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A Decomposition-Based Multiobjective Evolutionary Algorithm for Sparse Reconstruction

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Advances in Swarm Intelligence (ICSI 2018)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 10941))

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Abstract

Sparse reconstruction is an important method aiming at obtaining an approximation to an original signal from observed data. It can be deemed as a multiobjective optimization problem for the sparsity and the observational error terms, which are considered as two conflicting objectives in evolutionary algorithm. In this paper, a novel decomposition based multiobjective evolutionary algorithm is proposed to optimize the two objectives and reconstruct the original signal more exactly. In our algorithm, a sparse constraint specific differential evolution is designed to guarantee that the solution remains sparse in the next generation. In addition, a neighborhood-based local search approach is proposed to obtain better solutions and improve the speed of convergence. Therefore, a set of solutions is obtained efficiently and is able to closely approximate the original signal.

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Acknowledgements

This work is supported by the National Natural Science Foundation of China (61602397, 61672447, 61602398, 61711540306) and the Postgraduate Research and Innovation Project of Hunan Province of China (CX2017B338) and CERNET Innovation Project (NGII20160310) and Natural Science Foundation of Hunan Province of China (2017JJ3316) and the Research Foundation of Hunan Provincial Educational Department of China (16C1547).

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Authors and Affiliations

  1. College of Information Engineering, Xiangtan University, Xiangtan, 411105, China

    Jiang Zhu, Muyao Cai, Shujuan Tian, Yanbing Xu & Tingrui Pei

  2. National Engineering Laboratory for Robot Visual Perception and Control Technology, Hunan University, Changsha, 410082, China

    Jiang Zhu

Authors
  1. Jiang Zhu
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  2. Muyao Cai
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  3. Shujuan Tian
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  4. Yanbing Xu
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  5. Tingrui Pei
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Corresponding author

Correspondence to Shujuan Tian .

Editor information

Editors and Affiliations

  1. Peking University, Beijing, China

    Ying Tan

  2. Southern University of Science and Technology, Shenzhen, China

    Yuhui Shi

  3. Tongji University, Shanghai, China

    Qirong Tang

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Zhu, J., Cai, M., Tian, S., Xu, Y., Pei, T. (2018). A Decomposition-Based Multiobjective Evolutionary Algorithm for Sparse Reconstruction. In: Tan, Y., Shi, Y., Tang, Q. (eds) Advances in Swarm Intelligence. ICSI 2018. Lecture Notes in Computer Science(), vol 10941. Springer, Cham. https://doi.org/10.1007/978-3-319-93815-8_48

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  • DOI: https://doi.org/10.1007/978-3-319-93815-8_48

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-93814-1

  • Online ISBN: 978-3-319-93815-8

  • eBook Packages: Computer ScienceComputer Science (R0)

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