A hybrid evolutionary algorithm for multiobjective sparse reconstruction
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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.
KeywordsMultiobjective sparse reconstruction Compressed sensing Hybrid evolutionary algorithm Linearized Bregman
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Conflict of interest
The authors declare that there is no conflict of interest regarding the publication of this paper.
- 3.Zhang S: A biologically inspired appearance model for robust visual tracking. IEEE Trans. Neural Netw. Learn. Syst. 1–14 (2016)Google Scholar
- 15.Kim, S.J., Koh, K., Lustig, M., Boyd, S., Gorinevsky, D.: An interior-point method for large-scale \(\ell \)1-regularized least squares. IEEE J. Sel. Top. Signal Process. 1, 606–617 (2007)Google Scholar
- 21.Malioutov, D.M., Cetin, M., Willsky, A.S.: Homotopy continuation for sparse signal representation[C]. Proc. (ICASSP ’05). IEEE Int. Conf. Acoust. Speech Signal Process. 5, 733–736 (2005)Google Scholar
- 22.Hale, E.T., Yin, W., Zhang, Y.: A Fixed-Point Continuation Method for ’1-Regularized Minimization with Applications to Compressed Sensing. Caam Tr (2007)Google Scholar
- 25.Price, K.V.: Differential evolution versus the functions of the 2nd, ICEO[C]. IEEE Int. Conf. Evolut. Comput. IEEE, 153–157 (1997)Google Scholar
- 26.Mierswa, I., Wurst, M.: Information preserving multi-objective feature selection for unsupervised learning[C]. Conf. Genet. Evolut. Comput. ACM, 1545–1552 (2006)Google Scholar
- 28.Zitzler, E., Laumanns, M., Thiele, L.: SPEA2: improving the strength pareto evolutionary algorithm (2001)Google Scholar
- 29.Dehnad, K.: Density estimation for statistics and data analysis. Technometrics 29(4), 296–297 (1986)Google Scholar
- 30.Deb, K., Thiele, L., Laumanns M, et al. Scalable multi-objective optimization test problems[C] Evolutionary Computation, 2002. CEC ’02. Proceedings of the 2002 Congress on. IEEE, (2002) :825–830Google Scholar