Algorithm Research & Explore
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App. Res. of Comp. Printed Article
Vol. 37, 2020 No. 4
1010-1014

Sparse reconstruction algorithm based on improved genetic algorithm

Pan Meihong
Zheng Qin
College of Electronic & Information Engineering, Nanjing University of Aeronautics & Astronautics, Nanjing 211106, China
DOI: 10.19734/j.issn.1001-3695.2018.10.0727

Abstract

Convex relaxation methods present drawback in terms of computational complexity, meanwhile, greedy pursuit methods have disadvantages in their reconstruction accuracy. Based on the inspiration of iterative optimization of genetic algorithm and combining with the advantages of simulated annealing and multi-population algorithm, this paper proposed two heuristic sparse reconstruction algorithms based on simulated annealing genetic algorithm and multi-population genetic algorithm. Aiming at the defects of the traditional genetic algorithm that often trapped in the local optimal solutions, it implemented two strategies to search global optimal solutions of the sparse reconstruction via maintaining the differences among individuals and increasing the diversity of the population, respectively. The validity of the proposed algorithms on parameters selection and search strategy was proved by theoretical analysis. The proposed algorithms could be applied to the DoA estimation of multiple spatial sources in array signal processing to verify the effectiveness. Simulation results show that, compared with the OMP algorithm and l1-SVD algorithm, the proposed algorithms have improved the accuracy and reduced the computational complexity, which can converge to the global optimal solution in a fast manner.

Foundation Support

中央高校基本科研业务费基金资助项目(3082017NP2017421)
南京航空航天大学研究生创新基地(实验室)开放基金资助项目(kfjj20170403)

Publish Information

DOI: 10.19734/j.issn.1001-3695.2018.10.0727
Publish at: Application Research of Computers Printed Article, Vol. 37, 2020 No. 4
Section: Algorithm Research & Explore
Pages: 1010-1014
Serial Number: 1001-3695(2020)04-011-1010-05

Publish History

[2020-04-05] Printed Article

Cite This Article

潘美虹, 郑芹. 基于改进遗传算法的稀疏重构算法 [J]. 计算机应用研究, 2020, 37 (4): 1010-1014. (Pan Meihong, Zheng Qin. Sparse reconstruction algorithm based on improved genetic algorithm [J]. Application Research of Computers, 2020, 37 (4): 1010-1014. )

About the Journal

  • Application Research of Computers Monthly Journal
  • Journal ID ISSN 1001-3695
    CN  51-1196/TP

Application Research of Computers, founded in 1984, is an academic journal of computing technology sponsored by Sichuan Institute of Computer Sciences under the Science and Technology Department of Sichuan Province.

Aiming at the urgently needed cutting-edge technology in this discipline, Application Research of Computers reflects the mainstream technology, hot technology and the latest development trend of computer application research at home and abroad in a timely manner. The main contents of the journal include high-level academic papers in this discipline, the latest scientific research results and major application results. The contents of the columns involve new theories of computer discipline, basic computer theory, algorithm theory research, algorithm design and analysis, blockchain technology, system software and software engineering technology, pattern recognition and artificial intelligence, architecture, advanced computing, parallel processing, database technology, computer network and communication technology, information security technology, computer image graphics and its latest hot application technology.

Application Research of Computers has many high-level readers and authors, and its readers are mainly senior and middle-level researchers and engineers engaged in the field of computer science, as well as teachers and students majoring in computer science and related majors in colleges and universities. Over the years, the total citation frequency and Web download rate of Application Research of Computers have been ranked among the top of similar academic journals in this discipline, and the academic papers published are highly popular among the readers for their novelty, academics, foresight, orientation and practicality.

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