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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)

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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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

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