Comparison of Dimensionality Reduction Schemes for Parallel Global Optimization Algorithms
This work considers a parallel algorithms for solving multi-extremal optimization problems. Algorithms are developed within the framework of the information-statistical approach and implemented in a parallel solver Globalizer. The optimization problem is solved by reducing the multidimensional problem to a set of joint one-dimensional problems that are solved in parallel. Five types of Peano-type space-filling curves are employed to reduce dimension. The results of computational experiments carried out on several hundred test problems are discussed.
KeywordsGlobal optimization Dimension reduction Parallel algorithms Multidimensional multiextremal optimization Global search algorithms Parallel computations
The study was supported by the Russian Science Foundation, project No 16-11-10150.
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