Optimal filter design using an improved artificial bee colony algorithm

Bose, Digbalay; Biswas, Subhodip; Vasilakos, Athanasios V.; Laha, Sougata

doi:10.1016/j.ins.2014.05.033

Cited by 62 publications

(23 citation statements)

References 31 publications

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“…The first one, Figure A, is a fourth‐order Butterworth LPF, while the second one, Figure B, is a second‐order state variable LPF. These are the same filters studied in previous works using different evolutionary optimization methods. The goal is to choose the values of the passive components (resistors and capacitors) such that a specific LPF response is obtained.…”

Section: Problem Formulationmentioning

confidence: 99%

“…This procedure limits the flexibility of the design, even though it allows obtaining acceptable results with relatively reduced design effort. Recently, to make the design more reliable and flexible, different evolutionary optimization algorithms and techniques have been used in the design of active filters . In Jiang et al, the clonal selection algorithm was applied to obtain the optimal components values and reduce the design error of a fourth‐order Butterworth low‐pass filter (LPF).…”

Section: Introductionmentioning

confidence: 99%

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Optimal design of analog active filters using symbiotic organisms search

Dib

El-Asir

2018

Int J Numerical Modelling

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This paper investigates the optimal design of analog active filters using the symbiotic organisms search (SOS) algorithm. Symbiotic organisms search is a newly proposed global optimization method that is inspired by the symbiotic interaction strategies between different organisms in an ecosystem. In SOS, the 3 common types of symbiotic relationships (mutualism, commensalism, and parasitism) are modeled using simple expressions, which are used to find the global minimum of the fitness function. Unlike other methods, SOS is free of tuning parameters, which makes it an attractive optimization method. Here, SOS is used to determine the values of the passive components (resistors and capacitors) used in active filters. Two active filter topologies, fourth‐order Butterworth low‐pass filter and second‐order state variable low‐pass filter, are considered. The components values are chosen from several standard industrial series (E12, E24, and E96). The results obtained using SOS are compared with those obtained using several optimization methods, like particle swarm optimization, seeker optimization algorithm, differential evolution, and other ones. It is shown that the SOS is a robust straightforward evolutionary algorithm that can outperform other well‐known methods.

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Section: Problem Formulationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Optimal design of analog active filters using symbiotic organisms search

Dib

El-Asir

2018

Int J Numerical Modelling

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“…To improve global capability of the basic ABC algorithm, some modified versions of the basic method were proposed for solving continuous optimization problems [24,25]. Besides improvements of ABC, The ABC algorithm has been applied to solve a huge number problems such as designing digital IIR filters [26], to estimate electricity energy demand [27], image processing and clustering [28,29], dynamic deployment of wireless sensor networks [30], neural network training [31,32], to optimal filter design [33] and antenna array design [34].…”

Section: A Brief Literature Review On Improvements Of Abcmentioning

confidence: 99%

Artificial bee colony algorithm with distribution-based update rule

Babaoğlu

2015

Applied Soft Computing

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“…Kang et al's Rosenbrock ABC algorithm [13] used a modified Rosenbrock's rotational direction method to implement the exploitation phase to assist ABC in solving complex problems. Bose et al [14] proposed the idea of decentralization of attraction from super-fit members along with neighborhood information and wider exploration of search space and applied it to optimal filter design problems.…”

Section: B Improved Variants Of Abcmentioning

confidence: 99%

A Hybrid Artificial Bee Colony with Differential Evolution

Worasucheep¹

2015

IJMLC

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Abstract-Artificial bee colony (ABC) is a relatively new stochastic algorithm with competitive performance and minimal tuning parameter. This paper proposes a hybrid ABC algorithm with differential evolution (DE), but without additional parameters. DE is a well-known efficient evolutionary algorithm with proven records but its parameter setting is complicated. This proposed hybrid algorithm called ABCDE incorporates the powerful mutation strategies of DE into ABC, in order to increase convergence while diversity is not compromised. The performance of ABCDE is evaluated against both original ABC and opposition-based DE (ODE), a recent DE variant with high performance. The experiment uses twelve widely accepted non-linear benchmark functions with various characteristics, such as difficult landscape, multimodality, shift and rotation, to evaluate the ABCDE's performance on many complex functions. The experimental results demonstrate a superior performance of ABCDE against original ABC and ODE.

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Optimal filter design using an improved artificial bee colony algorithm

Cited by 62 publications

References 31 publications

Optimal design of analog active filters using symbiotic organisms search

Optimal design of analog active filters using symbiotic organisms search

Artificial bee colony algorithm with distribution-based update rule

A Hybrid Artificial Bee Colony with Differential Evolution

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