A multi-objective shuffled frog leaping algorithm for in-core fuel management optimization

Abstract The efficient operation and in-core fuel management of PWRs are of utmost importance. In the present work, a core reload optimization using Shuffled Frog Leaping (SFL) algorithm is addressed and mapped on nuclear fuel loading pattern optimization. SFL is one of the latest meta-heuristic optimization algorithms which is used for solving the discrete optimization problems and inspired from social behavior of frogs. The algorithm initiates the search from an initial population and carries forward to draw out an optimum result. This algorithm employs the use of memetic evolution by exchanging ideas between the members of the population in each local search. The local search of SFL is similar to particle swarm optimization (PSO) and applying shuffling process accomplishes the information exchange between several local searches to obtain an overall optimum result. To evaluate the proposed technique, Shekel’s Foxholes and a VVER-1000 reactor are used as test cases to illustrate performance of SFL. Among numerous neutronic and thermal-hydraulic objectives necessary for a fuel management problem to reach an overall optimum, this paper deals with two neutronic objectives, i.e., maximizing effective multiplication factor and flattening power distribution in the core, to evaluate the capability of applying SFL algorithm for a fuel management problem. The results, convergence rate and reliability of the method are quite promising and show the potential and efficiency of the technique for other optimization applications in the nuclear engineering field.

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