Image 1D OMP sparse decomposition with modified fruit-fly optimization algorithm

The Fruit-fly optimization algorithm (FOA) is good at parallel search ability in the evolution process, but it traps in local optimum sometimes. Simulated Annealing (SA) algorithm accepts the second-optimum solution with Mrtropolis criterion so as to jump out of the local optimum. So, combined the advantages of two algorithms, modified FOA (FOA-SA) algorithm is presented in this paper. In FOA-SA, the smell concentration function is improved as well, so as to get the whole searching directions for fruit-fly. At the same time, in order to solve the problem of the computational complexity in image 2D sparse decomposition, image 1D orthogonal matching pursuit (OMP) algorithm with FOA-SA algorithm is implemented. Experimental results show that the convergence of FOA-SA is better than that in FOA, and the speed of image 1D sparse algorithm is 2.41 times faster than 2D for the 512 $$\times $$× 512 image under the same conditions.

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