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Prediction of yarn strength based on an expert weighted neural network optimized by particle swarm optimization

TEXTILE RESEARCH JOURNAL 2021年第23-24期91卷 2911-2924页

作者： Zhang, Baowei Song, Jiuxiang Zhao, Suna Jiang, Hao Wei, Jingdian Wang, Yonghua Zhengzhou Univ Light Ind Zhengzhou 450002 Henan Peoples R China

Aiming at solving the problem that existing artificial neural networks (ANNs) still have low accuracy in predicting yarn strength, this study combines traditional expert experience and an ANN to propose a hybrid network, named the expert weighted neural network. Many studies have shown that it is reliable to predict yarn strength based on ANN technology. However, most ANN training models face with problems of low accuracy and easy trapping into their local minima. The strength prediction of traditional yarns relies on expert experience. Obvious expert experience can help the model perform preliminary learning and help the algorithm model achieve higher accuracy. Therefore, this study proposes a neural network model that combines expert weights and particle swarm optimization (PSO). The model uses PSO to optimize the weights of experts and investigates its effectiveness in yarn strength prediction.

关键词： Yarn strength expert weights artificial neural network expert weighted neural network particle swarm optimization algorithm

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An efficient task scheduling in a cloud computing environment using hybrid Genetic algorithm - particle swarm optimization (GA-PSO) algorithm

An efficient task scheduling in a cloud computing environmen...

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International Conference on Intelligent Sustainable Systems (ICISS)

作者： Kumar, A. M. Senthil Parthiban, K. Shankar, Siva S. Koneru Lakshmaiah Educ Fdn Dept CSE Guntur 522502 Andhra Pradesh India

ISBN: (纸本)9781538677995

Cloud computing provides the computational machines as a support of the clients utilizing cloud organize. In cloud computing, the user inputs are executed with required machines to convey the administrations. Numerous task scheduling methods are utilized to plan the client tasks to the machines. In this paper, another successful hybrid task scheduling is proposed to minimize the total execution time using Genetic algorithm (GA) and particle swarm optimization (PSO) algorithms. In hybrid Genetic algorithm - particle swarm optimization (GA-PSO) algorithm, PSO helped GA to obtain better results compare to a standard genetic algorithm, Min-Min, and Max-Min algorithms results.

关键词： Cloud computing Task scheduling algorithm Genetic algorithm particle swarm optimization algorithm

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A Weighted Ensemble Learning algorithm Based on Diversity Using a Novel particle swarm optimization Approach

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algorithmS 2020年第10期13卷 255-255页

作者： You, Gui-Rong Shiue, Yeou-Ren Yeh, Wei-Chang Chen, Xi-Li Chen, Chih-Ming Fujian Business Univ Dept Management Engn Fuzhou 350012 Peoples R China Fujian Business Univ Informat Technol Ctr Fuzhou 350012 Peoples R China Natl Tsing Hua Univ Dept Ind Engn & Management Engn Hsinchu 300 Taiwan Fujian Business Univ Dept Finance Fuzhou 350012 Peoples R China

In ensemble learning, accuracy and diversity are the main factors affecting its performance. In previous studies, diversity was regarded only as a regularization term, which does not sufficiently indicate that diversity should implicitly be treated as an accuracy factor. In this study, a two-stage weighted ensemble learning method using the particle swarm optimization (PSO) algorithm is proposed to balance the diversity and accuracy in ensemble learning. The first stage is to enhance the diversity of the individual learner, which can be achieved by manipulating the datasets and the input features via a mixed-binary PSO algorithm to search for a set of individual learners with appropriate diversity. The purpose of the second stage is to improve the accuracy of the ensemble classifier using a weighted ensemble method that considers both diversity and accuracy. The set of weighted classifier ensembles is obtained by optimization via the PSO algorithm. The experimental results on 30 UCI datasets demonstrate that the proposed algorithm outperforms other state-of-the-art baselines.

