Signal, Image and Video Processing

, Volume 11, Issue 6, pp 993–1000 | Cite as

A hybrid evolutionary algorithm for multiobjective sparse reconstruction

  • Bai Yan
  • Qi Zhao
  • Zhihai WangEmail author
  • Xinyuan Zhao
Original Paper


Sparse reconstruction (SR) algorithms are widely used in acquiring high-quality recovery results in compressed sensing. Existing algorithms solve SR problem by combining two contradictory objectives (measurement error and sparsity) using a regularizing coefficient. However, this coefficient is hard to determine and has a large impact on recovery quality. To address this concern, this paper converts the traditional SR problem to a multiobjective SR problem which tackles the two objectives simultaneously. A hybrid evolutionary paradigm is proposed, in which differential evolution is employed and adaptively configured for exploration and a local search operator is designed for exploitation. Another contribution is that the traditional linearized Bregman method is improved and used as the local search operator to increase the exploitation capability. Numerical simulations validate the effectiveness and competitiveness of the proposed hybrid evolutionary algorithm with LB-based local search in comparison with other algorithms.


Multiobjective sparse reconstruction Compressed sensing Hybrid evolutionary algorithm Linearized Bregman 


Compliance with ethical standards

Conflict of interest

The authors declare that there is no conflict of interest regarding the publication of this paper.


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Copyright information

© Springer-Verlag London 2017

Authors and Affiliations

  1. 1.Institute of Laser EngineeringBeijing University of TechnologyBeijingChina
  2. 2.School of Economics and ManagementBeijing University of TechnologyBeijingChina
  3. 3.Key Laboratory of Optoelectronics Technology, Ministry of EducationBeijing University of TechnologyBeijingChina
  4. 4.Beijing Institute for Scientific and Engineering ComputingBeijing University of TechnologyBeijingChina

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