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Information sharing in bee colony for detecting multiple niches in non-stationary environments

Published: 06 July 2013 Publication History

Abstract

This paper proposes an information sharing model of artificial bee colony for locating multiple peaks in dynamic environments. The concept of niching is implemented by using a hybridized approach that combines a modified variant of the fitness sharing technique with a speciation based response to the changing environment. The informative dynamic niching bee colony algorithm helps to synchronize the employer and onlooker forager swarms by synergizing the local information with a modified perturbation strategy. This main crux of our algorithm is its independency of problem dependent control parameter, like niche radius, and the absence of any hard-partitioning clustering technique that leads to high computational burden. Our framework aims at bringing about a simple, robust approach that can be applied to a variety of problems. Experimental investigation is undertaken pertaining to the competitive performance of our algorithm with the existing techniques in order to highlight the significance of our work.

References

[1]
Karaboga, D. 2005. An idea based on honey bee swarm for numerical optimization. Technical Report TR06. Computer Engineering Department. Engineering Faculty, Erciyes University.
[2]
Goldberg, D. E., and Richardson, J. 1987. Genetic algorithms with sharing for multimodal function optimization. In Proc. of the Second International Conference on Genetic Algorithms and their application,41--49.
[3]
Branke, J. 1999.Memory Enhanced Evolutionary Algorithms for Changing Optimization Problems. In Proc of the Congress on Evolutionary Computation. 3, 1875--1882.
[4]
Yang, S., and Li, C. 2010. A clustering particle swarm optimizer for locating and tracking multiple optima in dynamic environments. IEEE Trans. on Evol. Comput. 14, 6 (Dec, 2010), 951--974.
[5]
Parrott, D., and Li, X. 2006. Locating and Tracking Multiple Dynamic Optima by a Particle Swarm Model Using Speciation. IEEE Trans. on Evol. Comput. 10, 4 (Aug. 2006), 440 - 458.

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  • (2022) A Differential Evolution Algorithm With Adaptive Niching and K -Means Operation for Data Clustering IEEE Transactions on Cybernetics10.1109/TCYB.2020.303588752:7(6181-6195)Online publication date: Jul-2022
  • (2021)Clustering and Memory-based Parent-Child Swarm Meta-heuristic Algorithm for Dynamic OptimizationSignal and Data Processing10.52547/jsdp.18.3.12718:3(127-146)Online publication date: 1-Dec-2021
  • (2021)A gravitational search algorithm with hierarchy and distributed frameworkKnowledge-Based Systems10.1016/j.knosys.2021.106877(106877)Online publication date: Feb-2021
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Published In

cover image ACM Conferences
GECCO '13 Companion: Proceedings of the 15th annual conference companion on Genetic and evolutionary computation
July 2013
1798 pages
ISBN:9781450319645
DOI:10.1145/2464576
  • Editor:
  • Christian Blum,
  • General Chair:
  • Enrique Alba
Permission to make digital or hard copies of part or all of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for third-party components of this work must be honored. For all other uses, contact the Owner/Author.

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 06 July 2013

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Author Tags

  1. artificial bee colony
  2. change-detection
  3. dynamic niching
  4. local information
  5. niching parameter free
  6. sharing
  7. speciation

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  • Abstract

Conference

GECCO '13
Sponsor:
GECCO '13: Genetic and Evolutionary Computation Conference
July 6 - 10, 2013
Amsterdam, The Netherlands

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Overall Acceptance Rate 1,669 of 4,410 submissions, 38%

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Cited By

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  • (2022) A Differential Evolution Algorithm With Adaptive Niching and K -Means Operation for Data Clustering IEEE Transactions on Cybernetics10.1109/TCYB.2020.303588752:7(6181-6195)Online publication date: Jul-2022
  • (2021)Clustering and Memory-based Parent-Child Swarm Meta-heuristic Algorithm for Dynamic OptimizationSignal and Data Processing10.52547/jsdp.18.3.12718:3(127-146)Online publication date: 1-Dec-2021
  • (2021)A gravitational search algorithm with hierarchy and distributed frameworkKnowledge-Based Systems10.1016/j.knosys.2021.106877(106877)Online publication date: Feb-2021
  • (2019)BTC-2019: The 2019 Billion Triple Challenge DatasetThe Semantic Web – ISWC 201910.1007/978-3-030-30796-7_11(163-180)Online publication date: 26-Oct-2019
  • (2019)The KEEN UniverseThe Semantic Web – ISWC 201910.1007/978-3-030-30796-7_1(3-18)Online publication date: 26-Oct-2019
  • (2018)A restructured artificial bee colony optimizer combining life-cycle, local search and crossover operations for droplet property prediction in printable electronics fabricationJournal of Intelligent Manufacturing10.1007/s10845-015-1092-y29:1(109-134)Online publication date: 1-Jan-2018
  • (2017)Neighborhood-adaptive differential evolution for global numerical optimizationApplied Soft Computing10.1016/j.asoc.2017.06.00259:C(659-706)Online publication date: 1-Oct-2017
  • (2016)History-Driven Particle Swarm Optimization in dynamic and uncertain environmentsNeurocomputing10.1016/j.neucom.2015.05.115172:C(356-370)Online publication date: 8-Jan-2016
  • (2016)A hybrid approach to artificial bee colony algorithmNeural Computing and Applications10.1007/s00521-015-1851-x27:2(387-409)Online publication date: 1-Feb-2016
  • (2015)MPSICAInformation Sciences: an International Journal10.1016/j.ins.2015.03.001308:C(49-60)Online publication date: 1-Jul-2015
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