Engineering Evolutionary Intelligent Systems: Methodologies, Architectures and Reviews
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, viz., 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. All the randomly generated architecture of the initial population are trained by BP algorithm for a fixed number of epochs. The learning rate and momentum of the BP algorithm have been adapted suiting the generated data of the MCDM problem. The selection of FMS are made according to the error output of the results found from the MCDM model.