Abstract
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.
Keywords
- 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.
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References
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. 101–111
Baker, J. E.: “Reducing bias and inefficiency in the selection algorithm”. In: Grefenstette [10], pp. 14–21
Becker, R. G., A. J. Heunis, D. Q. Mayne: Computer-aided design of control systems via optimization. IEE Proc. D 126(1979)573–578
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. 42–50
Fonseca, C. M.: Multiobjective Genetic Algorithms with Application to Control Engineering Problems. PhD thesis, University of Sheffield, 1995
Fonseca, C. M., P. J. Fleming: An overview of evolutionary algorithms in multiobjective optimization. Evolutionary Computation 3(1995)1–16
Goldberg, D. E.: Genetic Algorithms in Search, Optimization and Machine Learning. Addison-Wesley, Reading, Massachusetts, 1989
Goldberg, D. E., J. Richardson: “Genetic algorithms with sharing for multimodal function optimization”. In: Grefenstette [10], pp. 41–49
Goldberg, D. E., P. Segrest: “Finite markov chain analysis of genetic algorithms”. In: Grefenstette [10], pp. 1–8
Grefenstette, J. J., (ed): Genetic Algorithms and Their Applications: Proceedings of the Second International Conference on Genetic Algorithms. Lawrence Erlbaum, 1987
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. 137–144
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, 1979
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)286–292
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, 1990
Metropolis, N., A. Rosenbluth, M. Rosenbluth, A. Teller, E. Teller: Equation of state calculations by fast computing machines. J. Chem. Phys. 21(1953)1087–1092
Mühlenbein, H., D. Schlierkamp-Voosen: Predictive models for the breeder genetic algorithm: I. continuous parameter optimization. Evolutionary Computation, 1(1993)25–49
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. 175–182
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)1589–1603
Silverman, B. W.: Density Estimation for Statistics and Data Analysis, Vol. 26 of Monographs on Statistics and Applied Probability. Chapman and Hall, London, 1986
Sinha, P. K.: Electromagnetic Suspension: Dynamics and Control. Peter Peregrinus Ltd., London, 1987
Skogestad, S., I. Postlethwaite: Multivariable Feedback Control: Analysis and Design. John Wiley and Sons Ltd., Chichester, England, 1996
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. 442–459
Vanderbilt, D., S. G. Louie: A Monte Carlo simulated annealing approach to optimization over continuous variables. J. Comp. Physics 56(1984)259–271
Whidborne, J. F.: EMS control system design for a maglev vehicle — A critical system. Automatica 29(1993)1345–1349
Whidborne, J. F., D.-W. Gu, I. Postlethwaite: Simulated annealing for multi-objective control system design. IEE Proc. D 144(1996)582–588
Whidborne, J. F., G. P. Liu: Critical Control Systems: Theory, Design and Applications. Research Studies Press, Taunton, U. K., 1993
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)455–461
Zakian, V.: A performance criterion. Int. J. Control 43(1986)921–931
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Chipperfield, A.J., Whidborne, J.F., Fleming, P.J. (1999). Evolutionary Algorithms and Simulated Annealing for MCDM. In: Gal, T., Stewart, T.J., Hanne, T. (eds) Multicriteria Decision Making. International Series in Operations Research & Management Science, vol 21. Springer, Boston, MA. https://doi.org/10.1007/978-1-4615-5025-9_16
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DOI: https://doi.org/10.1007/978-1-4615-5025-9_16
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