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A Prediction Model of Coal Seam Roof Water Abundance Based on PSO-GA-BP Neural Network

WATER 2023年第23期15卷 4117-4117页

作者： Dai, Xue Li, Xiaoqin Zhang, Yuguang Li, Wenping Meng, Xiangsheng Li, Liangning Han, Yanbo China Univ Min & Technol Sch Resources & Geosci Xuzhou 221116 Peoples R China State Investment Hami Energy Dev Co Ltd Hami 839000 Peoples R China

With the gradual increase of coal production capacity, the issue of water hazards in coal seam roofs is increasing in prominence. Accurate and effective prediction of the water content of the roof aquifer, based on limited hydrogeological data, is critical to the identification of the central area of prevention and control of coal seam roof water damage and the reduction of the incidence of such accidents in coal mines. In this paper, we establish a prediction model for the water abundance of the roof slab aquifer, using a PSO-GA-BP neural network. Our model is based on five key factors: aquifer thickness, permeability coefficient, core recovery, number of sandstone and mudstone interbedded layers, and fold fluctuation. The model integrates the genetic algorithm (GA) into the particle swarm optimization (PSO) algorithm, with the particle swarm optimization algorithm serving as the primary approach. It utilizes adaptive inertia weight and quadratic optimization of the weights and thresholds of the backpropagation neural network to minimize the output error threshold for the purpose of minimizing output errors. The prediction model is applied to hydrogeology and coal mine production for the first time. The model is trained using 100 data samples collected by the Surfer 13 software. These samples help to accurately predict the unit inflow of water. The model is then compared with traditional forecasting methods such as FAHP, BP, and GA-BP neural network models to determine its efficiency. The study found that the PSO-GA-BP neural network model accurately predicts aquifer water abundance with higher precision. The root mean square error (RMSE) of the test set is determined to be 8.7 x 10-4, and the fitting result is measured at 0.9999, indicating minimal error with actual values of the sample. According to the prediction results of the test set, the water abundance capacity of the No. 7 coal mine in Hami Danan Lake is divided, and it is found that the overall difference

关键词： water abundance particle swarm optimization algorithm genetic algorithm BP neural network FAHP

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Using particle swarm optimization algorithm in Multimedia CDN Content Placement

Using Particle Swarm Optimization Algorithm in Multimedia CD...

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5th International Symposium on Parallel Architectures, algorithms and Programming (PAAP)

作者： Chen, Jian-Bo Chen, Chu-Chuan Ming Chuan Univ Dept Informat & Telecommun Engn Tao Yuan Taiwan

ISBN: (纸本)9780769548982;9781467345668

Multimedia Content Delivery Networks (CDN) is used to improve the performance and reliability on Internet. In CDN architecture, the multimedia contents are replicated from the origin server to replica servers in order to improve the performance and minimize the use of network bandwidth. Efficient placing the multimedia contents in CDN is a challenging problem. There are five factors that can be used to determine the placement of multimedia contents, they are bandwidth availability, connection availability, storage availability, CPU availability, and memory availability. In this paper, a particle swarm optimization (PSO) algorithm is adopted to solve this issue. PSO algorithm uses these five different input parameters as different dimensions. In this five dimension searching space, PSO algorithm can find out the global optimal solution. With this global optimal solution, it is the most appropriate replica server that must place the multimedia content. The simulation results show that the PSO algorithm can achieve a better performance than other algorithms.

关键词： component CDN Content Placement particle swarm optimization algorithm

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A novel conformable fractional-order accumulation grey model and its applications in forecasting energy consumption of China

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SCIENTIFIC REPORTS 2024年第1期14卷 1-23页

作者： Chen, Yuzhen Gong, Wenhao Li, Suzhen Guo, Shuangbing Henan Inst Sci & Technol Sch Math Sci Xinxiang 453003 Peoples R China Univ Shanghai Sci & Technol Sch Management Shanghai 200093 Peoples R China

Accurate forecasting of energy consumption demand is crucial to optimize resources and achieve sustainable development goals. However, the energy system is affected by many factors, which are complex and highly uncertain. Therefore, a novel grey model (IBCFGMP (1,1,N)) is proposed, integrating multiple optimization techniques such as background value optimization, initial condition optimization, fractional-order accumulation optimization, and grey action quantity optimization. First, this paper deduces the time response function of the optimization model. The relevant parameters of the model can be found using the particle swarm optimization algorithm. Then, the properties of the model are studied, and it is found that the optimized model have stronger universality and stability. Finally, the model is applied to predict and analyze the energy consumption of China. The prediction results indicate that China's consumption of hydroelectricity, nuclear energy, and coal will be 12.693 exajoules, 5.550 exajoules, and 98.850 exajoules in 2026, respectively. The research results will provide a scientific basis for rationally optimizing resource allocation and realizing the sustainable development of clean energy.

