Improved Self-Learning Particle Swarm Algorithm for Calibrating a Three-Axis Measuring System

This paper introduces self-learning mechanism into the basic particle swarm algorithm to present the improved particle swarm algorithm. The self-learning mechanism ensures that the particles can change their speed and positions base on self learning and the overall experience. The novel algorithm can dynamically increase the diversity of particle swarm offspring and reduce the human intervention in evolutionary process. Then, use the improved particle swarm algorithm to calibrate the errors of three-axis measuring system. Simulation results show that the improved particle swarm algorithm is effective and feasible and has a good performance in calibrating the errors of three-axis measuring system.