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2024 IEEE Cyber Science and Technology Congress

作者： Sun, Houtao Wen, Chuanxin Shi, Chunhu Pu, Qiangsheng Lv, Pengpeng Luo, Fei NARI Technol CO LTD State Grid Elect Power Res Inst Nanjing Peoples R China State Grid Jiangsu Elect Power CO LTD State Grid Jiangsu Elect Power Res Inst Nanjing Peoples R China

ISBN: (纸本)9798331532109;9798331532093

Determining the location and size of distributed power supplies is a key component of distribution network planning, often requiring consideration of multiple factors. In this paper, we address investment costs, network losses, and voltage stability by developing a three-objective mathematical model. To solve for the Pareto optimal solutions, we use a hybrid approach combining simulated annealing with particle swarm optimization. Model constraints are handled by incorporating penalty functions into the objective function calculations. The approach not only yields a set of multi-objective Pareto solutions but also identifies the best optimization results through adaptive evaluation of local optima and weighted multi-objective analysis. The proposed algorithm's effectiveness for determining distributed power supply locations and capacities is demonstrated using the IEEE 69-node system.

关键词： Simulated annealing algorithm particle swarm algorithm Distributed power Siting and capacity determination Distribution network Multi-objective optimization

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Forecast of agricultural water resources demand based on particle swarm algorithm

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Acta Agriculturae Scandinavica Section B: Soil and Plant Science 2022年第1期72卷 30-42页

作者： Yi, Wenzhou Information Engineering College Guangdong Polytechnic of Engineering Guangzhou China

The planning and management of water resources are becoming more and more important, and the forecast of water demand as the prerequisite and foundation of the entire planning has become a very important task in agricultural development. This paper combines the particle swarm algorithm to construct the agricultural water resource demand forecasting model, analyzes the shortcomings of the traditional particle swarm algorithm, and makes appropriate improvements to the quantum particle swarm algorithm. Moreover, this paper constructs the functional structure of the agricultural water resource demand forecast model based on the forecast demand of water resources, and analyzes the application process of the particle swarm algorithm in the system of this paper. After the model is constructed, the performance of the model is verified, and the simulation test is designed to evaluate the effect of system forecast with actual data. At the same time, this paper uses the model constructed in this paper to analyze the factors affecting water resources forecast demand. From the results of the experimental analysis, it can be seen that the model constructed in this paper is more effective in the forecast of water resources demand. © 2021 The Author(s). Published by Informa UK Limited, trading as Taylor & Francis Group.

关键词： agricultural water resources demand forecast machine learning particle swarm algorithm

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A Multi-Objective particle swarm algorithm for the Optimization of IMRT Inverse Planning

A Multi-Objective Particle Swarm Algorithm for the Optimizat...

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2010 3rd International Conference on Biomedical Engineering and Informatics (BMEI 2010)

作者： Li, Guoli Cao, Dongzhi Zhejiang Univ Technol Coll Informat Engn Hangzhou Zhejiang Peoples R China Zhejiang Univ Technol Coll Comp Sci Hangzhou Peoples R China

ISBN: (纸本)9781424464968

A Multi-Objective particle swarm algorithm(MOPSA) is applied to the optimization of Intensity Modulated Radiation Treatment inverse planning. The test cases of irregular field under the condition of water phantom prove the practicability of the algorithm. The optimization results compared with other algorithms proposed by our group show that the algorithm has clearly advantages on convergence speed.

关键词： IMRT multi-objective optimization particle swarm algorithm

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Solving NoC Mapping Problem with Improved particle swarm algorithm

Solving NoC Mapping Problem with Improved Particle Swarm Alg...

