Efficient multicriterial optimization based on intensive reuse of search information

Victor Gergel () and Evgeny Kozinov ()
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Victor Gergel: Lobachevsky State University of Nizhni Novgorod
Evgeny Kozinov: Lobachevsky State University of Nizhni Novgorod

Journal of Global Optimization, 2018, vol. 71, issue 1, No 6, 73-90

Abstract: Abstract This paper proposes an efficient method for solving complex multicriterial optimization problems, for which the optimality criteria may be multiextremal and the calculations of the criteria values may be time-consuming. The approach involves reducing multicriterial problems to global optimization ones through minimax convolution of partial criteria, reducing dimensionality by using Peano curves and implementing efficient information-statistical methods for global optimization. To efficiently find the set of Pareto-optimal solutions, it is proposed to reuse all the search information obtained in the course of optimization. The results of computational experiments indicate that the proposed approach greatly reduces the computational complexity of solving multicriterial optimization problems.

Keywords: Decision making; Multicriterial optimization; Scalarization; Dimensionality reduction; Global optimization algorithm; Search information; Computational complexity (search for similar items in EconPapers)
Date: 2018
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Citations: View citations in EconPapers (3)

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DOI: 10.1007/s10898-018-0624-3

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