Computational effort comparison of genetic algorithm and particle swarm optimization algorithms for the proportional-integral-derivative controller tuning of a pressurized water nuclear reactor

Power level control is important in nuclear power plants, and some dynamic parameters depend on the output power. One of the popular controllers is proportional-integral-derivative (PID), that the researchers are interested in tuning its gains. In this paper, computational effort comparison of genetic algorithm (GA) and particle swarm optimization (PSO) algorithm is aimed to tune the PID gains for the typical pressurized water reactor (PWR) load-following, based on the point kinetics model with and without the parametric uncertainties. The integral of time-weighted square error (ITSE) performance index is used in the GA and PSO metaheuristic algorithms. The number of function evaluations (NFE) is used for the computational effort comparison as the novelty of this work. The results of both optimization methods show the high performance of the closed-loop metaheuristic-PID controller for the desired load following with robustness to the parametric uncertainties. However, PSO achieves the optimal solution in less NFE. (C) 2019 Elsevier Ltd. All rights reserved.