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6th International Conference on Advanced Computational Intelligence (ICACI)

作者： Li, Zhengxue Liu, Yang Cheng, Mingsong Dalian Univ Technol Sch Math Sci Dalian Peoples R China Huawei Technol Co Ltd Shenzhen Peoples R China

ISBN: (纸本)9781467363433

Network on chip (NoC) mapping problem belongs to quadratic assignment problem, and quadratic assignment problem is a classical combinatorial optimization problem which has been proved to be a NP-complete problem. In this paper, the widely used 2D-mesh as a NoC topology is studied, and the important factors influencing the performance of the system-power consumption and delay are modeled. Then the approximate optimal solution is found using the improved discrete particle swarm optimization algorithm. Simulation experiments show that very good optimal results are obtained.

关键词： particle swarm algorithm Nondeterministic polynomial-time hard MAPPING(MATHEMATICAL) algorithm simulation System performance channel assignment problem network-on-chip Quadratic

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Improving the particle swarm algorithm and Optimizing the Network Intrusion Detection of Neural Network 6

Improving the Particle Swarm Algorithm and Optimizing the Ne...

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6th International Conference on Intelligent Systems Design and Engineering Applications (ISDEA)

作者： Yang, Xu Hui, Zhao Beijing Inst Tracking & Telecommun Technol Beijing 100094 Peoples R China

ISBN: (纸本)9781467393935

According to the connections between the feature subset and RBF neural network parameter, in order to improve the accuracy rate of intrusion detection, a network intrusion detection model (IPSO-BPNN) improving the article swarm and optimizing the neural network is put forward. Take the network feature subset and RBF neural network parameter as a particle, and discover the optimum network feature subset and RBF neural network parameter through the coordination and information exchange between particles to establish the optimum network intrusion detection model, and adopt KDD Cup99 data set to perform the simulation experiment. The results of simulation experiment show that, IPSO-RBF neural network reduces the feature dimensions, and obtains better RBF neural network parameter, which is a network intrusion detection model of high detection accuracy rate and speed.

关键词： Network intrusion particle swarm algorithm Neural network Feature selection

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Optimal actuator and sensor locations for active vibration control Using improved particle swarm algorithm

Optimal actuator and sensor locations for active vibration c...

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4th World Congress on Software Engineering (WCSE)

作者： Wang Tian-xiong Bo Yu-ming Wang Xiang-ming Zhao Gao-peng Nanjing Univ Sci & Technol Sch Automat Nanjing Jiangsu Peoples R China

ISBN: (纸本)9781479928828

This paper deals with the optimization of actuators and sensors locations for active vibration control. An optimization criterion is defined, ensuring good observability and controlability of the structure, and avoiding the spillover effect. An improved particle swarm algorithm is used to find the optimal configurations. Some simulations for a cantilever beam are shown.

关键词： active control modes optimization criterion particle swarm algorithm

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Dimensionality Reduction particle swarm algorithm for High Dimensional Clustering

Dimensionality Reduction Particle Swarm Algorithm for High D...

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IEEE swarm Intelligence Symposium

作者： Cui, Xiaohui Beaver, Justin M. Charles, Jesse St. Potok, Thomas E. Oak Ridge Natl Lab Computat Sci & Engn Div Oak Ridge TN 37831 USA Univ Tennessee Chattanooga TN 37996 USA

ISBN: (纸本)9781424427048

The particle swarm Optimization (PSO) clustering algorithm can generate more compact clustering results than the traditional K-means clustering algorithm. However, when clustering high dimensional datasets, the PSO clustering algorithm is notoriously slow because its computation cost increases exponentially with the size of the dataset dimension. Dimensionality reduction techniques offer solutions that both significantly improve the computation time, and yield reasonably accurate clustering results in high dimensional data analysis. In this paper, we introduce research that combines different dimensionality reduction techniques with the PSO clustering algorithm in order to reduce the complexity of high dimensional datasets and speed up the PSO clustering process. We report significant improvements in total runtime. Moreover, the clustering accuracy of the dimensionality reduction PSO clustering algorithm is comparable to the one that uses full dimension space.

