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A cooperative swarm intelligence algorithm for multi-objective discrete optimization with application to the knapsack problem

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  • Zouache, Djaafar
  • Moussaoui, Abdelouahab
  • Ben Abdelaziz, Fouad

Abstract

We propose a novel cooperative swarm intelligence algorithm to solve multi-objective discrete optimization problems (MODP). Our algorithm combines a firefly algorithm (FA) and a particle swarm optimization (PSO). Basically, we address three main points: the effect of FA and PSO cooperation on the exploration of the search space, the discretization of the two algorithms using a transfer function, and finally, the use of the epsilon dominance relation to manage the size of the external archive and to guarantee the convergence and the diversity of Pareto optimal solutions.

Suggested Citation

  • Zouache, Djaafar & Moussaoui, Abdelouahab & Ben Abdelaziz, Fouad, 2018. "A cooperative swarm intelligence algorithm for multi-objective discrete optimization with application to the knapsack problem," European Journal of Operational Research, Elsevier, vol. 264(1), pages 74-88.
    • Handle: RePEc:eee:ejores:v:264:y:2018:i:1:p:74-88
    DOI: 10.1016/j.ejor.2017.06.058
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    References listed on IDEAS

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    6. 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.
    7. Przybylski, Anthony & Gandibleux, Xavier & Ehrgott, Matthias, 2008. "Two phase algorithms for the bi-objective assignment problem," European Journal of Operational Research, Elsevier, vol. 185(2), pages 509-533, March.
    8. Jaszkiewicz, Andrzej, 2002. "Genetic local search for multi-objective combinatorial optimization," European Journal of Operational Research, Elsevier, vol. 137(1), pages 50-71, February.
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    Cited by:

    1. Elias Munapo & Santosh Kumar, 2021. "Reducing the complexity of the knapsack linear integer problem by reformulation techniques," International Journal of System Assurance Engineering and Management, Springer;The Society for Reliability, Engineering Quality and Operations Management (SREQOM),India, and Division of Operation and Maintenance, Lulea University of Technology, Sweden, vol. 12(6), pages 1087-1093, December.
    2. Chou, Jui-Sheng & Truong, Dinh-Nhat, 2020. "Multiobjective optimization inspired by behavior of jellyfish for solving structural design problems," Chaos, Solitons & Fractals, Elsevier, vol. 135(C).
    3. Djaafar Zouache & Fouad Ben Abdelaziz & Mira Lefkir & Nour El-Houda Chalabi, 2021. "Guided Moth–Flame optimiser for multi-objective optimization problems," Annals of Operations Research, Springer, vol. 296(1), pages 877-899, January.
    4. Jakubik, Johannes & Binding, Adrian & Feuerriegel, Stefan, 2021. "Directed particle swarm optimization with Gaussian-process-based function forecasting," European Journal of Operational Research, Elsevier, vol. 295(1), pages 157-169.
    5. Nour Elhouda Chalabi & Abdelouahab Attia & Khalid Abdulaziz Alnowibet & Hossam M. Zawbaa & Hatem Masri & Ali Wagdy Mohamed, 2023. "A Multi–Objective Gaining–Sharing Knowledge-Based Optimization Algorithm for Solving Engineering Problems," Mathematics, MDPI, vol. 11(14), pages 1-37, July.

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