关键词: 拉凡格氏算法, 果蝇优化算法, 粒子群优化算法, 稀疏分解

Abstract: Signal sparse representation has important applications in many aspects of signal processing, but the problems of having large memory capacity and huge computational complexity in sparse decomposition, restrict its practical usage. Particle Swarm Optimization（PSO） algorithm and Fruit Fly Optimization Algorithm（FOA） being iterations random algorithms do better in finding the optimization solution with greater probability, so they can greatly improve the speed of the speech signal sparse decomposition, but they have the weakness of slow convergence in the later and less precision. Levenberg Marquardt（LM） algorithm has the advantage of the fast convergence speed and high precision, but LM algorithm depends on the initial value, which restricts its practical usage. Combining the advantages of FOA algorithms and LM algorithms, the initial parameters of Gabor atom are solved by FOA algorithm, and then the LM is used to optimize the initial parameters. The simulation results show that the solution of the optimization can be easily and quickly obtained by FOA-LM.

Key words: Levenberg Marquardt（LM） algorithm, Fruit Fly Optimization Algorithm（FOA）, Particle Swarm Optimization algorithm（PSO）, sparse decomposition