An Integrated Approach to Correlated Multi-Response Optimization

Abstract

As the manufacturing problem grows more complexity, particularly in the situations where the correlation and goal conflict among multiple responses need to be considered simultaneously, conventional optimization algorithms may fail to find the global optimum. In this case, an alternative approach is proposed that using grey relational analysis (GRA) in conjunction with principal component analysis (PCA) to obtain the grey relational grade (GRG), and using BP neural network to construct a process model. Then, the optimal parameters setting can be obtained by using a hybrid approach combined the global search advantage of genetic algorithm (GA) and the local search advantage of pattern search (PS). Finally, an example from the literature is illustrated to confirm the feasibility and effectiveness of the proposed approach.