Fitting geoidal undulation using quantum-behaved particle swarm optimization: A case study in Taiwan area

Abstract

This research proposes a version of the local geoidal undulation model that is generated from GNSS geodetic height, orthometric height and proper curve fitting surface model. We adopted here the algorithm of the quantum-behaved particle swarm optimization (QPSO) which has been improved from the original PSO. The original particle swarm optimization (PSO) is used to solve the optimization problem, and can solve the surface equation efficiently. The QPSO is a global convergence guaranteed and requires less parameters compared with the original PSO. The QPSO can search the best 2nd curve surface model more efficiently to fit the geoidal undulation of Taiwan area as well as to find the most appropriate partition fitting range.