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A Comparison of Evolutionary-Based Strategies for Mixed Discrete Multilevel Design Problems

  • Kai Chen
  • Ian C. Parmee
Conference paper

Abstract

This paper presents a comparison of evolutionary strategies which employ individual mutation schemes for different types of decision variables in optimal design and control problems. The work utilises the GAANT[9] algorithm as an improvement on previous work involving a dual-agent GA integrated with a nuclear power station whole plant design problem. The objective of the algorithm is to maintain diversity across both discrete and continuous variables. The algorithm is important during the preliminary design stages of industrial design problems with a limited number of discrete paths and heavy constraint. Particularly a nuclear power station re-design problem has been studied in depth.

Keywords

Nuclear Power Station Simple Genetic Algorithm Preliminary Design Stage Boiler Feed Water Dual Mutation 
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-Verlag London Limited 1998

Authors and Affiliations

  • Kai Chen
    • 1
  • Ian C. Parmee
    • 1
  1. 1.Plymouth Engineering Design Centre (PEDC)Plymouth UniversityPlymouthUK

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