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An improved two-layer model for rumor propagation considering time delay and event-triggered impulsive control strategy

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CHAOS SOLITONS & FRACTALS 2022年 164卷

作者： Dong, Yafang Huo, Liang'an Zhao, Laijun Univ Shanghai Sci & Technol Business Sch Shanghai 200093 Peoples R China

The rapid development of the Internet has broadened the existing channels of rumor propagation, it has added many new features during the dynamic process of the rumor propagation that are different from those in the past. In this paper, we construct an improved XYZ -ISR two-layer model considering time delay to describe the dynamic process of rumor propagation in multiple channels. Firstly, we obtain the basic regeneration number R0 by the next generation matrix method. Secondly, we derive the stability condition of the equilibrium point by constructing Lyapunov functions. In order to reduce the negative impact caused by rumor propagation at min-imum cost, we design an event-triggered impulsive control strategy and optimize the control parameters by using a particle swarm optimization algorithm. Then, by comparing the rumor propagation process in the XYZ -ISR two-layer model and the classical rumor DK model, we find that although rumors propagate faster in the XYZ -ISR model in the initial stage, the rumor spreads on a smaller scale than in the DK model in terms of the whole life cycle of the rumor due to the official supervision and timely release of corresponding rumor-debunking infor-mation. Besides, individuals need a certain amount of careful consideration time between receiving the infor-mation and deciding whether to forward it or not, and this time delay slows the behavior spreading and reduces its scale. Finally, we verify the reasonableness of the above theoretical results through numerical simulations. Moreover, by simulating a rumor case on the microblog with real data, we find that the model proposed in this paper can fit the real rumor propagation process well, and the event-triggered impulsive control strategy can control the rumor propagation more effectively in multiple channels.

关键词： Rumor propagation Two -layer model Time delay Event -triggered impulsive control particle swarm optimization algorithm

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particle swarm-differential evolution algorithm with multiple random mutation

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APPLIED SOFT COMPUTING 2022年 120卷

作者： Lin, Meijin Wang, Zhenyu Chen, Danfeng Zheng, Weijia Foshan Univ Sch Mechatron Engn & Automat Foshan 528000 Peoples R China

Differential evolution (DE) algorithm has attracted considerable attention because of its effectiveness and simplicity. However, previous studies have validated that DE still suffers from some limitations such as premature convergence and slow convergence especially dealing with multimodal optimization problems. To address these concerning issues, we propose an innovative optimization method named particle swarm-differential evolution algorithm with multiple random mutation (MRPSODE) in this paper. The proposed MRPSODE algorithm is based on multiple random mutation framework cooperating with mean particle swarm mutation strategy, DE/current-to-rand/1 mutation strategy and disturbance strategy. Firstly, we incorporate the modified mean particle swarm mutation strategy into DE algorithm to improve the global convergence ability. Secondly, DE/current-to-rand/1 mutation strategy is adopted to increase the population diversity and produce perturbations to avoid the algorithm trapping into a local optimum. Thirdly, we propose a disturbance strategy to help the population escape from local optima, so as to enhance the exploration ability. Finally, to ensure that the proposed algorithm can get satisfactory solutions with a fast convergence speed, we design a multiple random mutation framework, in which these three mutation strategies can effectively play their advantages and make up for the shortcomings of others. To evaluate the performance of the proposed algorithm, three different experiments are constructed on twenty-nine classical benchmark functions. The simulation results demonstrate that, (1) MRPSODE significantly outperforms conventional PSO and DE algorithms, (2) MRPSODE can achieve better performance than nine well-known DE variants in terms of solution quality and robustness, (3) MRPSODE is superior to nine latest heuristic-based algorithms. Furthermore, MRPSODE is successfully applied to seven typical constrained optimization problems and performs better than a

关键词： particle swarm optimization algorithm Differential evolution Disturbance strategy Multiple random mutation Constrained optimization problems

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Short-Term Wind Generation Combined Forecast Considering Meteorological Similarity

Short-Term Wind Generation Combined Forecast Considering Met...

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IEEE IAS Conference on Industrial and Commercial Power System Asia (IEEE I and CPS Asia)

作者： Liu, Yating Yang, Ming Yu, Yixiao Ding, Tingting Si, Zhiyuan Li, Menglin Shandong Univ Key Lab Power Syst Intelligent Dispatch & Control Jinan Peoples R China

ISBN: (纸本)9781665434980

High-precision short-term wind generation prediction results are conducive to making a scientific generation plan and improving the wind power absorption capacity of the power grids. Based on the analysis of the relationship between the numerical weather prediction and wind power, this paper proposes a short-term wind generation combined forecast model considering meteorological similarity to improve the prediction accuracy of short-term wind power. In this method, the meteorological similarity day model, the extreme gradient boosting algorithm and the back propagation neural network algorithm are selected for achieving the short-term wind power prediction. Then, the particle swarm optimization algorithm is applied to determine the weight of each single forecasting model. Finally, the prediction results are obtained through the combination of the single model prediction results. With the realistic wind power data collected from a wind farm in Xinjiang province, the short-term wind forecasting task is achieved by the proposed method. The simulation results illustrate that the combined model proposed in this paper can effectively improve the forecasting performance of the benchmark models.

