Premium
Integrative optimization by RBF network and particle swarm optimization
Author(s) -
Kitayama Satoshi,
Yasuda Keiichiro,
Yamazaki Koetsu
Publication year - 2009
Publication title -
electronics and communications in japan
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.131
H-Index - 13
eISSN - 1942-9541
pISSN - 1942-9533
DOI - 10.1002/ecj.10187
Subject(s) - particle swarm optimization , global optimization , mathematical optimization , artificial neural network , function (biology) , surface (topology) , computer science , sampling (signal processing) , multi swarm optimization , optimization problem , radial basis function , mathematics , algorithm , artificial intelligence , geometry , filter (signal processing) , evolutionary biology , computer vision , biology
Abstract This paper presents a method for the integrative optimization system. Recently, many methods for global optimization have been proposed. The objective of these methods is to find a global minimum of nonconvex function. However, large numbers of function evaluations are required, in general. We utilize the response surface method to approximate function space to reduce the function evaluations. The response surface method is constructed from sampling points. The RBF Network, which is one of the neural networks, is utilized to approximate the function space. Then Particle Swarm Optimization (PSO) is applied to the response surface. The proposed system consists of three parts: (Part 1) generation of the sampling points, (Part 2) construction of response surface by RBF Network, (Part 3) optimization by PSO. By iterating these three parts, it is expected that the approximate global minimum of nonconvex function can be obtained with a small number of function evaluations. Through numerical examples, the effectiveness and validity are examined. © 2009 Wiley Periodicals, Inc. Electron Comm Jpn, 92(12): 31–42, 2009; Published online in Wiley InterScience ( www.interscience. wiley.com ). DOI 10.1002/ecj.10187