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Enhancing the Performance of JADE Using Two-phase Parameter Control Scheme and Its Application

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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 TPCJADE, 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-theart 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.

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References

  1. R. Storn, K. Price. Differential Evolution-A Simple and Efficient Adaptive Scheme for Global Optimization over Continuous Spaces, Berkeley, USA: ICSI, 1995.

    MATH  Google Scholar 

  2. F. Neri, V. Tirronen. Recent advances in differential evolution: A survey and experimental analysis. Artificial Intelligence Review, vol. 33, no. 1–2, pp. 61–106, 2010. DOI: 10.1007/s10462-009-9137-2.

    Article  Google Scholar 

  3. S. Das, P. N. Suganthan. Differential evolution: A survey of the state-of-the-art. IEEE Transactions on Evolutionary Computation, vol. 15, no. 1, pp. 4–31, 2011. DOI: 10.1109/TEVC.2010.2059031.

    Article  Google Scholar 

  4. R. Gämperle, S. D. Müller, P. Koumoutsakos. A parameter study for differential evolution. In Advances in Intelligent Systems, Fuzzy Systems, Evolutionary Computation, Interlaken, Switzerland: WSEAS Press, pp. 293–298, 2002.

    Google Scholar 

  5. R. Storn, 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. DOI: 10.1023/A:1008202821328.

    Article  MathSciNet  MATH  Google Scholar 

  6. J. Ronkkonen, S. Kukkonen, K. V. Price. Real-parameter optimization with differential evolution. In Proceedings of IEEE Congress on Evolutionary Computation, IEEE, Edinburgh, Scotland, pp. 506–513, 2005. DOI: 10.1109/CEC. 2005.1554725.

    Google Scholar 

  7. A. K. Qin, V. L. Huang, P. N. Suganthan. Differential evolution algorithm with strategy adaptation for global numerical optimization. IEEE Transactions on Evolutionary Computation, vol. 13, no. 2, pp. 398–417, 2009. DOI: 10.1109/TEVC.2008.927706.

    Article  Google Scholar 

  8. J. Liu, J. Lampinen. A fuzzy adaptive differential evolution algorithm. Soft Computing, vol. 9, no. 6, pp. 448–462, 2005. DOI: 10.1007/s00500-004-0363-x.

    Article  MATH  Google Scholar 

  9. J. Brest, S. Greiner, B. Boskovic, M. Mernik, V. Zumer. Self-adapting control parameters in differential evolution: A comparative study on numerical benchmark problems. IEEE Transactions on Evolutionary Computation, vol. 10, no. 6, pp. 646–657, 2006. DOI: 10.1109/TEVC.2006.872133.

    Article  Google Scholar 

  10. J. Q. Zhang, A. C. Sanderson. JADE: Adaptive differential evolution with optional external archive. IEEE Transactions on Evolutionary Computation, vol. 13, no. 5, pp. 945–958, 2009. DOI: 10.1109/TEVC.2009.2014613.

    Article  Google Scholar 

  11. R. Mallipeddi, P. N. Suganthan, Q. K. Pan, M. F. Tasgetiren. Differential evolution algorithm with ensemble of parameters and mutation strategies. Applied Soft Computing, vol. 11, no. 2, pp. 1679–1696, 2011. DOI: 10.1016/j. asoc.2010.04.024.

    Article  Google Scholar 

  12. Y. Wang, Z. X. Cai, Q. F. Zhang. Differential evolution with composite trial vector generation strategies and control parameters. IEEE Transactions on Evolutionary Computation, vol. 15, no. 1, pp. 55–66, 2011. DOI: 10.1109/TEVC.2010.2087271.

    Article  Google Scholar 

  13. Y. Wang, H. X. Li, T. W. Huang, L. Li. Differential evolution based on covariance matrix learning and bimodal distribution parameter setting. Applied Soft Computing, vol. 18, pp. 232–247, 2014. DOI: 10.1016/j.asoc.2014.01.038.

    Article  Google Scholar 

  14. Q. Q. Fan, X. F. Yan. Differential evolution algorithm with self-adaptive strategy and control parameters for Pxylene oxidation process optimization. Soft Computing, vol. 19, no. 5, pp. 1363–1391, 2015. DOI: 10.1007/s00500-014-1349-y.

