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A hybrid evolutionary algorithm for multiobjective sparse reconstruction

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Abstract

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.

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Authors and Affiliations

  1. Institute of Laser Engineering, Beijing University of Technology, Beijing, 100124, China

    Bai Yan

  2. School of Economics and Management, Beijing University of Technology, Beijing, 100124, China

    Qi Zhao

  3. Key Laboratory of Optoelectronics Technology, Ministry of Education, Beijing University of Technology, Beijing, 100124, China

    Zhihai Wang

  4. Beijing Institute for Scientific and Engineering Computing, Beijing University of Technology, Beijing, 100124, China

    Xinyuan Zhao

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  1. Bai Yan
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  2. Qi Zhao
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  3. Zhihai Wang
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  4. Xinyuan Zhao
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Correspondence to Zhihai Wang.

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Yan, B., Zhao, Q., Wang, Z. et al. A hybrid evolutionary algorithm for multiobjective sparse reconstruction. SIViP 11, 993–1000 (2017). https://doi.org/10.1007/s11760-016-1049-4

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  • DOI: https://doi.org/10.1007/s11760-016-1049-4

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