Estimation of distribution algorithms for nuclear reactor fuel management optimisation

作     者：Jiang, S. Ziver, A. K. Carter, J. N. Pain, C. C. Goddard, A. J. H. Franklin, S. Phillips, H. J. 

作者机构：Univ London Imperial Coll Sci Technol & Med Dept Earth Sci & Engn Appl Modelling & Computat Grp London SW7 2AZ England Univ London Imperial Coll Sci Technol & Med Reactor Ctr Ascot SL5 7TE Berks England 

出 版 物：《ANNALS OF NUCLEAR ENERGY》 (核能纪事)

年 卷 期：2006年第33卷第11-12期

页      面：1039-1057页

核心收录：

学科分类：08[工学] 0827[工学-核科学与技术] 

主　　题：Optimization Procedure Optimization algorithms Allocation Algorithm CASING INSPECTION algorithms Nuclear fuels 

摘      要：In this paper, estimation of distribution algorithms (EDAs) are used to solve nuclear reactor fuel management optimisation (NRFMO) problems. Similar to typical population based optimisation algorithms, e.g. genetic algorithms (GAs), EDAs maintain a population of solutions and evolve them during the optimisation process. Unlike GAs, new solutions are suggested by sampling the distribution estimated from all the solutions evaluated so far. We have developed new algorithms based on the EDAs approach, which are applied to maximize the effective multiplication factor (K-eff) of the CONSORT research reactor of Imperial College London. In the new algorithms, a new elite-guided strategy and the stand-alone K-eff with fuel coupling is used as heuristic information to improve the optimisation. A detailed comparison study between the EDAs and GAs with previously published crossover operators is presented. A trained three-layer feed-forward artificial neural network (ANN) was used as a fast approximate model to replace the three-dimensional finite element reactor simulation code EVENT in predicting the K-eff. Results from the numerical experiments have shown that the EDAs used provide accurate, efficient and robust algorithms for the test case studied here. This encourages further investigation of the performance of EDAs on more realistic problems. (c) 2006 Elsevier Ltd. All rights reserved.