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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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Vehicle path planning method based on particle swarm optimization algorithm

Vehicle path planning method based on particle swarm optimiz...

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3rd International Conference on Manufacturing Science and Engineering (ICMSE 2012)

作者： Yu, Sheng-long Bo, Yu-ming Chen, Zhi-min Zhu, Kai Nanjing Univ Sci & Technol Sch Automat Nanjing 210094 Jiangsu Peoples R China

ISBN: (纸本)9783037853696

A particle swarm optimization algorithm (PSO) is presented for vehicle path planning in the paper. particle swarm optimization proposed by Kennedy and Eberhart is derived from the social behavior of the birds foraging. particle swarm optimization algorithm a kind of swarm-based optimization *** simulation experiments performed in this study show the better vehicle path planning ability of PSO than that of adaptive genetic algorithm and genetic algorithm. The experimental results show that the vehicle path planning by using PSO algorithm has the least cost and it is indicated that PSO algorithm has more excellent vehicle path planning ability than adaptive genetic algorithm,genetic algorithm.

关键词： particle swarm optimization algorithm genetic algorithm vehicle path planning

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Optimizing the Operation Sequence of Chip Shooter Machine Based on the particle swarm optimization algorithm

Optimizing the Operation Sequence of Chip Shooter Machine Ba...

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2nd International Conference on Advanced Design and Manufacturing Engineering (ADME 2012)

作者： Liang, Wanlei Guan, Xiaodan North China Inst Aerosp Engn Dept Elect Engn Langfang 065000 Peoples R China

ISBN: (纸本)9783037855034

The mounting process is the key factor of the placement efficiency, it is also important for the improvement of the efficiency of whole production line and decrease of the cost. This paper analyzed the mounting process of the Chip Shooter machine, applied the PSO algorithm, constructed the corresponding coding system, proposed the corresponding particle update mechanism, introduced the partially matched crossover idea of the genetic algorithm into the PSO algorithm, and designed the new re-scheduling method of feeder position assignment to optimize the position assignment of feeders and the pickup and placement sequence of components, thus improved the placement efficiency. After comparing the results before and after the simulation test for selected 8 pieces of PCB, the average efficiency of this algorithm is 7.09% higher than genetic algorithm method that is based on sort encoding. The experimental result shows that, this algorithm is more efficiency on the improvement placement efficiency and decrease of the placement time for the chip shooter machine.

关键词： Chip shooter machine Operation sequence optimization particle swarm optimization algorithm

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

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WATER 2023年第23期15卷

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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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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Improving collaborative recommender system using hybrid clustering and optimized singular value decomposition

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ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE 2023年 126卷

作者： Movafegh, Zahra Rezapour, Abdoreza Mehrastan Univ Dept Comp Engn Guilan Iran Islamic Azad Univ Dept Comp Engn Astaneh Ashrafieh Branch Guilan Iran

In the era of internet and big data, recommender systems are necessary to filter out useless information. Collaborative filtering (CF) is one of the most successful technique used in recommender systems, this technique suffers from a large number of users and items, or the scalability issue. In this paper, a new hybrid method based on K-means clustering (KM) and Singular Value Decomposition (SVD) which uses evolutionary algorithms is proposed to deal with scalability issue. On the one hand, KM optimized by particle swarm optimization (PSO) and denoising by Density-Based Spatial Clustering of Applications with Noise (DBSCAN) is used to cluster users and reduce the number of comparisons between the target user and other users. On the other hand, SVD optimized by Genetic algorithm (GA) is used to reduce the number of items. The proposed method is assessed on three standard datasets and the results are compared with basic CF and other extended versions that use clustering algorithms, evolutionary algorithms and dimensionality reduction techniques. The results show that our proposed method performed better than other methods in terms of precision, recall and MAE, and the scalability problem was improved by reducing the time complexity. Also, the combined clustering method was optimized in terms of Davies-Bouldin and the Dunn's index compared to the basic clustering methods.

关键词： Recommender system Collaborative filtering Clustering Singular value decomposition particle swarm optimization algorithm Genetic 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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An improved Fourier Ptychography algorithm for ultrasonic array imaging

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COMPUTERS IN BIOLOGY AND MEDICINE 2023年 163卷 107157-107157页

作者： Yang, Yaoyao Wu, Runcong Chen, Dongdong Fei, Chunlong Li, Di Yang, Yintang Xidian Univ Sch Microelect Xian 710071 Peoples R China Chinese Acad Sci Shenzhen Inst Adv Technol Shenzhen 518055 Peoples R China

Inspired by the optical imaging algorithm, the Fourier Ptychography (FP) algorithm is adopted to improve the resolution of ultrasonic array imaging. In the FP algorithm, the steady-state spectrum is utilized to recover the high-resolution ultrasonic images. Meanwhile, the parameters of FP algorithm are empirical, which can affect the imaging quality of ultrasonic array. Then the particle swarm optimization (PSO) algorithm is used to optimize the parameters of FP algorithm to further improve the imaging quality of ultrasonic array. The tungsten imaging experiments and pig eye imaging experiments are conducted to demonstrate the feasibility and effectiveness of the developed algorithm. In addition, the proposed algorithm and the coherent wave superposition (CWS) al-gorithm are both based on single plane wave (SPW) algorithms and they are then compared. The results show that the CWS algorithm and FP algorithm have good longitudinal and lateral resolutions, respectively. The particle swarm optimization-based FP (PSOFP) imaging algorithm has both excellent lateral and longitudinal resolutions. The average lateral resolution of PSOFP imaging algorithm is improved by 34.47% compared with CWS imaging algorithm in the tungsten wires experiments, and the lateral boundary structure width of the lens is improved by 49.48% in the pig eye experiments. The proposed algorithm can effectively improve the ultrasonic imaging quality for medical application.

关键词： Fourier Ptychography Ultrasonic imaging particle swarm optimization algorithm Medical imaging

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