Comparison of Parallel Genetic Algorithm and Particle Swarm Optimization for Parameter Calibration in Hydrological Simulation

Parameter calibration is an important part of hydrological simulation and affects the final simulation results. In this paper, we introduce heuristic optimization algorithms, Genetic Algorithm (GA) and Particle Swarm Optimization Algorithm (PSO), to cope with the complexity of the parameter calibration problem. In large scale hydrological simulations, we use a multilevel parallel parameter calibration algorithm framework to make full use of processor resources and accelerate the process of solving high dimensional parameter calibration. The results of parameter calibration with GA and PSO can basically reach the ideal value of 0.65 and above, with PSO achieving a speedup of 7.67 on TianHe-2 supercomputer. The experimental results indicate that by using a parallel implementation on multicore CPUs, high dimensional parameter calibration in large scale hydrological simulation is possible. Moreover, our comparison of the two algorithms shows that the GA obtains better calibration results and the PSO has a more pronounced acceleration effect.