    Article  Google Scholar 

  15. Q. Q. Fan, X. F. Yan. Self-adaptive differential evolution algorithm with zoning evolution of control parameters and adaptive mutation strategies. IEEE Transactions on Cybernetics, vol. 46, no. 1, pp. 219–232, 2016. DOI: 10.1109/TCYB.2015.2399478.

    Article  Google Scholar 

  16. R. A. Sarker, S. M. Elsayed, T. Ray. Differential evolution with dynamic parameters selection for optimization problems. IEEE Transactions on Evolutionary Computation, vol. 18, no. 5, pp. 689–707, 2014. DOI: 10.1109/TEVC.2013. 2281528.

    Article  Google Scholar 

  17. S. Rahnamayan, H. R. Tizhoosh, M. M. A. Salama. Opposition-based differential evolution. IEEE Transactions on Evolutionary Computation, vol. 12, no. 1, pp. 64–79, 2008. DOI: 10.1109/TEVC.2007.894200.

    Article  Google Scholar 

  18. W. Y. Gong, Z. H. Cai, Y. Wang. Repairing the crossover rate in adaptive differential evolution. Applied Soft Computing, vol. 15, pp. 149–168, 2014. DOI: 10.1016/j.asoc.2013. 11.005.

    Article  Google Scholar 

  19. W. Y. Gong, Z. H. Cai, D. W. Liang. Adaptive ranking mutation operator based differential evolution for constrained optimization. IEEE Transactions of Cybernetics, vol. 45, no. 4, pp. 716–727, 2015. DOI: 10.1109/TCYB.2014. 2334692.

    Article  Google Scholar 

  20. M. Yang, C. H. Li, Z. H. Cai, J. Guan. Differential evolution with auto-enhanced population diversity. IEEE Transactions ofCybernetics, vol. 45, no. 2, pp. 302–315, 2015. DOI: 10.1109/TCYB.2014.2339495.

    Article  Google Scholar 

  21. Q. Q. Fan, X. F. Yan, Y. L. Zhang. Auto-selection mechanism of differential evolution algorithm variants and its application. European Journal of Operational Research, to be published. DOI: 10.1016/j.ejor.2017.10.013.

  22. S. M. Guo, C. C. Yang. Enhancing differential evolution utilizing eigenvector-based crossover operator. IEEE Transactions on Evolutionary Computation, vol. 19, no. 1, pp. 31–49, 2015. DOI: 10.1109/TEVC.2013.2297160.

    Article  MathSciNet  Google Scholar 

  23. J. H. Zhong, M. E. Shen, J. Zhang, H. S. H. Chung, Y. H. Shi, Y. Li. A differential evolution algorithm with dual populations for solving periodic railway timetable scheduling problem. IEEE Transactions on Evolutionary Computation, vol. 17, no. 4, pp. 512–527, 2013. DOI: 10.1109/TEVC. 2012.2206394.

    Article  Google Scholar 

  24. S. Biswas, S. Kundu, S. Das. Inducing niching behavior in differential evolution through local information sharing. IEEE Transactions on Evolutionary Computation, vol. 19, no. 2, pp. 246–263, 2015. DOI: 10.1109/TEVC.2014.2313659.

    Article  Google Scholar 

  25. R. Tanabe, A. S. Fukunaga. Improving the search performance of SHADE using linear population size reduction. In Proceedings of IEEE Congress on Evolutionary Computation, IEEE, Beijing, China, pp. 1658–1665, 2014. DOI: 10.1109/CEC.2014.6900380.

    Google Scholar 

  26. Y. L. Li, Z. H. Zhan, Y. J. Gong, W. N. Chen, J. Zhang, Y. Li. Differential evolution with an evolution path: A deep evolutionary algorithm. IEEE Transactions of Cybernetics, vol. 45, no. 9, pp. 1798–1810, 2015. DOI: 10.1109/TCYB. 2014.2360752.

    Article  Google Scholar 

  27. S. M. Guo, C. C. Yang, P. H. Hsu, J. S. H. Tsai. Improving differential evolution with a successful-parent-selecting framework. IEEE Transactions on Evolutionary Computation, vol. 19, no. 5, pp. 717–730, 2015. DOI: 10.1109/TEVC.2014.2375933.

    Article  Google Scholar 

  28. H. Lu, J. Yin, Y. X. Yuan, J. H. Wang, Y. Z. Lin, X. L. 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. DOI: 10.1252/jcej.13we233.

