Skip to main content
SpringerLink
Log in

Pareto-optimal solutions based multi-objective particle swarm optimization control for batch processes

  • LSMS2010 and ICSEE 2010
  • Published:
Neural Computing and Applications Aims and scope Submit manuscript

Abstract

In order to maximize the amount of the final product while reducing the amount of the by-product in batch process, an improved multi-objective particle swarm optimization based on Pareto-optimal solutions is proposed in this paper. A novel diversity preservation strategy that combines the information of distance and angle into similarity judgment is employed to select global best and thus the convergence and diversity of the Pareto front is guaranteed. As a result, enough Pareto solutions are distributed evenly in the Pareto front. To test the effectiveness of the proposed algorithm, some benchmark functions are used and a comparison with its conventional counterparts is made. Furthermore, the algorithm is applied to two classical batch processes. The results show that the quality at the end of each batch can approximate the desire value sufficiently and the input trajectory converges, thus verify the efficiency and practicability of the proposed algorithm.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11

Similar content being viewed by others

References

  1. Bonvin D (1998) Optimal operation of batch reactors: a personal view. J Process Contr 8:355–368

    Google Scholar 

  2. Li X (2004) Better spread and convergence: particle swarm multiobjective optimization using the maximin fitness function. Lecture Notes in Computer Science, 2004, vol 3102. pp 117–128

  3. Hu X, Eberhart R (2002) Multiobjective optimization using dynamic neighborhood particle swarm optimization. In: Proceedings of the evolutionary computation, vol 2. IEEE, pp 1677–1681

  4. Parsopoulos KE, Vrahatis MN (2002) Particle swarm optimization method in multiobjective problems. ACM, New york, pp 603–607

    Google Scholar 

  5. Mostaghim S, Teich J (2003) Strategies for finding good local guides in multi-objective particle swarm optimization (MOPSO). Swarm Intelligence Symposium, 2003. SIS '03. Proceedings of the 2003 IEEE. pp 26–33

  6. Kenned YJ, Eberhart RC (1995) Particle swarm optimization. In: Proceedings of the IEEE international conference on neural networks. IEEE, Piscataway, pp 1942–1948

  7. Fieldsend JE, Everson RM, Singh S (2003) Using unconstrained elite archives for multiobjective optimization. IEEE Trans Evol Comput 7:305–323

    Article  Google Scholar 

  8. Leung Y-W, Wang Y (2003) U-measure: a quality measure for multiobjective programming. IEEE Trans Syst Man Cybern A Syst Hum 33:337–343

    Article  Google Scholar 

  9. Joanna L, Eiben AE (1997) A multi-sexual genetic algorithm for multiobjective optimization. In: Fukuda T, Furuhashi T (eds) Evolutionary Computation, IEEE international conference on evolutionary computation. pp 59–64

  10. Zitzler E, Deb K, Thiele L (2000) Comparison of multiobjective evolutionary algorithms: empirical results. Evol Comput 8(2):173–195

    Article  Google Scholar 

  11. Libiao Z (2006) Research on optimization algorithm based on particle swarm optimization and differential evolution. University of Jilin, Jilin

    Google Scholar 

  12. Deb K, Thiele L, Laumanns M, Zitzler E (2005) Scalable multi-objective optimization test problems. In: IEEE proceedings, evolutionary multiobjective optimization. pp 105–145

  13. Wei J (2009) Evolutionary algorithms for single-objective and multi-objective optimization problems

  14. Liu H, Jia L, Liu Q (2007) Batch-to-batch control of batch processes based on multilayer recurrent fuzzy neural network. In: Proceedings of the international conference on intelligent systems and knowledge engineering, vol 1369. Atlantis Press, France, pp 1234–1239

  15. Lu N, Gao F (2005) Stage-based process analysis and quality prediction for batch processes. Ind Eng Chem Res 44:3547–3555

    Article  Google Scholar 

  16. Jia L (2009) Run-to-run product quality control of batch processes. J Shanghai Univ (English Edition) 13:267–269

    Article  Google Scholar 

  17. Terwiesch P (1998) Semi-batch process optimization under uncertainty: theory and experiments. Comput Chem Eng 22:201–213

    Article  Google Scholar 

Download references

Acknowledgments

Supported by National Natural Science Foundation of China (61004019), Research Fund for the Doctoral Program of Higher Education of China (20093108120013), Shanghai Science Technology commission (08160512100 and 09JC1406300), Grant from Shanghai Municipal Education commission (09YZ08), Shanghai University, “11th Five-Year Plan” 211 Construction Project and Innovation Project for Postgraduate Student Granted by Shanghai University (SHUCX102221).

Author information

Authors and Affiliations

  1. Shanghai Key Laboratory of Power Station Automation Technology, Department of Automation, College of Mechatronics Engineering and Automation, Shanghai University, Shanghai, 200072, China

    Li Jia & Dashuai Cheng

  2. Faculty of Engineering, National University of Singapore, Singapore, Singapore

    Min-Sen Chiu

Authors
  1. Li Jia
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Dashuai Cheng
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Min-Sen Chiu
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Li Jia.

Rights and permissions

Reprints and permissions

About this article

Cite this article

Jia, L., Cheng, D. & Chiu, MS. Pareto-optimal solutions based multi-objective particle swarm optimization control for batch processes. Neural Comput & Applic 21, 1107–1116 (2012). https://doi.org/10.1007/s00521-011-0659-6

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00521-011-0659-6

Keywords

Search

Navigation