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Soft Computing
Erschienen in: Soft Computing 24/2019

27.02.2019 | Methodologies and Application

Improved quick artificial bee colony (iqABC) algorithm for global optimization

verfasst von: Selcuk Aslan, Hasan Badem, Dervis Karaboga

Erschienen in: Soft Computing | Ausgabe 24/2019

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Abstract

Artificial bee colony (ABC) algorithm inspired by the complex behaviors of honey bees in foraging is one of the most significant swarm intelligence-based meta-heuristics and has been successfully applied to a number of numerical and combinatorial optimization problems. In this study, for increasing the early convergence performance of the ABC algorithm while protecting the qualities of the final solutions, a new exploitation mechanism from the best food source that is managed by the number of evaluations is described and its efficiency on both employed and onlooker bee phases is analyzed. The results of the experimental studies obtained from a set of benchmark problems showed that the ABC algorithm with the proposed method performs significantly better than the standard implementation of ABC algorithm and its other variants in terms of convergence speed and solution quality especially for the difficult problems that should be solved before completion of the relatively small number of fitness evaluations.
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Metadaten
Titel
Improved quick artificial bee colony (iqABC) algorithm for global optimization
verfasst von
Selcuk Aslan
Hasan Badem
Dervis Karaboga
Publikationsdatum
27.02.2019
Verlag
Springer Berlin Heidelberg
Erschienen in
Soft Computing / Ausgabe 24/2019
Print ISSN: 1432-7643
Elektronische ISSN: 1433-7479
DOI
https://doi.org/10.1007/s00500-019-03858-y

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