Prediction of Damage Level of Inner Conventional Rubble Mound Breakwater of Tandem Breakwater Using Swarm Intelligence-Based Neural Network (PSO-ANN) Approach

Kuntoji, Geetha; Rao, Subba; Manu, M.; Reddy, Eluru Nava Bharath

doi:10.1007/978-981-13-1595-4_35

Cited by 3 publications

(6 citation statements)

References 4 publications

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“…In [20], [21], [22], [23], [24], [25], [26], [27], [28], [29], [30], [31], [32], [33], [34], [35], [36], [37], [38], [39], [40], [41], [42], [43], [44], [45], and [46] swarm intelligence algorithms are employed for optimizing feedforward multilayer perceptrons. A multilayer perceptron is a neural network consisting of more than one perceptron.…”

Section: Optimizing Different Types Of Artificial Neural Networkmentioning

confidence: 99%

“…In [33], one MLP with structure 2-12-1 is used. In [35], five MLPs with structures 8-1-1, 8-2-1, 8-3-1, 8-4-1 and 8-5-1 are used. In [36], an MLP with structure 20-10-1 is used.…”

Section: Optimizing Different Types Of Artificial Neural Networkmentioning

confidence: 99%

“…These biases are also optimized by taking the error of the neural network as the objective function. In [20], [21], [23], [25], [26], [27], [28], [29], [31], [32], [33], [34], [35], [36], [37], [38], [39], [40], [41], [43], [44], [45], [46], [51], and [52], swarm intelligence algorithms are used for optimizing the connection weights and biases of feedforward neural networks.…”

Section: Optimizing Artificial Neural Network Weights Biases and Stru...mentioning

confidence: 99%

“…In [22], [23], [29], [32], [33], [35], [39], [40], [42], [44], [45], [48], and [50], the original Particle Swarm Optimization (PSO) algorithm is employed for training ANNs. PSO is a swarm intelligence algorithm which simulates the movement of bird flocks or fish schools.…”

Section: A Feedforward Neural Networkmentioning

confidence: 99%

“…In [22], [23], [26], [28], [29], [30], [31], [32], [33], [34], [35], [36], [38], [40], [42], [44], [45], [51], and [52], the feedforward neural networks trained by swarm intelligence algorithms have been used for regression problems. In [22], and [23], the ANNs are employed for fault prediction in object-oriented systems and behaviour prediction of channel connectors embedded in normal and high-strength concrete respectively.…”

Section: Optimizing Neural Network For Different Supervised Learning ...mentioning

confidence: 99%

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A Survey on the Optimization of Artificial Neural Networks Using Swarm Intelligence Algorithms

2023

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Artificial Neural Networks (ANNs) are becoming increasingly useful in numerous areas as they have a myriad of applications. Prior to using ANNs, the network structure needs to be determined and the ANN needs to be trained. The network structure is usually chosen based on trial and error. The training, which consists of finding the optimal connection weights and biases of the ANN, is usually done using gradient-descent algorithms. It has been found that swarm intelligence algorithms are favorable for both determining the network structure and for the training of ANNs. This is because they are able to determine the network structure in an intelligent way, and they are better at finding the most optimal connection weights and biases during the training as opposed to conventional algorithms. Recently, a number of swarm intelligence algorithms have been employed for optimizing different types of neural networks. However, there is no comprehensive survey on the swarm intelligence algorithms used for optimizing ANNs. In this paper, we present a review of the different types of ANNs optimized using swarm intelligence algorithms, the way the ANNs are optimized, the different swarm intelligence algorithms used, and the applications of the ANNs optimized by swarm intelligence algorithms.INDEX TERMS Artificial neural network, swarm intelligence, optimization. I. INTRODUCTIONArtificial Neural Networks (ANNs) are computational models that simulate the biological neural network that constitutes the human brain to generate inferences based on certain given information. They are suitable for both supervised and unsupervised learning for solving a myriad of classification, regression, clustering, and association problems in a multitude of areas. Notably, ANN has been a prominent algorithm in the domain of machine learning, and has paved the way for the advancement in multiple areas such as natural language processing, fraud detection, computational biology, computer vision, unassisted control of vehicles, speech recognition, medical diagnosis and recommendation systems [1]. Recently, ANNs have been applied for making decisions in healthcare organizations [2], for forecasting the energy use in buildings [3], for the development of greenhouse technol-The associate editor coordinating the review of this manuscript and approving it for publication was Yeliz Karaca .

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Section: Optimizing Different Types Of Artificial Neural Networkmentioning

confidence: 99%

“…In [33], one MLP with structure 2-12-1 is used. In [35], five MLPs with structures 8-1-1, 8-2-1, 8-3-1, 8-4-1 and 8-5-1 are used. In [36], an MLP with structure 20-10-1 is used.…”

Section: Optimizing Different Types Of Artificial Neural Networkmentioning

confidence: 99%

Section: Optimizing Artificial Neural Network Weights Biases and Stru...mentioning

confidence: 99%

Section: A Feedforward Neural Networkmentioning

confidence: 99%

Section: Optimizing Neural Network For Different Supervised Learning ...mentioning

confidence: 99%

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A Survey on the Optimization of Artificial Neural Networks Using Swarm Intelligence Algorithms

2023

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Comparative Analysis of Intelligent Solutions Searching Algorithms of Particle Swarm Optimization and Ant Colony Optimization for Artificial Neural Networks Target Dataset

Alfa

Misra

Abayomi‐Alli

et al. 2021

Proceedings of Second International Conference on Computing, Communications, and Cyber-Security

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An Application of Machine Learning Algorithms on the Prediction of the Damage Level of Rubble-Mound Breakwaters

Saha

Changdar

2023

Journal of Offshore Mechanics and Arctic Engineering

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The stability analysis of breakwaters is very important to have a safe and economic design of these coastal protective structures and the damage level is one of the most important parameter in this context. In the recent past, machine learning techniques showed immense potential in transforming many industries and processes, for making them more efficient and accurate. In this study, five advanced machine learning algorithms; support vector regression, random forest, adaboost, gradient boosting and deep artificial neural network were employed and analysed on estimation of the damage level of rubble-mound breakwaters. A large experimental dataset, considering almost every stability variables with their whole ranges, was used in this purpose. Also, a detailed feature analysis is presented to have an insight into the relations between these variables. It was found that the present study had overcome all of the limitations of existing studies related to this field and delivered the highest level of accuracy.

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Prediction of Damage Level of Inner Conventional Rubble Mound Breakwater of Tandem Breakwater Using Swarm Intelligence-Based Neural Network (PSO-ANN) Approach

Cited by 3 publications

References 4 publications

A Survey on the Optimization of Artificial Neural Networks Using Swarm Intelligence Algorithms

A Survey on the Optimization of Artificial Neural Networks Using Swarm Intelligence Algorithms

Comparative Analysis of Intelligent Solutions Searching Algorithms of Particle Swarm Optimization and Ant Colony Optimization for Artificial Neural Networks Target Dataset

An Application of Machine Learning Algorithms on the Prediction of the Damage Level of Rubble-Mound Breakwaters

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