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2011 International Conference on Control, Automation and Systems Engineering(CASE)

作者： Li Wenna Zhang Zhenyu School of Electrical Engineering & Automation Tianjin Polytechnic University School of Information & Communication Engineering Tianjin polytechnic University

ISBN: (纸本)9781457708596

In order to improve the efficiency and accuracy of the particle swarm optimization algorithm in optimization, the parallel CUDA-based particle swarm algorithm is proposed and developed. With the Compute Unified Device Architecture(CUDA) technology, the parallel data structure is defined, and the mechanism of computing tasks mapping to CUDA is described. From the optimization experiments results of 4 benchmark functions it shows that the CUDAbased parallel algorithm can greatly save computing time and improve computing accuracy. This new PSO algorithm is more suitable for the relevant application of the particle swarm algorithm.

关键词： CUDA particle swarm algorithm parallel computing

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The Implicit Construction of Management System Based on particle swarm algorithm 2

The Implicit Construction of Management System Based on Part...

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2nd IEEE International Conference on Integrated Circuits and Communication Systems, ICICACS 2024

作者： Min, Kai Sun, Xiaojing Jain, Amar Yunan Medical Health College Kunming China Yunnan College of Business Management Kunming China Symbiosis International Deemed University Symbiosis Institute of Technology Department of Civil Engineering Pune India

ISBN: (纸本)9798350317558

In a class, the role of management system is very important, and a good educational atmosphere can enable students to develop good learning habits. Ordinary construction methods cannot solve the problem of class culture management in class management. Therefore, this paper proposes a particle swarm algorithm for class culture management analysis. First, the computer is used to construct cultural management, and the indicators are divided according to the requirements of class culture management to reduce class cultural management in the interfering factor. Then, the computer implicitly constructs the class culture management to form the class culture management program, and carries out the class culture management results Comprehensive analysis. MATLAB simulation shows the feasibility of particle swarm algorithm for the construction of class culture management of class management under certain evaluation criteria The rationality is better than ordinary construction methods. © 2024 IEEE.

关键词： class management Cultural management Computer particle swarm algorithm

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Energy Storage Optimization Technology of Source-Grid-Load-Storage Microgrid Based on particle swarm algorithm

Energy Storage Optimization Technology of Source-Grid-Load-S...

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2024 International Conference on Optimization Computing and Wireless Communication, ICOCWC 2024

作者： Liu, Zhilu Mo, Changming Zheng, Jianfeng Zhou, Longzhou Xian, Zhongye Foshan Power Supply Bureau of Guangdong Power Grid Co. Ltd. Guangdong Foshan China Tianjin Tianda Qiushi Electric Power High Technology CO. LTD Tianjin China

ISBN: (纸本)9798350383348

swarm algorithm is an effective optimization technique, which originates from the research on the behavior of birds and fish in nature. In the field of microgrid energy storage optimization, this algorithm is applied to manage and dispatch renewable energy (such as solar energy and wind energy) and traditional energy (such as micro gas turbine and diesel generator), and the load demand and charging and discharging strategy of energy storage system are considered to optimize the performance of the whole system. © 2024 IEEE.

关键词： computer technology Energy storage particle swarm algorithm source-grid-Ioad-storage microgrid

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A Research on Pricing and Replenishment Decision of Vegetable Items Based on XGBoost Prediction and particle swarm algorithm

A Research on Pricing and Replenishment Decision of Vegetabl...

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2023 IEEE International Conference on Electrical, Automation and Computer Engineering, ICEACE 2023

作者： Song, Tianyang Wang, Jingying Huang, Yang Xiangtan University Xiangtan China

ISBN: (纸本)9798350309614

In this paper, we conducted an in-depth study on the sales and replenishment of vegetables in fresh food superstores and used the multi-product newsboy model to conduct a detailed analysis and model construction of commodity sales data. First, after data preprocessing, the sales trend of each category and single product is plotted according to the total monthly sales volume, and the conclusion of the stability of the daily sales pattern of the superstore is drawn. Second, the relationship between the total sales volume is analyzed by using the XGBoost and Random Forest models in combination with the sales unit price and wholesale price, and the optimal revenue of the superstore is determined by the particle swarm algorithm. Lastly, the unit of the single product of the vegetable sales volume is taken into account during the process. Finally, the corresponding replenishment and pricing strategies are formulated by considering the vegetable sales unit, which provides a scientific basis for the superstore to realize efficient management. © 2023 IEEE.

关键词： particle swarm algorithm Random Forest Sales Analytics XGBoost

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Research on vegetable commodity pricing replenishment decision based on particle swarm algorithm

Research on vegetable commodity pricing replenishment decisi...

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2023 IEEE International Conference on Electrical, Automation and Computer Engineering, ICEACE 2023

作者： Geng, Jing Zhai, Yifan Yao, Hongyi Qinghai University Xining China

ISBN: (纸本)9798350309614

In order to solve the problem of replenishment and pricing decisions in superstores, we analyzed the relationship between the total sales volume and cost-plus pricing of each vegetable category, which provides a basis for superstores to make reasonable replenishment and pricing decisions. Due to the seasonality of agricultural products and the specificity of the forecasting unit, the data were preprocessed and then the ARIMA time-series forecasting model was used to forecast different individual products and categories. After that, we introduced the demand elasticity pricing model to make replenishment and pricing decisions and built a planning model to optimize the decisions using the relationship between the elasticity coefficient, revenues and costs, taking into account the competitive environment in which the superstore is located and the inconsistency of the elasticity of supply and demand. For this, we used black hole algorithm and particle swarm algorithm to optimize the objective function respectively. By comparing the results, we found that the particle swarm algorithm is significantly better than the black hole algorithm in terms of optimization finding results. The total amount of replenishment as well as the pricing strategy were obtained from the particle swarm algorithm. © 2023 IEEE.

