Cat Swarm Optimization algorithm for optimal linear phase FIR filter design
Cat Swarm Optimization algorithm for optimal linear phase FIR filter design
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DOI：
10.1016/j.isatra.2013.07.009
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发表时间：
2013-11-01
期刊：
影响因子：
7.3
通讯作者：
Mandal, Durbadal
中科院分区：
文献类型：
--
作者：
Saha, Suman Kumar;Ghoshal, Sakti Prasad;Mandal, Durbadal
本文采用一种新的亚启发式搜索方法--猫群优化(CSO)算法来确定FIR低通、高通、带通和带阻滤波器的最优冲激响应系数，以满足各自理想的频率响应特性。CSO是通过观察猫的行为而产生的，由两个子模型组成。在CSO中，可以决定迭代中使用了多少只猫。每只猫都有自己的位置，由M个维度组成，每个维度的速度，代表猫对适应度函数的适应程度的适应值，以及识别猫是处于寻找模式还是跟踪模式的标志。最终的解决方案将是其中一只猫的最佳位置。CSO将保留最优解，直到迭代结束。将该方法与实数编码遗传算法(RGA)、标准粒子群算法(PSO)和差分进化算法(DE)等常用优化方法的结果进行了比较。基于CSO的结果证实了所提出的CSO在解决FIR滤波器设计问题上的优越性。实验证明，基于CSO设计的FIR滤波器的性能优于RGA、传统PSO和DE。仿真结果还表明，在其他相关技术中，CSO是最优的，不仅在收敛速度上，而且在所设计的滤波器的性能上也是最优的。(C)2013年《国际行政程序法》。爱思唯尔有限公司出版。保留所有权利。
In this paper a new meta-heuristic search method, called Cat Swarm Optimization (CSO) algorithm is applied to determine the best optimal impulse response coefficients of FIR low pass, high pass, band pass and band stop filters, trying to meet the respective ideal frequency response characteristics. CSO is generated by observing the behaviour of cats and composed of two sub-models. In CSO, one can decide how many cats are used in the iteration. Every cat has its' own position composed of M dimensions, velocities for each dimension, a fitness value which represents the accommodation of the cat to the fitness function, and a flag to identify whether the cat is in seeking mode or tracing mode. The final solution would be the best position of one of the cats. CSO keeps the best solution until it reaches the end of the iteration. The results of the proposed CSO based approach have been compared to those of other well-known optimization methods such as Real Coded Genetic Algorithm (RGA), standard Particle Swarm Optimization (PSO) and Differential Evolution (DE). The CSO based results confirm the superiority of the proposed CSO for solving FIR filter design problems. The performances of the CSO based designed FIR filters have proven to be superior as compared to those obtained by RGA, conventional PSO and DE. The simulation results also demonstrate that the CSO is the best optimizer among other relevant techniques, not only in the convergence speed but also in the optimal performances of the designed filters. (C) 2013 ISA. Published by Elsevier Ltd. All rights reserved.