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On local optima in multiobjective combinatorial optimization problems

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  • Luis Paquete
  • Tommaso Schiavinotto
  • Thomas Stützle

Abstract

In this article, local optimality in multiobjective combinatorial optimization is used as a baseline for the design and analysis of two iterative improvement algorithms. Both algorithms search in a neighborhood that is defined on a collection of sets of feasible solutions and their acceptance criterion is based on outperformance relations. Proofs of the soundness and completeness of these algorithms are given. Copyright Springer Science+Business Media, LLC 2007

Suggested Citation

  • Luis Paquete & Tommaso Schiavinotto & Thomas Stützle, 2007. "On local optima in multiobjective combinatorial optimization problems," Annals of Operations Research, Springer, vol. 156(1), pages 83-97, December.
    • Handle: RePEc:spr:annopr:v:156:y:2007:i:1:p:83-97:10.1007/s10479-007-0230-0
    DOI: 10.1007/s10479-007-0230-0
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    References listed on IDEAS

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    1. Matthias Ehrgott & Xavier Gandibleux, 2004. "Approximative solution methods for multiobjective combinatorial optimization," TOP: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 12(1), pages 1-63, June.
    2. Paquete, Luis & Stutzle, Thomas, 2006. "A study of stochastic local search algorithms for the biobjective QAP with correlated flow matrices," European Journal of Operational Research, Elsevier, vol. 169(3), pages 943-959, March.
    3. Thomas Erlebach & Hans Kellerer & Ulrich Pferschy, 2002. "Approximating Multiobjective Knapsack Problems," Management Science, INFORMS, vol. 48(12), pages 1603-1612, December.
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    Cited by:

    1. Diaz, Juan Esteban & López-Ibáñez, Manuel, 2021. "Incorporating decision-maker’s preferences into the automatic configuration of bi-objective optimisation algorithms," European Journal of Operational Research, Elsevier, vol. 289(3), pages 1209-1222.
    2. Aritra Pal & Hadi Charkhgard, 2019. "A Feasibility Pump and Local Search Based Heuristic for Bi-Objective Pure Integer Linear Programming," INFORMS Journal on Computing, INFORMS, vol. 31(1), pages 115-133, February.
    3. Andrzej Jaszkiewicz & Thibaut Lust, 2017. "Proper balance between search towards and along Pareto front: biobjective TSP case study," Annals of Operations Research, Springer, vol. 254(1), pages 111-130, July.
    4. Justus Bonz, 2021. "Application of a multi-objective multi traveling salesperson problem with time windows," Public Transport, Springer, vol. 13(1), pages 35-57, March.
    5. Jaszkiewicz, Andrzej, 2018. "Many-Objective Pareto Local Search," European Journal of Operational Research, Elsevier, vol. 271(3), pages 1001-1013.
    6. Mădălina M. Drugan, 2018. "Scaling-up many-objective combinatorial optimization with Cartesian products of scalarization functions," Journal of Heuristics, Springer, vol. 24(2), pages 135-172, April.

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