基于改进PSO的发酵补料速率的优化控制
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摘要
发酵工业是现代生化工程和生物技术及其产业化的基础。如何提高发酵工业的技术水平和生产率,是发酵工业研究的热点。蛋白质发酵在发酵工业中占有重要地位,因为利用蛋白质发酵工艺可以生产很多结构复杂且不易合成的生物产品。随着现代生物技术的发展,对于蛋白质发酵工艺的摸索和优化主要集中在两个方面：一方面学者们从生物学角度出发,将研究工作集中在菌种的选取和改造上,通过诱发菌种变异、基因重组和菌种培养,使菌种的特性得到改善,提高蛋白质发酵产品的产量；另一方面,将智能优化算法应用到发酵过程参数的优化控制中,在不改变菌种的基础上,提高发酵产品的产量。
近年来,随着群体智能算法的发展,群体智能算法已经是优化控制领域的研究热点。粒子群优化算法作为群智能优化算法的一种,具有结构简单、易于实现、寻优能力强、适合复杂优化问题的求解等特性,可以用于发酵过程的优化。但蛋白质发酵是复杂间歇式的生物机理反应过程,其参数多且反应条件多变。所以标准粒子群优化算法应用于蛋白质发酵过程中,其收敛性差、易于陷入局部最优、寻优速度慢等缺点很容易就暴露出来,常常使得发酵工艺摸索过程中不能找到理想的发酵方案。本文在传统粒子群算法的基础上,针对标准粒子群算法(PSO-S)出现的上述几个主要问题,分别采用了不同的改进方案,最后基于前几种改进方案的基本思想,提出一种可以同时针对粒子群算法的多个缺点进行改进的混沌式量子粒子群分步更新算法(WQFS-PSO)。采用标准测试函数对所提的粒子群算法进行测试,验证出改进算法相对于传统算法在精度和优化速度上均有不同程度的提高。最后将传统算法和改进算法同时用于HPV蛋白发酵和白介素发酵过程优化中,通过对比,验证出所提出的WQFS-PSO算法对这两种蛋白质的发酵过程优化均取得了比较好的效果。发酵优化结果表明,WQFS-PSO方法改进了标准粒子群算法收敛性差、易于陷入局部最优的问题,可以应用于蛋白质发酵工艺的摸索和优化中。
Fermentation engineering is the foundation of modern biochemical engineering and biological technology and industrialization. How to improve the level of protein fermentation and productivity is the hot topic of protein fermentation industry research. Protein fermentation plays an important role as fermentation product. It is because of by the using of protein fermentation, it can realize a lot of popular biological products that has complex structure and not easy synthesized. With the development of modern biological technology, the exploration and optimization of protein fermentation mainly concentrated in two aspects. On one side many scholars concentrate their research work on the selection and the transformation of bacteria. They used induce bacteria variation, gene recombination and bacteria training to improve the characteristics of strains; on the other side they improve the output of the products of the fermentation through the optimization of the parameters in the protein fermentation process and control protein fermentation process without changing the bacteria.
In recent years, with the development of group swarm intelligence algorithm, it has been the hot topic in the optimization control field. As a group intelligent algorithm, particle swarm optimization algorithm, which is simple, easy to be realized, optimization ability strong and suitable for complicated solution of the optimization problems provides an effective way for the fermentation optimization. But because the protein fermentation is the complex intermittent biological mechanism reaction process, it involves many parameters and the reaction conditions are complicated. So applying in the protein fermentation process, particle swarm optimization algorithm is worst at the convergence, easy to fall into local optimum and slow at finding the optimum. It makes the improvement of the traditional particle swarm algorithm become inevitable. In this paper, in the foundation of the traditional particle swarm algorithm, we adopt different improvement plan according to the above problems in PSO-S, and at last we put forward a new chaos type quantum particle group of step-by-step updating algorithm (WQFS-PSO), which can improve multi-disadvantage of the particle swarm optimization. And by using the standard test function to test the improved particle swarm algorithm, we can get that the new algorithm is better in precision and the speed of optimization than the traditional algorithm. At last, we imply both the traditional and the new algorithm in the process of HPV protein fermentation and the interleukin fermentation process. Through the comparison, we can test out the proposed WQFS-PSO algorithm has a good effect on the fermentation process of the two kinds protein. The fermentation results show that the WQFS-PSO method dose a better job in solving the bad convergence, easy to fall into local optimum problems, and it can be applied to the optimization process of protein fermentation technology, and it can be used in the exploration and optimization of protein fermentation technology.
