GSA-LA: gravitational search algorithm based on learning automata

Alirezanejad, Mehdi; Enayatifar, Rasul; Motameni, Homayun; Nematzadeh, Hossein

doi:10.1080/0952813x.2020.1725650

Cited by 20 publications

(5 citation statements)

References 34 publications

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“…A gravitational search method based on learning automata (GSA-LA) for continuous problem optimization is also provided by [25]. The precision of the search is modified by a key parameter called the gravitational constant G(t).…”

Section: Literature Reviewmentioning

confidence: 99%

An Offline Handwriting Age Range Prediction System Using an Optimized Deep Learning Technique

Omolara¹,

Oyedepo²,

Wasiat³

et al. 2023

IJETAE

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One of the distinguishing features of an individual is handwriting and it has been established that everyone has unique handwriting differing from one another. This unique feature evolves with time and is influenced by a variety of factors such as gender, physical and mental health, and age among others. Also, the recent development in using individual peculiar features for forensic investigations in banks and other allied institutions either for signature verification or identification spelled the need to develop a smart system that can predict the age range with offline handwriting recognition. It is on this background that this research employed an optimized deep learning technique comprising of Gravitational Search Algorithm and Convolutional Neural Network (CNN-GSA) for offline handwriting age range prediction. A local database was populated with samples of the signature captured with a digital camera (5 megapixels), the CNN was employed for feature extraction, GSA was utilized to select optimal CNN parameters used for classification while the combined CNNGSA was utilized for an offline handwritten-based age prediction system. The performance evaluation of the approach proposed was done using sensitivity, specificity, precision, false positive rate, recognition accuracy, and processing time for all the variants, while the superiority of the system developed was ascertained by comparing it with the original CNN.

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Section: Literature Reviewmentioning

confidence: 99%

An Offline Handwriting Age Range Prediction System Using an Optimized Deep Learning Technique

Omolara¹,

Oyedepo²,

Wasiat³

et al. 2023

IJETAE

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“…Many variants of GSA have been proposed by researchers till now. GSA variant using learning automata proposed by Alirezanejad et al 33 . In Reference 34, authors proposed GSA with hierarchy and distributed framework.…”

Section: Related Workmentioning

confidence: 99%

“…GSA variant using learning automata proposed by Alirezanejad et al. 33 In Reference 34, authors proposed GSA with hierarchy and distributed…”

Section: Related Workmentioning

confidence: 99%

Gravitational search algorithm‐driven missing links prediction in social networks

Singh

2022

Concurrency and Computation

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The increased usage of online social networks in recent days attracts the attention of researchers. That makes analysis of social networks a significant concern. Link prediction is one of the problem of social networks, where links are predicted between users by analyzing the relationships between the nodes. As the size and usability of the social network grows, the accurate prediction of connections with limited public information is a challenge. This article introduces a novel approach for predicting missing links using a nature-inspired method, the gravitational search algorithm. We selected seven real-world networks and five widely used algorithms and also considered the sparsity of networks for the experimental evaluation of the proposed approach. The proposed GSA-driven algorithm has shown a better or comparable AUC result than the other standard algorithms on these networks.

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“…In the selected algorithm, the four aspects of each mass are position, active gravitational mass (𝑀𝑎𝑖), inertial mass (𝑀𝑖𝑖), and passive gravitational mass (𝑀𝑝𝑖). For solving optimization problems with GSA firstly, the position of a system is defined with 𝑁 masses (dimension of the search space) [14].…”

Section: The Gravitational Search Algorithmmentioning

confidence: 99%

Performance of High Voltage Transmission Line Based on Thyristor-Control Series Compensator

Wais

Majeed

2021

JEASD

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This research aims to improve the steady-state performance of high-voltage power transmission lines, which is a significant challenge for the power system grid's safe and reliable operation. The research adopted Flexible Alternating Current Transmission System for electronic devices. The flexible alternating current transmission system technology will be used in this specific work as Thyristor-Controlled Series Compensators. Thyristor-controlled series compensator devices increase transmission systems' capacity and enhance the voltage profile by regulating transmission line reactance. Gravitational Search Algorithm is a meta-heuristic approach applied to find the optimum size and optimal location of thyristor-controlled series compensators in power systems. The gravitational search algorithm is used to solve the multi-objective and integrated optimum power flow problem in reactive energy management during normal and contingency operations. The results showed reduced voltage deviation, reduced active/reactive power losses, and increased transmission line reserves above thermal limits, in addition to reducing the installed cost and number of thyristor series compensators devices. The simulation part was created with MATLAB programming language and validated with MATPOWER software. Applied to IEEE 30 bus system and 400kV super high voltage grid system.

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GSA-LA: gravitational search algorithm based on learning automata

Cited by 20 publications

References 34 publications

An Offline Handwriting Age Range Prediction System Using an Optimized Deep Learning Technique

An Offline Handwriting Age Range Prediction System Using an Optimized Deep Learning Technique

Gravitational search algorithm‐driven missing links prediction in social networks

Performance of High Voltage Transmission Line Based on Thyristor-Control Series Compensator

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