云模型改进萤火虫算法优化的模拟电路故障诊断

Analog circuit fault diagnosis optimized by cloud model improved firefly algorithm

针对萤火虫算法(FA)优化最小二乘支持向量机(LSSVM)的结构参数时,存在早熟和后期收敛速度慢等问题,提出了一种云模型改进型萤火虫算法(CCAFA)优化LSSVM参数的算法。首先,利用混沌映射初始化FA的初始位置,以获得群体的多样性;其次,依据萤火虫的适应度值将种群划分为三个区间,利用云自适应进化策略调整某一区间的惯性权重,之后采用云模型对萤火虫的初始位置实施变异操作;最后,使用混沌序列对群体最优位置进行优化处理,增强群体的全局搜索能力。通过对典型的4个参考函数进行测试,以测验该算法的可行性。并将CCAFA-LSSVM模型应用于模拟电路的故障诊断中,结果表明,改进型算法的收敛速度快、全局搜索能力强,有一定的参考价值。

In view of the firefly algorithm (FA) optimizing structural parameters of least squares support vector machine (LSSVM), there are problems such as premature convergence and slow convergence in the late stage, an improved cloud model firefly algorithm (CCAFA) algorithm for optimizing LSSVM parameters is proposed in this paper. Firstly, The chaotic map initializes the initial position of the FA to obtain the diversity of the population;Secondly, the population is divided into three intervals according to the fitness value of firefly, the inertia weight of a certain interval is adjusted by cloud adaptive evolution strategy, then the cloud model is used to mutate the initial position of the firefly;Finally, chaotic sequences are used to optimize the optimal population position and enhance the global search ability of the population. The typical four benchmark functions were tested to verify the feasibility of the algorithm. The CCAFA-LSSVM model is applied to the fault diagnosis of analog circuits, the results show that the improved algorithm has fast convergence speed and strong global search capability, which the proposed algorithm has certain reference value.