Multi-Objective Optimization Using Evolutionary Algorithms
Multi-Objective Optimization Using Evolutionary Algorithms
Evolutionary Algorithms for Solving Multi-Objective Problems (Genetic and Evolutionary Computation)
Evolutionary Algorithms for Solving Multi-Objective Problems (Genetic and Evolutionary Computation)
Multiobjective Optimization in Bioinformatics and Computational Biology
IEEE/ACM Transactions on Computational Biology and Bioinformatics (TCBB)
Locating the critical failure surface in a slope stability analysis by genetic algorithm
Applied Soft Computing
Adaptive parameter control of evolutionary algorithms to improve quality-time trade-off
Applied Soft Computing
A constraint-guided method with evolutionary algorithms for economic problems
Applied Soft Computing
An EMO algorithm using the hypervolume measure as selection criterion
EMO'05 Proceedings of the Third international conference on Evolutionary Multi-Criterion Optimization
Optimal Design of Magnitude Responses of Rational Infinite Impulse Response Filters
IEEE Transactions on Signal Processing
A WISE method for designing IIR filters
IEEE Transactions on Signal Processing
Automatic design of frequency sampling filters by hybrid geneticalgorithm techniques
IEEE Transactions on Signal Processing
Evolutionary programming made faster
IEEE Transactions on Evolutionary Computation
A fast and elitist multiobjective genetic algorithm: NSGA-II
IEEE Transactions on Evolutionary Computation
Multiobjective evolutionary algorithms for electric power dispatch problem
IEEE Transactions on Evolutionary Computation
RM-MEDA: A Regularity Model-Based Multiobjective Estimation of Distribution Algorithm
IEEE Transactions on Evolutionary Computation
Evaluation of two-stage ensemble evolutionary algorithm for numerical optimization
ICSI'11 Proceedings of the Second international conference on Advances in swarm intelligence - Volume Part I
Two-stage ensemble memetic algorithm: Function optimization and digital IIR filter design
Information Sciences: an International Journal
Fixed-point digital IIR filter design using two-stage ensemble evolutionary algorithm
Applied Soft Computing
Hi-index | 0.00 |
The research of applying evolutionary algorithms (EAs) to digital infinite-impulse response (IIR) filter design has gained much attention in recent years. Previously, most works treated digital IIR filter design as a single objective optimization problem of minimizing the magnitude response error with supplementary conditions. While the lack of considering the linear phase response error and the order may result in the loss of control on the structural flexibility, the distortion of output, and the dependency on pre-knowledge. The aim of this paper is to develop proper IIR filter designing method that (1) can provide relatively more complete optimal solutions with equal consideration of magnitude response, linear phase response and the order of structure; (2) can simultaneously optimize the structure and coefficients of digital IIR filter to obtain relatively better linear phase response and lower order, besides the good magnitude response. To achieve these targets, the digital IIR filter design problem is treated as a multi-objective optimization problem in this paper. A new local search operator enhanced multi-objective evolutionary algorithm (LS-MOEA) is specifically proposed for such kind of multi-objective optimization problems. To evaluate the effectiveness and efficiency of LS-MOEA, we experimentally compare it with classical methods and previously effective EAs for digital IIR filter design on four typical IIR filter design cases. Experimental results show that the proposed method can effectively improve the linear phase response of the designed filter, and can obtain filter of lower order. Besides, it achieves these by relatively much lower computational cost than compared EAs.