Artificial Intelligence Methods Applied to the In-Core Nuclear Fuel Management Optimization

Abstract

The In-Core Fuel Management Optimization (ICFMO), also known as Loading Pattern (LP) design optimization problem or nuclear reactor reload problem, is a classical problem in Nuclear Engineering. During the nuclear reactor fuel reloading operation periodically executed in Nuclear Power Plants (NPPs), part of the nuclear fuel is substituted. It is a realworld problem studied for more than four decades and several techniques have been used for its solution, such as optimization techniques and human expert knowledge. For example, early applications of Mathematical Programming methods for the solution of the ICFMO were made with Dynamic Programming (Wall & Fenech, 1965), and with Linear and Quadratic Programming (Tabak, 1968). The ICFMO presents characteristics such as high-dimensionality, the large number of feasible solutions, disconnected feasible regions in the search space (Stevens et al., 1995) as well as the high computational cost of the evaluation function and lack of derivative information, which contribute to the challenge of the optimization of the ICFMO. Notwithstanding, algorithms known as generic heuristic methods, or metaheuristics (Taillard et al., 2001), have demonstrated an outstanding capability of dealing with complex search spaces, specially in the case of the ICFMO. Such Artificial Intelligence (AI) algorithms, besides the low coupling to the specificities of the problems, have some characteristics such as the memorization of solutions (or characteristics of solutions), which allows the algorithm to retain intrinsic patterns of optimal or near-optimal solutions or, in other words, “inner” heuristics as described by Gendreau & Potvin (2005). As search methodologies, metaheuristics may have in common: diversification, in order to to explore different areas; mechanisms of intensification, in order to exploit specific areas of the search space; memory, in order to retain the best solutions; and tuning of parameters (Siarry & Zbigniew, 2008). Metaheuristics such as Simulated Annealing (SA; Kirkpatrick et al., 1983), Genetic Algorithm (GA; Goldberg, 1989), Population-Based Incremental Learning (PBIL; Baluja, 1994), Ant Colony System (ACS; Dorigo & Gambardella, 1997) and Particle Swarm Optimization (PSO; Kennedy & Eberhart, 2001) have been applied to several problems in different areas with considerable success. In the case of the ICFMO, metaheuristics have provided outstanding results since the earliest applications of the SA to this problem (Parks, 5