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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.
    2. Edmund K Burke & Michel Gendreau & Matthew Hyde & Graham Kendall & Gabriela Ochoa & Ender Özcan & Rong Qu, 2013. "Hyper-heuristics: a survey of the state of the art," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 64(12), pages 1695-1724, December.
    3. Johannes Bader & Kalyanmoy Deb & Eckart Zitzler, 2010. "Faster Hypervolume-Based Search Using Monte Carlo Sampling," Lecture Notes in Economics and Mathematical Systems, in: Matthias Ehrgott & Boris Naujoks & Theodor J. Stewart & Jyrki Wallenius (ed.), Multiple Criteria Decision Making for Sustainable Energy and Transportation Systems, pages 313-326, Springer.
    4. Laumanns, Marco & Zenklusen, Rico, 2011. "Stochastic convergence of random search methods to fixed size Pareto front approximations," European Journal of Operational Research, Elsevier, vol. 213(2), pages 414-421, September.
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    Cited by:

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