Particle Swarm Optimization applied to the nuclear reload problem of a Pressurized Water Reactor

Abstract

The concept of Swarm Intelligence is based on the ability of individuals to learn with their own experience in a group as well as to take advantage of the performance of other individuals, which are social–collaborative aspects of intelligence. In 1995, Kennedy and Eberhart presented the Particle Swarm Optimization (PSO), a Computational Intelligence metaheuristic technique. Since then, some PSO models for discrete search spaces have been developed for combinatorial optimization, although none of them presented satisfactory results to optimize a combinatorial problem such as the Nuclear Reactor Reload Problem (NRRP). In this sense, we have developed the Particle Swarm Optimization with Random Keys (PSORK) to optimize combinatorial problems. PSORK has been tested for benchmarks to validate its performance and to be compared to other techniques such as Ant Systems and Genetic Algorithms, and in order to analyze parameters to be applied to the NRRP. We also describe and discuss its performance and applications to the NRRP with a survey of the research and development of techniques to optimize the reloading operation of Angra 1 nuclear power plant, located at the Southeast of Brazil.