关键词： ensemble learning diversity machine learning particle swarm optimization algorithm weighted ensemble

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particle swarm optimization for the shortest path problem

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JOURNAL OF INTELLIGENT & FUZZY SYSTEMS 2021年第1期41卷 1353-1373页

作者： Yang, Lehua Li, Dongmei Tan, Ruipu Fujian Jiangxia Univ Coll Elect & Informat Sci Fuzhou Fujian Peoples R China Res Inst Data Anal & Intelligent Decis Making Fuzhou Fujian Peoples R China Fujian Jiangxia Univ Coll Foreign Languages Fuzhou Fujian Peoples R China

Solving the shortest path problem is very difficult in situations such as emergency rescue after a typhoon: roaddamage caused by a typhoon causes the weight of the rescue path to be uncertain and impossible to represent using single, precise numbers. In such uncertain environments, neutrosophic numbers can express the edge distance more effectively: membership in a neutrosophic set has different degrees of truth, indeterminacy, and falsity. This paper proposes a shortest path solution method for interval-valued neutrosophic graphs using the particle swarm optimization algorithm. Furthermore, by comparing the proposed algorithm with the Dijkstra, Bellman, and ant colony algorithms, potential shortcomings and advantages of the proposed method are deeply explored, and its effectiveness is verified. Sensitivity analysis performed using a 2020 typhoon as a case study is presented, as well as an investigation on the efficiency of the algorithm under different parameter settings to determine the most reasonable settings. particle swarm optimization is a promising method for dealing with neutrosophic graphs and thus with uncertain real-world situations.

关键词： Interval-valued neutrosophic numbers neutrosophic graph particle swarm optimization algorithm shortest path problem

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particle swarm optimization-Based H∞ Tracking Fault Tolerant Control for Batch Processes

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IEEE ACCESS 2021年 9卷 84241-84251页

作者： Wang, Jinru Yu, Jingxian Shenyang Sport Univ Sch Teaching & Res Dept Gen Educ Sect Comp Shenyang 110102 Peoples R China Liaoning Petrochem Univ Sch Sci Fushun 113001 Peoples R China

This paper focuses on particle swarm optimization algorithm (PSOA)-based H infinity tracking fault-tolerant control for batch processes to resist the influence of actuator faults and unknown disturbances. First, according to a given actual process model, by introducing output tracking error, state difference and new states including output tracking error, an extended equivalent model is constructed. Then, a linear-quadratic performance function is introduced. By using the PSOA to adjust those parameters in the function, a new state space H infinity tracking fault-tolerant control law is proposed under optimal control theory. Actuator faults are regarded as uncertainties here. The Lyapunov stability theory is used to solve the allowable disturbances in a certain range. The greatest merit of this design is that it has better tracking performance and stronger anti-fault and interference ability. Finally, the injection molding process and nonlinear batch reactor are taken as examples to compare with the genetic algorithm method (GA) and the traditional control method (TC), which shows that the method proposed is more practical and effective.

关键词： Batch processes new extended state space model particle swarm optimization algorithm linear-quadratic H infinity tracking fault-tolerant control actuator fault

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The Retinex-based image dehazing using a particle swarm optimization method

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MULTIMEDIA TOOLS AND APPLICATIONS 2021年第3期80卷 3425-3442页

作者： Yao, Li-Ping Pan, Zhong-liang South China Normal Univ Sch Phys & Telecommun Engn Guangzhou 510006 Guangdong Peoples R China Guangdong Inst Med Instruments Guangzhou 510000 Guangdong Peoples R China

To the best of our knowledge, currently the physical model based method is still an ill posed problem. Additionally, the image enhancement approaches also suffer from the texture preservation issue. Retinex-based approach is proved its effectiveness in image dehazing while the parameter should be turned properly. Therefore, in this paper, the particle swarm optimization (PSO) algorithm is firstly performed to optimize the parameter and the hazed image is converted into hue, saturation, intensity(HSI) for color compensation, In the other hand, the multi-scale local detail upgrading and the bilateral filtering approaches are designed to overcome the dehazing artefacts and edge preservation, which could further improve the overall visual effect of images. Experimental results on natural and synthetic images by using qualitative analysis and frequently used quantitative evaluation metrics illustrate the approving defogging effect of the proposed method. For instance, in a natural image road, our method achieves the higher e for 0.63, gamma for 3.21 andHfor 7.81, respectively and lower sigma for 0.04. In a synthetic image poster, the higher PSNR for 18.17 and SSIM for 0.78 are also acquired compared to other explored approaches in this paper. Besides, the results performed on other underwater and aerial images in this study further demonstrates its defog effectiveness.

