Optimization of the Test Intervals of a Nuclear Safety System by Genetic Algorithms, Solution Clustering and Fuzzy Preference Assignment

Abstract : In this paper, a procedure is developed for identifying a number of representative solutions manageable for decision-making in a multiobjective optimization problem concerning the test intervals of the components of a safety system of a nuclear power plant. Pareto Front solutions are identified by a genetic algorithm and then clustered by subtractive clustering into ''families''. On the basis of the decision maker's preferences, each family is then synthetically represented by a ''head of the family'' solution. This is done by introducing a scoring system that ranks the solutions with respect to the different objectives: a fuzzy preference assignment is employed to this purpose. Level Diagrams are then used to represent, analyze and interpret the Pareto Fronts reduced to the head-of-the-family solutions.
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Journal articles
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https://hal-supelec.archives-ouvertes.fr/hal-00610483
Contributor : Yanfu Li <>
Submitted on : Friday, July 22, 2011 - 10:41:39 AM
Last modification on : Tuesday, August 13, 2019 - 11:10:04 AM

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  • HAL Id : hal-00610483, version 1

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Enrico Zio, R. Bazzo. Optimization of the Test Intervals of a Nuclear Safety System by Genetic Algorithms, Solution Clustering and Fuzzy Preference Assignment. Nuclear Engineering and Technology, 2010, 42 (4), pp.414-425. ⟨hal-00610483⟩

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