Solution of large-scale problems of global optimization on the basis of parallel algorithms and cluster implementation of computing processes

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. © 2009 Springer Berlin Heidelberg.