Home  |  About Journal  |  Editorial Board  |  For Authors  |  For Referees  |  For Readers  |  Subscription  |  Contract Us
International Journal of Automation and Computing 2018, Vol. 15 Issue (4) :462-473    DOI: 10.1007/s11633-018-1119-x
Research Article Current Issue | Next Issue | Archive | Adv Search << Previous Articles | Next Articles >>
Enhancing the Performance of JADE Using Two-phase Parameter Control Scheme and Its Application
Qin-Qin Fan1,2, Yi-Lian Zhang3, Xue-Feng Yan2, Zhi-Huan Wang1
1. Logistics Research Center, Shanghai Maritime University, Shanghai 201306, China;
2. Key Laboratory of Advanced Control and Optimization for Chemical Processes of Ministry of Education, East China University of Science and Technology, Shanghai 200237, China;
3. Key Laboratory of Marine Technology and Control Engineering Ministry of Communications, Shanghai Maritime University, Shanghai 201306, China
Download: [PDF 2056KB] HTML()   Export: BibTeX or EndNote (RIS)      Supporting Info
Abstract The search efficiency of differential evolution (DE) algorithm is greatly impacted by its control parameters. Although many adaptation/self-adaptation techniques can automatically find suitable control parameters for the DE, most techniques are based on population information which may be misleading in solving complex optimization problems. Therefore, a self-adaptive DE (i.e., JADE) using two-phase parameter control scheme (TPC-JADE) is proposed to enhance the performance of DE in the current study. In the TPC-JADE, an adaptation technique is utilized to generate the control parameters in the early population evolution, and a well-known empirical guideline is used to update the control parameters in the later evolution stages. The TPC-JADE is compared with four state-of-the-art DE variants on two famous test suites (i.e., IEEE CEC2005 and IEEE CEC2015). Results indicate that the overall performance of the TPC-JADE is better than that of the other compared algorithms. In addition, the proposed algorithm is utilized to obtain optimal nutrient and inducer feeding for the Lee-Ramirez bioreactor. Experimental results show that the TPC-JADE can perform well on an actual dynamic optimization problem.
Service
Email this article
Add to my bookshelf
Add to citation manager
Email Alert
RSS
Articles by authors
KeywordsDifferential evolution (DE) algorithm   evolutionary computation   dynamic optimization   control parameter adaptation   chemical processes     
Received: 2017-05-05; published: 2018-02-09
Fund:

This work was supported by National Natural Science Foundation of China (Nos. 61272394, 61201395 and 61472119), the program for Science & Technology Innovation Talents in Universities of Henan Province (No. 13HASTIT039), Henan Polytechnic University Innovative Research Team (No. T2014-3), and Henan Polytechnic University Fund for Distinguished Young Scholars (No. J2013-2).

Corresponding Authors: Qin-Qin Fan     Email: forever123fan@163.com
About author: Qin-Qin Fan received the B.Sc. degree in automation from Institute of Technology, China in 2007, the M.Sc. degree in control science and engineering from East China University of Science and Technology, China in 2011. E-mail: forever123fan@163.com;Yi-Lian Zhang received the Ph.D. degree in control science and engineering from East China University of Science and Technology, China in 2015.E-mail: zyl1030@126.com;Xue-Feng Yan received the Ph.D. degree in control science and engineering from Zhejiang University, China. He is now a professor of East China University of Science and Technology, China.E-mail: xfyan@ecust.edu.cn;Zhi-Huan Wang received the B.Sc. degree in mechanical manufacture and automation from Harbin Institute of Technology, China in 2002, and the M.Sc. degree in information management and information system from Monash University, Australia in 2009.E-mail: zhwang@shmtu.edu.cn
Cite this article:   
Qin-Qin Fan, Yi-Lian Zhang, Xue-Feng Yan, Zhi-Huan Wang. Enhancing the Performance of JADE Using Two-phase Parameter Control Scheme and Its Application[J]. International Journal of Automation and Computing , vol. 15, no. 4, pp. 462-473, 2018.
