粒子群优化的GPSR-BB压缩感知重构算法

为了减少压缩感知中梯度投影稀疏重构法算法(GPSR-BB)的运行时间和迭代次数,有效地提高算法的重构性能,将具有全局搜索能力的粒子群算法应用到GPSR-BB算法中。利用粒子群优化算法的全局开发能力和GPSR-BB算法的局部搜索能力,加快了算法的收敛速度,减少了算法的迭代次数;通过对GPSR-BB算法中线搜索条件的改进,有效地提高了算法的重构精度。仿真实验表明:改进的GPSR-BB算法比传统的GPSR-BB算法运行时间缩短了43%、迭代次数降低了39.7%。在观测维数一定的条件下,改进的GPSR-BB算法重构成功概率高于传统的算法0.04,重构误差低于传统的0.09,具有较好的重构性能。

Gradient Projection for Sparse Reconstruction-Barzilai-Borwein Algorithm Based on Particle Swarm Optimization

In order to decrease the running time, the number of iteration and effectively improve the reconstruction performance of Gradient Projection for Sparse reconstruction-Barzilai-Borwein algorithm, Particle Swarm Optimization which has the global search ability is introduced in it. Using PSO''s global development ability and the local search ability of GPSR-BB algorithm, the convergence speed is increased and the running time is reduced. By the improvement of algorithm line search conditions, the reconstruction precision is improved effectively. Simulation results show that the improved GPSR-BB algorithm is shorter than the traditional algorithm by 43% in running time and by 39.7% in number of iteration. With the condition of a certain measurement dimension, the improved GPSR-BB algorithm is higher than the traditional one by 0.04 in average probability of success and lower than the traditional one by 0.09 in reconstruction error.