Application of Genetic Algorithms (GA) and Threshold Acceptance (TA) to a Ternary Liquid-Liquid Equilibrium System

Boultif, Ayache; Kabouche, A.; Ladjel, Segni

doi:10.15866/iremos.v9i1.8029

Cited by 4 publications

(1 citation statement)

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“…Here, the GA composes of perfect hopeful arrangements in look spaces for every issue; this is deliberated by a chromosomal series of qualities. A string of 0's and 1's 22 develops each chromosome. The gathered individual chromosomes were known as populace.…”

Section: Proposed Structure Optimization Of Nn Using Hybrid Ga‐sfla F...mentioning

confidence: 99%

Hybrid genetic and shuffled frog‐leaping algorithm for neural network structure optimization and learning model to predict free spectrum in cognitive radio

Supraja

Babu

Gayathri

et al. 2020

Int J Communication

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Summary Cognitive radio, which is called an intelligent radio, will dynamically access the available spectrum. This mechanism will bring revolt in wireless communication, which lightens the spectrum utilization problem. Machine learning plays a vital role in every technology. Here, in cognitive radio, it helps train the model to predict the free spectrum. The cognitive clients utilize range‐detecting procedures to detect the groups previously transmitting on them to stay away from impact with the authorized clients that prompts deferral and moderates more vitality. To decrease postponement and vitality utilization and to foresee the future use of channels, range expectation procedures are utilized. Spectrum prediction is used to anticipate the future channel status in light of gathered chronicled information; here, to take care of the issue, the neural system‐based spectrum prediction utilizes a backpropagation training model that has been proposed. To enhance the structure of the neural system and to decrease the forceful weight auxiliary pattern, genetic algorithm (GA) along with the hybrid combination of shuffled frog‐leaping algorithm (SFLA) is proposed. Here, GA has been utilized to abstain from catching nearby ideal solutions. The selection, crossover, and mutation functions were performed to build the haphazardness, which stretches out the populace unite to the set that contains the global ideal solution. SFLA has been proposed for structure improvement; the paired structure has been recommended to demonstrate the memes with the motivation behind building up a subaccumulation with lesser measurements than that of the original group where recognizing affectability and precision would be versatile with that of the primary status. Simulation results show the GA‐SFLA‐based hybrid algorithm that is used has increased the results of getting the best weights by improving the system; additionally, the proposed conspire results show high forecast accuracy.

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Section: Proposed Structure Optimization Of Nn Using Hybrid Ga‐sfla F...mentioning

confidence: 99%

Hybrid genetic and shuffled frog‐leaping algorithm for neural network structure optimization and learning model to predict free spectrum in cognitive radio

Supraja

Babu

Gayathri

et al. 2020

Int J Communication

View full text Add to dashboard Cite

show abstract

Optimized neural network for spectrum prediction using genetic algorithm in cognitive radio networks

Supraja

Gayathri

Pitchai³

2018

Cluster Comput

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Modeling of electromagnetic behavior of composite thin layers using genetic algorithm

Reddaf

Ferroudji

Boudjerda

et al. 2017

2017 5th International Conference on Electrical Engineering - Boumerdes (ICEE-B)

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Application of Genetic Algorithms (GA) and Threshold Acceptance (TA) to a Ternary Liquid-Liquid Equilibrium System

Cited by 4 publications

References 0 publications

Hybrid genetic and shuffled frog‐leaping algorithm for neural network structure optimization and learning model to predict free spectrum in cognitive radio

Hybrid genetic and shuffled frog‐leaping algorithm for neural network structure optimization and learning model to predict free spectrum in cognitive radio

Optimized neural network for spectrum prediction using genetic algorithm in cognitive radio networks

Modeling of electromagnetic behavior of composite thin layers using genetic algorithm

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