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ABC+ES: a novel hybrid artificial bee colony algorithm with evolution strategies

Published: 12 July 2014 Publication History

Abstract

This paper has the purpose of presenting a new hybridization of the Artificial Bee Colony Algorithm (ABC) based on the evolutionary strategies (ES) found on the Evolutionary Particle Swarm Optimization (EPSO). The main motivation of this approach is to augment the original ABC in a way that combines the effectiveness and simplicity of the ABC with the robustness and increased exploitation of the Evolution Strategies.
The algorithm is intended to be tested on two large-scale engineering design problem and its results compared to other optimization techniques.

References

[1]
B. Akay and D. Karaboga. "Artificial Bee Colony Algorithm for Large-Scale Problems and Engineering Design Optimization". In: Journal of Intelligent Manufacturing 23.4 (2012), pp. 1001--1014.
[2]
Q. He and L. Wang. "An Effective Co-evolutionary Particle Swarm Optimization for Constrained Engineering esign Problems". In: Engineering Applications of Artificial Intelligence (2007), pp. 89--99.
[3]
D. Karaboga. An Idea Based on Honey Bee Swarm for Numerical Optimization. Tech. rep. Erciyes University, 2005.
[4]
D. Karaboga et al. "A Comprehensive Survey: Artificial Bee Colony (ABC) Algorithm and Applications". In: Artificial Intelligence Review (2012), pp. 1--37.
[5]
V. Miranda and N. Fonseca. "EPSO - Evolutionary Particle Swarm Optimization, a New Algorithm with Applications in Power Systems". In: Transmission and Distribution Conference and Exhibition 2002: Asia Pacific. IEEE/PES. Vol. 2. IEEE Press, 2002, pp. 745--750.
[6]
D. L. Souza et al. "A New Cooperative Evolutionary Multi-Swarm Optimizer Algorithm Based on CUDA Architecture Applied to Engineering Optimization". In: Combinations of Intelligent Methods and Applications. Ed. by I. Hatzilygeroudis and V. Palade. Vol. 23. Springer-Verlag, 2013, pp. 95--115.
[7]
O. N. Teixeira et al. "Algoritmo Genético com Interação Social na Resolução de Problemas de Otimização Global com Restrições". In: 1st ed. Editora OMNIPAX, 2011. Chap. 10, pp. 197--223.

Cited By

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  • (2017)Evolutionary artificial bee colony for neural networks training2017 13th International Conference on Natural Computation, Fuzzy Systems and Knowledge Discovery (ICNC-FSKD)10.1109/FSKD.2017.8393309(44-49)Online publication date: Jul-2017
  • (2016)Application of Artificial Bee Colony Algorithms to Antenna Design Problems for RFID ApplicationsHandbook of Research on Modern Optimization Algorithms and Applications in Engineering and Economics10.4018/978-1-4666-9644-0.ch009(236-265)Online publication date: 2016
  • (2015)ABC+ES: Combining Artificial Bee Colony Algorithm and Evolution Strategies on Engineering Design Problems and Benchmark FunctionsHybrid Intelligent Systems10.1007/978-3-319-27221-4_5(53-66)Online publication date: 17-Nov-2015

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  1. ABC+ES: a novel hybrid artificial bee colony algorithm with evolution strategies

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    cover image ACM Conferences
    GECCO Comp '14: Proceedings of the Companion Publication of the 2014 Annual Conference on Genetic and Evolutionary Computation
    July 2014
    1524 pages
    ISBN:9781450328814
    DOI:10.1145/2598394
    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: 12 July 2014

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    Author Tags

    1. artificial bee colony
    2. constrained optimization
    3. evolution strategies
    4. hybrid metaheuristic
    5. swarm intelligence

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    GECCO '14
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    GECCO '14: Genetic and Evolutionary Computation Conference
    July 12 - 16, 2014
    BC, Vancouver, Canada

    Acceptance Rates

    GECCO Comp '14 Paper Acceptance Rate 180 of 544 submissions, 33%;
    Overall Acceptance Rate 1,669 of 4,410 submissions, 38%

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    View all
    • (2017)Evolutionary artificial bee colony for neural networks training2017 13th International Conference on Natural Computation, Fuzzy Systems and Knowledge Discovery (ICNC-FSKD)10.1109/FSKD.2017.8393309(44-49)Online publication date: Jul-2017
    • (2016)Application of Artificial Bee Colony Algorithms to Antenna Design Problems for RFID ApplicationsHandbook of Research on Modern Optimization Algorithms and Applications in Engineering and Economics10.4018/978-1-4666-9644-0.ch009(236-265)Online publication date: 2016
    • (2015)ABC+ES: Combining Artificial Bee Colony Algorithm and Evolution Strategies on Engineering Design Problems and Benchmark FunctionsHybrid Intelligent Systems10.1007/978-3-319-27221-4_5(53-66)Online publication date: 17-Nov-2015

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