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Synchronous and asynchronous Pareto-based multi-objective Artificial Bee Colony algorithms

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  • Bahriye Akay

Abstract

Pareto-based multi-objective optimization algorithms prefer non-dominated solutions over dominated solutions and maintain as much as possible diversity in the Pareto optimal set to represent the whole Pareto-front. This paper proposes three multi-objective Artificial Bee Colony (ABC) algorithms based on synchronous and asynchronous models using Pareto-dominance and non-dominated sorting: asynchronous multi-objective ABC using only Pareto-dominance rule (A-MOABC/PD), asynchronous multi-objective ABC using non-dominated sorting procedure (A-MOABC/NS) and synchronous multi-objective ABC using non-dominated sorting procedure (S-MOABC/NS). These algorithms were investigated in terms of the inverted generational distance, hypervolume and spread performance metrics, running time, approximation to whole Pareto-front and Pareto-solutions spaces. It was shown that S-MOABC/NS is more scalable and efficient compared to its asynchronous counterpart and more efficient and robust than A-MOABC/PD. An investigation on parameter sensitivity of S-MOABC/NS was presented to relate the behavior of the algorithm to the values of the control parameters. The results of S-MOABC/NS were compared to some state-of-the art algorithms. Results show that S-MOABC/NS can provide good approximations to well distributed and high quality non-dominated fronts and can be used as a promising alternative tool to solve multi-objective problems with the advantage of being simple and employing a few control parameters. Copyright Springer Science+Business Media New York 2013

Suggested Citation

  • Bahriye Akay, 2013. "Synchronous and asynchronous Pareto-based multi-objective Artificial Bee Colony algorithms," Journal of Global Optimization, Springer, vol. 57(2), pages 415-445, October.
    • Handle: RePEc:spr:jglopt:v:57:y:2013:i:2:p:415-445
    DOI: 10.1007/s10898-012-9993-1
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    Citations

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    Cited by:

    1. Jonathan Oesterle & Lionel Amodeo & Farouk Yalaoui, 2019. "A comparative study of Multi-Objective Algorithms for the Assembly Line Balancing and Equipment Selection Problem under consideration of Product Design Alternatives," Journal of Intelligent Manufacturing, Springer, vol. 30(3), pages 1021-1046, March.
    2. Xiang, Yi & Zhou, Yuren & Liu, Hailin, 2015. "An elitism based multi-objective artificial bee colony algorithm," European Journal of Operational Research, Elsevier, vol. 245(1), pages 168-193.
    3. Ling Wang & Lu An & Jiaxing Pi & Minrui Fei & Panos M. Pardalos, 2017. "A diverse human learning optimization algorithm," Journal of Global Optimization, Springer, vol. 67(1), pages 283-323, January.
    4. Nien-Che Yang & Danish Mehmood & Kai-You Lai, 2021. "Multi-Objective Artificial Bee Colony Algorithm with Minimum Manhattan Distance for Passive Power Filter Optimization Problems," Mathematics, MDPI, vol. 9(24), pages 1-19, December.

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