东北大学学报（自然科学版）

东北大学学报(自然科学版) ›› 2012, Vol. 33 ›› Issue (10): 1369-1372.DOI: -

• 论著 •    下一篇

基于改进粒子群算法的诺西肽发酵过程优化

牛大鹏;张楠;何大阔;常玉清;   

  1. 东北大学信息科学与工程学院;上海通用(沈阳)北盛汽车有限公司;
  • 收稿日期:2013-06-19 修回日期:2013-06-19 出版日期:2012-10-15 发布日期:2013-04-04
  • 通讯作者: -
  • 作者简介:-
  • 基金资助:
    国家自然科学基金资助项目(61074074,61174130,61004083);;

Nosiheptide fermentation process optimization based on improved PSO

Niu, Da-Peng (1); Zhang, Nan (2); He, Da-Kuo (1); Chang, Yu-Qing (1)   

  1. (1) School of Information Science and Engineering, Northeastern University, Shenyang 110819, China; (2) Shanghai GM (Shenyang) Norsom Motor Co. Ltd., Shenyang 110044, China
  • Received:2013-06-19 Revised:2013-06-19 Online:2012-10-15 Published:2013-04-04
  • Contact: Niu, D.-P.
  • About author:-
  • Supported by:
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摘要： 在诺西肽补料分批发酵动力学模型的基础上建立了诺西肽发酵过程产量优化模型,根据发酵工艺选取了决策变量,并确定了变量的边界约束范围.针对标准粒子群算法在求解复杂优化问题时易于陷入局部最优的问题,利用混沌序列具有随机性和遍历性的特点,引入混沌迁移算子,提出了一种改进的粒子群算法.利用改进算法对所建立的诺西肽发酵优化模型进行求解,大大提高了最终产物的产量,证明了所提改进粒子群算法的有效性.

关键词: 诺西肽, 补料分批发酵, 产量优化, 粒子群优化算法, 混沌迁移

Abstract: A production optimization model of nosiheptide fermentation process was built on the basis of the kinetic models of nosiheptide fed-batch fermentation. Decision variables were selected according to the technical flow, and the scope of the variable boundary constraints was decided. PSO (particle swarm optimization) algorithm is easy to fall into local optimum when solving complex optimization problems. To solve this problem, according to the randomicity and ergodicity of chaotic sequences, an improved PSO algorithm was proposed by introducing chaotic migration operator into PSO. The improved PSO algorithm was used to solve the production optimization model of nosiheptide fermentation, and the end time fermentation production was greatly improved. The results showed the effectiveness of the proposed algorithm.

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