关键词： Image enhancement Retinex particle swarm optimization algorithm Nature and synthetic images Underwater and aerial images

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Semantic and Trade-Off Aware Location Privacy Protection in Road Networks Via Improved Multi-Objective particle swarm optimization

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IEEE ACCESS 2021年 9卷 54264-54275页

作者： Tian, Cenxi Xu, Hongyun Lu, Tao Jiang, Rui Kuang, Yong South China Univ Technol Sch Comp Sci & Engn Guangzhou 510006 Peoples R China

Location privacy protection is an essential but challenging topic in the field of network security. Although the existing research methods, such as k-anonymity, mix zone, and differential privacy, show significant success, they usually neglect the location semantic and the proper trade-off between privacy and utility, which may allow attackers to obtain user privacy information by revealing the semantic correlation between the anonymous region and user's real location, thus causing privacy leakage. To solve this problem, we propose a location privacy protection scheme based on the k-anonymity technique, which provides practical location privacy-preserving through generating an anonymous set. This paper proposes a new location privacy attack strategy termed semantic relativity attack (SRA), which considers the location semantic problem. Correspondingly, a semantic and trade-off aware location privacy protection mechanism (STA-LPPM) is presented to achieve privacy protection with both high-level privacy and utility. To be specific, we model the location privacy protection as a multi-objective optimization problem and propose the Improved Multi-Objective particle swarm optimization (IMOPSO) to generate the optimal anonymous set calculating the well-design fitness functions of the multi-objective optimization problem. In this way, the privacy scheme can provide mobile users with the right balance of privacy protection and service quality. Experiments reveal that our privacy scheme can effectively resist the semantic relativity attack while preventing significant utility degrading.

关键词： K-anonymity location privacy location semantic multi-objective optimization problem particle swarm optimization algorithm road networks

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Detection of hemorrhage in retinal images using linear classifiers and iterative thresholding approaches based on firefly and particle swarm optimization algorithms

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TURKISH JOURNAL OF ELECTRICAL ENGINEERING AND COMPUTER SCIENCES 2019年第1期27卷 499-515页

作者： Adem, Kemal Hekim, Mahmut Demir, Selim Tokat Gaziosmanpasa Univ Fac Arts & Sci Dept Informat Tokat Turkey Tokat Gaziosmanpasa Univ Fac Engn Dept Elect & Elect Engn Tokat Turkey Tokat Gaziosmanpasa Univ Fac Med Dept Ophthalmol Tokat Turkey

We propose a novel iterative thresholding approach based on firefly and particle swarm optimization to be used for the detection of hemorrhages, one of the signs of diabetic retinopathy disease. This approach consists of the enhancement of the image using basic preprocessing methods, the segmentation of vessels with the help of Gabor and Top-hat transformation for the removal of the vessels from the image, the determination of the number of regions with hemorrhages and pixel counts in these regions using firefly algorithm (FFA) and particle swarm optimization algorithm (PSOA)-based iterative thresholding, and the detection of hemorrhages with the help of a support vector machine (SVM) and linear regression (LR)-based classifier. In the preprocessing step, color space selection, brightness and contrast adjustment, and adaptive histogram equalization are applied to enhance retinal images, respectively. In the step of segmentation, blood vessels are detected by using Gabor and Top-hat transformations and are removed from the image to avoid confusion with hemorrhagic regions in the retinal image. In the iterative thresholding step, the number of hemorrhagic regions and pixel counts in these regions are determined by using an iterative thresholding approach that generates different thresholding values with the FFA/PSOA. In the classification step, the hemorrhagic regions and pixel counts obtained by the iterative thresholding are used as inputs in the LR/SVM-based classifier. PSOA-based iterative thresholding and the SVM classifier achieved 96.7% sensitivity, 91.4% specificity, and 94.1% accuracy for hemorrhage detection. Finally, the experiments show that the correct classification rates and time performances of the PSOA-based iterative thresholding algorithm are better than those of the FFA in hemorrhage detection. In addition, the proposed approach can be used as a diagnostic decision support system for detecting hemorrhages with high success rate.

