Study on Different Crossover Mechanisms of Genetic Algorithm for Test Interval Optimization for Nuclear Power Plants

Автор: Molly Mehra, M.L. Jayalal, A. John Arul, S. Rajeswari, K. K. Kuriakose, S.A.V. Satya Murty

Журнал: International Journal of Intelligent Systems and Applications(IJISA) @ijisa

Статья в выпуске: 1 vol.6, 2013 года.

Бесплатный доступ

Surveillance tests are performed periodically on standby systems of a Nuclear Power Plant (NPP), as they improve the systems’ availability on demand. High availability of safety critical systems is very essential to NPP safety, hence, careful analysis is required to schedule the surveillance activities for such systems in a cost effective way without compromising the plant safety. This forms an optimization problem wherein, two different cases can be formulated for deciding the value of Surveillance Test Interval. In one case, cost is the objective function to be minimized while unavailability is constrained to be at a given level and in another case, unavailability is minimized for a given cost level. Here, optimization is done using Genetic Algorithm (GA) and real encoding has been employed as it caters well to the requirements of this problem. A detailed procedure for GA formulation is described in this paper. Two different crossover methods, arithmetical crossover and blend crossover are explored and compared in this study to arrive at the most suitable crossover method for such type of problems.

Еще

Genetic Algorithm, Arithmetical Crossover, Blend Crossover, Surveillance Test Interval, Nuclear Power Plants, Safety Grade Decay Heat Removal System, Prototype Fast Breeder Reactor

Короткий адрес: https://sciup.org/15010511

IDR: 15010511

Список литературы Study on Different Crossover Mechanisms of Genetic Algorithm for Test Interval Optimization for Nuclear Power Plants

  • Molly Mehra, M.L. Jayalal, A. John Arul, S. Rajeswari, K. Kuriakose, S.A.V. Satya Murty, Design and Development of Genetic Algorithm for Test Interval Optimization of Safety Critical System for a Nuclear Power Plant, Online Proceedings on Trends in Innovative Computing, Intelligent Systems Design and Applications Conference, Kochi, India (2012) 166 – 170.
  • Martorell S, Carlos S, Sanchez A, Serradell V, Constrained optimization of test intervals using steady-state genetic algorithms, Reliability Engineering System Safety (2000) 67:215–32.
  • Confidential Internal Report: “Probabilistic Safety Assessment, Level 1: Internal Events for PFBR, System Reliability Analysis” Volume II, April 2011.
  • Confidential Internal Report: “Probabilistic Safety Assessment, Level 1: Internal Events for PFBR, Event Tree and Cutsets,” Volume III & Systems Basic Events Volume IV, April 2011.
  • Haupt R.L and Haupt S.E, Practical Genetic Algorithms, John Wiley & Sons, 1998.
  • Goldberg D.E, Genetic Algorithms in Search Optimization and Machine Learning, Addison-Wesley Publishing Company, 1989.
  • Michalewicz Z, Genetic Algorithm + Data Structure = Evolution Programs, Springer-Verlag, New York, 1994.
  • Deb K, Multi-Objective Optimization using Evolutionary Algorithms, John Wiley & Sons, 2008.
Еще
Статья научная