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Safety Systems Optimum Design by Multicriteria Evolutionary Algorithms

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Evolutionary Multi-Criterion Optimization (EMO 2003)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 2632))

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Abstract

In this work new safety systems multiobjective optimum design methodologies are introduced and compared. Various multicriteria evolutionary algorithms are analysed (SPEA2, NSGAII and controlled elitist-NSGAII) and applied to a Containment Spray Injection System of a nuclear power plant. Influence of various mutation rates is considered. A double minimization is handled: unavailability and cost of the system. The comparative statistical results of the test case show a convergence study during evolution by means of certain metrics that measure front coverage and distance to the optimal front. Results succeed in solving the problem.

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Authors and Affiliations

  1. Evolutionary Computation and Applications Division-CEANI, Intelligent Systems and Numerical Applications in Engineering Institute-IUSIANI, Las Palmas de Gran Canaria University, 35017, Campus de Tafira Baja, Spain

    David Greiner, Blas Galván & Gabriel Winter

Authors
  1. David Greiner
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  2. Blas Galván
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  3. Gabriel Winter
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Editor information

Editors and Affiliations

  1. Faculty of Sciences and Technology, Centre for Intelligent Systems, University of the Algarve, Campus de Gambelas, 8005-139, Faro, Portugal

    Carlos M. Fonseca

  2. Department of Automatic Control and Systems Engineering, University of Sheffield, Mappin Street, Sheffield, S1 3JD, UK

    Peter J. Fleming

  3. Department of Electrical Engineering, Computer Engineering and Networks Laboratory, Swiss Federal Institute of Technology, Gloriastraße 35, 8092, Zürich, Switzerland

    Eckart Zitzler  & Lothar Thiele  & 

  4. Department of Mechanical Engineering, Kanpur Genetic Algorithms Laboratory, Indian Institute of Technology, Kanpur, UP, 208 016, India

    Kalyanmoy Deb

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© 2003 Springer-Verlag Berlin Heidelberg

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Greiner, D., Galván, B., Winter, G. (2003). Safety Systems Optimum Design by Multicriteria Evolutionary Algorithms. In: Fonseca, C.M., Fleming, P.J., Zitzler, E., Thiele, L., Deb, K. (eds) Evolutionary Multi-Criterion Optimization. EMO 2003. Lecture Notes in Computer Science, vol 2632. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-36970-8_51

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  • DOI: https://doi.org/10.1007/3-540-36970-8_51

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  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-01869-8

  • Online ISBN: 978-3-540-36970-7

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