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Extension of the Directed Search Domain algorithm for multi-objective optimization to higher dimensions

Author

Listed:
  • Boxi Yu

    (University of Manchester)

  • Sergey Utyuzhnikov

    (University of Manchester)

Abstract

This paper addresses the problem of generating an evenly distributed set of Pareto solutions. It appears in real-life applications related to multi-objective optimization when it is important to represent the entire Pareto front with a minimal cost. There exist only a few algorithms which are able to tackle this problem in a general formulation. The Directed Search Domain (DSD) algorithm has proved to be efficient and quite universal. It has successfully been applied to different challengeable test cases. In this paper for the first time the DSD approach is systematically extended and applied to problems with higher dimensions. The modified algorithm does not have any formal limitation on the number of objective functions that is important for practical applications. The efficacy of the algorithm is demonstrated on a number of test cases.

Suggested Citation

  • Boxi Yu & Sergey Utyuzhnikov, 2024. "Extension of the Directed Search Domain algorithm for multi-objective optimization to higher dimensions," Annals of Operations Research, Springer, vol. 340(2), pages 1125-1160, September.
    • Handle: RePEc:spr:annopr:v:340:y:2024:i:2:d:10.1007_s10479-024-06117-y
    DOI: 10.1007/s10479-024-06117-y
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    References listed on IDEAS

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    1. Adler, Nicole & Friedman, Lea & Sinuany-Stern, Zilla, 2002. "Review of ranking methods in the data envelopment analysis context," European Journal of Operational Research, Elsevier, vol. 140(2), pages 249-265, July.
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