Performance comparison of meta-heuristic algorithms for training artificial neural networks in modelling laser cutting

Authors:
Miloš Madić;Danijel Marković;Miroslav Radovanović
Affiliations:
Faculty of Mechanical Engineering in Niš, University of Niš, A. Medvedeva 14, Niš, Serbia;Faculty of Mechanical Engineering in Niš, University of Niš, A. Medvedeva 14, Niš, Serbia;Faculty of Mechanical Engineering in Niš, University of Niš, A. Medvedeva 14, Niš, Serbia
Venue:
International Journal of Advanced Intelligence Paradigms
Year:
2012

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Abstract

The application of artificial neural networks ANNs for modelling laser cutting is broad and ever increasing. The practical application of ANNs is mostly dependent on the success of the training process which is a complex task. Considering the disadvantages of backpropagation BP such as the convergence to local minima and slow convergence, this paper aims at investigating the possibilities of using novel meta-heuristic algorithms such as improved harmony search algorithm IHSA and cuckoo search algorithm CSA for training ANNs in modelling laser cutting. The validity and efficiency of the algorithms were verified by comparing the results with ANN model trained with real coded genetic algorithm RCGA whichs superiority over BP has been well-documented. Statistical methods of the correlation coefficient and absolute percentage error indicate that the search space exploration capability of the IHSA and CSA are comparable to RCGA. It was shown that all three algorithms could be efficiently used for training of ANNs in modelling laser cutting.