关键词： Hemorrhage firefly algorithm particle swarm optimization algorithm iterative thresholding

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Improved Ionospheric Total Electron Content Maps over China Using Spatial Gridding Approach

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ATMOSPHERE 2024年第3期15卷 351页

作者： Song, Fucheng Shi, Shuangshuang Linyi Univ Coll Resources & Environm Shandong Prov Key Lab Water & Soil Conservat & Env Linyi 276000 Peoples R China China Univ Min & Technol Jiangsu Key Lab Resources & Environm Informat Engn Xuzhou 221116 Peoples R China China Univ Min & Technol Sch Environm Sci & Spatial Informat Xuzhou 221116 Peoples R China

Precise regional ionospheric total electron content (TEC) models play a crucial role in correcting ionospheric delays for single-frequency receivers and studying variations in the Earth's space environment. A particle swarm optimization neural network (PSO-NN)-based model for ionospheric TEC over China has been developed using a long-term (2008-2021) ground-based global positioning system (GPS), COSMIC, and Fengyun data under geomagnetic quiet conditions. In this study, a spatial gridding approach is utilized to propose an improved version of the PSO-NN model, named the PSO-NN-GRID. The root-mean-square error (RMSE) and mean absolute error (MAE) of the TECs estimated from the PSO-NN-GRID model on the test data set are 3.614 and 2.257 TECU, respectively, which are 7.5% and 5.5% smaller than those of the PSO-NN model. The improvements of the PSO-NN-GRID model over the PSO-NN model during the equinox, summer, and winter of 2015 are 0.4-22.1%, 0.1-12.8%, and 0.2-26.2%, respectively. Similarly, in 2019, the corresponding improvements are 0.5-13.6%, 0-10.1%, and 0-16.1%, respectively. The performance of the PSO-NN-GRID model is also verified under different solar activity conditions. The results reveal that the RMSEs for the TECs estimated by the PSO-NN-GRID model, with F10.7 values ranging within [0, 80), [80, 100), [100, 130), [130, 160), [160, 190), [190, 220), and [220, +), are, respectively, 1.0%, 2.8%, 4.7%, 5.5%, 10.1%, 9.1%, and 28.4% smaller than those calculated by the PSO-NN model.

关键词： China ionosphere maps total electron content spatial gridding approach particle swarm optimization algorithm artificial neural network

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Multimodal Renewable Energy Hybrid Supply optimization Model Based on Heterogeneous Cloud Wireless Access

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IEEE ACCESS 2024年 12卷 78286-78303页

作者： Tian, Feng Wang, Hongjiang Jiang, He Zhao, Baida Shenyang Inst Engn Coll Informat Shenyang 110036 Peoples R China Shenyang Inst Engn Sch Renewable Energy Shenyang 110036 Peoples R China Shenyang Inst Engn Innovat & Entrepreneurship Educ Ctr Shenyang 110036 Peoples R China

With the increasing emphasis on environmental issues, the utilization of renewable energy has been recognized as a feasible solution to address the energy crisis and reduce environmental pollution. In view of this, this article proposes a multi-modal renewable energy hybrid power supply optimization model based on heterogeneous cloud wireless access. The model innovatively combines heterogeneous cloud wireless access technology and various intelligent optimization algorithms, including k-clustering algorithm, particle swarm optimization algorithm, and whale optimization algorithm, forming a hybrid optimization algorithm. In order to comprehensively evaluate the actual performance of the model, this study recruited 20 experts to provide detailed ratings on four core dimensions: cost-benefit ratio, reliability, robustness, and user satisfaction. The results showed that the model scored 95.1, 96.4, 95.6, and 96.2 in the four dimensions of cost-benefit ratio, reliability indicators, robustness, and user satisfaction, respectively. This series of significant data not only confirms the theoretical superiority of the model, but also demonstrates its strong potential and practical value in practical applications. In summary, this study provides a promising and innovative solution for the field of renewable energy supply.

关键词： optimization Clustering algorithms Heuristic algorithms Renewable energy sources Wireless communication Cloud computing Whale optimization algorithms K-clustering algorithm heterogeneous cloud radio access particle swarm optimization algorithm WOA algorithm energy supply optimization

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