An Improved Differential Evolution for Optimization of Chemical Process

›› 2008, Vol. 16 ›› Issue (2): 228-234.

• SYSTEM ENGINEERING • Previous Articles Next Articles

An Improved Differential Evolution for Optimization of Chemical Process

WU Yanling^1,2, LU Jiangang¹, SUN Youxian¹

1. State Key Laboratory of Industrial Control Technology, Zhejiang University, Hangzhou 310027, China;
2. Department of Automation, Anhui University, Hefei 230039, China

Received:2006-10-08 Revised:2007-12-10 Online:2008-04-28 Published:2008-04-28
Supported by:
the National Natural Science Foundation of China(60736021);the National High Technology Research and Development Program of China(2006AA04Z184,2007AA041406);the Key Technologies R&D Program of Zhejiang Province(2006C11066,2006C31051)

An Improved Differential Evolution for Optimization of Chemical Process

吴燕玲^1,2, 卢建刚¹, 孙优贤¹

1. State Key Laboratory of Industrial Control Technology, Zhejiang University, Hangzhou 310027, China;
2. Department of Automation, Anhui University, Hefei 230039, China

通讯作者: LU Jiangang,E-mail:jglu@iipc.zju.edu.cn
基金资助:
the National Natural Science Foundation of China(60736021);the National High Technology Research and Development Program of China(2006AA04Z184,2007AA041406);the Key Technologies R&D Program of Zhejiang Province(2006C11066,2006C31051)

Abstract

Abstract: Differential evolution(DE)is an evolutionary optimization method,which has been successfully used in many practical cases.However,DE involves large computation time,especially,when used to optimize the computationally expensive objective function.To overcome this difficulty,the concept of immunity based on vaccination is used to help proliferate excellent schemata and to restrain the degenerate phenomenon.To improve the effectiveness of vaccines,a new vaccine autonomous obtaining method,and a method of deciding the probability of vaccination are proposed.In addition,a method for modifying the search space dynamically is proposed to enhance the possibility of converging to the true global optimum.Experiments showed that the improved DE performs better than the classical DE significantly.

Key words: differential evolution, vaccine, convergence, search space, optimization

摘要： Differential evolution(DE)is an evolutionary optimization method,which has been successfully used in many practical cases.However,DE involves large computation time,especially,when used to optimize the computationally expensive objective function.To overcome this difficulty,the concept of immunity based on vaccination is used to help proliferate excellent schemata and to restrain the degenerate phenomenon.To improve the effectiveness of vaccines,a new vaccine autonomous obtaining method,and a method of deciding the probability of vaccination are proposed.In addition,a method for modifying the search space dynamically is proposed to enhance the possibility of converging to the true global optimum.Experiments showed that the improved DE performs better than the classical DE significantly.

关键词: differential evolution, vaccine, convergence, search space, optimization

WU Yanling, LU Jiangang, SUN Youxian. An Improved Differential Evolution for Optimization of Chemical Process[J]. , 2008, 16(2): 228-234.

吴燕玲, 卢建刚, 孙优贤. An Improved Differential Evolution for Optimization of Chemical Process[J]. , 2008, 16(2): 228-234.

References

1 Liu, P.L., Zou, L.S., Luo, H.A., Wang, L.J., Zheng, J.H., “Estimation of kinetic parameters for autocatalytic oxidation of cyclohexane based on a modified adaptive genetic algorithm”, Chin. J. Chem. Eng., 12 (1), 49-54 (2004).
2 Wu, L.Y., Hu, Y.D., Xu, D.M., Hua, B., “A genetic algorithm-based approach to scheduling of batch production with maximum profit”, Chin. J. Chem. Eng., 13 (1), 68-73 (2005).
3 Storn, R., Price, K., “Differential evolution-A simple and efficient heuristic for global optimization over continuous spaces”, Journal of Global Optimization, 11, 341-359 (1997).
4 Storn, R., Price, K., “Minimizing the real functions of the ICEC’96 contest by differential evolution”, In: IEEE Conference on Evolutionary Computation, Nagoya, Japan, 842-844 (1996).
5 Castro, L.N.de, Femando, J., Zuben, V., “Learning and optimization using the clonal selection principle”, IEEE Trans on Evolutionary Computation, 6 (3), 239-251 (2002).
6 Gonzalez, F., Dasgupta, D., “Anomaly detection using real-valued negative selection”, Genetic Programming and Evolvable Machines, 4 (4), 383-403 (2003).
7 Karanikas, C., Proios, G., “A nonlinear discrete transform for pattern recognition of discrete chaotic system”, Chaos, Solition and Fractals, 5 (17), 195-201 (2003).
8 Chen, S.L., Tsay, M.T., Gow, H.J., “Scheduling of cogeneration plants considering electricity wheeling using enhanced immune algorithm”, Electrical Power and Energy System, 27 (1), 31-38 (2005).
9 Wang, L., Intelligent Optimization Algorithm and Its Applying, Tsinghua University Press, Beijing (2001).
10 Han, X., Hong, B., Meng, W., “An immune genetic algorithm based on vaccine autonomous obtaining and updating”, Journal of Computer Research and Development, 42 (5), 740-745 (2005).
11 Duan, Y., “A kind of new immunity genetic algorithm and its application”, Control and Decision, 10, 1185-1188 (2005).
12 Li, C.J., Wang, L., “A novel genetic algorithm based on immunity”, IEEE Trans on Systems, Man, and Cybernetics (A) Systems and Humans, 30 (5), 552-561 (2000).
13 Kaelo, P., Ali, M.M., “A numerical study of some modified differential evolution algorithms”, European Journal of Operational Research, 169 (3), 1176-1184 (2006).
14 Chen, Z.X., Lu, W., Fang, Z.H., Huang, C.J., Ye, H., Liu, J., “Intrinsic kinetics of sulphur dioxide oxidation on Rb-Cs-V catalyst”, J. Cent. South Univ. Technol., 31 (2), 127-130 (2001).
15 Froment, G.F., Bischoff, K.B., Chemical Reactor Analysis and Design, Wiley, NewYork (1979).

