Hybrid Differential Evolution with Covariance Matrix Adaptation for Digital Filter Design

被引:0
|
作者
Walczak, Krzysztof [1 ]
机构
[1] Silesian Tech Univ, Inst Elect, Gliwice, Poland
来源
2011 IEEE SYMPOSIUM ON DIFFERENTIAL EVOLUTION (SDE) | 2011年
关键词
OPTIMIZATION; STRATEGY;
D O I
暂无
中图分类号
TM [电工技术]; TN [电子技术、通信技术];
学科分类号
0808 ; 0809 ;
摘要
Digital filter design method utilizing a hybrid algorithm based on a Differential Evolution (DE) and Covariance Matrix Adaptation Evolution Strategy (CMA-ES) is presented. The main goal of the algorithm is to optimize the finite impulse response (FIR) filter coefficients which lead to the minimum error between the actual and the ideal filter frequency response. DE performs the global exploration and optimizes the parameters of exponential functions that define the bounded search space. Next, CMA-ES is used as a local search engine. Its initial search point and boundary constraint estimates are provided by DE. Additionally, periodic feedback from CMA-ES is provided to the DE. The hybrid approach and the idea of search space boundary estimation seems to be a promising method for the FIR filter design task, especially for relatively high dimension filters. The algorithm performance is compared with the classical filter design methods and the other evolutionary proposals found in literature.
引用
收藏
页码:120 / 126
页数:7
相关论文
共 50 条
  • [31] Covariance matrix adaptation evolution strategy based on ensemble of mutations for parking navigation and maneuver of autonomous vehicles
    Aboyeji, Esther Tolulope
    Ajani, Oladayo S.
    Mallipeddi, Rammohan
    EXPERT SYSTEMS WITH APPLICATIONS, 2024, 249
  • [32] On the steady state analysis of covariance matrix self-adaptation evolution strategies on the noisy ellipsoid model
    Hellwig, Michael
    Beyer, Hans-Georg
    THEORETICAL COMPUTER SCIENCE, 2020, 832 : 98 - 122
  • [33] Covariance matrix self-adaptation evolution strategies and other metaheuristic techniques for neural adaptive learning
    Peterson, Leif E.
    SOFT COMPUTING, 2011, 15 (08) : 1483 - 1495
  • [34] A Reference Vector-Based Simplified Covariance Matrix Adaptation Evolution Strategy for Constrained Global Optimization
    Kumar, Abhishek
    Das, Swagatam
    Mallipeddi, Rammohan
    IEEE TRANSACTIONS ON CYBERNETICS, 2022, 52 (05) : 3696 - 3709
  • [35] Surrogate-Assisted Automatic Parameter Adaptation Design for Differential Evolution
    Stanovov, Vladimir
    Semenkin, Eugene
    MATHEMATICS, 2023, 11 (13)
  • [36] Pareto Optimal Microwave Filter Design Using Multiobjective Differential Evolution
    Goudos, Sotirios K.
    Sahalos, John N.
    IEEE TRANSACTIONS ON ANTENNAS AND PROPAGATION, 2010, 58 (01) : 132 - 144
  • [37] Ensemble Sinusoidal Differential Covariance Matrix Adaptation with Euclidean Neighborhood for Solving CEC2017 Benchmark Problems
    Awad, Noor H.
    Ali, Mostafa Z.
    Suganthan, Ponnuthurai N.
    2017 IEEE CONGRESS ON EVOLUTIONARY COMPUTATION (CEC), 2017, : 372 - 379
  • [38] Circuit Tolerance Design by Differential Evolution with Hybrid Analysis Method
    Zhong, Fugui
    Li, Bin
    Yuan, Bo
    2016 EIGHTH INTERNATIONAL CONFERENCE ON ADVANCED COMPUTATIONAL INTELLIGENCE (ICACI), 2016, : 74 - 78
  • [39] Parameters Adaptation in Differential Evolution
    Elsayed, Saber M.
    Sarker, Ruhul A.
    Ray, Tapabrata
    2012 IEEE CONGRESS ON EVOLUTIONARY COMPUTATION (CEC), 2012,
  • [40] Inverse Profiling of Inhomogeneous Subsurface Targets With Arbitrary Cross Sections Using Covariance Matrix Adaptation Evolution Strategy
    Hajebi, Maryam
    Hoorfar, Ahmad
    Bou-Daher, Elie
    Tavakoli, Ahad
    IEEE GEOSCIENCE AND REMOTE SENSING LETTERS, 2017, 14 (05) : 612 - 616