Global optimization via neural network approximation of inverse coordinate mappings

Тип публикации: статья из журнала

Год издания: 2011

Идентификатор DOI: 10.3103/S1060992X1103009X

Ключевые слова: generalized regression neural networks, global optimization, heuristic methods, neural network approximation of inverse coordinate mappings, neural networks, Coordinate mapping, Generalized regression neural networks, Neural network approximation, neural network approximation of inverse coordinate mappings, Objective functions, Search spaces, Smooth functions, Experiments, Global optimization, Heuristic methods, Mapping, Optimization, Neural networks

Аннотация: The novel heuristic method of global optimization via neural network approximation of inverse coordinate mappings is suggested. The method deals with continuous objective functions of multiple variables with multiple extremes. The search space must be a multidimensional box. No derivatives of a function are required to exist. The method is built on Generalized Regression Neural Networks (GRNNs). An example of application of the method to a smooth function with multiple extremes is provided. The method has been compared with the genetic algorithm and particle swarm optimization through an experiment. The description and results of the experiment are included. © 2011 Allerton Press, Inc.

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Издание

Журнал: Optical Memory and Neural Networks (Information Optics)

Выпуск журнала: Vol. 20, Is. 3

Номера страниц: 181-193

ISSN журнала: 1060992X

Авторы

  • Koshur V.D. (Institute of Space and Information Technology of Siberian Federal University, Ul. Kirenskogo, d. 26, korp. ULK, Krasnoyarsk 660074, Russian Federation)
  • Pushkaryov K.V. (Institute of Space and Information Technology of Siberian Federal University, Ul. Kirenskogo, d. 26, korp. ULK, Krasnoyarsk 660074, Russian Federation)

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