URL:  
http://www.ijac.net/EN/10.1007/s11633-018-1119-x      或     http://www.ijac.net/EN/Y2018/V15/I4/462
 
[1] R. Storn and K. Price. Differential evolution-a simple and efficient adaptive scheme for global optimization over continuous spaces:ICSI Berkeley, 1995.
[2] F. Neri and V. Tirronen. Recent advances in differential evolution:a survey and experimental analysis. Artificial Intelligence Review, vol. 33, no. 1-2, pp. 61-106, 2010.
[3] S. Das and P. N. Suganthan. Differential evolution:A survey of the state-of-the-art. Evolutionary Computation, IEEE Transactions on, vol. 15, no. 1, pp. 4-31, 2011.
[4] R. Gämperle, S. D. Müller and P. Koumoutsakos. A parameter study for differential evolution. Advances in intelligent systems, fuzzy systems, evolutionary computation, vol. 10, pp. 293-298, 2002.
[5] R. Storn and K. Price. Differential evolution-a simple and efficient heuristic for global optimization over continuous spaces. Journal of global optimization, vol. 11, no. 4, pp. 341-359, 1997.
[6] J. Ronkkonen, S. Kukkonen and K. V. Price. Real-parameter optimization with differential evolution. In Proc. IEEE CEC, pp. 506-513, 2005.
[7] A. K. Qin, V. L. Huang and P. N. Suganthan. Differential evolution algorithm with strategy adaptation for global numerical optimization. Evolutionary Computation, IEEE Transactions on, vol. 13, no. 2, pp. 398-417, 2009.
[8] J. Liu and J. Lampinen. A fuzzy adaptive differential evolution algorithm. Soft Computing, vol. 9, no. 6, pp. 448-462, 2005.
[9] J. Brest, S. Greiner, B. Boškovi?, M. Mernik and V. Zumer. Self-adapting control parameters in differential evolution:a comparative study on numerical benchmark problems. Evolutionary Computation, IEEE Transactions on, vol. 10, no. 6, pp. 646-657, 2006.
[10] J. Zhang and A. C. Sanderson. JADE:adaptive differential evolution with optional external archive. Evolutionary Computation, IEEE Transactions on, vol. 13, no. 5, pp. 945-958, 2009.
[11] R. Mallipeddi, P. N. Suganthan, Q.-K. Pan and M. F. Tasgetiren. Differential evolution algorithm with ensemble of parameters and mutation strategies. Applied Soft Computing, vol. 11, no. 2, pp. 1679-1696, 2011.
[12] Y. Wang, Z. Cai and Q. Zhang. Differential evolution with composite trial vector generation strategies and control parameters. Evolutionary Computation, IEEE Transactions on, vol. 15, no. 1, pp. 55-66, 2011.
[13] Y. Wang, H.X. Li, T. Huang and L. Li. Differential evolution based on covariance matrix learning and bimodal distribution parameter setting. Applied Soft Computing, vol. 18, pp. 232-247, 2014.
[14] Q. Fan and X. Yan. Differential evolution algorithm with self-adaptive strategy and control parameters for P-xylene oxidation process optimization. Soft Computing, vol. 19, no. 5, pp. 1363-1391, 2015.
[15] Q. Fan and X. Yan. Self-Adaptive Differential Evolution Algorithm With Zoning Evolution of Control Parameters and Adaptive Mutation Strategies, Cybernetics, IEEE Transactions on, vol. 46, no. 1, pp. 219-232, 2016.
[16] R. Sarker, S. Elsayed and T. Ray. Differential Evolution with Dynamic Parameters Selection for Optimization Problems. Evolutionary Computation, IEEE Transactions on, vol. 18, no. 5, pp. 689-707, 2014.
[17] S. Rahnamayan, H. R. Tizhoosh and M. Salama. Opposition-based differential evolution. Evolutionary Computation, IEEE Transactions on, vol. 12, no. 1, pp. 64-79, 2008.
