Preprint Article Version 1 NOT YET PEER-REVIEWED

Grouped Bees Algorithm: A Grouped Version of the Bees Algorithm

  1. Electrical and Computer Engineering Department, University of Manitoba, Winnipeg, MB R3T 2N2, Canada
  2. Electrical and Computer Engineering Faculty, Technical University of Kaiserslautern, Kaiserslautern 67663, Germany
  3. Electrical and Computer Engineering Faculty, K.N. Toosi University of Technology, Tehran 19697 64499, Iran
Version 1 : Received: 13 November 2016 / Approved: 14 November 2016 / Online: 14 November 2016 (04:48:54 CET)

How to cite: Nasrinpour, H.; Massah Bavani, A.; Teshnehlab, M. Grouped Bees Algorithm: A Grouped Version of the Bees Algorithm. Preprints 2016, 2016110070 (doi: 10.20944/preprints201611.0070.v1). Nasrinpour, H.; Massah Bavani, A.; Teshnehlab, M. Grouped Bees Algorithm: A Grouped Version of the Bees Algorithm. Preprints 2016, 2016110070 (doi: 10.20944/preprints201611.0070.v1).

Abstract

As with many of the non-deterministic search algorithms, particularly those are analogous to complex biological systems, there are a number of inherent difficulties, and the Bees Algorithm (BA) is no exception. Basic versions and variations of the BA have their own drawbacks. Some of these drawbacks are a large number of parameters to be set, lack of methodology for parameter setting and computational complexity. This paper describes a Grouped version of the Bees Algorithm (GBA) addressing these issues. Unlike its conventional version, in this algorithm bees are grouped to search different sites with different neighbourhood sizes rather than just discovering two types of sites, namely elite and selected. Following a description of the GBA, the results gained for 12 benchmark functions are presented and compared with those of the basic BA, enhanced BA, standard BA and modified BA to demonstrate the efficacy of the proposed algorithm. Compared to the conventional implementations of the BA, the proposed version requires setting of fewer parameters, while producing the optimum solutions much faster.

Subject Areas

bees algorithm; swarm intelligence; evolutionary optimization; grouped bees algorithm

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