Multi-objective optimization of biochemical system production using an improve Newton Competitive differential evolution method

Mohd Arfian Ismail, Vitaliy Mezhuyev, Safaai Deris, Mohd Saberi Mohamad, Shahreen Kasim, Rd Rohmat Saedudin

Research output: Contribution to journalArticlepeer-review

13 Citations (Scopus)

Abstract

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.

Original languageEnglish
Pages (from-to)1535-1542
Number of pages8
JournalInternational Journal on Advanced Science, Engineering and Information Technology
Volume7
Issue number4-2 Special Issue
DOIs
Publication statusPublished - 2017
Externally publishedYes

Keywords

  • Biochemical system
  • Competitive co-evolutionary algorithm
  • Differential evolution algorithm
  • Newton method

ASJC Scopus subject areas

  • General Computer Science
  • General Agricultural and Biological Sciences
  • General Engineering

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