    Article  Google Scholar 

  29. X. H. Qiu, Y. T. Hu, B. Li. Sequential fault diagnosis using an inertial velocity differential evolution algorithm. International Journal of Automation and Computing, Online First. DOI: 10.1007/s11633-016-1008-0.

  30. G. H. Lin, J. Zhang, Z. H. Liu. Hybrid particle swarm optimization with differential evolution for numerical and engineering optimization. International Journal of Automation and Computing, vol. 15, no. 1, pp. 103–114, 2018. DOI: 10.1007/s11633-016-0990-6.

    Article  Google Scholar 

  31. H. T. Ye, Z. Q. Li. PID neural network decoupling control based on hybrid particle swarm optimization and differential evolution. International Journal of Automation and Computing, Online First. DOI: 10.1007/s11633-015-0917-7.

  32. Q. Q. Fan, X. F. Yan, Y. Xue. Prior knowledge guided differential evolution. Soft Computing, vol. 21, no. 22, pp. 6841–6858, 2017. DOI: 10.1007/s00500-016-2235-6.

    Article  Google Scholar 

  33. P. N. Suganthan, N. Hansen, J. J. Liang, K. Deb, Y. P. Chen, A. Auger, S. Tiwari. Problem Definitions and Evaluation Criteria for the CEC 2005 Special Session on Real-Parameter Optimization, KanGAL Report 2005005, Nanyang Technological University, Singapore, 2005.

    Google Scholar 

  34. J. J. Liang, B. Y. Qu, P. N. Suganthan, Q. Chen. Problem Definitions and Evaluation Criteria for the CEC 2015 Competition on Learning-based Real-parameter Single Objective Optimization, Technical Report 201411A, Computational Intelligence Laboratory, Zhengzhou University, China, 2014.

    Google Scholar 

  35. J. R. Banga, E. Balsa-Canto, C. G. Moles, A. A. Alonso. Dynamic optimization of bioprocesses: Efficient and robust numerical strategies. Journal of Biotechnology, vol. 117, no. 4, pp. 407–419, 2005. DOI: 10.1016/j.jbiotec. 2005.02.013.

    Article  Google Scholar 

  36. B. Srinivasan, S. Palanki, D. Bonvin. Dynamic optimization of batch processes: I. Characterization of the nominal solution. Computers & Chemica. Engineering, vol. 27, no. 1, pp. 1–26, 2003. DOI: 10.1016/S0098-1354(02)00116-3.

    Google Scholar 

  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. 767–769, 1956. DOI: 10.1073/pnas.42.10.767.

    Article  MathSciNet  MATH  Google Scholar 

  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. DOI: 10.1109/9.173155.

    Article  MathSciNet  MATH  Google Scholar 

  39. W. H. Ray, J. Szekely. Process Optimization with Applications in Metallurgy and Chemical Engineering, New York, USA: John Wiley & Sons, 1973.

    Google Scholar 

  40. A. E. Bryson. Dynamic Optimization, Menlo Park, USA: Addison Wesley Longman, 1999.

    Google Scholar 

  41. D. Sarkar, J. M. Modak. Optimisation of fed-batch bioreactors using genetic algorithms. Chemical Engineering Science, vol. 58, no. 11, pp. 2283–2296, 2003. DOI: 10.1016/S0009-2509(03)00095-2.

    Article  Google Scholar 

  42. R. W. H. Sargent, G. R. Sullivan. The development of an efficient optimal control package. In Proceedings of the 8th IFIP Conference on Optimization Techniques, Springer, Wurzburg, Germany, pp. 158–168, 1978. DOI: 10.1007/BFb0006520.

    Chapter  Google Scholar 

  43. K. V. Price, R. M. Storn, J. A. Lampinen. Differential Evolution: A Practical Approach to Global Optimization, Berlin, Germany: Springer Heidelberg, 2005. DOI: 10.1007/3-540-31306-0.

    MATH  Google Scholar 

  44. S. Das, A. Konar, U. K. Chakraborty. Two improved differential evolution schemes for faster global search. In Proceedings of the Conference on Genetic and Evolutionary Computation, ACM, Washington DC, USA, pp. 991–998, 2005. DOI: 10.1145/1068009.1068177.