近年来,随着群体智能算法的发展,群体智能算法已经是优化控制领域的研究热点。粒子群优化算法作为群智能优化算法的一种,具有结构简单、易于实现、寻优能力强、适合复杂优化问题的求解等特性,可以用于发酵过程的优化。但蛋白质发酵是复杂间歇式的生物机理反应过程,其参数多且反应条件多变。所以标准粒子群优化算法应用于蛋白质发酵过程中,其收敛性差、易于陷入局部最优、寻优速度慢等缺点很容易就暴露出来,常常使得发酵工艺摸索过程中不能找到理想的发酵方案。本文在传统粒子群算法的基础上,针对标准粒子群算法(PSO-S)出现的上述几个主要问题,分别采用了不同的改进方案,最后基于前几种改进方案的基本思想,提出一种可以同时针对粒子群算法的多个缺点进行改进的混沌式量子粒子群分步更新算法(WQFS-PSO)。采用标准测试函数对所提的粒子群算法进行测试,验证出改进算法相对于传统算法在精度和优化速度上均有不同程度的提高。最后将传统算法和改进算法同时用于HPV蛋白发酵和白介素发酵过程优化中,通过对比,验证出所提出的WQFS-PSO算法对这两种蛋白质的发酵过程优化均取得了比较好的效果。发酵优化结果表明,WQFS-PSO方法改进了标准粒子群算法收敛性差、易于陷入局部最优的问题,可以应用于蛋白质发酵工艺的摸索和优化中。
Fermentation engineering is the foundation of modern biochemical engineering and biological technology and industrialization. How to improve the level of protein fermentation and productivity is the hot topic of protein fermentation industry research. Protein fermentation plays an important role as fermentation product. It is because of by the using of protein fermentation, it can realize a lot of popular biological products that has complex structure and not easy synthesized. With the development of modern biological technology, the exploration and optimization of protein fermentation mainly concentrated in two aspects. On one side many scholars concentrate their research work on the selection and the transformation of bacteria. They used induce bacteria variation, gene recombination and bacteria training to improve the characteristics of strains; on the other side they improve the output of the products of the fermentation through the optimization of the parameters in the protein fermentation process and control protein fermentation process without changing the bacteria.
In recent years, with the development of group swarm intelligence algorithm, it has been the hot topic in the optimization control field. As a group intelligent algorithm, particle swarm optimization algorithm, which is simple, easy to be realized, optimization ability strong and suitable for complicated solution of the optimization problems provides an effective way for the fermentation optimization. But because the protein fermentation is the complex intermittent biological mechanism reaction process, it involves many parameters and the reaction conditions are complicated. So applying in the protein fermentation process, particle swarm optimization algorithm is worst at the convergence, easy to fall into local optimum and slow at finding the optimum. It makes the improvement of the traditional particle swarm algorithm become inevitable. In this paper, in the foundation of the traditional particle swarm algorithm, we adopt different improvement plan according to the above problems in PSO-S, and at last we put forward a new chaos type quantum particle group of step-by-step updating algorithm (WQFS-PSO), which can improve multi-disadvantage of the particle swarm optimization. And by using the standard test function to test the improved particle swarm algorithm, we can get that the new algorithm is better in precision and the speed of optimization than the traditional algorithm. At last, we imply both the traditional and the new algorithm in the process of HPV protein fermentation and the interleukin fermentation process. Through the comparison, we can test out the proposed WQFS-PSO algorithm has a good effect on the fermentation process of the two kinds protein. The fermentation results show that the WQFS-PSO method dose a better job in solving the bad convergence, easy to fall into local optimum problems, and it can be applied to the optimization process of protein fermentation technology, and it can be used in the exploration and optimization of protein fermentation technology.
引文
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[2]F. van den Bergh, A. P. Engelbrecht. A study of particle swarm optimization particle trajectories [J]. Information Sciences,2006(176):937-971.
[3]Andre B. de Carvalho, Aurora Pozo. Measuring the convergence and diversity of CDAS Multi-Objective Particle Swarm Optimization Algorithms:A study of many objective problems [J]. Neurocomputing,2011(3):1-9.
[4]胡成玉.面向动态环境的粒子群算法研究[D].湖北：湖中科技大学控制理论与控制工程专业,2010.
[5]田野.粒子群优化算法及其应用研究[D].吉林：吉林大学计算机科学与技术学院,2010.
[6]Girolamo Fornarelli, Antonio Giaquinto. Adaptive particle swarm optimization for CNN associative memories design [J]. Neurocomputing,2009(72):3851-3862.
[7]R. Btits, A. P. Engelbrecht, F. van den Bergh. Locating multiple optima using particle swarm optimization [J]. Applied Mathematics and Computation,2007 (189):1859-1883.
[8]高芳,崔刚,吴智博等.一种新型多步式位置可选择更新粒子群优化算法[J].电子学报,2009,37(3)：529-534.
[9]Ahmad Nickabadi, Mohammad Mehdi Ebadzadeh, Reza Safabakhsh. A novel particle swarm optimization algorithm with adaptive inertia weight [J]. Applied Soft Computing, 2011 (11):3658-3670.
[10]Leandro dos Santos Coelho. An efficient particle swarm approach for mixed-integer programming in reliability-redundancy optimization applications [J]. Reliability Engineering and System Safety,2009(94):830-837.
