caa a22 4500 605468532 CHVBK 20210128100317.0 cr unu---uuuuu 210128e20150601xx s 000 0 eng 10.1007/s00500-014-1334-5 doi (NATIONALLICENCE)springer-10.1007/s00500-014-1334-5 Training neural networks with ant colony optimization algorithms for pattern classification [Elektronische Daten] [Michalis Mavrovouniotis, Shengxiang Yang] Feed-forward neural networks are commonly used for pattern classification. The classification accuracy of feed-forward neural networks depends on the configuration selected and the training process. Once the architecture of the network is decided, training algorithms, usually gradient descent techniques, are used to determine the connection weights of the feed-forward neural network. However, gradient descent techniques often get trapped in local optima of the search landscape. To address this issue, an ant colony optimization (ACO) algorithm is applied to train feed-forward neural networks for pattern classification in this paper. In addition, the ACO training algorithm is hybridized with gradient descent training. Both standalone and hybrid ACO training algorithms are evaluated on several benchmark pattern classification problems, and compared with other swarm intelligence, evolutionary and traditional training algorithms. The experimental results show the efficiency of the proposed ACO training algorithms for feed-forward neural networks for pattern classification. Springer-Verlag Berlin Heidelberg, 2014 Neural networks nationallicence Pattern classification nationallicence Ant colony optimization nationallicence Mavrovouniotis Michalis Centre for Computational Intelligence (CCI), School of Computer Science and Informatics, De Montfort University, The Gateway, LE1 9BH, Leicester, UK aut Yang Shengxiang Centre for Computational Intelligence (CCI), School of Computer Science and Informatics, De Montfort University, The Gateway, LE1 9BH, Leicester, UK aut Soft Computing Springer Berlin Heidelberg 19/6(2015-06-01), 1511-1522 1432-7643 19:6<1511 2015 19 500 https://doi.org/10.1007/s00500-014-1334-5 text/html Onlinezugriff via DOI BK010053 XK010053 XK010000 Metadata rights reserved Springer special CC-BY-NC licence nationallicence 1 research-article jats NATIONALLICENCE NATIONALLICENCE NL-springer NATIONALLICENCE 856 40 https://doi.org/10.1007/s00500-014-1334-5 text/html Onlinezugriff via DOI NATIONALLICENCE 700 1- Mavrovouniotis Michalis Centre for Computational Intelligence (CCI), School of Computer Science and Informatics, De Montfort University, The Gateway, LE1 9BH, Leicester, UK aut NATIONALLICENCE 700 1- Yang Shengxiang Centre for Computational Intelligence (CCI), School of Computer Science and Informatics, De Montfort University, The Gateway, LE1 9BH, Leicester, UK aut NATIONALLICENCE 773 0- Soft Computing Springer Berlin Heidelberg 19/6(2015-06-01), 1511-1522 1432-7643 19:6<1511 2015 19 500