基于改进粒子群算法的图像匹配技术研究

摘要

在分析基本粒子群优化算法的基础上，对学习因子进行非线性异步策略调整，改变其固定常数模式，平衡算法在迭代过程中的局部和全局搜索能力；同时引入活力因子，对失活粒子执行变异操作，提高种群多样性。改进算法可以提升对多维空间的全局寻优能力，避免粒子产生早熟收敛现象。将改进粒子群算法引入图像匹配优化问题中，提出了一种基于改进粒子群算法的图像匹配算法，实验结果表明，该算法具有更快的匹配速度以及更高的匹配精度，具有强鲁棒性。%Based on the analysis of the basic particle swarm optimization algorithm,the thesis adjusted the learn⁃ing factor by the strategy of nonlinear asynchronous,changed the model of fixed constant,and balanced the global and local search ability in the process of iteration;simultaneously the thesis introduced the activity factor to im⁃prove the population diversity which performed mutation for the particles who lost energy.The improved algorithm can improve the global search capability in the multidimensional space,and avoided premature convergence phe⁃nomenon.The improved particle swarm algorithm was introduced into image matching optimization problem,and proposed an image matching algorithm based on the improved particle swarm algorithm,the experimental results showed that The algorithm has the advantages of faster matching speed and higher matching accuracy,and has strong robustness.