Optimization of Nuclear Reactors Loading Patterns with Computational Intelligence Methods

Abstract

The goal of the Loading Pattern (LP) optimization problem is to determine an optimal (or near-optimal) distribution of Fuel Assemblies of a Nuclear Reactor for producing full power within adequate safety margins. Also known as In-Core Fuel Management Optimization, the LP optimization is a prominent real-world problem in Nuclear Engineering with high complexity due to its combinatorial formulation with a large number of feasible solutions, a large number of sub-optimal solutions, disconnected feasible regions, high dimensionality, complex and time-consuming evaluation functions with Reactor Physics calculations. In the present chapter, we discuss LP optimization problem and four computational intelligence optimization methods, also known as optimization metaheuristics or generic heuristic methods, namely the Cross-Entropy algorithm, the Particle Swarm Optimization, Artificial Bee Colonies, and Population-Based Incremental Learning. Results using actual models are described and also discussed.