On the performance of an artificial bee colony optimization algorithm applied to the accident diagnosis in a PWR nuclear power plant

The swarm-based algorithm described in this paper is a new search algorithm capable of locating good solutions efficiently and within a reasonable running time. The work presents a population-based search algorithm that mimics the food foraging behavior of honey bee swarms and can be regarded as belonging to the category of intelligent optimization tools. In its basic version, the algorithm performs a kind of random search combined with neighborhood search and can be used for solving multi-dimensional numeric problems. Following a description of the algorithm, this paper presents a new event classification system based exclusively on the ability of the algorithm to find the best centroid positions that correctly identifies an accident in a PWR nuclear power plant, thus maximizing the number of correct classification of transients. The simulation results show that the performance of the proposed algorithm is comparable to other population-based algorithms when applied to the same problem, with the advantage of employing fewer control parameters. (author)