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

  • Kai Chen
  • Ian C. Parmee
Conference paper


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.


Nuclear Power Station Simple Genetic Algorithm Preliminary Design Stage Boiler Feed Water Dual Mutation 
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  1. [1]
    G. Bilchev and I. C. Parmee. The ant colony metaphor for searching continuous design spaces. In Terence C. Fogarty, editor, Proceedings of AISB Workshop on Evolutionary Computing, Lecture notes in computer science 993, pages 25–39. Springer-Verlag, ISBN3540604693, 1995.Google Scholar
  2. [2]
    K. Chen, I. C. Parmee, and C. R. Gane. Dual mutation strategies for mixed-integer optimisation in power station design. In Proceedings of the 1997 IEEE International Conference on Evolutionary Computations (ICEC’97), pages 385–390. Indianapolis, USA, 13–16 April, 1997.CrossRefGoogle Scholar
  3. [3]
    K. Chen, I. C. Parmee, and C. R. Gane. A genetic algorithm for mixed-integer optimisation in power and water system design and control. In D. Ruan, editor, Intelligent hybrid systems: Fuzzy logic, neural networks, and genetic algorithms, pages 311–351. Kluwer Academic Publishers, Boston, September, 1997.Google Scholar
  4. [4]
    A. Coloni, Dorigo M., and Maniezzo V. An investigation of some properties of the ant algorithm. In Proceedings of PPSN’92, pages 509–520. Elsevier Publishing, 1992.Google Scholar
  5. [5]
    D. Dasguta and D. McGregor. A structured genetic algorithm. Technical Report Research Report IKBS-2–91, University of Strathclyde, Gloasgow, UK, 1991.Google Scholar
  6. [6]
    K. De Jong. An analysis of the behaviour of a class of genetic adaptive systems. PhD thesis, University of Michigan, Diss. Abstr. Int. 36(10), 5140B, University Microfilms No. 76–9381, 1975.Google Scholar
  7. [7]
    A. Geist and et al. PVM: Parallel Virtual Machine. The MIT Press, Cambridge, Massachusetts, 1994.MATHGoogle Scholar
  8. [8]
    I. C. Parmee. Diverse evolutionary search for preliminary whole system design. In Proceedings of 4th International Conference on AI in Civil and Structural Engineering. Cambridge University, Civil-Comp Press, August, 1995.Google Scholar
  9. [9]
    I. C. Parmee. The development of a dual-agent strategy for efficient search across whole system engineering hierarchies. In Proceedings of the 4th International Conference on Parallel Solving from Nature. Berlin, 22–27 September, 1996.Google Scholar
  10. [10]
    I. C. Parmee. Towards an optimal engineering design process using appropriate adaptive search techniques. Journal of Engineering Design, 7 (4): 341–362, December 1996.CrossRefGoogle Scholar
  11. [11]
    I. C. Parmee, C. R. Gane, M. Donne, and K. Chen. Genetic strategies for the design and optimal operation of thermal systems. In Proceedings of the 4th European Congress on Intelligent Techniques and Soft Computing. Aachen, Germany, 2–5 September, 1996.Google Scholar

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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