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|Title:||Computing with the collective intelligence of honey bees – A survey||Authors:||Rajasekhar, Anguluri
Suganthan, P. N.
Nature inspired computing
|Issue Date:||2016||Source:||Rajasekhar, A., Lynn, N., Das, S., & Suganthan, P. N. (2017). Computing with the collective intelligence of honey bees – A survey. Swarm and Evolutionary Computation, 32, 25-48.||Series/Report no.:||Swarm and Evolutionary Computation||Abstract:||Over past few decades, families of algorithms based on the intelligent group behaviors of social creatures like ants, birds, fishes, and bacteria have been extensively studied and applied for computer-aided optimization. Recently there has been a surge of interest in developing algorithms for search, optimization, and communication by simulating different aspects of the social life of a very well-known creature: the honey bee. Several articles reporting the success of the heuristics based on swarming, mating, and foraging behaviors of the honey bees are being published on a regular basis. In this paper we provide a brief but comprehensive survey of the entire horizon of research so far undertaken on the algorithms inspired by the honey bees. Starting with the biological perspectives and motivations, we outline the major bees-inspired algorithms, their prospects in the respective problem domains and their similarities and dissimilarities with the other swarm intelligence algorithms. We also provide an account of the engineering applications of these algorithms. Finally we identify some open research issues and promising application areas for the bees-inspired computing techniques.||URI:||https://hdl.handle.net/10356/83447
|ISSN:||2210-6502||DOI:||10.1016/j.swevo.2016.06.001||Rights:||© 2016 Elsevier. This is the author created version of a work that has been peer reviewed and accepted for publication by Swarm and Evolutionary Computation, Elsevier. It incorporates referee’s comments but changes resulting from the publishing process, such as copyediting, structural formatting, may not be reflected in this document. The published version is available at: [http://dx.doi.org/10.1016/j.swevo.2016.06.001].||Fulltext Permission:||open||Fulltext Availability:||With Fulltext|
|Appears in Collections:||EEE Journal Articles|
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