Two-stage ensemble memetic algorithm: Function optimization and digital IIR filter design
Two-stage ensemble memetic algorithm: Function optimization and digital IIR filter design
复制标题
两级集成模因算法：功能优化和数字 IIR 滤波器设计
DOI：
10.1016/j.ins.2012.07.041
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发表时间：
2013
影响因子：
8.1
通讯作者：
Weise, Thomas
中科院分区：
文献类型：
--
作者：
Wang, Yu;Li, Bin;Weise, Thomas
基于优化技术的无限脉冲响应（IIR）滤波器的优化设计研究近年来备受关注。然而，由于所应用的优化技术的性能有限，在以往的研究中，可以得到的滤波器阶数很低。模因算法（Memetic algorithms, MAs）被广泛认为具有比传统算法更好的收敛能力。然而，MAs的通用性，例如解决各种数字IIR滤波器设计的能力仍然有限。在本文中，我们设计了一个两阶段集成模因算法（Two-Stage ensemble Memetic Algorithm， TSMA）框架，以更恰当地综合进化全局搜索和局部搜索技术的优势。在第一个优化阶段，在候选局部搜索技术之间进行竞争。其主要思想是选择最佳的局部搜索技术并获得良好的初始状态。第二优化阶段是继承第一阶段的良好信息，实施有效的自适应遗传算法，追求高质量的解。本文的实验研究包括三个方面：(1)通过比较TSMA及其次优化器和最近有效的进化算法（EAs）在26个测试函数上的优势，对TSMA框架进行了实验研究；然后(2)将TSMA与4ma在CEC05功能上进行比较，全面展示TSMA的优势；(3)应用TSMA和6种最先进的算法设计高阶数字无限脉冲响应（IIR）滤波器。实验结果明确地证明了TSMA在功能优化和数字IIR滤波器设计任务上的卓越有效性、高效性和可靠性。
The research on optimal design of infinite-impulse response (IIR) filters based on optimization techniques has gained much attention in recent years. However, due to the limited performance of the applied optimization techniques, the orders of the filters, which can be obtained, are very low in the previous research. Memetic algorithms (MAs) are widely recognized to have better convergence capability than their conventional counterparts. However, the universality of the MAs, e.g. the ability of solving diverse kinds of digital IIR filter designs, is still limited. In this paper, we design a Two-Stage ensemble Memetic Algorithm (TSMA) framework to more appropriately synthesize the strengths of the evolutionary global search and local search techniques. In the first optimization stage, a competition is held among the candidate local search techniques. Its major idea is to choose the best local search technique and to obtain good initial state. Inheriting the good information of the first stage, the second optimization stage is to implement effective adaptive MA to pursue high-quality solution. The experimental studies presented in this paper contain three aspects: (1) the benefits of the TSMA framework are experimentally investigated by comparing TSMA with its sub-optimizers and recent effective evolutionary algorithms (EAs) on 26 test functions; then (2) TSMA is compared with 4 MAs on the CEC05 functions to comprehensively show the advantages of TSMA; and (3) the TSMA and 6 state-of-the-art algorithms are applied to design high-order digital infinite-impulse response (IIR) filters. The experimental results definitely demonstrate the excellent effectiveness, efficiency and reliability of TSMA on both function optimization and digital IIR filter design tasks.
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DOI：
--
发表时间：
2007
期刊：
--
影响因子：
--
作者：
C. Houck;M. Kay
通讯作者：
C. Houck;M. Kay
DOI：
10.1109/cec.2008.4631330
发表时间：
2008-06
期刊：
2008 IEEE Congress on Evolutionary Computation (IEEE World Congress on Computational Intelligence)
影响因子：
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作者：
Yu Wang;Bin Li
通讯作者：
Yu Wang;Bin Li
影响因子：
1.4
作者：
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通讯作者：
POWELL, MJD
DOI：
10.1109/tsmcb.2006.883268
发表时间：
2007-02
期刊：
IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics)
影响因子：
--
作者：
Maolin Tang;X. Yao
通讯作者：
Maolin Tang;X. Yao
DOI：
10.1109/cec.2005.1554902
发表时间：
2005-12
期刊：
2005 IEEE Congress on Evolutionary Computation
影响因子：
--
作者：
A. Auger;N. Hansen
通讯作者：
A. Auger;N. Hansen