Journal of Heuristics

, Volume 25, Issue 1, pp 71–105 | Cite as

Archivers for the representation of the set of approximate solutions for MOPs

  • O. Schütze
  • C. HernándezEmail author
  • E-G. Talbi
  • J. Q. Sun
  • Y. Naranjani
  • F.-R. Xiong


In this paper we address the problem of computing suitable representations of the set of approximate solutions of a given multi-objective optimization problem via stochastic search algorithms. For this, we will propose different archiving strategies for the selection of the candidate solutions maintained by the generation process of the stochastic search process, and investigate them further on analytically and empirically. For all archivers we will provide upper bounds on the approximation quality as well as on the cardinality of the limit solution set. We conclude this work by a comparative study on some test problems in order to visualize the effect of all novel archiving strategies.


Multi-objective optimization Pareto set Approximate solutions Convergence Stochastic search algorithm 


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Copyright information

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

Authors and Affiliations

  • O. Schütze
    • 1
  • C. Hernández
    • 1
    Email author
  • E-G. Talbi
    • 2
  • J. Q. Sun
    • 3
  • Y. Naranjani
    • 3
  • F.-R. Xiong
    • 4
  1. 1.Computer Science DepartmentCinvestav-IPNMexico CityMexico
  2. 2.University of Lille 1Villeneuve d’AscqFrance
  3. 3.School of EngineeringUniversity of CaliforniaMercedUSA
  4. 4.Department of MechanicsTianjin UniversityTianjinChina

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