    Google Scholar 

  45. J. Montgomery, S. Chen. An analysis of the operation of differential evolution at high and low crossover rates. In Proceedings of IEEE Congress on Evolutionary Computation, IEEE, Barcelona, Spain, 2010. DOI: 10.1109/CEC. 2010.5586128.

    Google Scholar 

  46. F. Wilcoxon. Individual comparisons by ranking methods. Biometric. Bulletin, vol. 1, no. 6, pp. 80–83, 1945. DOI: 10.2307/3001968.

    Google Scholar 

  47. M. Friedman. The use of ranks to avoid the assumption of normality implicit in the analysis of variance. AJournal of the American Statistical ssociation, vol. 32, no. 200, pp. 675–701, 1937. DOI: 10.2307/2279372.

    Article  MATH  Google Scholar 

  48. J. A. Roubos, C. D. D. Gooijer, G. Van Straten, A. J. B. Van Boxtel. Comparison of optimization methods for fedbatch cultures of hybridoma cells. Bioprocess Engineering, vol. 17, no. 2, pp. 99–102, 1997. DOI: 10.1007/s004490050360.

    Article  Google Scholar 

  49. J. Lee, 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. DOI: 10.1002/aic.690400516.

    Article  Google Scholar 

  50. Q. Q. Fan, Z. M. Lu, X. F. Yan, M. J. Guo. Chemical process dynamic optimization based on hybrid differential evolution algorithm integrated with Alopex. Journal of Central South University, vol. 20, no. 4, pp. 950–959, 2013. DOI: 10.1007/s11771-013-1570-3.

    Article  Google Scholar 

  51. J. A. Roubos, G. Va. Straten, A. J. B. Van Boxtel. An evolutionary strategy for fed-batch bioreactor optimization; concepts and performance. Journal of Biotechnology, vol. 67, no. 2–3, pp. 173–187, 1999. DOI: 10.1016/S0168-1656(98)00174-6.

    Article  Google Scholar 

  52. D. Sarkar, J. M. Modak. ANNSA: A hybrid artificial neural network/simulated annealing algorithm for optimal control problems. Chemical Engineerin Science, vol. 58, no. 14, pp. 3131–3142, 2003. DOI: 10.1016/S0009-2509(03)00168-4.

    Article  Google Scholar 

  53. B. Zhang, D. Z. Chen, W. X. 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. DOI: 10.1016/j.compchemeng. 2005.05.020.

    Article  Google Scholar 

  54. Q. Q. Fan, X. H. Wang, 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, no. 6, pp. 2227–2237, 2015. DOI: 10.1007/s11771-015-2747-8.

    Article  Google Scholar 

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Acknowledgements

This work was supported by National Natural Science Foundation of China (Nos. 61603244 and 41505001) and Fundamental Research Funds for the Central Universities (No. 222201717006).

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Authors and Affiliations

  1. Logistics Research Center, Shanghai Maritime University, Shanghai, 201306, China

    Qin-Qin Fan & Zhi-Huan Wang

  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

    Qin-Qin Fan & Xue-Feng Yan

  3. Key Laboratory of Marine Technology and Control Engineering Ministry of Communications, Shanghai Maritime University, Shanghai, 201306, China

    Yi-Lian Zhang

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  1. Qin-Qin Fan
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Correspondence to Qin-Qin Fan.

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Recommended by Associate Editor Matjaz Gams

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, and the Ph. D. degree control science and engineering from East China University of Science and Technology, China in 2015, respectively. He is currently a lecturer with Shanghai Maritime University, China.

His research interests include differential evolution algorithm, particle swarm optimization, constrained optimization, multiobjective optimization, and their real-world applications.

Yi-Lian Zhang received the Ph. D. degree in control science and engineering from East China University of Science and Technology, China in 2015. She is now a lecturer with Shanghai Maritime University, China.

Her research interests include networked control systems, set-membership filtering and evolutionary computation.

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.

His research interests include complex chemical process modeling, optimizing and controlling, process monitoring, fault diagnosis and intelligent information processing.

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.

His research interests include big data, evolutionary computation and intelligent information processing.

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Fan, QQ., Zhang, YL., Yan, XF. et al. Enhancing the Performance of JADE Using Two-phase Parameter Control Scheme and Its Application. Int. J. Autom. Comput. 15, 462–473 (2018). https://doi.org/10.1007/s11633-018-1119-x

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