Binary TLBO algorithm assisted to investigate the supper scattering plasmonic nano tubes

Balvasi, Mohsen; Akhlaghi, Mehdi; Shahmirzaee, Hossein

doi:10.1016/j.spmi.2015.10.040

Cited by 12 publications

(9 citation statements)

References 35 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In the following, calculate the average position of the students (Xmean). The reason for calculating the student knowledge average is that the teacher gives the training according to the average level of the class.By considering "r" as a random number as well as Tf as a constant coefficient, it is possible to model the movement of students in the first step by the following relation [13][14][15][16]:…”

Section: Tlbo (Teacher Learn Based Optimization) Algorithmmentioning

confidence: 99%

“…In the second stage, the teaching process is the responsibility of the students,so that each student selects another student randomly and shares knowledgewith each other's and also updates his / her position; thus trying to use the otherstudent information to raise his / her level of awareness and knowledge. This phase can be modeled as following formulations [13][14][15][16]:…”

Section: Tlbo (Teacher Learn Based Optimization) Algorithmmentioning

confidence: 99%

“…Signal to Noise Ratio (SNR)is a statistical difference metric which is used to measure the similitude between the undistorted original audio signal and the distorted watermarked audio signal. The SNR computation is done according to following equation: (15) where A corresponds to the original audio signal, and A' corresponds to the watermarked signal. Although SNR is a simple way to measure the noise introduced by the embedded watermark and can give a general idea of imperceptibility, it does not take into account the robustness of the proposed algorithm.…”

Section: Watermark Extraction Procedurementioning

confidence: 99%

See 2 more Smart Citations

High-capacity, transparent and robust blind audio watermarking scheme based DWT and TLBO algorithm

Joudaki

Abdeyazdan

Mosleh

2020

Preprint

View full text Add to dashboard Cite

Digital watermarking is one of the best solutions again the copyright infringement, duplicates, verifies data and illegal distribution of digital media. Recently, the protection of digital audio signals is one of the attracting and interesting topics for scientific and researchers. In this paper we propose a blind audio watermarking mechanism in which it has high capacity, transparency and resistance simultaneously based on digital wavelet transform (DWT) algorithm. The key principle of this work is that in the DWT procedure, using two filters; break down the original audio signal into several sub-bands and transform them on a specific frequency range. It should be noted that the 8 bits of watermarked signal is selected and transform to the original signal. In order to increase the watermarking resistance, framing the high frequency coefficients of the third level of the wavelet and calculate the frames average and place them in the frame memory prime. Moreover, TLBO algorithm used to determination of embedding and extraction coefficients in order to increase the SNR ratio in the embedding process and decrease the bit error rate (BER) in the extraction process. This method increases the embedding payload capacity while the audio SNR and extracted image BER have good qualify. Moreover, experimental results shown that this method has 13kbs hiding rate,ascendency imperceptibility, good payload capacity and intense robustness when resisting against various attacks such as MP3 compression, re-quantization, low-pass filtering, amplitude scaling, re-sampling, echo addition and noise corruption.

show abstract

Section: Tlbo (Teacher Learn Based Optimization) Algorithmmentioning

confidence: 99%

Section: Tlbo (Teacher Learn Based Optimization) Algorithmmentioning

confidence: 99%

Section: Watermark Extraction Procedurementioning

confidence: 99%

See 1 more Smart Citation

High-capacity, transparent and robust blind audio watermarking scheme based DWT and TLBO algorithm

Joudaki

Abdeyazdan

Mosleh

2020

Preprint

View full text Add to dashboard Cite

show abstract

“…The TLBO algorithm is a teaching-learning process inspired algorithm proposed by Rao et al (2011) and Rao and Patel (2013), based on the effect of the influence of a teacher on the output of learners in a class. The algorithm simulates two fundamental modes of learning as follows: through teacher and interacting with the other learners (Balvasi et al, 2016). TLBO is a population-based algorithm, where a group of students (i.e learner) is considered as population and the different subjects offered to the learners is analogous with the different design variables of the optimization problem.…”

Section: Teaching-learning Based Optimization Algorithmmentioning

confidence: 99%

Improved version of teaching learning-based optimization algorithm using random local search

Kheireddine

Belli

Hacib

2019

COMPEL

View full text Add to dashboard Cite

Purpose This paper aims to deal with the development of a newly improved version of teaching learning based optimization (TLBO) algorithm. Design/methodology/approach Random local search part was added to the classic optimization process with TLBO. The new version is called TLBO algorithm with random local search (TLBO-RLS). Findings At first step and to validate the effectiveness of the new proposed version of the TLBO algorithm, it was applied to a set of two standard benchmark problems. After, it was used jointly with two-dimensional non-linear finite element method to solve the TEAM workshop problem 25, where the results were compared with those resulting from classical TLBO, bat algorithm, hybrid TLBO, Nelder–Mead simplex method and other referenced work. Originality value New TLBO-RLS proposed algorithm contains a part of random local search, which allows good exploitation of the solution space. Therefore, TLBO-RLS provides better solution quality than classic TLBO.

show abstract

“…If the new solution X new is better, it is accepted in the population. The algorithm will continue until the termination condition is met (Balvasi et al, 2016).…”

Section: Pso and Tlbo Algorithmsmentioning

confidence: 99%

Application of PSO and TLBO algorithms with neural network for optimal design of electrical machines

Kheireddine

Zoubida

Hacib

et al. 2018

COMPEL

View full text Add to dashboard Cite

PurposeThis study aims to focus on the application of the stochastic algorithms for optimal design of electrical machines. Among them, the authors are interested in particle swarm optimization and teaching–learning-based optimization. Design/methodology/approachThe optimization process is realized by the coupling of the above methods to finite element analysis of the electromagnetic field. FindingsTo improve the performance of these algorithms and reduce their computation time, a coupling with the artificial neuron network has been realized. Originality/valueThe proposed strategy is applied to solve two optimization problems: Team workshop problem 25 and switched reluctance motor with flux barriers.

show abstract

Binary TLBO algorithm assisted to investigate the supper scattering plasmonic nano tubes

Cited by 12 publications

References 35 publications

High-capacity, transparent and robust blind audio watermarking scheme based DWT and TLBO algorithm

High-capacity, transparent and robust blind audio watermarking scheme based DWT and TLBO algorithm

Improved version of teaching learning-based optimization algorithm using random local search

Application of PSO and TLBO algorithms with neural network for optimal design of electrical machines

Contact Info

Product

Resources

About