In this paper, a learning automata (LA) method is introduced based on dimensionality ranking (called DRLA), and its application for designing analog active filters is investigated. The basic idea in the DRLA is that the number of iterations and ordering selection of dimensions for the next phase is according to effect of each dimension in the fitness improvement of the current phase or previous phases. This strategy improves the exploration of search space and escaping the local minimums. Also, to achieve the proper balance in exploration and exploitation, the control parameter $\tau$ is changed by a linear control manner. To validate the applicability, accuracy, and performance of the proposed approach, it is first evaluated on 23 benchmark functions and then, as a new application of LA, applied to the optimal design of two different analog active filter topologies as real-world engineering problems. The filter component values are estimated within E96 series for resistors and capacitors, and a single-objective function is also considered as average quality factors deviation and cut-off frequency deviation. Simulation results compared to some rival single-objective metaheuristic optimization methods reveal that the improved method has competitive performance in terms of fitness, convergence, statistical analyses, box plot, and bode curve as well as runtime.

本文介绍了一种基于维数排序的学习自动机方法，称为DRLA，并研究了其在模拟有源滤波器设计中的应用。DRLA中的基本思想是，下一阶段的迭代次数和维度的排序选择是根据每个维度在当前阶段或先前阶段的适应性改善中的效果而定的。此策略可改善对搜索空间的探索并避免局部最小值。另外，为了在勘探和开发中达到适当的平衡，控制参数$\tau$通过线性控制方式改变。为了验证所提出方法的适用性，准确性和性能，首先对23个基准功能进行了评估，然后作为LA的新应用，将其作为现实工程问题应用于两种不同模拟有源滤波器拓扑的优化设计。 。E96系列中的电阻器和电容器的滤波器组件值是估计值，并且单目标函数也被视为平均品质因数偏差和截止频率偏差。与一些竞争对手的单目标元启发式优化方法相比，仿真结果表明，改进的方法在适应性，收敛性，统计分析，箱形图和波特曲线以及运行时间方面均具有竞争优势。