Multiple-Response Optimization by using Neural Networks and Metaheuristic Algorithms

Abstract

An important problem in manufacturing or product and process design is optimization of several responses simultaneously. Common approaches for multiple response optimization problems often begin with estimating the relationship between responses as outputs and control factors as inputs. Among these methods, response surface methodology (RSM), has attracted more attention in recent years, but in certain cases, relationship between responses and control factors are far too complex to be efficiently estimated by regression models and RSM method especially when we want optimize several responses simultaneously. An alternative approach proposed in this paper is to use artificial neural network (ANN) to estimate the response functions, Because of high mean square error (MSE) in neural network training step we use heuristic algorithms instead of Descent Gradient based algorithms. In the optimization phase a particle swarm optimization (PSO) and desirability function are considered to determine the optimal settings for the control factors. Two case study from literature are prepared to illustrate the strength of proposed approach in optimization of multiple response problems.