基于Gbest引导布谷鸟搜索算法的IIR滤波器设计

Niloy Chakraborty, Adrika Mukherjee, Supriya Dhabal
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引用次数: 1

摘要

IIR滤波器的设计是一项具有挑战性的任务,本文采用鲸鱼优化算法(WOA)、人工蜂群(ABC)算法、布谷鸟搜索算法(CSA)和Gbest Guided Cuckoo Search (GCS)算法设计了IIR滤波器。最近开发的元启发式算法CSA的修改版本是GCS,其中实现了一些更改以使算法更快且参数独立。不同实验设计案例(分别对二阶、三阶和四阶传递函数)的仿真结果表明,GCS收敛速度更快;与WOA、ABC和CSA相比,计算时间更短。研究发现,在GCS情况下,采用二阶、三阶和四阶传递函数设计的IIR滤波器每100次迭代所需的计算时间分别为0.00043 s、0.00035 s和0.0000569 s。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Design of IIR filter using Gbest guided cuckoo search algorithm
The design of IIR filter is a challenging task and this paper presents a design of IIR filter using Whale Optimisation Algorithm (WOA), Artificial Bee Colony (ABC) Algorithm, Cuckoo Search Algorithm (CSA) and Gbest Guided Cuckoo Search (GCS) Algorithm. A modified version of a recently developed meta-heuristic algorithm CSA is GCS, where some changes are implemented to make the algorithm faster and parameter independent. Simulation results from different experimental design cases (on the transfer function of 2nd, 3rd and 4th order respectively) affirm that GCS converges faster; furthermore, the computational time required is less than WOA, ABC and CSA. It is found that in the case of GCS, the computational time required for designed IIR filter using 2nd, 3rd and 4th order transfer functions are 0.00043 s., 0.00035 s and 0.0000569 s, every 100 iterations respectively.
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