Meta-Learning evolutionary artificial neural network for selecting flexible manufacturing systems

  • Authors:

  • Arijit Bhattacharya;Ajith Abraham;Crina Grosan;Pandian Vasant;Sangyong Han

  • Affiliations:

  • The Patent Office, Kolkata, West Bengal, India;School of Computer Science and Engineering, Chung-Ang University, Seoul, Korea;Department of Computer Science, Babes-Bolyai University, Romania;Universiti Teknologi Petronas, Tronoh, BSI, Perak DR, Malaysia;School of Computer Science and Engineering, Chung-Ang University, Seoul, Korea

  • Venue:
  • ISNN'06 Proceedings of the Third international conference on Advances in Neural Networks - Volume Part III
  • Year:
  • 2006

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Abstract

This paper proposes the application of Meta-Learning Evolutionary Artificial Neural Network (MLEANN) in selecting flexible manufacturing systems (FMS) from a group of candidate FMS’s. First, multi-criteria decisionmaking (MCDM) methodology using an improved S-shaped membership function has been developed for finding out the ‘best candidate FMS alternative’ from a set of candidate-FMSs. The MCDM model trade-offs among various parameters, namely, design parameters, economic considerations, etc., affecting the FMS selection process in multi-criteria decision-making environment. Genetic algorithm is used to evolve the architecture and weights of the proposed neural network method. Further, a back-propagation (BP) algorithm is used as the local search algorithm. The selection of FMS is made according to the error output of the results found from the MCDM model.