Hybrid evolutionary optimization for nutraceutical manufacturing processes

Abstract

In this paper, an intelligent approach, called HERON (hybrid evolutionary optimization for nutraceutical manufacturing), is proposed to optimize a variety of manufacturing processes in the nutraceutical field. The approach integrates the Taguchi method, an artificial neural network (ANN), and a genetic algorithm (GA). The Taguchi method is used to cost-effectively gather the data on the process parameters. Data obtained by the Taguchi method are divided into input and output data for an ANN’s input and output parameters, respectively. The ANN trains itself to develop the relationship between its input and output parameters. The trained ANN is then integrated into a GA as the fitness function, such that the GA can evolutionarily obtain the optimal process parameters. The HERON is validated through a manufacturing process on soft-shell turtle soft-capsules. The objective is to minimize the soft-capsule defect rate. Compared to the defect rates obtained by the empirical and Taguchi methods, the HERON reduces the defect rate by 43.75 and 32.5 %, respectively. In addition, compared to the manufacturing costs obtained by the empirical and Taguchi methods, the HERON reduces the manufacturing cost by 11.81 and 25.29 %, respectively.