Improved firefly algorithm based optimal design of special signal blocking IIR filters

Dash, Judhisthir; Dam, Bivas; Swain, Ramakrushna

doi:10.1016/j.measurement.2019.106986

Cited by 18 publications

(6 citation statements)

References 20 publications

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“…Owing to its hypothetical nature, various chaotic functions and benchmark functions are discussed to obtain better optimal results. In addition to this, the proposed CSCF algorithms are compared with several other optimization algorithms such as FireFly (FF) algorithm [19], Sine Cosine Algorithm (SCA) [20], Particle Swarm Optimization (PSO) approach [23], Artificial Bee Colony (ABC) optimization algorithms [24] to evaluate the effectiveness of the CSCF algorithm.…”

Section: Resultsmentioning

confidence: 99%

“…Here, an additional term is added to the standard firefly algorithm to achieve better randomness and efficiency of the firefly. Then the difference among the arbitrary firefly and the th X position of the firefly is obtained thereby achieving the effective randomness of the firefly [19].…”

Section: ➢ Improved Firefly Algorithmmentioning

confidence: 99%

“…This section demonstrates the comparative analysis of the proposed CSCF algorithm with various other optimization algorithms such as FireFly (FF) algorithm [19], Sine Cosine Algorithm (SCA) [20], Particle Swarm Optimization (PSO) approach [23], Artificial Bee Colony (ABC) optimization algorithms [24] to evaluate the effectiveness of the CSCF algorithm. Here, the dimension value range is assumed to be D=100; where each algorithm is implemented by using 20 benchmark functions.…”

Section: Comparison Of Cscf With Various Optimization Algorithmsmentioning

confidence: 99%

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CSCF: a chaotic sine cosine firefly algorithm for practical application problems

Hassan

2020

Neural Comput & Applic

117

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Recently, numerous meta-heuristic based approaches are deliberated to reduce the computational complexities of several existing approaches that include tricky derivations, very large memory space requirement, initial value sensitivity etc. However, several optimization algorithms namely firefly algorithm, sine cosine algorithm, particle swarm optimization algorithm have few drawbacks such as computational complexity, convergence speed etc. So to overcome such shortcomings, this paper aims in developing a novel Chaotic Sine Cosine Firefly (CSCF) algorithm with numerous variants to solve optimization problems. Here, the chaotic form of two algorithms namely the sine cosine algorithm (SCA) and the Firefly (FF) algorithms are integrated to improve the convergence speed and efficiency thus minimizing several complexity issues. Moreover, the proposed CSCF approach is operated under various chaotic phases and the optimal chaotic variants containing the best chaotic mapping is selected. Then numerous chaotic benchmark functions are utilized to examine the system performance of the CSCF algorithm. Finally, the simulation results for the problems based on engineering design are demonstrated to prove the efficiency, robustness and effectiveness of the proposed algorithm.

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Section: Resultsmentioning

confidence: 99%

Section: ➢ Improved Firefly Algorithmmentioning

confidence: 99%

Section: Comparison Of Cscf With Various Optimization Algorithmsmentioning

confidence: 99%

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CSCF: a chaotic sine cosine firefly algorithm for practical application problems

Hassan

2020

Neural Comput & Applic

117

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“…At a distance zero, the light source intensity is indicated as I 0 . Conversely, at a distance r , the flashing light intensity is indicated as I ( r ), respectively 28

I ((), r) = I_{0} e^{- α r^{2}}

…”

Section: Proposed Systemmentioning

confidence: 99%

Impulse noise reduction using hybrid neuro‐fuzzy filter with improved firefly algorithm from X‐ray bio‐images

Pugalenthi¹,

Oliver²,

Anuradha³

2020

Int J Imaging Syst Tech

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Noise filtering performance in medical images is improved using a neuro‐fuzy network developed with the combination of a post processor and two neuro‐fuzzy (NF) filters. By the fact, the Sugeno‐type is found to be less accurate during impulse noise reduction process. In this paper, we propose an improved firefly algorithm based hybrid neuro‐fuzzy filter in both the NF filters to improve noise reduction performance. The proposed noise reduction system combines the advantages of the neural, fuzzy and firefly algorithms. In addition, an improved version of firefly algorithm called searching diversity based particle swarm firefly algorithm is used to reduce the local trapping problem as well as to determine the optimal shape of membership function in fuzzy system. Experimental results show that the proposed filter has proved its effectiveness on reducing the impulse noise in medical images against different impulse noise density levels.

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“…IIR digital filter can be designed with analog filter or computer-aided methods, and their implementation process is also complex compare to FIR filter implementation (Agrawal et al 2019a;Agrawal et al 2017). However, the phase frequency characteristic of IIR filter is nonlinear, which limits its application range (Dash et al 2020). The phase of the FIR filter is strictly linear because of only Zeros in transfer function and the structure is non-recursive, therefore FIR filters are always stable.…”

Section: Introductionmentioning

confidence: 99%

FIR digital filter design based on improved artificial bee colony algorithm

Lian

Tian

2022

Soft Comput

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The traditional swarm intelligence optimization algorithm is prone to fall into local optimal solutions in finite impulse response (FIR) digital filter design, and has slow convergence speed. In order to optimize the design of FIR filter, a FIR digital filter design method based on improved bee colony (ABC) algorithm is proposed．This improved ABC algorithm can adaptively adjust the step size of the selected neighborhood of nectar source location. At the same time, the information of the global optimal solution is used to guide the search of candidate solution, which improves the global search ability of the algorithm. The improved ABC algorithm can balance the conflict between local search ability and global search ability, so it can achieve better optimization effect.The time and space complexity of the algorithm is analyzed in detail. Then, the improved ABC algorithm is used to design three typical FIR digital filters, namely low-pass, band-pass and band-stop filter. The performance of the designed filter is tested by simulation experiments. The experimental results show that compared with other state-of-the-art optimization algorithms, the proposed FIR filter design method has achieved better effect and performance. Meanwhile, the proposed design method has shorter optimization time. The superiority of the proposed method is verified.

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Improved firefly algorithm based optimal design of special signal blocking IIR filters

Cited by 18 publications

References 20 publications

CSCF: a chaotic sine cosine firefly algorithm for practical application problems

CSCF: a chaotic sine cosine firefly algorithm for practical application problems

Impulse noise reduction using hybrid neuro‐fuzzy filter with improved firefly algorithm from X‐ray bio‐images

FIR digital filter design based on improved artificial bee colony algorithm

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