关键词： Grey prediction model Conformable fractional-order particle swarm optimization algorithm Clean energy IBCFGMP (1,1,N) model

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Metaheuristic-based possibilistic fuzzy k-modes algorithms for categorical data clustering

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INFORMATION SCIENCES 2021年 557卷 1-15页

作者： Kuo, R. J. Zheng, Y. R. Thi Phuong Quyen Nguyen Natl Taiwan Univ Sci & Technol Dept Ind Management 43Sect 4Kee Lung Rd Taipei 106 Taiwan Univ Sci & Technol Univ Danang Fac Project Management 54 Nguyen Luong Bang Danang Vietnam Texas Instrument Taiwan Ltd New Taipei Taiwan

Smart devices and technology applications are used in many fields. Much information is now recorded and collected rapidly so data analysis, especially clustering analysis, is vital to the process of analyzing and obtaining valuable information from datasets. However, data has different types of attributes: numerical, categorical, and mixed attributes. Some datasets also contain noise and outliers. An appropriate clustering is necessary to exploit the data structure. This study proposes a clustering algorithm that is called a possibilistic fuzzy k-modes (PFKM) algorithm. This combines the concept of possibility with the fuzzy k-modes (FKM) algorithm to address the effect of outliers and to improve the clustering results for categorical data. This study also implements three metaheuristics to increase clustering performance: a genetic algorithm (GA), a particle swarm optimization (PSO) and the sine-cosine algorithm (SCA). Three clustering algorithms are proposed: the GAPFKM, PSO-PFKM, and SCA-PFKM algorithms. The performance of the algorithms is compared with that for the classical FKM algorithm using two indices: the sum-of-squared error (SSE) and the accuracy. The experimental results show that the PSO-PFKM and SCA-PFKM algorithms perform better for most datasets. (C) 2020 Elsevier Inc. All rights reserved.

关键词： Clustering analysis Categorical data Metaheuristic Genetic algorithm particle swarm optimization algorithm Sine cosine algorithm Fuzzy clustering Fuzzy k-modes algorithm

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Research on site selection of agricultural internet of things nodes based on rapid terrain sampling

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COMPUTERS AND ELECTRONICS IN AGRICULTURE 2023年 204卷

作者： Xie, Jiaxing Liang, Gaotian Gao, Peng Wang, Weixing Yin, Dongxiao Li, Jun South China Agr Univ Coll Elect Engn Coll Artificial Intelligence Guangzhou 510642 Peoples R China South China Agr Univ Guangdong Lab Lingnan Modern Agr Guangzhou 510642 Peoples R China Luoding Polytech Yunfu 527200 Peoples R China South China Agr Univ Coll Engn Guangzhou 510642 Peoples R China

With the development of Internet of Things (IoT) technology, modern agriculture is moving in the direction of 4.0. The Agricultural IoT is inseparable from wireless communication. However, in traditional agricultural IoT router and gateway site selection, the influence of the actual terrain on transmission loss is not considered, which results in node power wastage and increased maintenance costs. Based on a multi-sensor fusion algorithm, a fast terrain sampler is designed in this study to collect point-cloud data of the experimental site terrain. A reasonable objective function is then designed under the premise of consideration of the electromagnetic wave free-space and diffraction losses, and the locations of the routers and gateway are optimized based on k-means and particle swarm optimization (PSO) algorithm. Simulations show that the running time of the PSO algorithm is very sensitive to the changes in the execution parameters, and the improved PSO algorithm converges faster than the genetic algorithm (GA) in all three initialization methods. After collecting field terrain data, five interpolation methods were compared, and the nearest-neighbor algorithm is used to obtain the terrain model. On-site collection of received signal-strength indication (RSSI) shows that the communication quality of the optimal point selected via this algorithm is significantly higher than those of nearby points. At the same time, it is proved that the RSSI data has serious discontinuities, so the traditional gradient descent method is not suitable for solving the objective function in this work. Therefore, the algorithm used herein is of great significance for the site selection of agricultural Internet of Things nodes. However, since there are many ways to calculate the diffraction loss, the objective function of this work still needs more correction studies. The tool proposed in this work can obtain a 3D model of farmland faster than traditional surveying and mapping methods. Wit