[18] W. Gong, Z. Cai and Y. Wang. Repairing the crossover rate in adaptive differential evolution. Applied Soft Computing, vol. 15, pp. 149-168, 2014.
[19] W. Gong, Z. Cai and D. Liang. Adaptive ranking mutation operator based differential evolution for constrained optimization. Cybernetics, IEEE Transactions on, vol. 45, no. 4, pp. 716-727, 2015.
[20] M. Yang, C. Li, Z. Cai and J. Guan. Differential Evolution with Auto-Enhanced Population Diversity, Cybernetics, IEEE Transactions on, vol. 45, no. 2, pp. 302-315, 2015.
[21] Q. Fan, X. Yan and Y. Zhang. Auto-selection mechanism of differential evolution algorithm variants and its application. European Journal of Operational Research, 2017.
[22] S.-M. Guo and C.-C. Yang. Enhancing differential evolution utilizing eigenvector-based crossover operator. Evolutionary Computation, IEEE Transactions on, vol. 19, no. 1, pp. 31-49, 2015.
[23] J.-H. Zhong, M. Shen, J. Zhang, H. S.-H. Chung, Y.-H. Shi and Y. Li. A differential evolution algorithm with dual populations for solving periodic railway timetable scheduling problem. Evolutionary Computation, IEEE Transactions on, vol. 17, no. 4, pp. 512-527, 2013.
[24] S. Biswas, S. Kundu and S. Das. Inducing niching behavior in differential evolution through local information sharing. Evolutionary Computation, IEEE Transactions on, vol. 19, no. 2, pp. 246-263, 2015.
[25] R. Tanabe and A. S. Fukunaga. Improving the search performance of SHADE using linear population size reduction. In Evolutionary Computation (CEC), 2014 IEEE Congress on, IEEE, pp. 1658-1665, 2014.
[26] Y.-L. Li, Z.-H. Zhan, Y.-J. Gong, W.-N. Chen, J. Zhang and Y. Li. Differential evolution with an evolution path:a deep evolutionary algorithm, Cybernetics, IEEE Transactions on, vol. 45, no. 9, pp. 1798-1810, 2015.
[27] S. Guo, C. Yang, P. Hsu and J.-c. Tsai. Improving Differential Evolution with Successful-Parent-Selecting Framework. Evolutionary Computation, IEEE Transactions on, vol. 19, no. 5, pp. 717-730, 2015.
[28] H. Lu, J. Yin, Y. Yuan, J. Wang, Y. Lin and X. Wang. Energy Consumption Analysis of Sludge Transport Pipeline System Based on GA-DE Hybrid Algorithm. Journal of Chemical Engineering of Japan, vol. 47, no. 8, pp. 621-627, 2014.
[29] X.-H. Qiu, Y.-T. Hu and B. Li. Sequential fault diagnosis using an inertial velocity differential evolution algorithm. International Journal of Automation and Computing, pp. 1-9, 2016.
[30] G.-H. Lin, J. Zhang and Z.-H. Liu. Hybrid particle swarm optimization with differential evolution for numerical and engineering optimization. International Journal of Automation and Computing, pp. 1-12, 2016.
[31] H.-T. Ye and Z.-Q. Li. PID neural network decoupling control based on hybrid particle swarm optimization and differential evolution. International Journal of Automation and Computing, pp. 1-6, 2015.
[32] Q. Fan, X. Yan and Y. Xue. Prior knowledge guided differential evolution. Soft Computing, vol. 21, no. 22, pp. 6841-6858, 2017.
[33] P. N. Suganthan et al. Problem definitions and evaluation criteria for the CEC 2005 special session on real-parameter optimization. KanGAL report, vol. 2005005, 2005.
[34] J. Liang, B. Qu, P. Suganthan and Q. Chen. Problem definitions and evaluation criteria for the CEC 2015 competition on learning-based real-parameter single objective optimization. Technical Report201411A, Computational Intelligence Laboratory, Zhengzhou University, Zhengzhou China and Technical Report, Nanyang Technological University, Singapore, 2014.
