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An interactive evolutionary multi-objective optimization algorithm with a limited number of decision maker calls

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  • Sinha, Ankur
  • Korhonen, Pekka
  • Wallenius, Jyrki
  • Deb, Kalyanmoy

Abstract

This paper presents a preference-based method to handle optimization problems with multiple objectives. With an increase in the number of objectives the computational cost in solving a multi-objective optimization problem rises exponentially, and it becomes increasingly difficult for evolutionary multi-objective techniques to produce the entire Pareto-optimal front. In this paper, an evolutionary multi-objective procedure is combined with preference information from the decision maker during the intermediate stages of the algorithm leading to the most preferred point. The proposed approach is different from the existing approaches, as it tries to find the most preferred point with a limited budget of decision maker calls. In this paper, we incorporate the idea into a progressively interactive technique based on polyhedral cones. The idea is also tested on another progressively interactive approach based on value functions. Results are provided on two to five-objective unconstrained as well as constrained test problems.

Suggested Citation

  • Sinha, Ankur & Korhonen, Pekka & Wallenius, Jyrki & Deb, Kalyanmoy, 2014. "An interactive evolutionary multi-objective optimization algorithm with a limited number of decision maker calls," European Journal of Operational Research, Elsevier, vol. 233(3), pages 674-688.
    • Handle: RePEc:eee:ejores:v:233:y:2014:i:3:p:674-688
    DOI: 10.1016/j.ejor.2013.08.046
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    References listed on IDEAS

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    1. P. Korhonen & J. Karaivanova, 1998. "An Algorithm for Projecting a Reference Direction onto the Nondominated Set of Given Points," Working Papers ir98011, International Institute for Applied Systems Analysis.
    2. Selcen (Pamuk) Phelps & Murat Köksalan, 2003. "An Interactive Evolutionary Metaheuristic for Multiobjective Combinatorial Optimization," Management Science, INFORMS, vol. 49(12), pages 1726-1738, December.
    3. Fowler, John W. & Gel, Esma S. & Köksalan, Murat M. & Korhonen, Pekka & Marquis, Jon L. & Wallenius, Jyrki, 2010. "Interactive evolutionary multi-objective optimization for quasi-concave preference functions," European Journal of Operational Research, Elsevier, vol. 206(2), pages 417-425, October.
    4. Figueira, José Rui & Greco, Salvatore & Slowinski, Roman, 2009. "Building a set of additive value functions representing a reference preorder and intensities of preference: GRIP method," European Journal of Operational Research, Elsevier, vol. 195(2), pages 460-486, June.
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    Cited by:

    1. Sami Mnasri & Nejah Nasri & Malek Alrashidi & Adrien Bossche & Thierry Val, 2020. "IoT networks 3D deployment using hybrid many-objective optimization algorithms," Journal of Heuristics, Springer, vol. 26(5), pages 663-709, October.
    2. Kadziński, Miłosz & Tervonen, Tommi & Tomczyk, Michał K. & Dekker, Rommert, 2017. "Evaluation of multi-objective optimization approaches for solving green supply chain design problems," Omega, Elsevier, vol. 68(C), pages 168-184.
    3. Yadav, Deepanshu & Nagar, Deepak & Ramu, Palaniappan & Deb, Kalyanmoy, 2023. "Visualization-aided multi-criteria decision-making using interpretable self-organizing maps," European Journal of Operational Research, Elsevier, vol. 309(3), pages 1183-1200.

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