Multiobjective optimization by genetic algorithms: application to safety systems

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

“…The case study here considered is taken from [5] and regards the optimization of the test intervals (TIs) of the high pressure injection system (HPIS) of a pressurized water reactor (PWR), with respect to three objectives: maximize mean system availability, minimize cost and minimize workers time of exposure to radiation. Figure 1 shows a simplified schematics of a specific HPIS design.…”

“…The HPIS mean unavailability U -HPIS has been computed from the fault tree for the top event "no flow out of both injection paths A and B"; the Boolean reduction of the corresponding structure function allows us to determine the NMCS system minimal cut sets (MCS) [5]. Then, the system mean unavailability can be expressed as in the argument of the maximization (2) where nv is the number of basic events in the v-th minimal cut set and u -v h is the mean unavailability of the h-th component making up the v-th MCS, h=1,....,nv [8]:…”

“…The multiobjective optimization problem (2)- (4) has been solved using the MATLAB genetic algorithm toolbox, with a population size of 100 individuals and a stopping criterion of 500 generations, as indicated in [5]. The resulting Pareto Set (Θ) is made of 100 points, and the corresponding Pareto Front is shown in Figure 3 in the objective functions space.…”

“…Each family of solutions F j , j=1,....,K, obtained by the subtractive clustering algorithm contains a number n j of solutions of similar characteristics in the objective functions space and whose best representative solution, the head of the family H [5]. In this paper, the norm considered is the following 1-norm: 1-norm :…”

A Comparison of Methods for Selecting Preferred Solutions in Multiobjective Decision Making

“…9 The a posteriori procedure of analysis is applied to the Pareto Frontier and Set obtained by a standard Multiobjective Genetic Algorithm. 9 The remainder of the paper is organized as follows: Section 1.0 presents the case study to describe upfront the setting of the typical multiobjective optimization problem of interest; Section 1.Error! Reference source not found.…”