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An Empirical Comparison between the Artificial Bee Colony and Bat Algorithms on Continuous Function Optimization Problem

by Shifat Sharmin Shapla, Tanveer Ahmed Belal, Mohammad Shafiul Alam
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Communications on Applied Electronics
Foundation of Computer Science (FCS), NY, USA
Volume 5 - Number 8
Year of Publication: 2016
Authors: Shifat Sharmin Shapla, Tanveer Ahmed Belal, Mohammad Shafiul Alam
10.5120/cae2016652347

Shifat Sharmin Shapla, Tanveer Ahmed Belal, Mohammad Shafiul Alam . An Empirical Comparison between the Artificial Bee Colony and Bat Algorithms on Continuous Function Optimization Problem. Communications on Applied Electronics. 5, 8 ( Aug 2016), 24-28. DOI=10.5120/cae2016652347

@article{ 10.5120/cae2016652347,
author = { Shifat Sharmin Shapla, Tanveer Ahmed Belal, Mohammad Shafiul Alam },
title = { An Empirical Comparison between the Artificial Bee Colony and Bat Algorithms on Continuous Function Optimization Problem },
journal = { Communications on Applied Electronics },
issue_date = { Aug 2016 },
volume = { 5 },
number = { 8 },
month = { Aug },
year = { 2016 },
issn = { 2394-4714 },
pages = { 24-28 },
numpages = {9},
url = { https://www.caeaccess.org/archives/volume5/number8/640-2016652347/ },
doi = { 10.5120/cae2016652347 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2023-09-04T19:55:41.770075+05:30
%A Shifat Sharmin Shapla
%A Tanveer Ahmed Belal
%A Mohammad Shafiul Alam
%T An Empirical Comparison between the Artificial Bee Colony and Bat Algorithms on Continuous Function Optimization Problem
%J Communications on Applied Electronics
%@ 2394-4714
%V 5
%N 8
%P 24-28
%D 2016
%I Foundation of Computer Science (FCS), NY, USA
Abstract

Swarm intelligence is the collective and collaborative behavior of self-organized systems, natural or artificial. Swarm intelligence algorithms basically come from nature or biological behavior of nature. In this paper we have conducted an experimental comparison between two Swarm intelligence algorithms — the Artificial Bee Colony (ABC) algorithm and basic Bat algorithm on both unimodal and multimodal high dimensional continuous functions. The ABC algorithm has a well-balanced exploration and exploitation ability which is based on the intelligent food foraging behavior of honey bee swarm, proposed by Karaboga in the year 2005, while the Bat algorithm was inspired by the echolocation behavior of bats found in nature. The experimental results show that the ABC algorithm performs better than the BAT algorithm.

References
  1. K. Khan and A. Sahai, "A Comparison of BA, GA, PSO, BP and LM for Training Feed forward Neural Networks in eLearning Context," International Journal of Intelligent Systems and Applications, pp. 23-29, June 2012.
  2. A Rekaby, "Directed Artificial Bat Algorithm (DABA) – A New Bio-Inspired Algorithm," Advances in Computing, Communications and Informatics (ICACCI), 2013 International Conference on, Mysore, 2013, pp. 1241-1246.
  3. Y. Selim and U. K. Ecir, "Improved Bat Algorithm (IBA)on Continuous Optimization Problems," International Conference on Software and Computer Applications, Lecture Notes on Software Engineering, vol. 1, no. 3, pp. 279-283, 2013.
  4. V. Tereshko, A. Loengarov, Collective decision-making in honeybee foraging dynamics, Computing and Information Systems Journal 9 (3) (2005).
  5. D. Karaboga, “An idea based on honey bee swarm for numerical optimization”, Erciyes University, Kayseri, Turkey, Technical Report–TR06, 2005.
  6. J. D. Altringham, “Bats: Biology and Behaviour”, Oxford University Press, (1996).
  7. Y. Selim and U. K. Ecir, "Improved Bat Algorithm (IBA) on Continuous Optimization Problems," Lecture Notes on Software Engineering, vol. 1, no. 3, pp. 279-283, 2013.
  8. T. Colin, "The Varienty of Life", Oxford University Press, 2000.
  9. A. Faritha and C. Chandrasekar, "An optimized approachof modified bat algorithm to record deduplication," International Journal of Computer Applications, vol. 62, no. 1, pp. 10-15, 2012.
  10. Y. Xin-She, "Bat Algorithm for MultiobjectiveOptimization," International Journal Bio-InspiredComputation, vol. 3, no. 5, pp. 267-274, 2011.
  11. Y. Xin-She, "A New Metaheuristic BatInspired Algorithm, Nature Inspired Cooperative Strategies for Optimization (NISCO)”, Springer, vol. 284, no. Springer Berlin, pp. 65-74, 2010.
  12. Y. Xin-She, "Bat Algorithm for Multiobjective Optimization," International Journal Bio-Inspired Computation, vol. 3, no. 5, pp. 267-274, 2011.
  13. N. Sakib, S. Mustafizur, M. S. Alam, M. W. U. Kabir, "A Novel Adaptive Bat Algorithm to Control Explorations and Exploitations for Continuous Optimization Problems," International Journal of Computer Applications, vol. 94, no. 13, 2014.
  14. Y.Xin-She, "A New Metaheuristic Bat-InspiredAlgorithm, Nature Inspired Cooperative Strategies forOptimization (NISCO 2010)," Springer, vol. 284, no. Springer Berlin, pp. 65-74 , 2010.
  15. Y. Xin-She, "Bat Algorithm for MultiobjectiveOptimization," International Journal Bio-InspiredComputation, vol. 3, no. 5, pp. 267-274, 2011.
  16. X. S. Yang, "Harmony Search as a MetaheuristicAlgorithm, Music-Inspired Harmony Search Algorithm," Theory and Applications, Studies in ComputationalIntelligence, vol. 191, pp. 1-14, 2009.
Index Terms

Computer Science
Information Sciences

Keywords

Swarm intelligence artificial bee colony algorithm bat algorithm continuous function optimization.

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