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Weighted bee colony algorithm for discrete optimization problems with application to feature selection

Moayedikia, Alireza, Jensen, R, Wiil, UK and Forsati, R 2015, Weighted bee colony algorithm for discrete optimization problems with application to feature selection, Engineering Applications of Artificial Intelligence, vol. 44, pp. 153-167, doi: 10.1016/j.engappai.2015.06.003.

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Title Weighted bee colony algorithm for discrete optimization problems with application to feature selection
Author(s) Moayedikia, Alireza
Jensen, R
Wiil, UK
Forsati, R
Journal name Engineering Applications of Artificial Intelligence
Volume number 44
Start page 153
End page 167
Total pages 15
Publisher Elsevier
Place of publication Oxford, Eng.
Publication date 2015-01-01
ISSN 0952-1976
Keyword(s) Science & Technology
Technology
Automation & Control Systems
Computer Science, Artificial Intelligence
Engineering, Multidisciplinary
Engineering, Electrical & Electronic
Computer Science
Engineering
Bee colony optimization
Categorical optimization
Classification
Feature selection
Weighted bee colony optimization
Hybrid genetic algorithm
Summary The conventional bee colony optimization (BCO) algorithm, one of the recent swarm intelligence (SI) methods, is good at exploration whilst being weak at exploitation. In order to improve the exploitation power of BCO, in this paper we introduce a novel algorithm, dubbed as weighted BCO (wBCO), that allows the bees to search in the solution space deliberately while considering policies to share the attained information about the food sources heuristically. For this purpose, wBCO considers global and local weights for each food source, where the former is the rate of popularity of a given food source in the swarm and the latter is the relevancy of a food source to a category label. To preserve diversity in the population, we embedded new policies in the recruiter selection stage to ensure that uncommitted bees follow the most similar committed ones. Thus, the local food source weighting and recruiter selection strategies make the algorithm suitable for discrete optimization problems. To demonstrate the utility of wBCO, the feature selection (FS) problem is modeled as a discrete optimization task, and has been tackled by the proposed algorithm. The performance of wBCO and its effectiveness in dealing with feature selection problem are empirically evaluated on several standard benchmark optimization functions and datasets and compared to the state-of-the-art methods, exhibiting the superiority of wBCO over the competitor approaches.
Language eng
DOI 10.1016/j.engappai.2015.06.003
Indigenous content off
Field of Research 08 Information and Computing Sciences
09 Engineering
0806 Information Systems
HERDC Research category CN.1 Other journal article
ERA Research output type C Journal article
Copyright notice ©2015, Elsevier
Persistent URL http://hdl.handle.net/10536/DRO/DU:30119626

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Every reasonable effort has been made to ensure that permission has been obtained for items included in DRO. If you believe that your rights have been infringed by this repository, please contact drosupport@deakin.edu.au.