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.
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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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