Evolutionary Algorithms and Simulated Annealing for MCDM

  • Andrew J. Chipperfield
  • James F. Whidborne
  • Peter J. Fleming
Part of the International Series in Operations Research & Management Science book series (ISOR, volume 21)


This chapter describes two stochastic search and optimization techniques, evolutionary algorithms and simulated annealing, both inspired by models of natural processes (evolution and thermodynamics) and considers their role and application in multiple criteria decision making and analysis. The basic single criteria algorithms are first presented in each case and it is then demonstrated with an example problem how these may be modified and set up to deal with multiple design criteria. Whilst the example employed considers the design of a robust control system for a high speed maglev vehicle, the approaches and techniques have a far wider range of application.


Simulated Annealing Evolutionary Algorithm Multiobjective Optimization Multiobjective Optimization Problem Control System Design 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. [1]
    Baker, J. E.: “Adaptive selection methods for genetic algorithms”. In: John J. Grefenstette (ed): Genetic Algorithms and Their Applications: Proc. of the First International Conference on Genetic Algorithms, Lawrence Erlbaum, 1985, pp. 101–111Google Scholar
  2. [2]
    Baker, J. E.: “Reducing bias and inefficiency in the selection algorithm”. In: Grefenstette [10], pp. 14–21Google Scholar
  3. [3]
    Becker, R. G., A. J. Heunis, D. Q. Mayne: Computer-aided design of control systems via optimization. IEE Proc. D 126(1979)573–578Google Scholar
  4. [4]
    Deb, K., D. E. Goldberg: “An investigation of niche and species formation in genetic function optimization”. In: J. David Schaffer (ed): Proc. of the Third International Conference on Genetic Algorithms, Morgan Kaufmann, San Mateo, CA, 1989, pp. 42–50Google Scholar
  5. [5]
    Fonseca, C. M.: Multiobjective Genetic Algorithms with Application to Control Engineering Problems. PhD thesis, University of Sheffield, 1995Google Scholar
  6. [6]
    Fonseca, C. M., P. J. Fleming: An overview of evolutionary algorithms in multiobjective optimization. Evolutionary Computation 3(1995)1–16CrossRefGoogle Scholar
  7. [7]
    Goldberg, D. E.: Genetic Algorithms in Search, Optimization and Machine Learning. Addison-Wesley, Reading, Massachusetts, 1989Google Scholar
  8. [8]
    Goldberg, D. E., J. Richardson: “Genetic algorithms with sharing for multimodal function optimization”. In: Grefenstette [10], pp. 41–49Google Scholar
  9. [9]
    Goldberg, D. E., P. Segrest: “Finite markov chain analysis of genetic algorithms”. In: Grefenstette [10], pp. 1–8Google Scholar
  10. [10]
    Grefenstette, J. J., (ed): Genetic Algorithms and Their Applications: Proceedings of the Second International Conference on Genetic Algorithms. Lawrence Erlbaum, 1987Google Scholar
  11. [11]
    Grefenstette, J. J.: “Genetic algorithms for changing environments”. In: R. Männer and B. Manderick (eds): Parallel Problem Solving From Nature 2, North-Holland, 1992, pp. 137–144Google Scholar
  12. [12]
    Hwang, C.-L., A. S. M. Masud: Multiple Objective Decision Making — Methods and Applications, Vol. 164 of Lecture Notes in Economics and Mathematical Systems. Springer-Verlag, Berlin, 1979Google Scholar
  13. [13]
    Kortüm, W., A. Utzt: Control law design and dynamic evaluations for a maglev vehicle with a combined lift and guidance suspension system. ASME J. Dyn. Syst. Meas. & Control 106(1984)286–292CrossRefGoogle Scholar
  14. [14]
    McFarlane, D. C., K. Glover: Robust Controller Design Using Normalized Coprime Factor Plant Descriptions, Vol. 138 of Lect. Notes Control & Inf. Sci. Springer-Verlag, Berlin, 1990Google Scholar
  15. [15]
    Metropolis, N., A. Rosenbluth, M. Rosenbluth, A. Teller, E. Teller: Equation of state calculations by fast computing machines. J. Chem. Phys. 21(1953)1087–1092CrossRefGoogle Scholar
  16. [16]
    Mühlenbein, H., D. Schlierkamp-Voosen: Predictive models for the breeder genetic algorithm: I. continuous parameter optimization. Evolutionary Computation, 1(1993)25–49CrossRefGoogle Scholar
  17. [17]
    Müller, P. C.: “Design of optimal state-observers and its application to maglev vehicle suspension control”. In: Proc. 4th IFAC Symp. Multivariable Technological Systems, Fredericton, Canada, 1977, pp. 175–182Google Scholar
  18. [18]
    Ritzel, B. J., J. W. Eheart, S. Ranjithan: Using genetic algorithms to solve a multiple objective groundwater pollution containment problem. Water Resources Research 30(1994)1589–1603CrossRefGoogle Scholar
  19. [19]
    Silverman, B. W.: Density Estimation for Statistics and Data Analysis, Vol. 26 of Monographs on Statistics and Applied Probability. Chapman and Hall, London, 1986Google Scholar
  20. [20]
    Sinha, P. K.: Electromagnetic Suspension: Dynamics and Control. Peter Peregrinus Ltd., London, 1987Google Scholar
  21. [21]
    Skogestad, S., I. Postlethwaite: Multivariable Feedback Control: Analysis and Design. John Wiley and Sons Ltd., Chichester, England, 1996Google Scholar
  22. [22]
    Spears, W. M., :“An overview of evolutionary computation”. In: Machine Learning: ECML-93 European Conference on Machine Learning, Lecture notes in Artificial Intelligence, Vol. 667 of Lecture notes in Artificial Intelligence, Springer-Verlag, 1993, pp. 442–459Google Scholar
  23. [23]
    Vanderbilt, D., S. G. Louie: A Monte Carlo simulated annealing approach to optimization over continuous variables. J. Comp. Physics 56(1984)259–271CrossRefGoogle Scholar
  24. [24]
    Whidborne, J. F.: EMS control system design for a maglev vehicle — A critical system. Automatica 29(1993)1345–1349CrossRefGoogle Scholar
  25. [25]
    Whidborne, J. F., D.-W. Gu, I. Postlethwaite: Simulated annealing for multi-objective control system design. IEE Proc. D 144(1996)582–588Google Scholar
  26. [26]
    Whidborne, J. F., G. P. Liu: Critical Control Systems: Theory, Design and Applications. Research Studies Press, Taunton, U. K., 1993Google Scholar
  27. [27]
    Whidborne, J. F., I. Postlethwaite, D.-W. Gu: Robust controller design using Hø loop-shaping and the method of inequalities. IEEE Trans. on Contr. Syst. Technology 2(1994)455–461CrossRefGoogle Scholar
  28. [28]
    Zakian, V.: A performance criterion. Int. J. Control 43(1986)921–931CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media New York 1999

Authors and Affiliations

  • Andrew J. Chipperfield
  • James F. Whidborne
  • Peter J. Fleming

There are no affiliations available

Personalised recommendations