关键词： S ite selection planning Fast terrain sampling modeling particle swarm optimization algorithm Wireless transmission loss Extended Kalman filter

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A novel mapping algorithm for three-dimensional network on chip based on quantum-behaved particle swarm optimization

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Frontiers of Computer Science 2017年第4期11卷 622-631页

作者： Cui HUANG Dakun ZHANG Guozhi SONG School of Computer Science and Software Engineering Tianjin Polytechnic University Tianjin 300387 China

Mapping of three-dimensional network on chip is a key problem in the research of three-dimensional network on chip. The quality of the mapping algorithm used di- rectly affects the communication efficiency between IP cores and plays an important role in the optimization of power consumption and throughput of the whole chip. In this paper, ba- sic concepts and related work of three-dimensional network on chip are introduced. Quantum-behaved particle swarm op- timization algorithm is applied to the mapping problem of three-dimensional network on chip for the first time. Sim- ulation results show that the mapping algorithm based on quantum-behaved particle swarm algorithm has faster con- vergence speed with much better optimization performance compared with the mapping algorithm based on particle swarm algorithm. It also can effectively reduce the power consumption of mapping of three-dimensional network on chip.

关键词： three-dimensional network on chip mapping al-gorithm quantum-behaved particle swarm optimization al-gorithm particle swarm optimization algorithm low powerconsumption

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Network traffic prediction model based on improved VMD and PSO-ELM

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INTERNATIONAL JOURNAL OF COMMUNICATION SYSTEMS 2023年第7期36卷 e5448-e5448页

作者： Shi, Jinmei Zhou, Jinghe Feng, Junying Chen, Huandong Hainan Vocat Univ Sci & Technol Coll Informat Engn Haikou Peoples R China Hainan Normal Univ Coll Informat Sci & Technol Haikou Peoples R China Hainan Vocat Univ Sci & Technol Coll Informat Engn Haikou 571126 Hainan Peoples R China

The rapid update of computing power leads to exponential data traffic growth, and the incidence of network attacks is also increasing. It is significantly important to analyze and predict network traffic accurately in the early stage and take corresponding preventive measures. The existing network flow integrated forecasting models still have some bottlenecks that are difficult to solve, for example, the slow optimization speed of modal decomposition parameters, easy falling into local optimal solutions, the slow convergence speed of the training process, and poor generalization capability. In this paper, particle swarm optimization (PSO) is utilized to improve the parameters selection process of the variational mode decomposition (VMD) algorithm and the extreme learning machine (ELM) algorithm. First, the PSO-VMD combined with multi-scale permutation entropy (MPE) is utilized to decompose the original network flow, and multiple eigenmode components are obtained. Second, the PSO-ELM is utilized to train the network traffic prediction model, and the PSO parameters in PSO-ELM are updated through adaptive weight adjustment and synchronous learning factors to increase the training and prediction speed, and the component prediction results are reconstructed to get a high-precision network flow forecasting result. Finally, through the prediction and verification of the public network flow data of the WIDE backbone, the result of this experiment indicates that the VMD-PSO-ELM can break through the bottlenecks of slow optimization speed of VMD decomposition parameters, reduce the computational complexity of ELM, accelerate the convergence speed, and increase the forecasting accuracy.

关键词： extreme learning machine multi-scale permutation entropy network traffic prediction particle swarm optimization algorithm variational mode decomposition

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Active power filter module function to improve power quality conditions using GWO and PSO techniques for solar photovoltaic arrays and battery energy storage systems

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JOURNAL OF ENERGY STORAGE 2023年 72卷

作者： Abou Houran, Mohamad Sabzevari, Kiomars Hassan, Alaaeldien Oubelaid, Adel Tostado-Veliz, Marcos Khosravi, Nima Xi An Jiao Tong Univ Sch Elect Engn Xian 710049 Peoples R China Tech & Vocat Univ TVU Dept Elect Engn Tehran Iran Univ Bejaia Fac Technol Lab Technol Ind & Informat Bejaia 06000 Algeria Univ Jaen Dept Elect Engn Linares 23700 Spain R&D Management NPC Dept Elect & Instrumentat Engn Tehran Iran MOP Deputy Res & Dev Tehran Iran

