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雙交換遺傳算法及其在非周期天線陣方向圖綜合中的應(yīng)用

李東風(fēng),  龔中麟

李東風(fēng), 龔中麟. 雙交換遺傳算法及其在非周期天線陣方向圖綜合中的應(yīng)用[J]. 電子與信息學(xué)報, 2003, 25(2): 242-246.
引用本文: 李東風(fēng), 龔中麟. 雙交換遺傳算法及其在非周期天線陣方向圖綜合中的應(yīng)用[J]. 電子與信息學(xué)報, 2003, 25(2): 242-246.
Li Dongfeng, Gong Zhonglin. The double-crossover genetic algorithm and its application in radiation pattern synthesis of aperiodic antenna arrays[J]. Journal of Electronics & Information Technology, 2003, 25(2): 242-246.
Citation: Li Dongfeng, Gong Zhonglin. The double-crossover genetic algorithm and its application in radiation pattern synthesis of aperiodic antenna arrays[J]. Journal of Electronics & Information Technology, 2003, 25(2): 242-246.

雙交換遺傳算法及其在非周期天線陣方向圖綜合中的應(yīng)用

The double-crossover genetic algorithm and its application in radiation pattern synthesis of aperiodic antenna arrays

  • 摘要: 提出了一種雙交換遺傳出法,它拓展了傳統(tǒng)遺傳算法的基本思想,通過引入雙交換繁殖操作和參數(shù)化染色體,擺脫了傳統(tǒng)遺傳算法收斂對初始群體的依賴性,避免了傳統(tǒng)遺傳算法繁瑣的編解碼過程,因而,它比傳統(tǒng)遺傳算法更有效和更實用,為了說明雙交換遺傳算法的有效性和實用性,將雙交換遺傳算法應(yīng)用于非周期圓形天線陣掃描方向圖綜合,應(yīng)用表明雙交換遺傳算法可以成為一種通用的陣列天線輻射方向圖綜合工具。
    Abstract: The double-crossover Genetic Algorithm (GA) extending the basic idea of conventional Genetic Algorithms(GAs) is presented. The double-crossover GA with the double-crossover reproduction operation and parametric chromosomes gets rid of the dependence of the convergence of conventional GAs on the initial population, avoids troublesome encoding and decoding, and is more practical and effective than conventional GAs. To illustrate effectiveness and practicality, the double-crossover GA is applied to the scanning radiation pattern synthesis of aperiodic circular arrays. The successful application shows that the double-crossover GA can be used as a general tool for radiation pattern synthesis of antenna arrays.
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  • R.L. Haupt, Thinned arrays using genetic algorithms, IEEE Trans. on Antennas Propagat.,1994, 42(7), 993-999.[2]Dong Feng Li, Zhong Lin Gong, Design of hexagonal planar arrays using genetic algorithms for performance improvement, 2000 2nd International Conference on Microwave and Millimeter Wave Technology Proceedings, Beijing, China, Sept. 2000, 455-460.[3]J. Holland, Adaptation in Nature and Artificial Systems, Ann Arbor, The University of Michigan Press, 1975, chapter 3.[4]D.E. Goldberg, Genetic Algorithms in Search, Optimization, and Machine Learning, New York,Addison-Wesley, 1989, chapter 2.[5]L. Davis, Ed., Genetic Algorithms and Simulated Annealing, Los Altos, CA, Morgan Kaufmann,1987, chapter 2.[6]陳國良,王煦發(fā),莊鎮(zhèn)泉,王東生,遺傳算法及其應(yīng)用,北京,人民郵電出版社,1996,28-100.[7]劉勇,康立山,陳屏,非數(shù)值并行算法(第二冊),遺傳算法,北京,科學(xué)出版社,1995,38-108. ［8］K.K. Yan, Y. Lu, Sidelobe reduction in array-pattern synthesis using genetic algorithms, IEEE Trans. on Antennas Propagat., 1997, 45(7), 1117-1122.[8]D. Marcano, F. Duran, Synthesis of antenna arrays using genetic algorithms, IEEE Antennas and Propagation Magazine, 2000, 42(3), 12-20.[9]佘春峰,楊華中,胡冠章,汪蕙,浮點遺傳算法的收斂性及其在模型參數(shù)提取問題中的應(yīng)用,電子學(xué)報,2000,28(3),134-136.[10]R. C. Johnson, Antenna Engineering Handbook, Third edition, New York, McGraw-Hill, Inc.,1993, chapter 3.[11]R.J. Mailloux, Phased Array Antenna Handbook, Boston, Artech House, 1993, 81-87.[12]M.T. Ma, Theory and Application of Antenna Arrays, New York, Wiley, 1974, 191-202.
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出版歷程
  • 收稿日期:  2001-09-18
  • 修回日期:  2002-02-25
  • 刊出日期:  2003-02-19

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