Automatic selection of hidden neurons and weights in neural networks for data classification using hybrid particle swarm optimization, multi-verse optimization based on Lévy flight

Bousmaha, Rabab; Hamou, Reda Mohamed; Amine, Abdelmalek

doi:10.1007/s12065-021-00579-w

Cited by 12 publications

(6 citation statements)

References 37 publications

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“…This is due to the fact that modifying the hidden neurons drastically changes the topography of the network, making training more challenging and necessitating unique considerations. The authors of [11] propose a PSO with Levy flight-based multiverse optimization (MVO). PSO is a brand-new, quick algorithm that improves the harmony between development and exploration by preventing early convergence.…”

Section: Methodsmentioning

confidence: 99%

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Calculation of Neural Network Weights and Biases Using Particle Swarm Optimization

Selvan,

Potdar

2024

RAiSE-2023

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Section: Methodsmentioning

confidence: 99%

“…The mean squared error (MSE) for problems requiring real valued results (Regression) and the cross entropy for problems requiring classes (Target or labels) are the two most widely utilized error functions. The error function in this instance is the cross entropy [11]. Given two input-output pairs, the mean square error function is as follows:…”

Section: Methodsmentioning

confidence: 99%

Calculation of Neural Network Weights and Biases Using Particle Swarm Optimization

Selvan,

Potdar

2024

RAiSE-2023

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“…PLMVO offers better results than other training algorithms in all datasets. It is proven as capable as an alternative to conventional methods of training [12].…”

Section: Introductionmentioning

confidence: 99%

Optimizing Multi-Layer Perceptron using Variable Step Size Firefly Optimization Algorithm for Diabetes Data Classification

Behera¹,

Sarangi²,

Mishra³

et al. 2023

Int. J. Onl. Eng.

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According to a survey conducted by the International Diabetes Federation, the proportion of people living with diabetes is gradually rising. Diabetes mellitus is a chronic disorder caused by elevated blood sugar levels. For the early diagnosis and treatment of diabetes patients, efficient machine-learning methods are needed. Data Classification is a significant subject in many areas of life, and it is also a very challenging job in data mining. Clinical data mining has recently gained attention in complicated healthcare challenges relying on healthcare datasets. The principal objective of classification is to classify all data in a given dataset to a certain class label. In the healthcare field, classification is commonly employed in much research articles. A hybrid method for diabetes data classification is suggested by integrating multilayer perceptron with a modified firefly optimization algorithm for diabetes data classification. The performance of the proposed hybrid multilayer perceptron variable step size firefly algorithm is compared with other hybrid models such as the hybrid multilayer perceptron particle swarm optimization algorithm, hybrid multilayer perceptron differential evolution algorithm, and hybrid multilayer perceptron firefly optimization algorithm. The performance of these models is calculated based on accuracy, precision, recall, F1 score, and mean square error. In comparison to other models, the proposed hybrid model produces superior outcomes for diabetes data classification.

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“…Recently, meta-heuristics prove their performance in many recent fields like optimization of neural networks [4], cellular network planning [2], and WSN deployment [16]. In this work, we use a recent meta-heuristic called Whale Optimization Algorithm (WOA) [11], which proved its performance in recent works on the deployment of WSN such as in [13] and [8].…”

Section: Introductionmentioning

confidence: 99%

Optimization of the Deployment of Wireless Sensor Networks Dedicated to Fire Detection in Smart Car Parks using Chaos Whale Optimization Algorithm

Benghelima

Ould‐Khaoua

Benzerbadj

et al. 2022

ICC 2022 - IEEE International Conference on Communications

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Smart Car Parks (SCPs) based on Wireless Sensor Networks (WSNs) are one of the most interesting IoT applications. The present study addresses the deployment optimization problem of two-tiered WSNs dedicated to fire monitoring in smart parking lots. Networks deployed inside the SCP consist of three types of nodes: Sensor Nodes (SNs) which cover the spots within the parking area, Relay Nodes (RNs) which forward alert messages generated by SNs, and the Sink node which is connected to the outside world (e.g, firefighters), through a high bandwidth connection. This paper proposes an algorithm based on chaos theory and Whale Optimization Algorithm (WOA), which minimizes simultaneously the deployed number of SNs, RNs, and the maximum distance from SNs to the sink node (network diameter) while ensuring coverage and connectivity. To evaluate the effectiveness of our proposal, we have conducted extensive tests. The results show that the Chaos WOA (CWOA) outperforms the original WOA in terms of solution quality and computation time and by comparison with the exact method, CWOA finds results very close to the optimal in terms of fitness value and is efficient in terms of computational time when the problem becomes more complex.

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Automatic selection of hidden neurons and weights in neural networks for data classification using hybrid particle swarm optimization, multi-verse optimization based on Lévy flight

Cited by 12 publications

References 37 publications

Calculation of Neural Network Weights and Biases Using Particle Swarm Optimization

Calculation of Neural Network Weights and Biases Using Particle Swarm Optimization

Optimizing Multi-Layer Perceptron using Variable Step Size Firefly Optimization Algorithm for Diabetes Data Classification

Optimization of the Deployment of Wireless Sensor Networks Dedicated to Fire Detection in Smart Car Parks using Chaos Whale Optimization Algorithm

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