计算机应用 ›› 2017, Vol. 37 ›› Issue (5): 1516-1520.DOI: 10.11772/j.issn.1001-9081.2017.05.1516

• 应用前沿、交叉与综合 • 上一篇    

融合BFGS的自适应蜂群算法在谐波平衡分析中的应用

南敬昌, 张云雪, 高明明   

  1. 辽宁工程技术大学 电子与信息工程学院, 辽宁 葫芦岛 125105
  • 收稿日期:2016-10-10 修回日期:2016-11-25 出版日期:2017-05-10 发布日期:2017-05-16
  • 通讯作者: 高明明
  • 作者简介:南敬昌(1971-),男,河南滑县人,教授,博士,主要研究方向:射频电路与器件、多媒体信息编码、通信系统仿真;张云雪(1991-),女,天津人,硕士,主要研究方向:射频功率放大器谐波平衡方程求解算法;高明明(1980-),女,内蒙古平庄人,副教授,博士,主要研究方向:无线通信、射频通信。
  • 基金资助:
    国家自然科学基金资助项目(61372058);辽宁省高校重点实验室项目(LJZS007);辽宁省教育厅科学研究项目(L2015209)。

Adaptive bee colony algorithm combined with BFGS algorithm for microwave circuit harmonic balance analysis

NAN Jingchang, ZHANG Yunxue, GAO Mingming   

  1. School of Electrics and Information Engineering, Liaoning Technical University, Huludao Liaoning 125105, China
  • Received:2016-10-10 Revised:2016-11-25 Online:2017-05-10 Published:2017-05-16
  • Supported by:
    This work is partially supported by the National Natural Science Foundation of China (61372058), the Key Laboratory of Universities and colleges Program of the Liaoning Province (LJZS007), the Scientific Research General Program of Liaoning Provincial Department of Education (L2015209).

摘要: 针对谐波平衡分析中传统算法存在初值限制,以及智能算法收敛速度慢的缺点,提出一种基于BFGS(Broyden-Fleteher-Goldfarl-Shanno)算法局部搜索策略的自适应蜂群算法。该算法在基本蜂群算法的基础上引入非线性的动态调整因子代替蜂群算法搜索公式中的随机变量,增加搜索的自适应性,并将BFGS算法运用到自适应蜂群算法后期求解,提高其局部搜索能力。实验结果表明,改进算法较标准蜂群算法迭代次数减少51.9%,相对于传统BFGS算法和部分改进智能算法均表现出较好收敛性能。

关键词: 自适应蜂群算法, 动态调整因子, BFGS算法, 谐波平衡, 非线性分析

Abstract: In view of the shortcomings of the initial value limitation of traditional algorithms and slow convergence speed of intelligent algorithms in harmonic balance analysis, an adaptive bee colony algorithm based on local search strategy of Broyden-Fleteher-Goldfarl-Shanno (BFGS) algorithm was proposed. Based on the basic bee colony algorithm, nonlinear dynamic adjustment factor was introduced to replace the random variables in the formula, thus improving the adaptability of searching. Meanwhile, BFGS algorithm was applied to the later period of bee colony algorithm to speed up the local search capability. Simulation results show that compared with the standard bee colony algorithm, the number of iterations of the improved algorithm was reduced by 51.9%, and the proposed algorithm has better convergence performance compared with the traditional BFGS algorithm and some other improved intelligent algorithms.

Key words: adaptive bee colony algorithm, dynamic adjustment factor, Broyden-Fleteher-Goldfarl-Shanno (BFGS) algorithm, harmonic balance, nonlinear analysis

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