Abstract
In this paper, an approach, in which the decision making problems are reduced to solving the multicriteria time-consuming global optimization problems is proposed. The developed approach includes various methods of scalarization of the vector criteria, the dimensionality reduction with the use of the Peano space-filling curves and the efficient global search algorithms. In the course of computations, the optimization problem statements and the applied methods of the criteria scalarization can be altered in order to achieve more complete compliance to available requirements to the optimality. The overcoming of the computational complexity of the developed approach is provided by means of the reuse of the whole search information obtained in the course of computations. The performed numerical experiments have confirmed the reuse of the search information to allow reducing essentially the amount of computations for solving the global optimization problems.
This research was supported by the Russian Science Foundation, project No 16-11-10150 “Novel efficient methods and software tools for time-consuming decision making problems using supercomputers of superior performance.”
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Gergel, V., Kozinov, E. (2020). Multistage Global Search Using Various Scalarization Schemes in Multicriteria Optimization Problems. In: Le Thi, H., Le, H., Pham Dinh, T. (eds) Optimization of Complex Systems: Theory, Models, Algorithms and Applications. WCGO 2019. Advances in Intelligent Systems and Computing, vol 991. Springer, Cham. https://doi.org/10.1007/978-3-030-21803-4_64
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