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A Hybrid of Optimization Method for Multi-Objective Constraint Optimization of Biochemical System Production

机译:生化系统生产多目标约束优化的混合优化方法

摘要

In this paper, an advance method for multi-objective constraint optimization method of biochemical system udproduction was proposed and discussed in detail. The proposed method combines Newton method, Strength Pareto Evolutionary Algorithm (SPEA) and Cooperative Co-evolutionary Algorithm (CCA). The main objective of the proposed method was to improve the desired production and at the same time to reduce the total of component concentrations involved in producing the best result. The proposed method starts with Newton method by treating the biochemical system as a non-linear equations system. Then, Genetic Algorithm (GA) in SPEA and CCA were used to represent the variables in non-linear equations system into multiple sub-chromosomes. The used of GA was to improve the desired production while CCA to reduce the total of component concentrations involved. The effectiveness of the proposed method was evaluated using two benchmark biochemical systems and the experimental results showed that the proposed method was able to generate the highest results compare to other existing works.
机译:提出并详细讨论了生化系统生产的多目标约束优化方法的改进方法。该方法结合了牛顿法,强度帕累托进化算法(SPEA)和协作协同进化算法(CCA)。所提出方法的主要目的是提高所需的产量,同时减少产生最佳结果所涉及的组分浓度的总和。提出的方法从牛顿法开始,将生化系统视为非线性方程组。然后,使用SPEA和CCA中的遗传算法(GA)将非线性方程组中的变量表示为多个亚染色体。 GA的使用是为了提高所需的产量,而CCA则是为了减少所涉及的组分总浓度。使用两个基准生化系统评估了该方法的有效性,实验结果表明,与其他现有工作相比,该方法能够产生最高的结果。

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