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Parallel solving of multiple information-coordinated global optimization problems
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DOI：10.1016/j.jpdc.2021.04.009.png)
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This paper proposes an efficient approach for the parallel solution of computationally time consuming problems of multiple global optimization, in which minimized functions can be multiextremal and calculating function values may require huge amounts of computations. The proposed approach is based on the information-statistical theory of global optimization, within which a general computational scheme of global optimization methods is proposed. In the paper, this general scheme is expanded by the possible reuse of search information obtained in the process of computations when solving multiple global optimization problems. Within the framework of the proposed generalized scheme, parallel algorithms are proposed for computational systems with shared and distributed memory. Results of computational experiments demonstrated that the proposed approach can significantly reduce the computational complexity of solving multiple global optimization problems. (C) 2021 Elsevier Inc. All rights reserved.
Keyword:
Global optimization
Dimensionality reduction
Search information
Parallel methods of global search
Computational complexity
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