Multi-objective Optimization of Biochemical System Production Using an Improve Newton Competitive Differential Evolution Method

• Main Authors: Mohd Arfian, Ismail, Mezhuyev, Vitaliy, Safaai, Deris, Mohd Saberi, Mohamad, Shahreen, Kasim, Saedudin, Rd Rohmat
• Format: Article
• Language: English
• Published: Indonesian Society for Knowledge and Human Development 2017
• Subjects: QA76 Computer software
• Online Access: http://umpir.ump.edu.my/id/eprint/20028/1/3388-7557-1-PB.pdf; http://umpir.ump.edu.my/id/eprint/20028/; http://dx.doi.org/10.18517/ijaseit.7.4-2.3388
• Institution: Universiti Malaysia Pahang

In this paper, an improved method of multi-objective optimization for biochemical system production is presented and discussed in detail. The optimization process of biochemical system production become hard and difficult when involved a large biochemical system that contains many components. In addition, the multi-objective problem also needs to be considered. Due to that, this study proposed and improved a method that comprises with Newton method, differential evolution algorithm (DE) and competitive co-evolutionary algorithm(ComCA). The aim of the proposed method is to maximize the production and simultaneously minimize the total amount of chemical concentrations involves. The operation of the proposed method starts with Newton method by dealing with biochemical system production as a nonlinear equations system. Then DE and ComCA are used to represent the variables in nonlinear equation system and tune the variables in order to find the best solution. The used of DE is to maximize the production while ComCA is to minimize the total amount of chemical concentrations involves. The effectiveness of the proposed method is evaluated using two benchmark biochemical systems, and the experimental results show that the proposed method performs well compared to other works.