A Modified Wolf Pack Algorithm for Multiconstrained Sparse Linear Array Synthesis
The aim of the research is to propose a new optimization method for the multiconstrained optimization of sparse linear arrays (including the constraints of the number of elements, the aperture of arrays, and the minimum distance between adjacent elements). The new method is a modified wolf pack opti...
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| Format: | Article |
| Language: | English |
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Wiley
2020-01-01
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| Series: | International Journal of Antennas and Propagation |
| Online Access: | http://dx.doi.org/10.1155/2020/9483971 |
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| author | Ting Wang Ke-Wen Xia Hai-Lin Tang Su-Wei Zhang Mukase Sandrine |
| author_facet | Ting Wang Ke-Wen Xia Hai-Lin Tang Su-Wei Zhang Mukase Sandrine |
| author_sort | Ting Wang |
| collection | DOAJ |
| description | The aim of the research is to propose a new optimization method for the multiconstrained optimization of sparse linear arrays (including the constraints of the number of elements, the aperture of arrays, and the minimum distance between adjacent elements). The new method is a modified wolf pack optimization algorithm based on the quantum theory. In the new method, wolves are coded by Bloch spherical coordinates of quantum bits, updated by quantum revolving gates, and selectively adaptively mutated when performing poorly. Because of the three-coordinate characteristics of the sphere, the number of global optimum solutions is greatly expanded and ultimately can be searched with a higher probability. Selective mutation enhances the robustness of the algorithm and improves the search speed. Furthermore, because the size of each dimension of Bloch spherical coordinates is always [−1, 1], the variables transformed by solution space must satisfy the constraints of the aperture of arrays and the minimum distance between adjacent elements, which effectively avoids infallible solutions in the process of updating and mutating the position of the wolf group, reduces the judgment steps, and improves the efficiency of optimization. The validity and robustness of the proposed method are verified by the simulation of two typical examples, and the optimization efficiency of the proposed method is higher than the existing methods. |
| format | Article |
| id | doaj-art-b69dba44054b4dc2812dea8b7e46ca0c |
| institution | Kabale University |
| issn | 1687-5869 1687-5877 |
| language | English |
| publishDate | 2020-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | International Journal of Antennas and Propagation |
| spelling | doaj-art-b69dba44054b4dc2812dea8b7e46ca0c2025-02-03T01:05:06ZengWileyInternational Journal of Antennas and Propagation1687-58691687-58772020-01-01202010.1155/2020/94839719483971A Modified Wolf Pack Algorithm for Multiconstrained Sparse Linear Array SynthesisTing Wang0Ke-Wen Xia1Hai-Lin Tang2Su-Wei Zhang3Mukase Sandrine4School of Electronic Information Engineering, Hebei University of Technology, Tianjin 300000, ChinaSchool of Electronic Information Engineering, Hebei University of Technology, Tianjin 300000, ChinaPeople’s Liberation Army Air Force 93756, Tianjin 300000, ChinaYichang Testing Technology Research Institute, No. 58, Shengli Third Road, Yichang City, Hubei Province, ChinaSchool of Electronic Information Engineering, Hebei University of Technology, Tianjin 300000, ChinaThe aim of the research is to propose a new optimization method for the multiconstrained optimization of sparse linear arrays (including the constraints of the number of elements, the aperture of arrays, and the minimum distance between adjacent elements). The new method is a modified wolf pack optimization algorithm based on the quantum theory. In the new method, wolves are coded by Bloch spherical coordinates of quantum bits, updated by quantum revolving gates, and selectively adaptively mutated when performing poorly. Because of the three-coordinate characteristics of the sphere, the number of global optimum solutions is greatly expanded and ultimately can be searched with a higher probability. Selective mutation enhances the robustness of the algorithm and improves the search speed. Furthermore, because the size of each dimension of Bloch spherical coordinates is always [−1, 1], the variables transformed by solution space must satisfy the constraints of the aperture of arrays and the minimum distance between adjacent elements, which effectively avoids infallible solutions in the process of updating and mutating the position of the wolf group, reduces the judgment steps, and improves the efficiency of optimization. The validity and robustness of the proposed method are verified by the simulation of two typical examples, and the optimization efficiency of the proposed method is higher than the existing methods.http://dx.doi.org/10.1155/2020/9483971 |
| spellingShingle | Ting Wang Ke-Wen Xia Hai-Lin Tang Su-Wei Zhang Mukase Sandrine A Modified Wolf Pack Algorithm for Multiconstrained Sparse Linear Array Synthesis International Journal of Antennas and Propagation |
| title | A Modified Wolf Pack Algorithm for Multiconstrained Sparse Linear Array Synthesis |
| title_full | A Modified Wolf Pack Algorithm for Multiconstrained Sparse Linear Array Synthesis |
| title_fullStr | A Modified Wolf Pack Algorithm for Multiconstrained Sparse Linear Array Synthesis |
| title_full_unstemmed | A Modified Wolf Pack Algorithm for Multiconstrained Sparse Linear Array Synthesis |
| title_short | A Modified Wolf Pack Algorithm for Multiconstrained Sparse Linear Array Synthesis |
| title_sort | modified wolf pack algorithm for multiconstrained sparse linear array synthesis |
| url | http://dx.doi.org/10.1155/2020/9483971 |
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