关键词： ARIMA time series prediction black hole algorithm particle swarm algorithm vegetable commodities

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A scheduling method based on a hybrid genetic particle swarm algorithm for multifunction phased array radar

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Frontiers of Information Technology & Electronic Engineering 2017年第11期18卷 1806-1816页

作者： Hao-wei ZHANG Jun-wei XIE Wen-long LU Chuan SHENG Bin-feng ZONG Air and Missile Defense College Air Force Engineering University Xi'an 710051 China Troop 94225 Rizhao 276000 China Troop 94710 Wuxi 214000 China

A hybrid optimization approach combining a particle swarm algorithm, a genetic algorithm, and a heuristic interleaving algorithm is proposed for scheduling tasks in the multifunction phased array radar. By optimizing parameters using chaos theory, designing the dynamic inertia weight for the particle swarm algorithm as well as introducing crossover operation and mutation operation of the genetic algorithm, both the efficiency and exploration ability of the hybrid algorithm are improved. Under the frame of the intelligence algorithm, the heuristic interleaving scheduling algorithm is presented to further use the time resource of the task waiting duration. A large-scale simulation demonstrates that the proposed algorithm is more robust and efficient than existing algorithms.

关键词： Phased array radar Scheduling particle swarm algorithm Genetic algorithm Pulse interleave

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A kind of exponential inertia weight particle swarm algorithm

A kind of exponential inertia weight particle swarm algorith...

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2009 International Workshop on Intelligent Systems and Applications, ISA 2009

作者： Li, Qiang Wu, Jianxin Sun, Yan Mechanical Engineering Department North China University of Technology Beijing China Mechanical Engineering Department Inner Mongolia University of Technology Hohhot China

ISBN: (纸本)9781424438945

A new kind of exponential inertia weight particle swarm algorithm is presented and the astringency analysis is finished in the paper. The results of application example proved that practical optimization parameters can be obtained in limited time by the method, and it is a kind of effective way to solve such kind of problem. © 2009 IEEE.

关键词： Astringency analysis Exponential inertia weight particle swarm algorithm

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A Two-Stage Intelligent Task Offloading Approach for Cloud-Edge Collaborative Computing Based on particle swarm algorithm 5

A Two-Stage Intelligent Task Offloading Approach for Cloud-E...

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5th International Conference on Frontiers Technology of Information and Computer, ICFTIC 2023

作者： Yang, Zhongtian Beijing University of Posts and Telecommunications International School Beijing China

ISBN: (纸本)9798350309034

Cloud-edge collaborative computing plays a crucial role in the massive computing demands and enhancing application performance. However, this computing method faces numerous challenges, including network latency, resource imbalances, and changes in requirements. In this paper, we propose an intelligent two-level scheduling approach for tasks based on cloud-edge collaborative computing to meet the demand for real-time and low-latency in applications like IoT. First, the approach assigns tasks to edge or cloud according to the task types. Furthermore, it intelligently offloads some tasks to the cloud when the load on the edge is too high. Finally, it finds the optimal time and cost balance point through multi-objective optimization by particle swarm algorithm. Experimental results show that this method can fully utilize the edge and cloud resources, reduce data uploading, and control the cost while ensuring low latency. © 2023 IEEE.

关键词： Cloud-edge collaboration Edge computing particle swarm algorithm Task scheduling

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Adaptive VSG Control with Coefficient Optimization via Improved particle swarm algorithm 3

Adaptive VSG Control with Coefficient Optimization via Impro...

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3rd International Conference on Energy, Power and Electrical Engineering, EPEE 2023

作者： Chen, Jie Liu, Muyang Chen, Junru Zeb, Kamran Qureshi, Hassaan Khaliq School of Electrical Engineering Xinjiang University Urumqi China School of Electrical Engineering National University of Science and Technology Islamabad Pakistan

ISBN: (纸本)9798350318180

Virtual synchronous generator (VSG) control simulates the external characteristics of synchronous generators (SG) so that can provide inertia and frequency support to the grid in the similar behaviors of SGs. The classic VSG control use the fixed control parameters, which, however does not take advantage of the potential of fast-response feature of the power electronics. Basing on the stability features of the power system with high penetrations of inverter-based sources, the paper proposes an adaptive VSG control strategy with the virtual inertia and damping coefficients adaptively varied according to dynamic frequency of the power system. The paper analyses the effect of the proposed adaptive VSG control through the root-loci method and the small-signal stability analysis. The coefficients of the adaptive control can be optimized through the particle swarm algorithm to fulfill the best potential of the control on improving the frequency stability of the power system. A micro-grid model is served to validate the proposed adaptive VSG control and the coefficient optimization method. © 2023 IEEE.

关键词： Adaptive Control particle swarm algorithm Virtual Synchronous Machine

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Niche particle swarm algorithm and application study

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2nd International Conference on Future Control and Automation, ICFCA 2012

作者： Tang, Chao-Li Huang, You-Rui Li, Ji-Yun Institute of Electrical and Information Engineering Anhui University of Science and Technology Huainan Anhui 232001 China

ISBN: (纸本)9783642310027

To the multi-modal function optimization problem, after analyzing characteristics and deficiencies of traditional niche genetic and niche clonal selection algorithms, it proposes a niche particle swarm algorithm (NPSA), which based on principle of particle swarm algorithm. By the convergence analysis and simulation experiments of four typical multi-modal functions, conclusions show that the NPSA has eximious simplicity and high efficiency. © 2012 Springer-Verlag.

关键词： multi-modal function optimization niche technology particle swarm algorithm

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