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Neural Computing and Applications

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24.05.2016 | Original Article

Artificial bee colony algorithm with strategy and parameter adaptation for global optimization

verfasst von: Bin Zhang, Tingting Liu, Changsheng Zhang, Peng Wang

Erschienen in: Neural Computing and Applications | Sonderheft 1/2017

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Abstract

The artificial bee colony (ABC) algorithm has been successfully applied to solve a wide range of real-world optimization problems. However, the success of ABC in solving a specific problem crucially depends on appropriately choosing the foraging strategies and its associated parameters. In this paper, we propose a strategy and parameter self-adaptive selection ABC algorithm (SPaABC), in which both employed bees search strategies and their associated control parameter values are gradually self-adaptive by learning from their previous experiences in generating promising solutions. In order to verify the performance of our approach, SPaABC algorithm is compared to many recently related algorithms on eighteen benchmark functions. Experimental results indicate that the proposed algorithm achieves competitive performance on most test instances.

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The code was based on http://mf.erciyes.edu.tr/abc.

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Titel: Artificial bee colony algorithm with strategy and parameter adaptation for global optimization
verfasst von: Bin Zhang
Tingting Liu
Changsheng Zhang
Peng Wang
Publikationsdatum: 24.05.2016
Verlag: Springer London
Erschienen in: Neural Computing and Applications / Ausgabe Sonderheft 1/2017
Print ISSN: 0941-0643
Elektronische ISSN: 1433-3058
DOI: https://doi.org/10.1007/s00521-016-2348-y

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