A Hybrid Artificial Neural Network-based Scheduling Knowledge Acquisition Algorithm

schedulingknowledgeattributeselectiongasaann

摘要：It is a key issue that constructing successful knowledge base to satisfy an efficient adaptive scheduling for the com- plex manufacturing system.Therefore,a hybrid artificial neural network (ANN)-based scheduling knowledge acquisition algo- rithm is presented in this paper.We combined genetic algorithm (GA) with simulated annealing (SA) to develop a hybrid opti- mization method,in which GA was introduced to present parallel search architecture and SA was introduced to increase escaping probability from local optima and ability to neighbor search.The hybrid method was utilized to resolve the optimal attributes subset of manufacturing system and determine the optimal topology and parameters of ANN under different scheduling objectives;ANN was used to evaluate the fitness of chromosome in the method and generate the scheduling knowledge after obtaining the optimal at- tributes subset,optimal ANN’s topology and parameters.The experimental results demonstrate that the proposed algorithm pro- duces significant performance improvements over other machine learning-based algorithms.

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