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International Conference on Electrical and Information Technologies for Rail Transportation - Electrical Traction

作者： An, Yazheng Qin, Yong Meng, Xuelei Wang, Ziyang Beijing Jiaotong Univ Beijing Res Ctr Urban Traff Informat Sensing & Se Beijing 100044 Peoples R China Lanzhou Jiaotong Univ Sch Traff & Transportat Lanzhou 730070 Peoples R China

ISBN: (纸本)9783662493700;9783662493687

The paper establishes the operation adjustment model in accordance with constraints of the train operation based on the characteristic of high real-time and dynamic and the fitness function aiming at getting the least level of delay time and the number of delay times. In order to solve the established operation adjustment model, the method of cooperation is introduced to improve the typical particle swarm algorithm, which improves the efficiency of operation and the global optimum. Finally, taking the train operation adjustment of dedicated passenger line between Beijing and Shenyang as an example, the model and algorithm are verified. It can be seen that the method is efficient and feasible and it has a good guiding significance for train operation adjustment.

关键词： Operation adjustment Delay times Cooperation particle swarm algorithm

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Using the combination of particle swarm algorithms and fuzzy approach to provide a clustering method for network nodes with coverage maintenance in wireless sensor networks 7

Using the combination of particle swarm algorithms and fuzzy...

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7th International Conference on Computer and Knowledge Engineering (ICCKE)

作者： Heydariyan, Seyyed Amir Reza Taghdisi Mohajerzadeh, Amir Hussein Int Univ Imam Reza Software Engn Mashhad Iran Ferdowsi Univ Mashhad Comp Engn Mashhad Iran

ISBN: (纸本)9781538608043

Wireless sensor network (WSN) is an inexpensive newfound technology with many applications in various fields (such as biology Environment, war and natural disasters). A network consisting of a large number of sensor nodes and collecting information from the environment in a distributed environment. The main limitations include limited energy, low communication capacity, low storage volume, and low bandwidth. This study provides a clustering method for network nodes by optimized particle swarm and fuzzy approach This method determines the cluster head according to node's distances from each other and the left amount of energy and the distance from the sink and the density of each node, and determines the rest of the nodes in the network as sub clusters. Selecting the number and the place of cluster heads in order to have the most energy efficiency in the network is a Np-Hard problem, and on the other hand PSO algorithms is very flexible in solving dynamic problems. In this method we maintain the coverage and reduce the energy consumption by combining the particle swarm algorithms and fuzzy approach. This method is faster than the other methods.

关键词： wireless sensor networks reducing energy consumption coverage maintenance particle swarm algorithm fuzzy method

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Bi-dimensional Empirical Mode Decomposition algorithm Based on particle swarm-Fractal Interpolation

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KSII TRANSACTIONS ON INTERNET AND INFORMATION SYSTEMS 2018年第12期12卷 5955-5977页

作者： An, Feng-Ping He, Xin Huaiyin Normal Univ Sch Phys & Elect Elect Engn Huaian 223300 JS Peoples R China Beijing Inst Technol Sch Informat & Elect Beijing 100081 BJ Peoples R China Henan Univ Inst Intelligent Network Syst Kaifeng 475004 Peoples R China

Performance of the interpolation algorithm used in the technique of bi-dimensional empirical mode decomposition directly affects its popularization and application, so that the researchers pay more attention to the algorithm reasonable, accurate and fast. However, it has been a lack of an adaptive interpolation algorithm that is relatively satisfactory for the bi-dimensional empirical mode decomposition (BEMD) and is derived from the image characteristics. In view of this, this paper proposes an image interpolation algorithm based on the particle swarm and fractal. Its procedure includes: to analyze the given image by using the fractal brown function, to pick up the feature quantity from the image, and then to operate the adaptive image interpolation in terms of the obtained feature quantity. All parameters involved in the interpolation process are determined by using the particle swarm optimization algorithm. The presented interpolation algorithm can solve those problems of low efficiency and poor precision in the interpolation operation of bi-dimensional empirical mode decomposition and can also result in accurate and reliable bi-dimensional intrinsic modal functions with higher speed in the decomposition of the image. It lays the foundation for the further popularization and application of the bi-dimensional empirical mode decomposition algorithm.

