Research of Image Matching Algorithm Based on Hybrid Particle Swarm Optimization

This paper proposes an image matching method based on hybrid PSO. The method combines the advantage of the rapid global optimization ability of PSO, and introduces the idea of Population Category Evolution and the mechanism of SA to improve itself. Adopting different evolutionary strategies for different particle categories and making the individual optimal value of the particle accept a lower value of a certain probability, that speed up the convergence speed of the algorithm, and enhance the stability and correctness, and improve the convergence and the ability of global optimization. The simulation results indicate that this method can improve the speed and the efficiency of image matching under the premise of ensuring correctness, and is an effective method for image matching.