[11]Stefan Lessmann, Marco Caserta, Idle Montalvo Arango. Tuning met heuristics: A data mining based approach for particle swarm optimization [J]. Expert Systems with Applications,2011(38):12826-12838.
[12]Kravaris C, Chung C. Nonlinear state feedback synthesis by global Input/Output linearization [J]. AIChEJ,1987(33):592-603.
[13]Jonqensen S, Szederkenyi B. Nonlinear analysis and control of a continuous fermentation progress [J]. Computers and Chemical Engineering,2002, 26(4):659-670.
[14]宋毅芳,任元,杨虹等.生物发酵过程的微分几何变结构控制[J].控制系统,2007,23(2-1)：44-46.
[15]Bonastre A, Ors R, Peris M. Monitoring of a wort fermentation process by means of a distributed expert system [J]. Chemometrics and Intelligent Systems, 2000,50(2):235-242.
[16]Babuska R, Osterhoff J, Oudshoom A, et al. Fuzzy self-tuning PI control of pH in fermentation [J]. Engineering Applications of Artificial Intelligence,2003,15(1):3-15.
[17]Karakuzu C, Tutker M, Ozturk S. Modeling on-line state estimation and fuzzy control of production scale fed-batch baker's yeast fermentation [J]. Control Engineering Practice,2006,14(8):959-974.
[18]潘丰,补料分批发酵过程优化控制[J].自动化仪表,2004,25(8)：51-54.
[19]Shinzawa H, Jiang J H, Iwahashi M, et al. Self-modeling curve resolution (SMCR) by particle swarm optimization(PSO) [J]. Analytical Chemical Acta,2007,595(1):275-281.
[20]Xu B L, Wang Z Q. A multi-objective-ACO-based data association method for bearings-only multi-target tracking [J]. Communications in Nonlinear Science and Numerical Simulation,2007,7(3):1360-1369.
[21]Liu H B, Abtaham A, Clerc M. Chaotic dynamic characteristics in swarm intelligence [J]. Applied soft computing,2007,7(3):1019-1026.
[22]Zeljko Kanovic, Milan R, Zoran D. et al. Generalized particle swarm optimization algorithm-Theoretical and empirical analysis with application in fault detection [J]. Applied Methematics and Computation,2011,217:10175-10186.
[23]KENNEDY. J. E, EBERHART. R. C. Particle swarm optimization. Proceedings of the IEEE International Conference on Neural Networks [C]. Perth, Australia:IEEE Press, 1995,4:1942-1948.
[24]胡旺,李志蜀.一种更简化而高效的粒子群算法[J].软件学报,2007,18(4)：861-868.
[25]赫然,王永吉,王青等.一种改进的自适应逃逸微粒群算法及实验分析[J].软件学报,2005,16(12)：2036-2044.
[26]朱海梅,吴永萍.一种高速收敛粒子群优化算法[J].控制与决策,2010,25(1)：20-24.
[27]S. N. Omkar, Rahul Khandelwal, T. V.S. Ananth, G.Narayana Naik, S. Gopalakrishnan. Quantum behaved Particle Swarm Optimization (QPSO) for multi-objective design optimization of composite structures [J]. Expert Systems with Applications, 2009(36):11312-11322.
[28]周雅兰,王甲海,印鉴.一种基于分步估计的离散粒子群优化算法[J].电子学报,2008,36(6)：1242-1248.
[29]R. E. Pereza, K. Behdinan. Particle swarm approach for structural design optimization [J]. Computers and Structures,2007(85):1579-1588.
[30]孙俊.量子行为粒子群优化算法研究[D].江苏：江南大学控制理论与控制工程学院,2009.
[30]Rakesh Malviya, Dilip Kumar Pratihar. Tuning of neural networks using particle swarm optimization to model MIG walding process [J]. Swarm and Evolutionary Computation,2011(7):1-13.
[32]袁代林.改进的微粒群算法及其在结构拓扑优化中的应用[D].陕西：西安交通大学固体力学专业,2009.
[33]O. Tolga Altinoz, Asim Egemen Yilmaz. Particle Swarm Optimization with Parameter Dependency Walls and its sample application to the microstrip-like interconnect line design [J]. International Journal of Electronics and Comminications,2011(5):1-8.
[34]Jing Jie, Jiangchao Zeng, Chongzhao Han. et al. Knowledge-based cooperative particle swarm optimization [J].2008(205):861-873.
[35]Dawei Zhou, Xiang Cao, Guohai Liu. et al. Randomzation in particle swarm optimization for global search ability [J]. Experts Systems with Applications,2011(38):15356-15364.
[36]M. Senthil Arumugam, M. V. C. Rao. On the improved performances of the particle swarm optimization algorithms with adaptive parameters, cross-over operators and root mean square (RMS) variants for computing optimal control of a class of hybrid systems [J]. Applied Soft Computing,2008(8):324-336.
[37]薛尧予.群能量守恒粒子群算法及其在发酵过程控制中的应用研究[D].北京：北京化工大学控制理论与控制工程专业,2010.
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