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Parallel representation of local elimination algorithm for accelerating the solving sparse discrete optimization problems
Computer Research and Modeling, 2015, v. 7, no. 3, pp. 699-705Views (last year): 1.The decomposition algorithms provide approaches to deal with NP-hardness in solving discrete optimization problems (DOPs). In this article one of the promising ways to exploit sparse matrices — local elimination algorithm in parallel interpretation (LEAP) are demonstrated. That is a graph-based structural decomposition algorithm, which allows to compute a solution in stages such that each of them uses results from previous stages. At the same time LEAP heavily depends on elimination ordering which actually provides solving stages. Also paper considers tree- and block-parallel for LEAP and required realization process of it comparison of a several heuristics for obtaining a better elimination order and shows how is related graph structure, elimination ordering and solving time.
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Non-uniform cellular genetic algorithms
Computer Research and Modeling, 2015, v. 7, no. 3, pp. 775-780Views (last year): 9. Citations: 3 (RSCI).In this paper, we introduce the concept of non-uniform cellular genetic algorithm, in which a number of parameters that affect the operation of genetic operators is dependent on the location of the cells of a given cellular space. The results of numerical comparison of non-uniform cellular genetic algorithms with the standard genetic algorithms, showing the advantages of the proposed approach while minimizing multimodal functions with a large number of local extrema, are presented. The coarse-grained parallel implementation of the non-uniform algorithms using the technology of MPI is considered.
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International Interdisciplinary Conference "Mathematics. Computing. Education"