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Guided Moth–Flame optimiser for multi-objective optimization problems

Author

Listed:
  • Djaafar Zouache

    (University of Mohamed El Bachir Elibrahimi)

  • Fouad Ben Abdelaziz

    (NEOMA Business School, Rouen Campus)

  • Mira Lefkir

    (University of Mohamed El Bachir Elibrahimi)

  • Nour El-Houda Chalabi

    (University of Mohamed El Bachir Elibrahimi)

Abstract

This paper proposes a novel version of Moth–Flame optimiser for solving multi-objective problems (MOMFO). The main idea of this algorithm is that the Moth’s swarm explores the search space around the Flames set (leader solutions). To implement our approach, we integrate the unlimited external archive to guide the Moth’s swarm during the exploration search to find the Pareto solutions set. To ensure a good compromise between convergence and diversity when exploring the search space, we use the epsilon dominance principle to update the external archive. In addition, we use the non-dominated sort and crowding distance in updating the Flame solutions to ensure rapid convergence towards the Pareto solutions set. We validate the proposed algorithm on twelve test functions and compare our results with two well-known meta-heuristics. The results of the proposed algorithm show better convergence behavior with a better diversity of solutions.

Suggested Citation

  • 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.
    • Handle: RePEc:spr:annopr:v:296:y:2021:i:1:d:10.1007_s10479-019-03407-8
    DOI: 10.1007/s10479-019-03407-8
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    References listed on IDEAS

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    1. Wei Kun Li & Wan Liang Wang & Li Li, 2018. "Optimization of Water Resources Utilization by Multi-Objective Moth-Flame Algorithm," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 32(10), pages 3303-3316, August.
    2. Chatkaew Jariyatantiwait & Gary G. Yen, 2014. "Multiobjective Differential Evolution Based on Fuzzy Performance Feedback," International Journal of Swarm Intelligence Research (IJSIR), IGI Global, vol. 5(4), pages 45-64, October.
    3. 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.
    4. Kathrin Klamroth & Margaret M. Wiecek, 2000. "Dynamic programming approaches to the multiple criteria knapsack problem," Naval Research Logistics (NRL), John Wiley & Sons, vol. 47(1), pages 57-76, February.
    5. 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.
    6. 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. Nour Elhouda Chalabi & Abdelouahab Attia & Abderraouf Bouziane & Mahmoud Hassaballah & Abed Alanazi & Adel Binbusayyis, 2023. "An Archive-Guided Equilibrium Optimizer Based on Epsilon Dominance for Multi-Objective Optimization Problems," Mathematics, MDPI, vol. 11(12), pages 1-30, June.
    2. Abdelaziz, Fouad Ben & Maddah, Bacel & Flamand, Tülay & Azar, Jimmy, 2024. "Store-Wide space planning balancing impulse and convenience," European Journal of Operational Research, Elsevier, vol. 312(1), pages 211-226.
    3. Zhe Liu & Shurong Li, 2022. "A numerical method for interval multi-objective mixed-integer optimal control problems based on quantum heuristic algorithm," Annals of Operations Research, Springer, vol. 311(2), pages 853-898, April.

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