关键词： BP neural network algorithm Gaussian transform particle swarm optimization algorithm XGBoost short-term wind power combined forecast

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Multi-agent learning mechanism design and simulation of multi-echelon supply chain

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COMPUTERS & INDUSTRIAL ENGINEERING 2022年 168卷

作者： Sun, Jun-Yan Tang, Jian-Ming Chen, Zhi-Rui Shaanxi Univ Sci & Technol Fac Mech & Elect Engn Xian 710021 Peoples R China Xian Aerosp Precis Electromech Inst Xian 710010 Peoples R China

A supply chain multi-agent learning mechanism based on the particle swarm optimization algorithm is designed. The manufacturer's profit and the product utility are taken as objective functions to explore the influences of social learning ability and self-learning ability on the manufacturer's optimal price, optimal advertising in-vestment, optimal profit, product utility, and supply chain competition. This research demonstrates the in-fluences of different learning abilities on the evolution of the supply chain. The simulation results show that the most appropriate social learning ability can enhance competition between manufacturers and improve product utility. With the enhancement of self-learning ability, manufacturers have a wider range of pricing, products are more diverse, and consumers have a wider choice of goods. This study shows certain guiding significance for the scientific management of the supply chain and the optimization of the competitive strategy of the enterprises on the chain.

关键词： particle swarm optimization algorithm Multi-echelon supply chain Multi-agent Learning mechanism Simulation analysis

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Research on Image Segmentation optimization algorithm based on Chaotic particle swarm optimization and Fuzzy Clustering

Research on Image Segmentation Optimization Algorithm based ...

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2018年第七届软件与计算机应用国际会议

作者： Tan Linglong Chen Yehui Li Changkai Electronic communication engineering college Anhui Xinhua University

Fuzzy C-Means clustering, FCM, is an unsupervised learning algorithm. The algorithm is easily affected by noise points and depends on the initial values. When the sample value is large, the algorithm is easy to fall into local extremum. In this study, the traditional fuzzy clustering algorithm is improved, and the particle swarm optimization algorithm with global optimization ability is applied to the FCM algorithm, and chaotic technology is added. Chaotic variables produce a chaotic sequence based on the current global optimal position, using chaotic sequence has the best fitness value of particles randomly instead of a particle of the particle swarm, the improved algorithm can effectively avoid the stagnation of particles in the iteration, fast search to the global optimal solution, avoid convergence to local extremum. Experimental results indicate that this algorithm overcomes the dependence on the initial clustering centre of FCM, which brings high robustness and segmentation accuracy, and has more faster convergence speed.

关键词： FCM particle swarm optimization algorithm chaotic particle swarm optimization algorithm image segmentation

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optimization procedures for the inversion of impedance spectra to the distribution of relaxation times

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JOURNAL OF ELECTROANALYTICAL CHEMISTRY 2022年 911卷

作者： Wang, Chunlin Zhu, Guangyan Zhang, Pengbo Fang, Xiaohong Chinese Acad Sci Shanghai Adv Res Inst Shanghai 201210 Peoples R China Univ Chinese Acad Sci Beijing 100049 Peoples R China Pylon Technol Co Ltd Shanghai 201210 Peoples R China

Tikhonov regularization technology has been applied to solve the ill-posed inversion problem of electrochemical impedance spectra inversion to the distribution of relaxation times (DRT). However, the parameters contributing to the accuracy of the DRT obtained by this method, including types of the EIS data, regularization matrixes, regularization parameters, and shape factors, need sophisticated adjustment. In this work, the influences of these parameters were discussed in detail, and the results showed that the effects of some process parameters on the DRT worked complementarily. Therefore, the DRT inversion program with the particle swarm optimization algorithm (PSO), a multi-objective global optimization algorithm, was developed. It was demonstrated the accuracy, feasibility, and reliability of the proposed DRT program. The program was also applied to separate and quantify the electrochemical processes of the commercial LIBs.

关键词： Electrochemical impedance spectroscopy particle swarm optimization algorithm Distribution of relaxation time Tikhonov regularization

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Multiscale Joint optimization Strategy for Retinal Vascular Segmentation

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SENSORS 2022年第3期22卷 1258页

作者： Yan, Minghan Zhou, Jian Luo, Cong Xu, Tingfa Xing, Xiaoxue Changchun Univ Coll Elect Informat Engn Changchun 130012 Peoples R China Beijing Inst Technol Sch Opt & Photon Beijing 100081 Peoples R China

