

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Abstract

In this paper, weighted differential evolution algorithm (WDE) has been proposed for solving real-valued numerical optimization problems. When all parameters of WDE are determined randomly, in practice, WDE has no control parameter but the pattern size. WDE can solve unimodal, multimodal, separable, scalable, and hybrid problems. WDE has a very fast and quite simple structure, in addition, it can be parallelized due to its non-recursive nature. WDE has a strong exploration and exploitation capability. In this paper, WDE’s success in solving CEC’ 2013 problems was compared to 4 different EAs (i.e., CS, ABC, JADE, and BSA) statistically. One 3D geometric optimization problem (i.e., GPS network adjustment problem) and 4 constrained engineering design problems were used to examine the WDE’s ability to solve real-world problems. Results obtained from the performed tests showed that, in general, problem-solving success of WDE is statistically better than the comparison algorithms that have been used in this paper.
Vorheriger Artikel Intuitionistic fuzzy divergence measure-based ELECTRE method for performance of cellular mobile telephone service providers
Nächster Artikel A comparative study of artificial neural networks in predicting blast-induced air-blast overpressure at Deo Nai open-pit coal mine, Vietnam

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Metadaten
Titel
Weighted differential evolution algorithm for numerical function optimization: a comparative study with cuckoo search, artificial bee colony, adaptive differential evolution, and backtracking search optimization algorithms
verfasst von
Pinar Civicioglu
Erkan Besdok
Mehmet Akif Gunen
Umit Haluk Atasever
Publikationsdatum
26.10.2018
Verlag
Springer London
Erschienen in
Neural Computing and Applications / Ausgabe 8/2020
Print ISSN: 0941-0643
Elektronische ISSN: 1433-3058
DOI
https://doi.org/10.1007/s00521-018-3822-5

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