The application of artificial bee colony (ABC) algorithm in FIR filter design

Ji, Dan

doi:10.1109/fskd.2016.7603253

Cited by 11 publications

(7 citation statements)

References 22 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The most significant drawback of CSA is that parameter tuning is necessary, which makes it less effective than GCS. In GCS, no tuning of parameters are used in the form of 𝜆 and 𝑃 𝑎 as already discussed in Equations ( 10) & (11), respectively. In order to make GCS more efficient, Adaptive systematic Cuckoo Search Algorithm (ACSA) is proposed in which we varied mean free path (𝜆) as mentioned in Equation (10).…”

Section: Adaptive Systematic Cuckoo Search Algorithmmentioning

confidence: 99%

“…To create and improve FIR digital filters, an upgraded PSO algorithm called refrPSO that is based on the refracting opposite learning model is employed [10]. For frequency sampling-based FIR filter design, the Artificial Bee Colony (ABC) algorithm is used [11]. Cuckoo Search Algorithm (CSA) exhibits better performance in comparison to the GA [12], PSO [13,14] and craziness based PSO [15].…”

Section: Introductionmentioning

confidence: 99%

“…The parameter 𝑃 𝑎 can be appropriately adjusted to increase the convergence rate. Therefore, 𝑃 𝑎 is changed based the next equation to make the algorithm self-tunned:(11) 𝑃 𝑎 = rand 𝐷…”

mentioning

confidence: 99%

See 2 more Smart Citations

Optimal High Pass FIR Filter Based on Adaptive Systematic Cuckoo Search Algorithm

Bansal

Gill

2022

Cybernetics and Information Technologies

View full text Add to dashboard Cite

This paper presents the design of a desired linear phase digital Finite Impulse Response (FIR) High Pass (HP) filter based on Adaptive Systematic Cuckoo Search Algorithm (ACSA). The deviation, or error from the desired response, is assessed along with the stop-band and pass-band attenuation of the filter. The Cuckoo Search algorithm (CS) is used to avoid local minima because the error surface is typically non-differentiable, nonlinear, and multimodal. The ACSA is applied to the minimax criterion (L∞-norm) based error fitness function, which offers a better equiripple response for passband and stopband, high stopband attenuation, and rapid convergence for the developed optimal HP FIR filter algorithm. The simulation findings demonstrate that when compared to the Parks McClellan (PM), Particle Swarm Optimization (PSO), CRazy Particle Swarm Optimization (CRPSO), and Cuckoo Search algorithms, the proposed HP FIR filter employing ACSA leads to better solutions.

show abstract

Section: Adaptive Systematic Cuckoo Search Algorithmmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Optimal High Pass FIR Filter Based on Adaptive Systematic Cuckoo Search Algorithm

Bansal

Gill

2022

Cybernetics and Information Technologies

View full text Add to dashboard Cite

show abstract

“…ABC algorithm was used in many fields such as job shop scheduling [6,7], binary optimization [8][9][10], travelling salesman [11], digital signal processing [63], and so on. For example, Mizan et al [18] presented job scheduling in Hybrid cloud by modifying Bee Life algorithm and Greedy algorithm to achieve an affirmative response from the end users and utilize the resources.…”

Section: Overview Of the Abc Algorithmmentioning

confidence: 99%

Enhancing of Artificial Bee Colony Algorithm for Virtual Machine Scheduling and Load Balancing Problem in Cloud Computing

Kruekaew¹,

Kimpan²

2020

IJCIS

View full text Add to dashboard Cite

This paper proposes the combination of Swarm Intelligence algorithm of artificial bee colony with heuristic scheduling algorithm, called Heuristic Task Scheduling with Artificial Bee Colony (HABC). This algorithm is applied to improve virtual machines scheduling solution for cloud computing within homogeneous and heterogeneous environments. It was introduced to minimize makespan and balance the loads. The scheduling performance of the cloud computing system with HABC was compared to that supplemented with other swarm intelligence algorithms: Ant Colony Optimization (ACO) with standard heuristic algorithm, Particle Swarm Optimization (PSO) with standard heuristic algorithm and improved PSO (IPSO) with standard heuristic algorithm. In our experiments, CloudSim was used to simulate systems that used different supplementing algorithms for the purpose of comparing their makespan and load balancing capability. The experimental results can be concluded that virtual machine scheduling management with artificial bee colony algorithm and largest job first (HABC_LJF) outperformed those with ACO, PSO, and IPSO.

show abstract

“…An optimization is a process in which best possible variables are selected form a given variables under desired conditions. Different design algorithms such as Parks McClellan algorithm [7], Genetic Algorithm (GA) [8], Particle Swarm Optimization (PSO) [8] [9] [10], Differential Evolution (DE) [10] [11], Artificial Bee Colony Optimization (ABC) [12], Teaching Learning Based Optimization (TLBO) [13] [14], Cat Swarm optimization [14] and Cuckoo Search algorithm (CS) [15] have been used in the design procedure of FIR filters. Yang [16] developed a new algorithm known as Bat optimization algorithm which is based on the behaviour of bats and utilises their echolocation property.…”

Section: Introductionmentioning

confidence: 99%

Performance Analysis of Digital Fir Filter Using Bat Optimization

Kaur¹

2019

IJRAT

View full text Add to dashboard Cite

In this paper, filter design procedure is analysed for the designing of finite impulse response (FIR) filter with wanted parameter details using two optimization techniques known as Bat and Particle Swarm Optimization (PSO) optimization algorithms. Earlier optimization techniques were not efficient in solving non-differential functions as those for FIR filters and these stuck on local optimal solution. Therefore, in this PSO and BAT optimization algorithms are used to overcome this problem. These are based on the population method where a group of individuals are considered and their parameters are analysed and varied to reach at the optimal result. PSO works in correspondence to the swarm of birds whereas bat is based on the behaviour of bats and their echolocation property. Using these algorithms, the optimal impulse response coefficients of the different FIR filters are determined to meet their ideal response. Simulative results of both these techniques are compared on the basis of their pass band and stop ripples. These results demonstrate that the Bat is slightly better than PSO.

show abstract

The application of artificial bee colony (ABC) algorithm in FIR filter design

Cited by 11 publications

References 22 publications

Optimal High Pass FIR Filter Based on Adaptive Systematic Cuckoo Search Algorithm

Optimal High Pass FIR Filter Based on Adaptive Systematic Cuckoo Search Algorithm

Enhancing of Artificial Bee Colony Algorithm for Virtual Machine Scheduling and Load Balancing Problem in Cloud Computing

Performance Analysis of Digital Fir Filter Using Bat Optimization

Contact Info

Product

Resources

About