ABC-X: a generalized, automatically configurable artificial bee colony framework

The artificial bee colony (ABC) algorithm is a popular metaheuristic that was originally conceived for tackling continuous function optimization tasks. Over the last decade, a large number of variants of ABC have been proposed, making it by now a well-studied swarm intelligence algorithm. Typically,...

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Veröffentlicht in:Swarm intelligence Jg. 11; H. 1; S. 1 - 38
Hauptverfasser: Aydın, Doğan, Yavuz, Gürcan, Stützle, Thomas
Format: Journal Article
Sprache:Englisch
Veröffentlicht: New York Springer US 01.03.2017
Springer Nature B.V
Schlagworte:
Algorithms
Artificial Intelligence
Communications Engineering
Computer Communication Networks
Computer Science
Computer Systems Organization and Communication Networks
Intelligence
Mathematical and Computational Engineering
Mathematical models
Networks
Automatic algorithm configuration
Artificial bee colony
Algorithm framework
Continuous optimization
ISSN:1935-3812, 1935-3820
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Zusammenfassung:The artificial bee colony (ABC) algorithm is a popular metaheuristic that was originally conceived for tackling continuous function optimization tasks. Over the last decade, a large number of variants of ABC have been proposed, making it by now a well-studied swarm intelligence algorithm. Typically, in a paper on algorithmic variants of ABC algorithms, one or at most two of its algorithmic components are modified. Possible changes include variations on the search equations, the selection of candidate solutions to be explored, or the adoption of features from other algorithmic techniques. In this article, we propose to follow a different direction and to build a generalized ABC algorithm, which we call ABC-X. ABC-X collects algorithmic components available from known ABC algorithms into a common algorithm framework that allows not only to instantiate known ABC variants but, more importantly, also many ABC algorithm variants that have never been explored before in the literature. Automatic algorithm configuration techniques can generate from this template new ABC variants that perform better than known ABC algorithms, even when their numerical parameters are fine-tuned using the same automatic configuration process.
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ISSN:1935-3812
1935-3820
DOI:10.1007/s11721-017-0131-z