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Honey bee foraging algorithm for multimodal & dynamic optimization problems

Published: 07 July 2007 Publication History

Abstract

We present a new swarm based algorithm called Honey Bee Foraging (HBF). This algorithm is modeled after the food foraging behavior of the honey bees and performs a swarm based collective foraging for fitness in promising neighborhoods in combination with individual scouting searches in other areas. The strength of the algorithm lies in its continuous monitoring of the whole scouting and foraging process with dynamic relocation of the bees if more promising regions are found. The algorithm has the potential to be useful for optimization problems of multi-modal and dynamic nature.

References

[1]
Baig, A.R., and Rashid, M., "Foraging for Fitness: A Honey Bee Behavior based Algorithm for Function Optimization", Technical report, NUCES, Pakistan, Nov 2006.
[2]
Blackwell, T., and Branke, J., "Multiswarms, Exclusion, and Anti--Convergence in Dynamic Environments", IEEE Trans. on Evolutionary Computation, Vol. 10, No. 4, Aug 2006.

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    cover image ACM Conferences
    GECCO '07: Proceedings of the 9th annual conference on Genetic and evolutionary computation
    July 2007
    2313 pages
    ISBN:9781595936974
    DOI:10.1145/1276958

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    Association for Computing Machinery

    New York, NY, United States

    Publication History

    Published: 07 July 2007

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    Author Tags

    1. PSO
    2. dynamic functions
    3. honey bees
    4. multimodal functions
    5. optimization
    6. swarm intelligence

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    GECCO '07 Paper Acceptance Rate 266 of 577 submissions, 46%;
    Overall Acceptance Rate 1,669 of 4,410 submissions, 38%

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    • (2023)Optimizing microarray cancer gene selection using swarm intelligence: Recent developments and an exploratory studyEgyptian Informatics Journal10.1016/j.eij.2023.10041624:4(100416)Online publication date: Dec-2023
    • (2020)Swarm Intelligence–Based Energy‐Efficient Clustering Algorithms for WSN: Overview of Algorithms, Analysis, and ApplicationsSwarm Intelligence Optimization10.1002/9781119778868.ch12(207-261)Online publication date: 4-Dec-2020
    • (2019)A comprehensive review on nature inspired computing algorithms for the diagnosis of chronic disorders in human beingsProgress in Artificial Intelligence10.1007/s13748-019-00191-18:4(401-424)Online publication date: 1-Dec-2019
    • (2018)Swarm Intelligence Algorithms for Feature Selection: A ReviewApplied Sciences10.3390/app80915218:9(1521)Online publication date: 1-Sep-2018
    • (2018)New inspirations in swarm intelligence: a surveyInternational Journal of Bio-Inspired Computation10.1504/IJBIC.2011.0387003:1(1-16)Online publication date: 21-Dec-2018
    • (2014)The foraging behaviour of honey bees, Apis mellifera: a reviewVeterinární medicína10.17221/7240-VETMED59:1(1-10)Online publication date: 31-Jan-2014
    • (2014)Find robust solutions over time by two-layer multi-objective optimization method2014 IEEE Congress on Evolutionary Computation (CEC)10.1109/CEC.2014.6900241(1528-1535)Online publication date: Jul-2014
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    • (2010)An artificial bee colony algorithm for unknown parameters and time-delays identification of chaotic systems5th International Conference on Computer Sciences and Convergence Information Technology10.1109/ICCIT.2010.5711137(659-664)Online publication date: Nov-2010
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