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

Parameter design optimization problems have found extensive industrial appl ications, including product development, process design and operational con dition setting. The parameter design optimization problems are complex beca use non-lineal relationships and interactions may occur among parameters. T o resolve such problems, engineers commonly employ the Taguchi method. Howe ver, the Taguchi method has some limitations in practice. Therefore, in thi s work, we present a novel means of improving the effectiveness of the opti mization of parameter design. The proposed approach employs the neural netw ork and simulated annealing, and consists of two phases. Phase I formulates an objective function for a problem using a neural network method to predi ct the value of the response for a given parameter setting. Phase 2 applies the simulated annealing algorithm to search for the optimal parameter comb ination. A numerical example demonstrates the effectiveness of the proposed approach.