Тип публикации: доклад, тезисы доклада, статья из сборника материалов конференций
Конференция: International Conference on Parallel Computing Technologies; Novosibirsk, RUSSIA; Novosibirsk, RUSSIA
Год издания: 2009
Идентификатор DOI: 10.1007/978-3-642-03275-2_12
Ключевые слова: optimization, global optimization, large-scale problems solution, cluster, neural networks, Cluster implementation, Cluster systems, Computing process, Generalized regression neural networks, Global minima, Global optimization problems, Inverse neural network, Large-scale problem, Objective functions, Step-by-step, Computer science, Inverse problems, Parallel algorithms, Parallel architectures, Simulated annealing, Clustering algorithms
Аннотация: The parallel hybrid inverse neural network coordinate approximations algorithm (PHINNCA) for solution of large-scale global optimization problems is proposed in this work. The algorithm maps a trial value of an objective function into values of objective function arguments. It decreases a trial value step by step to find a global mПоказать полностьюinimum. 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 processes, they share the information about their progress and apply a simulated annealing procedure to it.
Журнал: PARALLEL COMPUTING TECHNOLOGIES, PROCEEDINGS
Выпуск журнала: Vol. 5698
Номера страниц: 121-125
ISSN журнала: 03029743
Место издания: BERLIN
Издатель: SPRINGER-VERLAG BERLIN