Neural Computing and Applications

, Volume 21, Issue 8, pp 1917–1929 | Cite as

Hypervolume-based multi-objective local search

  • Matthieu Basseur
  • Rong-Qiang Zeng
  • Jin-Kao Hao


This paper presents a multi-objective local search, where the selection is realized according to the hypervolume contribution of solutions. The HBMOLS algorithm proposed is inspired from the IBEA algorithm, an indicator-based multi-objective evolutionary algorithm proposed by Zitzler and Künzli in 2004, where the optimization goal is defined in terms of a binary indicator defining the selection operator. In this paper, we use the indicator optimization principle, and we apply it to an iterated local search algorithm, using hypervolume contribution indicator as selection mechanism. The methodology proposed here has been defined in order to be easily adaptable and to be as parameter-independent as possible. We carry out a range of experiments on the multi-objective flow shop problem and the multi-objective quadratic assignment problem, using the hypervolume contribution selection as well as two different binary indicators which were initially proposed in the IBEA algorithm. Experimental results indicate that the HBMOLS algorithm is highly effective in comparison with the algorithms based on binary indicators.


Hypervolume contribution Multi-objective Local search Flow shop problem Quadratic assignment problem 



We are grateful to the referees for their comments and questions which helped us to improve the paper. The work is partially supported by the “Pays de la Loire” Region (France) within the RaDaPop (2009–2013) and LigeRO (2010–2013) projects.


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Copyright information

© Springer-Verlag London Limited 2011

Authors and Affiliations

  • Matthieu Basseur
    • 1
  • Rong-Qiang Zeng
    • 1
  • Jin-Kao Hao
    • 1
  1. 1.Department of Computer ScienceUniversity of AngersAngers Cedex 01France

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