[1]	Chuang Liang, Zhihao Liu, Baochang Sun, Haikui Zou, Guangwen Chu. Improvement in discharge characteristics and energy yield of ozone generation via configuration optimization of a coaxial dielectric barrier discharge reactor [J]. Chinese Journal of Chemical Engineering, 2023, 60(8): 61-68.
[2]	Yaran Bu, Changchun Wu, Lili Zuo, Qian Chen. The calculation and optimal allocation of transmission capacity in natural gas networks with MINLP models [J]. Chinese Journal of Chemical Engineering, 2023, 59(7): 251-261.
[3]	Danlei Chen, Yiqing Luo, Xigang Yuan. Cascade refrigeration system synthesis based on hybrid simulated annealing and particle swarm optimization algorithm [J]. Chinese Journal of Chemical Engineering, 2023, 58(6): 244-255.
[4]	Jikai Dong, Bing Wang, Xinjie Wang, Chenxi Cao, Shikuan Chen, Wenli Du. Optimization of sensor deployment sequences for hazardous gas leakage monitoring and source term estimation [J]. Chinese Journal of Chemical Engineering, 2023, 56(4): 169-179.
[5]	Dandan Ren, Shanshan Xiang, Yuwen Yan, Ruiying Kong, Xingchu Gong. Design and optimization of purification process of sinomenine hydrochloride [J]. Chinese Journal of Chemical Engineering, 2023, 55(3): 63-72.
[6]	Yingjun Lin. Whole-process optimization for industrial production of glucosamine sulfate sodium chloride based on QbD concept [J]. Chinese Journal of Chemical Engineering, 2023, 54(2): 153-161.
[7]	Shan Liu, Wenqi Zhong, Xi Chen, Li Sun, Lukuan Yang. Multiobjective economic model predictive control using utopia-tracking for the wet flue gas desulphurization system [J]. Chinese Journal of Chemical Engineering, 2023, 54(2): 343-352.
[8]	Chao Zhang, Youzhi Liu, Weizhou Jiao, Hongyan Shen, Xigang Yuan, Shengkun Jia. An optimization method for enhancement of gas–liquid mass transfer in a bubble column reactor based on the entropy generation extremum principle [J]. Chinese Journal of Chemical Engineering, 2023, 53(1): 83-88.
[9]	Monique Juna L. Leite, Ingrid Ramalho Marques, Mariane Carolina Proner, Pedro H.H. Araújo, Alan Ambrosi, Marco Di Luccio. Catalytically active membranes for esterification: A review [J]. Chinese Journal of Chemical Engineering, 2023, 53(1): 142-154.
[10]	Xinqiang You, Kai Zhao, Ling Li, Ting Qiu. Ionic liquids as entrainer in extractive distillation for effectively separating 1-propanol–water azeotropic mixture [J]. Chinese Journal of Chemical Engineering, 2022, 49(9): 224-233.
[11]	Jia Ren, Zengqiang Chen, Mingwei Sun, Qinglin Sun, Zenghui Wang. Proportion integral-type active disturbance rejection generalized predictive control for distillation process based on grey wolf optimization parameter tuning [J]. Chinese Journal of Chemical Engineering, 2022, 49(9): 234-244.
[12]	Na Wang, Chong Peng, Zhenmin Cheng, Zhiming Zhou. Molecular reconstruction of vacuum gas oils using a general molecule library through entropy maximization [J]. Chinese Journal of Chemical Engineering, 2022, 48(8): 21-29.
[13]	Chun Deng, Xuantong Lu, Qixin Zhang, Jian Liu, Jui-Yuan Lee, Xiao Feng. Fuzzy optimization design of multicomponent refinery hydrogen network [J]. Chinese Journal of Chemical Engineering, 2022, 48(8): 125-139.
[14]	Zhigang Xu, Xiongfei Jin, Tao Zhou, Qian Zou, Longcheng Liu, zhongbo Wang, Hanbing Sheng, Huasheng Xie. Preparation of aldoxime through direct ammoximation using titanium silicalite-1 catalyst [J]. Chinese Journal of Chemical Engineering, 2022, 47(7): 11-17.
[15]	Junru Liu, Rui Hu, Xinlei Liu, Qunfeng Zhang, Guanghua Ye, Zhijun Sui, Xinggui Zhou. Modeling of propane dehydrogenation combined with chemical looping combustion of hydrogen in a fixed bed reactor [J]. Chinese Journal of Chemical Engineering, 2022, 47(7): 165-173.

An Improved Differential Evolution for Optimization of Chemical Process

An Improved Differential Evolution for Optimization of Chemical Process

PDF (PC)

Knowledge

Abstract

Cite this article

share this article

References

Related Articles 15

Recommended Articles

Metrics

Comments