Perpendicularity error has great influence on the quality and performance of the geometrical products, and it is of great importance to guarantee the interchangeability. Particle swarm optimization (PSO) is an extraordinarily useful intelligent optimization algorithm with several advantages of fast convergence rate and easy realization for computer in the multidimensional space function optimization and dynamic target optimization. As a result, it is very accurate and is accordant with the requirement of minimum zone method (MZC) using PSO to associate the base plane of perpendicularity error.
Parameter Selection and Performance Comparison of Particle Swarm Optimization in Sensor Networks Localization
