Designing digital IIR filters using ant colony optimisation algorithm

Karaboğa, Nurhan; Kalınlı, Adem; Karaboğa, Derviş

doi:10.1016/j.engappai.2004.02.009

Cited by 121 publications

(57 citation statements)

References 14 publications

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“…In this paper, we use the ABC algorithm to find its optimal weights. Artificial Bee Colony (ABC) algorithm was originally presented by Karaboga et al [18] under the inspiration of collective behavior on honey bees with better performance in function optimization problems compared with GA, differential evolution (DE), and particle swarm optimization (PSO) [19]. As is known, normal global optimization techniques conduct only one search operation in one iteration.…”

Section: Introductionmentioning

confidence: 99%

Magnetic Resonance Brain Image Classification by an Improved Artificial Bee Colony Algorithm

Zhang¹,

Wu²,

Wang³

2011

PIER

168

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Abstract-Automated and accurate classification of magnetic resonance (MR) brain images is a hot topic in the field of neuroimaging. Recently many different and innovative methods have been proposed to improve upon this technology. In this study, we presented a hybrid method based on forward neural network (FNN) to classify an MR brain image as normal or abnormal. The method first employed a discrete wavelet transform to extract features from images, and then applied the technique of principle component analysis (PCA) to reduce the size of the features. The reduced features were sent to an FNN, of which the parameters were optimized via an improved artificial bee colony (ABC) algorithm based on both fitness scaling and chaotic theory. We referred to the improved algorithm as scaled chaotic artificial bee colony (SCABC). Moreover, the K-fold stratified cross validation was employed to avoid overfitting. In the experiment, we applied the proposed method on the data set of T2-weighted MRI images consisting of 66 brain images (18 normal and 48 abnormal). The proposed SCABC was compared with traditional training methods such as BP, momentum BP, genetic algorithm, elite genetic algorithm with migration, simulated annealing, and ABC. Each algorithm was run 20 times to reduce randomness. The results show that our SCABC can obtain the least mean MSE and 100% classification accuracy.

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Section: Introductionmentioning

confidence: 99%

Magnetic Resonance Brain Image Classification by an Improved Artificial Bee Colony Algorithm

Zhang¹,

Wu²,

Wang³

2011

PIER

168

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“…Genetic Algorith m (GA ) is not only capable of searching multid imensional and multimodal spaces but also optimizes co mplex and discontinuous functions that are hard to analyze mathematically. Therefore, researchers have developed design methods based on modern heuristics optimizat ion algorith ms such as genetic algorith ms [2][3][4][5][6][7][8][9][10] , particle swarm optimization [11] , seeker-optimizat ion-algorithm-based evolutionary method [12] , simulated annealing [13] , tabu search [14] , ant colony optimization [15] , hybrid taguchi genetic algorith m (HTGA) [16] ,immune algorith m (TIA) [17] and many more.…”

Section: Introductionmentioning

confidence: 99%

Digital IIR Filter Design using Real Coded Genetic Algorithm

Kaur¹,

Patterh²,

Dhillon³

2013

IJITCS

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Abstract-The paper develops a technique for the robust and stable design of digital infinite impulse response (IIR) filters. As the error surface of IIR filters is generally mu lti-modal, g lobal optimization techniques are required to design efficient digital IIR filter in order to avoid local minima. In this paper a real-coded genetic algorithm (RCGA ) with arith meticaverage-bound-blend crossover and wavelet mutation is applied to design the digital IIR filter. A mu lticriterion optimization is emp loyed as the design criterion to obtain the optimal stable IIR filter that satisfies the different performance requirements like min imizing the L p -norm appro ximat ion error and min imizing the ripple magnitude. The proposed real-coded genetic algorithm is effect ively applied to solve the mult icriterion, mu ltiparameter optimization problems of lo w-pass, high-pass, band-pass, and band-stop digital filters design. The computational experiments show that the proposed method is superior or atleast comparable to other algorith ms and can be efficiently used for h igher order filter design.

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“…However, as the particles in PSO only search in a finite sampling space, it also lacks the ability to jump out of the local optima when solving complex multi-modal tasks. 16 Ant colony optimisation (ACO) 18 imitates the social behavior of real ant colonies. It may occasionally be trapped into local stagnation or premature convergence resulting in a low optimizing precision or even failure.…”

Section: Evolutionary Identification Methodsmentioning

confidence: 99%

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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Designing digital IIR filters using ant colony optimisation algorithm

Cited by 121 publications

References 14 publications

Magnetic Resonance Brain Image Classification by an Improved Artificial Bee Colony Algorithm

Magnetic Resonance Brain Image Classification by an Improved Artificial Bee Colony Algorithm

Digital IIR Filter Design using Real Coded Genetic Algorithm

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

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