Coarse-grained parallel genetic algorithm applied to a nuclear reactor core design optimization problem

This work extends the research related to genetic algorithms (GA) in core design optimization problems, which basic investigations were presented in previous work. Here we explore the use of the Island Genetic Algorithm (IGA), a coarse-grained parallel GA model, comparing its performance to that obtained by the application of a traditional non-parallel GA. The optimization problem consists on adjusting several reactor cell parameters, such as dimensions, enrichment and materials, in order to minimize the average peak-factor in a 3-enrichment zone reactor, considering restrictions on the average thermal flux, criticality and sub-moderation. Our IGA implementation runs as a distributed application on a conventional local area network (LAN), avoiding the use of expensive parallel computers or architectures. After exhaustive experiments, taking more than 1500 h in 550 MHz personal computers, we have observed that the IGA provided gains not only in terms of computational time, but also in the optimization outcome. Besides, we have also realized that, for such kind of problem, which fitness evaluation is itself time consuming, the time overhead in the IGA, due to the communication in LANs, is practically imperceptible, leading to the conclusion that the use of expensive parallel computers or architecture can be avoided.