Constrained optimization of test intervals using a steady-state genetic algorithm

There is a growing interest from both the regulatory authorities and the nu clear industry to stimulate the use of Probabilistic Risk Analysis (PRA) fo r risk-informed applications at Nuclear Power Plants (NPPs). Nowadays, spec ial attention is being paid on analyzing plant-specific changes to Test Int ervals (TIs) within the Technical Specifications (TSs) of NPPs and it seems to be a consensus on the need of making these requirements more risk-effec tive and less costly. Resource versus risk-control effectiveness principles formally enters in optimization problems. This paper presents an approach for using the PRA models in conducting the constrained optimization of TIs based on a steady-state genetic algorithm (SSGA) where the cost or the burd en is to be minimized while the risk or performance is constrained to be at a given level, or vice versa. The paper encompasses first with the problem formulation, where the objective function and constraints that apply in th e constrained optimization of TIs based on risk and cost models at system l evel are derived. Next, the foundation of the optimizer is given, which is derived by customizing a SSGA in order to allow optimizing TIs under constr aints. Also, a case study is performed using this approach, which shows the benefits of adopting both PRA models and genetic algorithms, in particular for the constrained optimization of TIs, although it is also expected a gr eat benefit of using this approach to solve other engineering optimization problems. However, care must be taken in using genetic algorithms in constr ained optimization problems as it is concluded in this paper. (C) 2000 Else vier Science Ltd. All rights reserved.