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- W4313407173 abstract "The paper is devoted to consideration of multiextremal multicriteria black-box problems with time-consuming criteria and proposes a new computational algorithm for solving such problems. This algorithm is based on combination of convolution approach, dimensionality reduction, and information-statistical global optimization algorithm. For approximating the Pareto set a family of maximum convolutions is considered generating problems of scalar multiextremal optimization. The latters are reduced by means of Peano mappings to the equivalent problems of univariate optimization that are solved by an accelerated information-statistical global optimization method. This method applies the technique of local refinements and uses in the course of current univariate optimization the information about criteria that was obtained when solving completed optimization problems. These features allow one to accelerate the search of global solution. The efficiency of the proposed algorithm is experimentally estimated on representative classes of multidimensional multiextremal test problems. Comparison with 5 nature-inspired particle swarm and genetic algorithms on the base of the hypervolume index reflecting quality of Pareto set evaluation demonstrates qualitative results of the proposed method." @default.
- W4313407173 created "2023-01-06" @default.
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- W4313407173 date "2022-01-01" @default.
- W4313407173 modified "2023-09-24" @default.
- W4313407173 title "An Accelerated Algorithm for Finding Efficient Solutions in Multiobjective Problems with Black-Box Multiextremal Criteria" @default.
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- W4313407173 doi "https://doi.org/10.1007/978-3-031-22543-7_4" @default.
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