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Interactive evolutionary multi-objective optimization for quasi-concave preference functions

Author

Listed:
  • Fowler, John W.
  • Gel, Esma S.
  • Köksalan, Murat M.
  • Korhonen, Pekka
  • Marquis, Jon L.
  • Wallenius, Jyrki

Abstract

We present a new hybrid approach to interactive evolutionary multi-objective optimization that uses a partial preference order to act as the fitness function in a customized genetic algorithm. We periodically send solutions to the decision maker (DM) for her evaluation and use the resulting preference information to form preference cones consisting of inferior solutions. The cones allow us to implicitly rank solutions that the DM has not considered. This technique avoids assuming an exact form for the preference function, but does assume that the preference function is quasi-concave. This paper describes the genetic algorithm and demonstrates its performance on the multi-objective knapsack problem.

Suggested Citation

  • 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.
    • Handle: RePEc:eee:ejores:v:206:y:2010:i:2:p:417-425
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    References listed on IDEAS

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    Cited by:

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    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. 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.
    4. Wang, Rui & Purshouse, Robin C. & Giagkiozis, Ioannis & Fleming, Peter J., 2015. "The iPICEA-g: a new hybrid evolutionary multi-criteria decision making approach using the brushing technique," European Journal of Operational Research, Elsevier, vol. 243(2), pages 442-453.
    5. Nasim Nasrabadi & Akram Dehnokhalaji & Pekka Korhonen & Jyrki Wallenius, 2019. "Using convex preference cones in multiple criteria decision making and related fields," Journal of Business Economics, Springer, vol. 89(6), pages 699-717, August.
    6. Branke, Juergen & Corrente, Salvatore & Greco, Salvatore & Słowiński, Roman & Zielniewicz, Piotr, 2016. "Using Choquet integral as preference model in interactive evolutionary multiobjective optimization," European Journal of Operational Research, Elsevier, vol. 250(3), pages 884-901.
    7. Shicheng Hu & Danping Li & Junmin Jia & Yang Liu, 2021. "A Self-Learning Based Preference Model for Portfolio Optimization," Mathematics, MDPI, vol. 9(20), pages 1-17, October.

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