The accurate segmentation of retinal vascular is of great significance for the diagnosis of diseases such as diabetes, hypertension, microaneurysms and arteriosclerosis. In order to segment more deep and small blood vessels and provide more information to doctors, a multi-scale joint optimization strategy for retinal vascular segmentation is presented in this paper. Firstly, the Multi-Scale Retinex (MSR) algorithm is used to improve the uneven illumination of fundus images. Then, the multi-scale Gaussian matched filtering method is used to enhance the contrast of the retinal images. Optimized by the particle swarm optimization (PSO) algorithm, Otsu algorithm (OTSU) multi-threshold segmentation is utilized to segment the retinal image extracted by the multi-scale matched filtering method. Finally, the image is post-processed, including binarization, morphological operation and edge-contour removal. The test experiments are implemented on the DRIVE and STARE datasets to evaluate the effectiveness and practicability of the proposed method. Compared with other existing methods, it can be concluded that the proposed method can segment more small blood vessels while ensuring the integrity of vascular structure and has a higher performance. The proposed method has more obvious targets, a higher contrast, more plentiful detailed information, and local features. The qualitative and quantitative analysis results show that the presented method is superior to the other advanced methods.

关键词： medical image retinal vascular matching filter multiscale particle swarm optimization algorithm

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Performance comparison of the optimized k-section squared-off cascades for enrichment of ¹²⁴Te using two meta-heuristic paradigms

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PROGRESS IN NUCLEAR ENERGY 2022年 145卷

作者： Ezazi, Farzaneh Mallah, Mohammad Hasan Safdari, Jaber Mirmohammadi, Seiedeh Leila Atom Energy Org Nucl Sci & Technol Res Inst Mat & Nucl Fuel Res Sch POB 11365-8486 Tehran Iran

Comparing the performance of square-off cascades with different number of sections and determining the extent of their differences in optimal parameters, can identify more alternatives from an economical point of view reducing the cost of the plant and increasing the incomes due to sales of more products. In this way, this study investigated the performance of optimum square, 2-section squared-off, and 3-section squared-off cascades in the separation of Te-124 to 85% concentration level. The optimization was performed with the particle swarm optimization and Antlion optimization algorithms using an objective function concentrated on maximizing the division factor, D, the total recovery factor, R, and the quantity of total product to be produced. The results obtained for cascades with 160 centrifuges showed that the 3-section squared-off cascade is more efficient than its 2-section counterpart, and both of them have outperformed the square cascade and have a higher recovery factor. It was also shown that PSO results are superior to ALO and can guarantee a better answer to the problem. The results of this study can be a good road map for operational separation of the desired isotope as well isotopes of other elements.

关键词： Squared-off cascade particle swarm optimization algorithm Antlion optimization algorithm Te-124 isotope Recovery factor Separation step

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Prediction of magnetic properties of a single-molecule magnetic metallofullerene cluster DySc2N@C80

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PHYSICA B-CONDENSED MATTER 2022年 647卷

作者： Wang, Tong-lun Wang, Wei Li, Qi Li, Bo-chen Shenyang Univ Technol Sch Sci Shenyang 110870 Peoples R China

In this work, we proposed a particle swarm optimization algorithm combined with Monte Carlo simulation method to investigate the magnetic properties of a single-molecule magnetic metallofullerene cluster DySc2N@C80. Based on the experimental results, we estimated the values of the exchange coupling and the anisotropy of the present system according to the critical temperature TC, which achieved a good agreement between simulated and experimental results. In addition, we also obtained the influence of size effect, the curves of magnetization, magnetic susceptibility and internal energy induced by external magnetic field and hysteresis behaviors at different temperatures.

关键词： Single -molecule magnetic metallofullerene cluster Critical temperature particle swarm optimization algorithm Monte Carlo simulation Hyteresis loops

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Three-phase Volume Rate Inversion of Asphalt Mixture Based on particle swarm optimization 3

Three-phase Volume Rate Inversion of Asphalt Mixture Based o...

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IEEE 3rd Information Technology, Networking, Electronic and Automation Control Conference (ITNEC)

作者： Zhang, Bei Xu, Shengjie Zhong, Yanhui Li, Xiaolong Zhengzhou Univ Coll Water Conservancy & Environm Zhengzhou Henan Peoples R China

ISBN: (纸本)9781538662434

Asphalt mixture is the construction material of asphalt pavement surface. Its thickness, compactness and asphalt content are the main indexes of pavement quality evaluation. The LR dielectric model is modified by experiments in this thesis. The modified dielectric model can represent the essential relationship between the composite dielectric properties of asphalt mixture and the three-phase volume ratio of aggregate, asphalt and air voids. Based on the finite-difference time-domain method, the particle swarm optimization (PSO) algorithm for inverse analysis of three-phase volume fraction (air voids, asphalt and aggregate volume fraction) of asphalt mixtures is established by taking the mean square error of actual received signal and analog forward signal as fitness function. Based on this method, the inverse analysis of three-phase asphalt volume fraction mixtures is realized. By comparing the theoretical waveform inversion results with the theoretical model, the feasibility of the algorithm for multi-parameter inversion was verified, and the accuracy of the algorithm was further verified by comparing the inversion results of field measured data with the results of core sampling.

关键词： asphalt mixture dielectric property model three-phase volume ratio particle swarm optimization algorithm inversion algorithm

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