Optimization of parameter design: An intelligent approach using neural network and simulated annealing

Abstract

Parameter design optimization problems have found extensive industrial applications, including product development, process design and operational condition setting. The parameter design optimization problems are complex because non-linear relationships and interactions may occur among parameters. To resolve such problems, engineers commonly employ the Taguchi method. However, the Taguchi method has some limitations in practice. Therefore, in this work, we present a novel means of improving the effectiveness of the optimization of parameter design. The proposed approach employs the neural network and simulated annealing, and consists of two phases. Phase 1 formulates an objective function for a problem using a neural network method to predict the value of the response for a given parameter setting. Phase 2 applies the simulated annealing algorithm to search for the optimal parameter combination. A numerical example demonstrates the effectiveness of the proposed approach.

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