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Diversity of Pareto front: A multiobjective genetic algorithm based on dominating information

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Abstract

In this paper, the diversity information included by dominating number is analyzed, and the probabilistic relationship between dominating number and diversity in the space of objective function is proved. A ranking method based on dominating number is proposed to build the Pareto front. Without increasing basic Pareto method’s computation complexity and introducing new parameters, a new multiobjective genetic algorithm based on proposed ranking method (MOGA-DN) is presented. Simulation results on function optimization and parameters optimization of control system verify the efficiency of MOGA-DN.

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References

  1. C. A. Coello Coello, D. A. Van Veldhuizen, G. B. Lamont. Evolutionary Algorithms for Solving Multiobjective Problems[M]. New York: Kluwer Academic Publishers, 2002.

    Google Scholar 

  2. C. A. Coello Coello. Evolutionary multiobjective optimizations: A historical view of the field[J]. IEEE Computational Intelligence Magazine, 2006, 1(1): 28–36.

    Article  MathSciNet  Google Scholar 

  3. T. Goel, R. Vaidyanathan, R. T. Haftka, et al. Response surface approximation of Pareto optimal front in multiobjective optimization[J]. Computer Methods in Applied Mechanics and Engineering, 2007, 196(4): 879–893.

    Article  MATH  Google Scholar 

  4. S. L. Ho, S. Yang, G. Ni. Incorporating a priori preferences in a vector PSO algorithm to find arbitrary fractions of the pareto front of multiobjective design problems[J]. IEEE Transactions on Magnetics, 2008, 44(6): 1038–1041.

    Article  Google Scholar 

  5. T. Back. Evolutionary Algorithms in Theory and Practice[M]. New York: Oxford University Press, 1996.

    Google Scholar 

  6. H. Tamak, H. Kita, S. Kobayashi. Multiobjective optimization by genetic algorithms: A review[C]//Proceedings of IEEE International Conference on Evolutionary Computation. New York: IEEE, 1996: 517–522.

    Chapter  Google Scholar 

  7. D. E. Goldberg. Genetic Algorithms in Search, Optimization, and Machine Learning[M]. Boston: Addison Wesley Longman Publisher, 1989.

    Google Scholar 

  8. A. Dietz, C. Azzaro-Pantel, L. Pibouleau, et al. Strategies for multiobjective genetic algorithm development: application to optimal batch plant design in process systems engineering[J]. Computers & Industrial Engineering, 2008, 54(3): 539–569.

    Article  Google Scholar 

  9. D. Sarkar, J. M. Modak. Pareto-optimal solutions for multiobjective optimization of fed-batch bioreactors using non-dominated sorting genetic algorithm[J]. Chemical Engineering Science, 2005, 60(2): 481–492.

    Article  Google Scholar 

  10. C. A. Coello Coello, E. M. Montes. Constraint-handling in genetic algorithm through the use of dominance-based tournament selection[J]. Advanced Engineering Informatics, 2002, 16(3): 193–203.

    Article  Google Scholar 

  11. F. Aherne, P. Rockett, N. Thacker. Automatic parameter selection for object recognition using a parallel multiobjective genetic algorithm[C]//The 7th International Conference on Computer Analysis of Images and Patterns. Berlin: Springer-Verlag, 1997: 559–566.

    Chapter  Google Scholar 

  12. E. Zitzler, L. Thiele. Multiobjective evolutionary algorithms: A comparative case study and the strength pareto approach[J]. IEEE Transactions on Evolutionary Computation, 1999, 3(4): 257–271.

    Article  Google Scholar 

  13. J. Branke, B. Scheckenbach, M. Stein, et al. Portfolio optimization with an envelope-based multiobjective evolutionary algorithm[J]. European Journal of Operational Research, 2009, 199(3): 684–693.

    Article  MATH  MathSciNet  Google Scholar 

  14. T. Marlin. Process Control: Designing Processes and Control System for Dynamic Performance[M]. New York: McGraw-Hill, Inc. 1995.

    Google Scholar 

  15. C. R. Cutler, B. L. Ramaker. Dynamic matrix control: a computer control algorithm[C]//Proceedings of Joint Automatic Control Conference. New York: IEEE, 1980.

    Google Scholar 

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Authors and Affiliations

  1. Department of Automation, Hefei University of Technology, Hefei Anhui, 230009, China

    Wei Chen, Mei Chen & Xin Li

  2. Department of Mechanical and Automation Engineering, the Chinese University of Hong Kong, Hong Kong, China

    Jingyu Yan

Authors
  1. Wei Chen
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  2. Jingyu Yan
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  3. Mei Chen
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  4. Xin Li
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Corresponding author

Correspondence to Wei Chen.

Additional information

The work was supported by the Academic Outstanding Youth Talented Person Fund of Anhui Province (No.2009SQR2014).

Wei CHEN is a lecturer at the Hefei University of Technology, Anhui Province, China. She obtains her bachelor of Engineering and her Ph.D. of Engineering from University of Science and Technology of China. A major area of her research is focused on nonlinear model predictive control, and a secondary area of her research is concerned with artificial intelligence, particularly genetic algorithm and the corresponding application.

Jingyu YAN is a Ph.D. candidate in the Department of Mechanical and Automation Engineering, the Chinese University of Hong Kong. He obtains his bachelor and master degrees of Engineering from University of Science and Technology of China. His current research interests are biomedical engineering, process control and optimization, and nonlinear model predictive control based on soft computing.

Mei CHEN is an associate professor at the Hefei University of Technology, Anhui Province, China. Her major research area is focused on computer control.

Xin LI is a lecturer at the Hefei University of Technology, Anhui Province, China. His major research area is focused on control strategies on power electronics and intelligent control.

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Chen, W., Yan, J., Chen, M. et al. Diversity of Pareto front: A multiobjective genetic algorithm based on dominating information. J. Control Theory Appl. 8, 222–228 (2010). https://doi.org/10.1007/s11768-010-7222-3

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  • DOI: https://doi.org/10.1007/s11768-010-7222-3

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