A Particle Swarm Optimization Based Deep Learning Model for Vehicle Classification

Alhudhaif, Adi; Saeed, Ammar; Imran, Talha; Kamran, Muhammad; Alghamdi, Ahmed S.; Aseeri, Ahmad O.; Alsubai, Shtwai

doi:10.32604/csse.2022.018430

Cited by 15 publications

(12 citation statements)

References 14 publications

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“…The swarm behavior of organisms is the basis of mimic that followed by swarm intelligence (SI) techniques, which lives and cooperates with each other in groups. Alhudhaif Adi et al used the PSO is also in deep learning for feature optimization and reduction using nature-based PSO [20]. As examples of the SI algorithms are PSO [21], ACO [22], Dragonfly Algorithm (DA) [23], Salp Swarm Algorithm (SSA) [24], Grey Wolf Optimizer (GWO) [25], FA [26], and SFLA [27].…”

Section: Introductionmentioning

confidence: 99%

Resource Allocation Based on SFLA Algorithm for D2D Multicast Communications

Mahdi¹,

Taşpınar²

2023

Computer Systems Science and Engineering

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Multicast device-to-device (D2D) communication technology is considered as one of the new technologies in the fifth generation (5G) networks that directly addresses the need for content sharing among internet users. In fact, when direct communication is available between devices, the spectral efficiency is improved by reusing the licensed cellular spectrum. The current studies show that D2D communication increases network capacity and reduces latency. In order to achieve the alternate capabilities, coordination is required to implement interference management. We considered subcarrier allocation for the uplink, in addition to the power control that takes place on the underlay network. The completed data rate in single multicast communication is significantly reduced and limited by nodes with lower channel quality. In this paper, we used Shuffled Frog Leaping Algorithm (SFLA) for resource allocation (RA) in D2D multicast communications. We compared the results of the SFLA algorithm with the Firefly Algorithm (FA), Ant Colony Optimization (ACO) and Particle Swarm Optimization (PSO); in terms of D2D user throughput, Cellular User (CU) throughput, network average throughput, network interference and signal interference noise ratio (SINR) target. The simulation results show that SFLA clearly outperforms other algorithms in terms of data rate under the high pressure of infeasibility.

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Section: Introductionmentioning

confidence: 99%

Resource Allocation Based on SFLA Algorithm for D2D Multicast Communications

Mahdi¹,

Taşpınar²

2023

Computer Systems Science and Engineering

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“…Most MAs are inspired by human intelligence, the social nature of biological groups, and the laws of natural phenomena. Some classic representatives of MAs, such as genetic algorithm (GA) [10], particle swarm optimization (PSO) [11], differential evolution (DE) [12], grey wolf optimizer (GWO) [13], Harris hawks optimizer (HHO) [14], bat algorithm (BA) [15], whale optimization algorithm (WOA) [16], salp swarm algorithm (SSA) [17], sine cosine algorithm (SCA) [18], water cycle algorithm (WCA) [19], and so on, have been successfully used to solve some complex optimization problems.…”

Section: Introductionmentioning

confidence: 99%

GPOFWA: A hybrid greedy political optimizer with fireworks algorithm for numerical and engineering optimization problems

Dong

Zou

et al. 2022

Preprint

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This paper proposes a novel hybrid optimization algorithm named GPOFWA, which integrates political optimizer (PO) with fireworks algorithm (FWA) to solve numerical and engineering optimization problems. The original PO uses subgroup optimal solutions such as party leaders and constituency winners to guide the movement of the search agent. However, the number of such subgroup optimal solutions is limited, which leads to insufficient global exploration capabilities of PO. In addition, the recent past-based position updating strategy (RPPUS) of PO lacks effective verification of the updated candidate solutions, which reduces the convergence speed of the algorithm. The proposed hybrid algorithm uses the spark explosion mechanism in FWA to perform explosion spark and Gauss explosion spark operations on the subgroup optimal solutions (party leader and constituency winner) respectively based on the greedy strategy, which optimizes the subgroup optimal solution and enhances the exploitative ability of the algorithm. Moreover, Gaussian explosion sparks are also used to correct the candidate solutions after RPPUS, which makes up for the shortcomings of the original PO. In addition, a new subgroup optimal solution called the Converged Mobile Center (CMC) based on two-way consideration is designed to guide the movement of search agents. We test the presented hybrid algorithm on 30 well-known benchmark test functions and three engineering optimization problems. The experimental results show that GPOFWA is superior to nine state-of-the-art methods in terms of the quality of the resulting solution.

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“…The hyper-parameter selection in deep neural networks using Parallel PSO too was discussed by Lorenzo et al ( 2017b ). A PSO based deep learning model for vehicle classification (Alhudhaif, 2022 ), image classification (Junior and Yen, 2019 ), hyper spectral image classification (Liu X. et al, 2021 ), and flash flood detection from satellite images (Tuyen et al, 2021 ) too was reported in the literature. A text feature selection using the PSO algorithm was implemented by Zahran and Kanaan ( 2009 ).…”

Section: Introductionmentioning

confidence: 99%

A Framework for Text Classification Using Evolutionary Contiguous Convolutional Neural Network and Swarm Based Deep Neural Network

Prabhakar

Rajaguru²,

So³

et al. 2022

Front. Comput. Neurosci.

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To classify the texts accurately, many machine learning techniques have been utilized in the field of Natural Language Processing (NLP). For many pattern classification applications, great success has been obtained when implemented with deep learning models rather than using ordinary machine learning techniques. Understanding the complex models and their respective relationships within the data determines the success of such deep learning techniques. But analyzing the suitable deep learning methods, techniques, and architectures for text classification is a huge challenge for researchers. In this work, a Contiguous Convolutional Neural Network (CCNN) based on Differential Evolution (DE) is initially proposed and named as Evolutionary Contiguous Convolutional Neural Network (ECCNN) where the data instances of the input point are considered along with the contiguous data points in the dataset so that a deeper understanding is provided for the classification of the respective input, thereby boosting the performance of the deep learning model. Secondly, a swarm-based Deep Neural Network (DNN) utilizing Particle Swarm Optimization (PSO) with DNN is proposed for the classification of text, and it is named Swarm DNN. This model is validated on two datasets and the best results are obtained when implemented with the Swarm DNN model as it produced a high classification accuracy of 97.32% when tested on the BBC newsgroup text dataset and 87.99% when tested on 20 newsgroup text datasets. Similarly, when implemented with the ECCNN model, it produced a high classification accuracy of 97.11% when tested on the BBC newsgroup text dataset and 88.76% when tested on 20 newsgroup text datasets.

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A Particle Swarm Optimization Based Deep Learning Model for Vehicle Classification

Cited by 15 publications

References 14 publications

Resource Allocation Based on SFLA Algorithm for D2D Multicast Communications

Resource Allocation Based on SFLA Algorithm for D2D Multicast Communications

GPOFWA: A hybrid greedy political optimizer with fireworks algorithm for numerical and engineering optimization problems

A Framework for Text Classification Using Evolutionary Contiguous Convolutional Neural Network and Swarm Based Deep Neural Network

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