A novel binary artificial bee colony algorithm

This paper presents a novel artificial bee colony algorithm for binary optimization in general. Our proposal, named NBABC, features a mechanism which limits the number of dimensions that can be changed in the employed and onlookers bees’ phase. We compare the NBABC to other five binary variants of t...

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Veröffentlicht in:Future generation computer systems Jg. 98; S. 180 - 196
Hauptverfasser: Santana, Clodomir J., Macedo, Mariana, Siqueira, Hugo, Gokhale, Anu, Bastos-Filho, Carmelo J.A.
Format: Journal Article
Sprache:Englisch
Veröffentlicht: Elsevier B.V 01.09.2019
Schlagworte:
Artificial bee colony (ABC)
Binary optimization
Feature selection
Knapsack problem
Swarm intelligence
Feature selection
Swarm intelligence
Binary optimization
Artificial bee colony (ABC)
Knapsack problem
ISSN:0167-739X, 1872-7115
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Zusammenfassung:This paper presents a novel artificial bee colony algorithm for binary optimization in general. Our proposal, named NBABC, features a mechanism which limits the number of dimensions that can be changed in the employed and onlookers bees’ phase. We compare the NBABC to other five binary variants of the continuous ABC, including the state-of-the-art versions for binary optimization, and other four well-known methods. We employ different types of tasks to assess the performance of all the algorithms including the OneMax problem, five variations of the 0/1 Knapsack problems and Feature Selection using eight public datasets. The experiments show that the new proposal obtained competitive results, and in some cases outperformed not only the binary-based ABCs but also the other binary swarm-based and evolutionary-based optimizers. •Replacement of arithmetic operations by flip operations over the binary vectors.•Adaptable mechanism which limits the maximum number of dimension changes.•Performance comparison between the NBABC and other 9 bio-inspired binary algorithms.•Competitive results in different problems with low and high number of dimensions.•One of the lowest average computational time among the algorithms studied.
ISSN:0167-739X
1872-7115
DOI:10.1016/j.future.2019.03.032