An efficient hybrid genetic algorithm to design finite impulse response filters

Boudjelaba, Kamal; Ros, Frédéric; Chikouche, Djamel

doi:10.1016/j.eswa.2014.03.034

Cited by 40 publications

(15 citation statements)

References 16 publications

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“…In the method, it is pre-requisite to get the impulse response from the desired frequency response, and then the impulse response is multiplied by a window function. The method is efficient and fast, but the truncation of the impulse response results in large ripples in pass band and stop band 6,7 in the frequency domain. Another popular method for FIR digital filter design is the frequency sampling method.…”

Section: Traditional Identification Methodsmentioning

confidence: 99%

“…In order to further optimize the solution, some researchers recently proposed improved or new methods. Hybrid genetic algorithm (HGA) 7 introduces a self-adaptive management of the balance between diversity and elitism during the genetic life. Only some promising reference chromosomes are submitted to a local optimization procedure.…”

Section: Evolutionary Identification Methodsmentioning

confidence: 99%

“…This method depends on fixed or floating point implementation, numerical accuracy as well as the filter order. 7 IIR filters are designed using different methods. One of the most commonly used methods is based on the reference analog prototype filter.…”

Section: Traditional Identification Methodsmentioning

confidence: 99%

See 2 more Smart Citations

Structure Evolution Based Optimization Algorithm for Low Pass IIR Digital Filter Design

Chen¹,

Liu²,

Yang³

et al. 2017

IJCIS

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Digital filters are generally designed by identifying the transfer functions. Most researches are focused on the goal of approaching the desired frequency response, and take less additional consideration of structure characteristics which can greatly affect the performance of the digital filter. This paper proposes a structure-evolution based optimization algorithm (SEOA) which allows the integrated consideration of structure issues and frequency response specifications in design stage. The method generates digital filter structures by a structurally automatic-generation algorithm (SAGA) which can randomly generate and effectively represent digital structures. The structures, seen as chromosomes, are evolved over genetic algorithm (GA) for the search of the optimal solution in structure space. They are evaluated according to the mean squared error (MSE) between the designed and the desired frequency responses. Simulation results validate that the algorithm designs diversified structures of digital filters and they meet target frequency specifications and structure constraints tightly. It is a promising way for optimized and automated design of digital filters.

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Section: Traditional Identification Methodsmentioning

confidence: 99%

Section: Evolutionary Identification Methodsmentioning

confidence: 99%

See 1 more Smart Citation

Structure Evolution Based Optimization Algorithm for Low Pass IIR Digital Filter Design

Chen¹,

Liu²,

Yang³

et al. 2017

IJCIS

View full text Add to dashboard Cite

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“…Evolutionary algorithms, because of their ability in converging to the global optima, have been widely used for FIR filter design (Ababneh and Bataineh, 2008;Ahmad and Antoniou, 2006;Boudjelaba et al, 2014;Ghoshal et al, 2012;Karaboga and Cetinkaya, 2006;Liu et al, 2010;Lu and Tzeng, 2000;Mandal et al, 2012;Najjarzadeh and Ayatollahi, 2008;Radecki et al, 2005;Saha et al, 2013aSaha et al, , 2013c. The various techniques reported in this context include simulated annealing (Radecki et al, 2005), genetic algorithms (GA) (Ahmad and Antoniou, 2006;Boudjelaba et al, 2014;Lu and Tzeng, 2000), particle swarm optimization (PSO) (Ababneh and Bataineh, 2008;Mandal et al, 2012), Differential evolution (DE) (Karaboga and Cetinkaya, 2006;Liu et al, 2010), Hybrid differential evolution and PSO (DEPSO) (Luitel and Venayagamoorthy, 2008), orthogonal harmony search algorithm (OHS) (Saha et al, 2013a), Cat swarm optimization (CSO) (Saha et al, 2013b), bacteria foraging optimization algorithm (Saha et al, 2013c) and Seeker optimization algorithm (SOA) . However, a common limitation with most of the optimization based FIR filter design techniques is that they aim at meeting a specific objective i.e.…”

Section: Introductionmentioning

confidence: 99%

Low power FIR filter design using modified multi-objective artificial bee colony algorithm

Dwivedi

Ghosh

Londhe

2016

Engineering Applications of Artificial Intelligence

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“…particle swarm method followed by a global minimization method (Vaz & Vicente, 2007, tabu search and simulated annealing algorithms as presented in Hedar et al (2002), Hedar and Fukushima (2006), or genetic algorithms (Boudjelaba, Ros, & Chikouche, 2014).…”

Section: Introductionmentioning

confidence: 99%

Adaptive Penalty and Barrier function based on Fuzzy Logic

Matias

Correia

Mestre

et al. 2015

Expert Systems with Applications

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Optimization methods have been used in many areas of knowledge, such as Engineering, Statistics, Chemistry, among others, to solve optimization problems. In many cases it is not possible to use derivative methods, due to the characteristics of the problem to be solved and/or its constraints, for example if the involved functions are non-smooth and/or their derivatives are not know. To solve this type of problems a Java based API has been implemented, which includes only derivative-free optimization methods, and that can be used to solve both constrained and unconstrained problems. For solving constrained problems, the classic Penalty and Barrier functions were included in the API. In this paper a new approach to Penalty and Barrier functions, based on Fuzzy Logic, is proposed. Two penalty functions, that impose a progressive penalization to solutions that violate the constraints, are discussed. The implemented functions impose a low penalization when the violation of the constraints is low and a heavy penalty when the violation is high. Numerical results, obtained using twentyeight test problems, comparing the proposed Fuzzy Logic based functions to six of the classic Penalty and Barrier functions are presented. Considering the achieved results, it can be concluded that the proposed penalty functions besides being very robust also have a very good performance.

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An efficient hybrid genetic algorithm to design finite impulse response filters

Cited by 40 publications

References 16 publications

Structure Evolution Based Optimization Algorithm for Low Pass IIR Digital Filter Design

Structure Evolution Based Optimization Algorithm for Low Pass IIR Digital Filter Design

Low power FIR filter design using modified multi-objective artificial bee colony algorithm

Adaptive Penalty and Barrier function based on Fuzzy Logic

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