[35] J. R. Banga, E. Balsa-Canto, C. G. Moles and A. A. Alonso. Dynamic optimization of bioprocesses:Efficient and robust numerical strategies. Journal of Biotechnology, vol. 117, no. 4, pp. 407-419, 2005.
[36] B. Srinivasan, S. Palanki and D. Bonvin. Dynamic optimization of batch processes:I. Characterization of the nominal solution. Computers & Chemical Engineering, vol. 27, no. 1, pp. 1-26, 2003.
[37] R. Bellman. Dynamic programming and Lagrange multipliers. Proceedings of the National Academy of Sciences of the United States of America, vol. 42, no. 10, pp. 767, 1956.
[38] R. Luus. On the application of iterative dynamic programming to singular optimal control problems. IEEE transactions on automatic control, vol. 37, no. 11, pp. 1802-1806, 1992.
[39] W. H. Ray and J. Szekely. Process optimization, with applications in metallurgy and chemical engineering:John Wiley & Sons, 1973.
[40] A. E. Bryson. Dynamic optimization:Addison Wesley Longman Menlo Park, CA, 1999.
[41] D. Sarkar and J. M. Modak. Optimisation of fed-batch bioreactors using genetic algorithms. Chemical Engineering Science, vol. 58, no. 11, pp. 2283-2296, 2003.
[42] R. Sargent and G. Sullivan. The development of an efficient optimal control package. Optimization Techniques:Springer, pp. 158-168, 1978.
[43] K. Price, R. M. Storn and J. A. Lampinen. Differential evolution:a practical approach to global optimization:Springer Science & Business Media, 2006.
[44] S. Das, A. Konar and U. K. Chakraborty. Two improved differential evolution schemes for faster global search. In Proceedings of the 2005 conference on Genetic and evolutionary computation, ACM, pp. 991-998, 2005.
[45] J. Montgomery and S. Chen. An analysis of the operation of differential evolution at high and low crossover rates. In Evolutionary Computation (CEC), 2010 IEEE Congress on, IEEE, pp. 1-8, 2010.
[46] F. Wilcoxon. Individual comparisons by ranking methods. Biometrics bulletin, vol. 1, no. 6, pp. 80-83, 1945.
[47] M. Friedman. The use of ranks to avoid the assumption of normality implicit in the analysis of variance. Journal of the american statistical association, vol. 32, no. 200, pp. 675-701, 1937.
[48] J. Roubos, C. De Gooijer, G. Van Straten and A. Van Boxtel. Comparison of optimization methods for fed-batch cultures of hybridoma cells. Bioprocess engineering, vol. 17, no. 2, pp. 99-102, 1997.
[49] J. Lee and W. F. Ramirez. Optimal fed-batch control of induced foreign protein production by recombinant bacteria. AIChE Journal, vol. 40, no. 5, pp. 899-907, 1994.
[50] Q. Fan, Z. Lü, X. Yan and M. Guo. Chemical process dynamic optimization based on hybrid differential evolution algorithm integrated with Alopex. Journal of Central South University, vol. 20, pp. 950-959, 2013.
[51] J. Roubos, G. Van Straten and A. Van Boxtel. An evolutionary strategy for fed-batch bioreactor optimization; concepts and performance. Journal of Biotechnology, vol. 67, no. 2, pp. 173-187, 1999.
[52] D. Sarkar and J. M. Modak. ANNSA:a hybrid artificial neural network/simulated annealing algorithm for optimal control problems. Chemical Engineering Science, vol. 58, no. 14, pp. 3131-3142, 2003.
[53] B. Zhang, D. Chen and W. Zhao. Iterative ant-colony algorithm and its application to dynamic optimization of chemical process. Computers & chemical engineering, vol. 29, no. 10, pp. 2078-2086, 2005.
[54] Q.-q. Fan, X.-h. Wang and X.-f. Yan. Harmony search algorithm with differential evolution based control parameter co-evolution and its application in chemical process dynamic optimization. Journal of Central South University, vol. 22, pp. 2227-2237, 2015.
Copyright 2010 by International Journal of Automation and Computing