Abstract
The parallel hybrid inverse neural network coordinate approxima tions algorithm (PHINNCA) for solution of large-scale global optimization problems is proposed in this work. The algorithm maps a trial value of an ob jective function into values of objective function arguments. It decreases a trial value step by step to find a global minimum. Dual generalized regression neural networks are used to perform the mapping. The algorithm is intended for cluster systems. A search is carried out concurrently. When there are multiple pro cesses, they share the information about their progress and apply a simulated annealing procedure to it.
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Koshur, V., Kuzmin, D., Legalov, A., Pushkaryov, K. (2009). Solution of Large-Scale Problems of Global Optimization on the Basis of Parallel Algorithms and Cluster Implementation of Computing Processes. In: Malyshkin, V. (eds) Parallel Computing Technologies. PaCT 2009. Lecture Notes in Computer Science, vol 5698. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-03275-2_12
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DOI: https://doi.org/10.1007/978-3-642-03275-2_12
Publisher Name: Springer, Berlin, Heidelberg
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