关键词： dimensionality reduction Clustering algorithms particle swarm optimization computational time algorithms DIMENSIONAL particle swarm algorithm Cluster Analysis Dataset

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Multibeam Optimal Line Planning Based on Improved particle swarm algorithm 3

Multibeam Optimal Line Planning Based on Improved Particle S...

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3rd IEEE International Conference on Electrical Engineering, Big Data and algorithms, EEBDA 2024

作者： Peng, Chengfan Zang, Chenhui Kang, Shengyu Cheng, Yuxuan School of Medical Imaging Hangzhou Medical College Hangzhou China

ISBN: (纸本)9798350380989

Multibeam bathymetric system is an important technology in the field of ocean exploration, which expands the range of depth measurement and improves the efficiency by transmitting multiple acoustic beams at the same time. This paper mainly starts from the geometric relationship between the beams and the seabed slope, the relationship between the coverage width and the depth, and explores the optimal layout design of the survey line by using optimization methods such as planning model, differential iteration and particle swarm algorithm. Specifically, firstly, a planar coordinate system is established to model the coverage width, analyze the geometrical relationship between the coverage width, transducer opening angle and seawater depth, and finally construct the model about the transducer opening angle and seawater depth through the sinusoidal theorem and angle transformation. Then the three-plane two-coordinate system is established to construct the model between the coverage width and the direction of the measuring line. Finally, the model is improved to get the establishment of the optimal survey line laying model in general. © 2024 IEEE.

关键词： multibeam bathymetric system optimized line layout particle swarm algorithm underwater topographic map

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Modification of particle swarm algorithm for the Problem of the SVM Classifier Development

Modification of Particle Swarm Algorithm for the Problem of ...

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2015 International Conference "Stability and Control Processes" in Memory of V.I. Zubov (SCP)

作者： Demidova, Liliya Sokolova, Yulia Ryazan State Radioengn Univ 59-1 Gagarina Str Ryazan 390005 Russia

ISBN: (纸本)9781467376983

The application of particle swarm algorithm and its modifications for the problem of the SVM classifier development is considered. The modification particle swarm algorithm, allowing to reduce the time required for the SVM classifier development during the search of the kernel function type, values of the kernel function parameters and value of the regularization parameter and providing high quality data classification, is offered. The experimental results confirming the efficiency of the modified particle swarm algorithm for the problem of the SVM classifier development are given.

关键词： particle swarm algorithm Support Vector Network Classifiers MODIFICATION

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Evolutionary System for Voltage Reactive Power Control in Distribution Network based on Improved particle swarm algorithm

Evolutionary System for Voltage Reactive Power Control in Di...

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2023 International Conference on Integrated Intelligence and Communication Systems, ICIICS 2023

作者： Jiang, Shibai Xiaokaiti, Maihebubai Ma, Zhanjun Xue, Gaoqian Simayi, Xuelati Information and Telecommunication Company State Grid Xinjiang Electric Power Co.LTD Urumqi China

ISBN: (纸本)9798350315455

This paper presents an evolutionary system for voltage reactive power control in distribution networks based on the Improved particle swarm algorithm (IPSA). Extensive experiments are conducted in various environmental scenarios, including urban areas, industrial settings, renewable energy integration, residential areas, commercial zones, and rural regions. Evolutionary system in minimizing reactive power losses, regulating voltages, improving power factor, and mitigating voltage violations. The findings of this study contribute to the field of power system optimization by providing a practical and efficient solution for voltage reactive power control. The proposed evolutionary system will be validated using realistic distribution network models and benchmarked against traditional optimization methods, such as genetic algorithms and basic PSO. The system offers a reliable approach to enhance the performance and efficiency of distribution networks in diverse environmental scenarios. The study presents a valuable contribution to the field of power system optimization and provides insights for power system operators and planners in optimizing voltage control strategies and enhancing the overall performance of distribution networks. © 2023 IEEE.

关键词： control strategies diverse environmental scenarios evolutionary system particle swarm algorithm

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