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Оптимизация формы множества Парето в задачах многокритериального программирования // Optimization of the Shape of the Pareto Set in the Problems of Multi-criterial Programming

Author

Listed:
  • B. Katendi A.

    (Moscow Institute of Physics and Technology (State University))

  • E. Umnov A.

    (Moscow Institute of Physics and Technology (State University))

  • A. Umnov E.

    (Moscow Institute of Physics and Technology (State University))

  • Б. Катенди А.

    (Московский физико-технический институт (Государственный университет))

  • Е. Умнов А.

    (Московский физико-технический институт (Государственный университет))

  • А. Умнов Е.

    (Московский физико-технический институт (Государственный университет))

Abstract

In this paper, a scheme for using the method of smooth penalty functions for the dependence of solutions of multi-criterial optimization problems on parameters is being considered. In particular, algorithms based on the method of smooth penalty functions are given to solve problems of optimization by the parameters of the level of consistency of the objective functions and to find the corresponding shape of the Pareto’s set. В работе рассматривается схема использования метода гладких штрафных функций для исследования зависимости решений задач многокритериальной оптимизации от параметров. Приводится описание алгоритмов, основанных на методе гладких штрафных функций, решения задачи оптимизации по параметрам уровня согласованности целевых функций и выбора соответствующей формы множества Парето.

Suggested Citation

  • B. Katendi A. & E. Umnov A. & A. Umnov E. & Б. Катенди А. & Е. Умнов А. & А. Умнов Е., 2018. "Оптимизация формы множества Парето в задачах многокритериального программирования // Optimization of the Shape of the Pareto Set in the Problems of Multi-criterial Programming," Review of Business and Economics Studies // Review of Business and Economics Studies, Финансовый Университет // Financial University, vol. 6(1), pages 5-16.
    • Handle: RePEc:scn:00rbes:y:2018:i:1:p:5-16
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