Hybrid Artificial Bee Colony algorithm for neural network training

A hybrid algorithm combining Artificial Bee Colony (ABC) algorithm with Levenberq-Marquardt (LM) algorithm is introduced to train artificial neural networks (ANN). Training an ANN is an optimization task where the goal is to find optimal weight set of the network in training process. Traditional tra...

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Bibliographic Details
Published in2011 IEEE Congress of Evolutionary Computation (CEC) pp. 84 - 88
Main Authors Ozturk, Celal, Karaboga, Dervis
Format Conference Proceeding
LanguageEnglish
Published IEEE 01.06.2011
Subjects
Approximation algorithms
Artificial bee colony algorithm
Artificial neural networks
Evolutionary computation
Hybrid algorithms
Levenberq-Marquardt algorithm
Neural network training
Neurons
Simulated annealing
Training
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ISBN1424478340
9781424478347
ISSN1089-778X
DOI10.1109/CEC.2011.5949602

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Summary:A hybrid algorithm combining Artificial Bee Colony (ABC) algorithm with Levenberq-Marquardt (LM) algorithm is introduced to train artificial neural networks (ANN). Training an ANN is an optimization task where the goal is to find optimal weight set of the network in training process. Traditional training algorithms might get stuck in local minima and the global search techniques might catch global minima very slow. Therefore, hybrid models combining global search algorithms and conventional techniques are employed to train neural networks. In this work, ABC algorithm is hybridized with the LM algorithm to apply training neural networks.
ISBN:1424478340
9781424478347
ISSN:1089-778X
DOI:10.1109/CEC.2011.5949602