Performance evaluation of PSO and GA in PWR core loading pattern optimization

The efficient operation and fuel management of PWRs are of utmost importance. Recently, genetic algorithm (GA) and particle swarm optimization (PSO) techniques have attracted considerable attention among various modern heuristic optimization techniques. GA is a powerful optimization technique, based upon the principles of natural selection and species evolution. GA is finding popularity as design tools because of its versatility, intuitiveness and ability to solve highly non-linear, mixed integer optimization problems. PSO refers to a relatively new family of algorithms and is mainly inspired by social behavior patterns of organisms that live within large group. This study addresses the application and performance comparison of PSO and GA optimization methods for nuclear fuel loading pattern problem. Flattening of power inside the reactor core of Bushehr nuclear power plant (WWER-1000 type) is chosen as an objective function to prove the validity of algorithms. In addition the performance of both optimization techniques in terms of convergence rate and computational time is compared. It is found that, from an evolutionary point of view, the performance of both GA and PSO is quite adequate. But, GA seems to arrive at its final parameter value in a fewer generations than the PSO. It is also noticed that, the computation time for implemented GA in this work is too high in comparison to PSO.