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Evaluating selection methods on hyper-heuristic multi-objective particle swarm optimization

Author

Listed:
  • Olacir R. Castro

    (Federal University of Paraná)

  • Gian Mauricio Fritsche

    (Federal University of Paraná)

  • Aurora Pozo

    (Federal University of Paraná)

Abstract

Multi-objective particle swarm optimization (MOPSO) is a promising meta-heuristic to solve multi-objective problems (MOPs). Previous works have shown that selecting a proper combination of leader and archiving methods, which is a challenging task, improves the search ability of the algorithm. A previous study has employed a simple hyper-heuristic to select these components, obtaining good results. In this research, an analysis is made to verify if using more advanced heuristic selection methods improves the search ability of the algorithm. Empirical studies are conducted to investigate this hypothesis. In these studies, first, four heuristic selection methods are compared: a choice function, a multi-armed bandit, a random one, and the previously proposed roulette wheel. A second study is made to identify if it is best to adapt only the leader method, the archiving method, or both simultaneously. Moreover, the influence of the interval used to replace the low-level heuristic is analyzed. At last, a final study compares the best variant to a hyper-heuristic framework that combines a Multi-Armed Bandit algorithm into the multi-objective optimization based on decomposition with dynamical resource allocation (MOEA/D-DRA) and a state-of-the-art MOPSO. Our results indicate that the resulting algorithm outperforms the hyper-heuristic framework in most of the problems investigated. Moreover, it achieves competitive results compared to a state-of-the-art MOPSO.

Suggested Citation

  • Olacir R. Castro & Gian Mauricio Fritsche & Aurora Pozo, 2018. "Evaluating selection methods on hyper-heuristic multi-objective particle swarm optimization," Journal of Heuristics, Springer, vol. 24(4), pages 581-616, August.
  • Handle: RePEc:spr:joheur:v:24:y:2018:i:4:d:10.1007_s10732-018-9369-x
    DOI: 10.1007/s10732-018-9369-x
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    References listed on IDEAS

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    1. Jacek Blazewicz & Edmund Burke & Graham Kendall & Wojciech Mruczkiewicz & Ceyda Oguz & Aleksandra Swiercz, 2013. "A hyper-heuristic approach to sequencing by hybridization of DNA sequences," Annals of Operations Research, Springer, vol. 207(1), pages 27-41, August.
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    Cited by:

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    3. Longlong Leng & Yanwei Zhao & Zheng Wang & Jingling Zhang & Wanliang Wang & Chunmiao Zhang, 2019. "A Novel Hyper-Heuristic for the Biobjective Regional Low-Carbon Location-Routing Problem with Multiple Constraints," Sustainability, MDPI, vol. 11(6), pages 1-31, March.

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