Paper Title:
Ant Colony Optimization for Neural Network
  Abstract

A new learning way for neural network (NN) in which its weights can be optimized by using the ant colony algorithm is presented in this paper. The learning of neural network belongs to continuous optimization. The ant colony algorithm is initially developed for hard combinatorial optimization. A kind of ant colony optimization (ACO) for continuous optimization, which includes global searching, local searching and definite searching, is developed based on the basic ant colony algorithm. A three-layer neural network, as an example, is trained to express nonlinear function. The efficiency of the new algorithm is examinated. It is found that the new developed method has the merits of both ant colony algorithm and neural network.

  Info
Periodical
Key Engineering Materials (Volumes 392-394)
Edited by
Guanglin Wang, Huifeng Wang and Jun Liu
Pages
677-681
DOI
10.4028/www.scientific.net/KEM.392-394.677
Citation
H. Mei, Y. Wang, "Ant Colony Optimization for Neural Network", Key Engineering Materials, Vols. 392-394, pp. 677-681, 2009
Online since
October 2008
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Price
$32.00
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