Design of a two-dimensional recursive filter using the bees algorithm

Pham, Duc Truong; Koç, Ebubekir

doi:10.1007/s11633-010-0520-x

Cited by 30 publications

(23 citation statements)

References 10 publications

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“…Shrinking-based BA is an improved version of BA that utilizes a neighborhood shrinking procedure [41] and Standard BA is an improved variant that employs both shrinking and site abandonment procedures [42]. In addition, we conduct an additional set of experiments to compare the performance of PLBA with other population-based metaheuristic algorithms.…”

Section: ) Parameter Settingsmentioning

confidence: 99%

An Improved Bees Algorithm for Real Parameter Optimization

Hussein¹,

Sahran²,

Abdullah³

2015

ijacsa

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Section: ) Parameter Settingsmentioning

confidence: 99%

An Improved Bees Algorithm for Real Parameter Optimization

Hussein¹,

Sahran²,

Abdullah³

2015

ijacsa

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“…The Bees Algorithm was also used to optimise noisy non-linear problems [79] and digital image filters [80], and for biometric fusion under missing data scenario [81].…”

Section: Applications Of the Bees Algorithmmentioning

confidence: 99%

Nature-Inspired Intelligent Optimisation Using the Bees Algorithm

Pham

Castellani

Thi

2014

Transactions on Computational Intelligence XIII

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Abstract.The Bees Algorithm models the foraging behaviour of honey bees in order to solve optimisation problems. The algorithm performs a kind of exploitative neighbourhood search combined with random explorative search. This paper describes the Bees Algorithm, and compares its functioning and performance with those of other state-of-the-art nature-inspired intelligent optimisation methods. Two application cases are presented: the minimisation of a set of well-known benchmark functions, and the training of neural networks to reproduce the inverse kinematics of a robot manipulator. In both cases, the Bees Algorithm proved its effectiveness and speed. Compared with other state-ofthe-art methods, the performance of the Bees Algorithm was very competitive.

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“…A signal is a function of one or more independent variables that contains information about a physical situation or phenomenon (Oppenheim et al 1983). For example, a one-dimensional (1-D) signal depends only on time.…”

Section: Introductionmentioning

confidence: 99%

“…The research area that conveys all of the above-mentioned signals and the processing of the mathematical functions and operators performed on the signals is known as signal processing (Priemer 1991). Signal processing can be categorized into three areas; analog signal processing, discrete time signal processing and digital signal processing (Oppenheim and Schafer 1975;Boashash 2003). In recent years, digital signals and systems have become an important interest area depending on semiconductors and integrated circuit technologies.…”

Section: Introductionmentioning

confidence: 99%

“…The digital filter needs a powerful digital processor such as a digital signal processor (DSP), a field programmable gate array (FPGA), or an ordinary PC to calculate the manipulations on the digital signals. The digital filter is classed in two main groups: the finite impulse response (FIR) filter and the infinite impulse response (IIR) filter (Oppenheim et al 1999;Proakis and Manolakis 1996). FIR and IIR filters can be referred to as the non-recursive filter and the recursive filter, respectively.…”

Section: Introductionmentioning

confidence: 99%

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The design approaches of two-dimensional digital filters based on metaheuristic optimization algorithms: a review of the literature

Koçkanat

Karaboğa

2015

Artif Intell Rev

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Recently, the design of the two-dimensional digital filter has become a subject of interest in the field of two-dimensional signal processing. The two-dimensional digital filter has been applied in many important areas such as image processing, television systems and seismic signal processing. In digital filter design, there are several indispensable aims such as stability, reduced computational complexity and computational time. Thus, researchers and practitioners have investigated various advanced methods based on metaheuristic optimization algorithms for the design of the two-dimensional digital filter. Metaheuristic optimization algorithms have been applied to solve different complicated problems in various fields and they have also been successfully used in digital filter design. This paper presents a review of the design approaches of two-dimensional digital filters based on metaheuristic optimization algorithms such as the genetic algorithm, differential evolution and particle swarm optimization. By comparing the proposed design approaches based on metaheuristic optimization algorithms, it is observed that the genetic algorithm is the most preferred algorithm and emerging novel algorithms using metaheuristic optimization algorithms have better performance in terms of computational complexity and computational time. It is hoped that this review will be helpful for researchers and practitioners studying the design of twodimensional digital filters.

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Design of a two-dimensional recursive filter using the bees algorithm

Cited by 30 publications

References 10 publications

An Improved Bees Algorithm for Real Parameter Optimization

An Improved Bees Algorithm for Real Parameter Optimization

Nature-Inspired Intelligent Optimisation Using the Bees Algorithm

The design approaches of two-dimensional digital filters based on metaheuristic optimization algorithms: a review of the literature

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