Abstract: The residual statics correction is a global optimization problem with multi-parameter and multi- extreme values. The ordinary genetic algorithm (GA) is almost impossible to avoid the premature phenomenon because of the large number of unknown parameters, so it is difficult to convergence to a global optimum. This paper presents a PSO (particle swarm optimization)-GA hybrid algorithm (PGHA) based on the advantage of PSO and GA. PGHA combines the rules of updating the velocity and situation of PSO with the idea of crossover and mutation of GA. Using PGHA and GA to experimentally deal with these two theoretical models shows that PGHA is better than GA in performance and adaptability and it is a practical method for calculating residual statics correction in complicated topographic conditions.

Key words: residual static correction, genetic algorithm, particle swarm optimization, PSO-GA hybrid algorithm