Journal of Heuristics

, Volume 24, Issue 4, pp 581–616 | Cite as

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

  • Olacir R. CastroJr.Email author
  • Gian Mauricio Fritsche
  • Aurora Pozo


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.


Multi-objective particle swarm optimization Multi-objective Hyper-heuristics Leader selection Archiving Fitness-rate-rank-based multi-armed bandit Adaptive choice function 



The authors would like to thank the Academic Publishing Advisory Center (Centro de Assessoria de Publicação Acadêmica, CAPA - of the Federal University of Paraná for assistance with English language editing. Also, the authors would like to thank CNPq (National Council for Scientific and Technological Development) and CAPES (Coordination for the Improvement of Higher Education Personnel) for the financial support.


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Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2018

Authors and Affiliations

  • Olacir R. CastroJr.
    • 1
    Email author
  • Gian Mauricio Fritsche
    • 1
  • Aurora Pozo
    • 1
  1. 1.Computer Science’s DepartmentFederal University of ParanáCuritibaBrazil

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