research-article

An improved artificial bee colony algorithm based on elite group guidance and combined breadth-depth search strategy

Authors:

Tianqing Chang,

Haoze SunAuthors Info & Claims

Information Sciences—Informatics and Computer Science, Intelligent Systems, Applications: An International Journal, Volume 442, Issue C

Pages 54 - 71

https://doi.org/10.1016/j.ins.2018.02.025

Published: 01 May 2018 Publication History

Abstract

Inspired by the natural phenomenon that honey bees follow the elite group in the foraging process, we propose a novel artificial bee colony algorithm named ECABC based on elite group guidance and the combined breadth-depth search strategy in this paper. Firstly, by simulating the behavior that bees follow the elite group, a novel neighborhood search equation is proposed. According to the equation, with the center of the elite group as the starting point of the search, under the guidance of the global optimum, neighborhood search is performed. Secondly, a combined search strategy is designed as follows: the stochastic breadth-first search strategy for employed bees and the stochastic depth-first search strategy for onlooker bees. Thirdly, the random selection method of elite bees is adopted to replace the probability selection method of onlooker bees. Besides, the influencing parameters of the optimization results are studied and the optimum parameters allowing the best comprehensive performance are obtained. In addition, the proposed algorithm is experimentally verified with 22 benchmark functions and then compared with other improved artificial bee colony algorithms. The comparison results show that the ECABC can effectively improve the convergence speed, convergence precision, and robustness.

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Cited By

Cui YHu WRahmani A(2024)Multi-robot path planning using learning-based Artificial Bee Colony algorithmEngineering Applications of Artificial Intelligence10.1016/j.engappai.2023.107579129:COnline publication date: 16-May-2024
https://dl.acm.org/doi/10.1016/j.engappai.2023.107579
Zhang QBu XGao HLi TZhang H(2024)A hierarchical learning based artificial bee colony algorithm for numerical global optimization and its applicationsApplied Intelligence10.1007/s10489-023-05202-254:1(169-200)Online publication date: 1-Jan-2024
https://dl.acm.org/doi/10.1007/s10489-023-05202-2
Tao RZhou HMeng ZLiu Z(2024)An integrated firefly algorithm for the optimization of constrained engineering design problemsSoft Computing - A Fusion of Foundations, Methodologies and Applications10.1007/s00500-023-09305-328:4(3207-3250)Online publication date: 1-Feb-2024
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Index Terms

An improved artificial bee colony algorithm based on elite group guidance and combined breadth-depth search strategy
1. Computing methodologies
  1. Artificial intelligence
    1. Search methodologies
      1. Heuristic function construction
2. Theory of computation
  1. Design and analysis of algorithms
    1. Mathematical optimization

Index terms have been assigned to the content through auto-classification.

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cover image Information Sciences: an International Journal

Information Sciences: an International Journal Volume 442, Issue C

May 2018

235 pages

ISSN:0020-0255

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Copyright © Elsevier Inc.

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Elsevier Science Inc.

United States

Publication History

Published: 01 May 2018

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Cited By

Cui YHu WRahmani A(2024)Multi-robot path planning using learning-based Artificial Bee Colony algorithmEngineering Applications of Artificial Intelligence10.1016/j.engappai.2023.107579129:COnline publication date: 16-May-2024
https://dl.acm.org/doi/10.1016/j.engappai.2023.107579
Zhang QBu XGao HLi TZhang H(2024)A hierarchical learning based artificial bee colony algorithm for numerical global optimization and its applicationsApplied Intelligence10.1007/s10489-023-05202-254:1(169-200)Online publication date: 1-Jan-2024
https://dl.acm.org/doi/10.1007/s10489-023-05202-2
Tao RZhou HMeng ZLiu Z(2024)An integrated firefly algorithm for the optimization of constrained engineering design problemsSoft Computing - A Fusion of Foundations, Methodologies and Applications10.1007/s00500-023-09305-328:4(3207-3250)Online publication date: 1-Feb-2024
https://dl.acm.org/doi/10.1007/s00500-023-09305-3
Wang JLiu YRao SZhou XHu J(2023)A novel self-adaptive multi-strategy artificial bee colony algorithm for coverage optimization in wireless sensor networksAd Hoc Networks10.1016/j.adhoc.2023.103284150:COnline publication date: 1-Nov-2023
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Zhou XTan GWu YWu S(2023)Neighborhood Learning for Artificial Bee Colony Algorithm: A Mini-surveyNeural Information Processing10.1007/978-981-99-8067-3_28(370-381)Online publication date: 20-Nov-2023
https://dl.acm.org/doi/10.1007/978-981-99-8067-3_28
Zhang XXia SLi XZhang T(2022)Multi-objective particle swarm optimization with multi-mode collaboration based on reinforcement learning for path planning of unmanned air vehiclesKnowledge-Based Systems10.1016/j.knosys.2022.109075250:COnline publication date: 17-Aug-2022
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Yavuz GDurmuş BAydın D(2022)Artificial Bee Colony Algorithm with Distant Savants for constrained optimizationApplied Soft Computing10.1016/j.asoc.2021.108343116:COnline publication date: 1-Feb-2022
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Zhou XHuang JTang HWang M(2022)Artificial bee colony algorithm with bi‐coordinate systems for global numerical optimizationInternational Journal of Intelligent Systems10.1002/int.2281637:9(5816-5858)Online publication date: 30-Jul-2022
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