PARTICLE SWARM OPTIMIZATION APPLIED TO THE COMBINATORIAL PROBLEM IN ORDER TO SOLVE THE NUCLEAR REACTOR FUEL RELOADING PROBLEM

摘要

This work focuses on the use of the Artificial Intelligence metaheuristic technique Particle Swarm Optimization (PSO) to optimize a nuclear reactor fuel reloading. This is a combinatorial problem, in which the goal is to find the best feasible solution, minimizing a specific objective function. However, in the first moment it is possible to compare the fuel reloading problem with the Traveling Salesman Problem (TSP), since both of them are combinatorial and similar in terms of complexity, with one advantage: the evaluation of the TSP objective function is more simple. Thus, the proposed method has been applied to two TSPs: Oliver 30 and Rykel 48. In 1995, KENNEDY and EBERHART presented the PSO technique to optimize non-linear continuous functions. Recently some PSO models for discrete search spaces have been developed for combinatorial optimization, although all of them have different formulation from the one presented in this work. Here we use the PSO theory associated with to the Random Keys (RK) model, used in some optimizations with Genetic Algorithms, as a way to transform the combinatorial problem into a continuous space search. The Particle Swarm Optimization with Random Keys (PSORK) results from this association, which combines PSO and RK. The adaptations and changings in the PSO aim to allow the appliance of the PSO at the nuclear fuel reloading problem. This work shows the PSORK applied to the TSP and the obtained results as well.