Cluster Computing

, Volume 20, Issue 4, pp 3015–3022 | Cite as

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

  • Ming Yang
  • Ning-bo Liu
  • Wei LiuEmail author


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.


Orthogonal sparse decomposition (OMP) Fruit-fly optimization algorithm (FOA) Simulated annealing (SA) algorithm Global optimum Computational complexity 



The research work is supported by “Twelfth Five-year” Scientific Research Program (No. [2013] 325) of Jilin Province Education Department of China.


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Copyright information

© Springer Science+Business Media New York 2017

Authors and Affiliations

  1. 1.College of Information and Control EngineeringJilin Institute of Chemical TechnologyJilinChina
  2. 2.Beijing Metstar Radar Co., LTD.BeijingChina

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