This study aims to unbalanced power quality (PQ) conditions analysis of solar photovoltaic arrays and battery energy storage system (PV-BESS) integrated active power filter module (APFM). Here, the APFM's role is to mitigate the PQ issues that existed by the nonlinear loads. The standalone PV-APFM design is negligibly reliable approximated to a hybrid PV-BESS system because of its fluctuation and high environmental reliance. Further, here the research challenge is to optimise the APFM controller gain coefficients by the grey wolf optimization (GWO) algorithm and apply this technique to current and voltage harmonic loops (CHL-VHL) to achieve the best answer to enhance the unbalanced PQ conditions. The multi-objective functions (MOFs) to crack the problem include the total harmonic distortion (THD) of current and voltage components. The particle swarm optimization (PSO) approach will be considered a complementary method for validation and comparison with the outcomes obtained from the GWO procedure. Four case studies in imbalance conditions have been considered to confirm the proposed method in MATLAB-Simulink software.

关键词： Harmonic condition Total harmonic distortion Active power filter module Grey wolf optimization particle swarm optimization algorithm

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Calculation and optimization of inter-regional available transfer capability considering wind power uncertainty and system improvement measures

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FRONTIERS IN ENERGY RESEARCH 2023年 11卷

作者： Liu, Wenli Zhang, Tao Yang, Xiaolei Wang, Jin China Three Gorges Univ Coll Elect Engn & New Energy Yichang Hubei Peoples R China State Grid Jiaxing Power Supply Co Jiaxing Zhejiang Peoples R China

The intermittency and uncertainty of wind energy make transmission congestion management increasingly complex. In this paper, we investigate the impact of wind energy on available transmission capacity (ATC) and then explore ways to mitigate transmission congestion from the grid and load side, respectively. The impact of a stochastic variable is considered by applying Latin Hypercube Sampling (LHS) and backward curtailment techniques to generate typical scenarios. ATC is improved by the optimal allocation of Thyristor Controlled Series Compensation (TCSC) on the grid side and the use of Demand Response (DR) on the load side. The source-grid-load cooperative optimisation model for ATC is solved by an improved particle swarm optimisation (PSO) algorithm. Based on the IEEE-30 bus system, an experimental scheme is designed and analytical calculations are performed. The results show that the joint application of TCSC and DR can help to improve ATC in a comprehensive way. The work done in this paper can achieve the purpose of promoting renewable energy consumption by improving the utilisation efficiency of the transmission network without changing the network structure.

关键词： wind power's stochastic characteristics available transfer capability demand response thyristor-controlled series compensation particle swarm optimization algorithm

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CSP-free adaptive Kriging surrogate model method for reliability analysis with small failure probability

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RELIABILITY ENGINEERING & SYSTEM SAFETY 2024年 243卷

作者： Li, Wenxiong Geng, Rong Chen, Suiyin South China Agr Univ Coll Water Conservancy & Civil Engn Guangzhou 510642 Peoples R China

In the field of reliability engineering, the active learning reliability method that amalgamates Kriging model and Monte Carlo simulation has been devised and proven to be efficacious in reliability analysis. Nevertheless, the performance of this method is sensitive to the magnitude of candidate sample pool, particularly for systems with small failure probabilities. To surmount these limitations, this paper proposes an active learning method that obviates the need for candidate sample pools. The proposed method comprises two stages: construction of surrogate model and Monte Carlo simulation for failure probability estimation. During the surrogate model construction stage, the surrogate model is iteratively refined based on the representative samples selected by solving the optimization problem facilitated by the particle swarm optimization algorithm. To achieve an optimal balance between solution accuracy and efficiency, the penalty intensity control and the density control for the experimental design points are incorporated to modify the objective function in optimization. The per-formance of the proposed method is evaluated using numerical examples, and results indicate that by leveraging an optimization algorithm to select representative samples, the proposed method overcomes the limitations of traditional active learning methods based on candidate sample pool and exhibits exceptional performance in addressing small failure probabilities.

关键词： Reliability analysis Kriging surrogate model particle swarm optimization algorithm Learning function Candidate sample pool

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