融合BFGS的自适应蜂群算法在谐波平衡分析中的应用

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

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。

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.