关键词： Fractal particle swarm algorithm Bi-dimensional Empirical Mode Decomposition Optimization Image Interpolation

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Composite Improved algorithm Based on Jellyfish, particle swarm and Genetics for UAV Path Planning in Complex Urban Terrain

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SENSORS 2024年第23期24卷 7679页

作者： Wang, Qi Yi, Wenjun Nanjing Univ Sci & Technol Natl Key Lab Transient Phys Nanjing 210094 Peoples R China

Path planning technology is of great consequence in the field of unmanned aerial vehicles (UAVs). In order to enhance the safety, path smoothness, and shortest path acquisition of UAVs undertaking tasks in complex urban multi-obstacle environments, this paper proposes an innovative composite improvement algorithm that integrates the advantages of the jellyfish search algorithm and the particle swarm algorithm. The algorithm effectively overcomes the shortcomings of a single algorithm, including parameter setting issues, slow convergence rates, and a tendency to become trapped in local optima. Additionally, it enhances the path smoothness, which improves the path optimisation. This enhances the capacity of UAVs to optimise their paths in environments characterised by multiple obstacles. To evaluate the practical effectiveness of the algorithm, a three-dimensional complex city model was constructed for the purposes of the study, and an adaptation function was designed for the purpose of evaluation. The experimental evaluation of 23 benchmark functions, the simulation test of the 3D city model, and 100 repetitive experiments demonstrate that the composite improved algorithm has a considerable advantage over the other comparative algorithms regarding performance. It exhibits fast convergence, high accuracy, and both global and local search capabilities, which enable the effective planning of a UAV flight path and the maintenance of good stability. In comparison to traditional algorithms, the composite improved algorithm demonstrably reduces the flight time and the number of obstacle avoidance manoeuvres required by the UAV. It provides robust technical support for the path planning of the UAV in complex urban environments and facilitates the advancement and implementation of related technologies.

关键词： jellyfish search algorithm particle swarm algorithm genetic algorithm UAV path planning complex urban terrain modelling CEC2005 benchmark functions

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Reconfiguration of low-voltage distributed power sources within electric power's distribution network based on improved particle swarm-fish swarm fusibility algorithm

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SCIENTIFIC REPORTS 2024年第1期14卷 1-14页

作者： Xu, Xiaowei Nie, Ding Xu, Wenhua Xiang, Enxin Chen, Shan Nie, Yongjie Fu, Xiao Xu, Wan Han, Yiming Yunnan Elect Power Grid Res Inst Kunming 650217 Peoples R China Kunming Univ Sci & Technol Fac Elect Power Engn Kunming 650504 Peoples R China

With the development of distributed power sources in the distribution network, the algorithm of distribution network reconfiguration is gaining attention from experts and scholars. Its goal is to reduce the power loss during power transmission, so as to reduce the power grid loss during power transmission. And weaken the electric heating effect in the process of electric energy transmission, thus maintaining the safety of the surrounding residents. Due to the wire impedance effect, a lot of electric energy of the circuit is lost to electric heating, which is easy to cause local overheating and lead to fire. This will not only cause power loss, but also endanger the safety of surrounding residents. To address the issue, experiments on distribution grid reconstruction are performed using the enhanced particle swarm-fish swarm algorithm with the Elecgrid self-constructed dataset. Initially, low-voltage distributed power sources in parallel are connected to the circuit, thereby decreasing internal resistance and electrical heat. Then, by controlling the circuit in the system, the double separation relay adjusts the inductance and capacitance of the conductor, thus reducing the reactance length. Additionally, particle swarm particles are mutated to enable them to jump out of the local optimum, and elite fish approach is used to expand the search area. Finally, the proposed fusion algorithm is applied to the self-built data set of Elecgrid and compared with the other three algorithms. The fusion algorithm serves as the standard test system for this comparison. The active power loss of the hybrid algorithm is 63 kW at an operating voltage of 0.74 V. The loss work of the other three algorithms is 74 kW, 97 kW and 109 kW respectively. The mixed algorithm has the lowest loss among the four algorithms. The experiments are repeated for six times, and the linear fitting degrees of the four algorithms are 0.9804, 0.9527, 0.9612 and 0.9503, respectively. The experimental results s

关键词： Distributed power supply Distribution network reconfiguration particle swarm algorithm Artificial fish swarm algorithm Objective function

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Anti-interference model of intelligent control for high-precision regulation of pressure in industrial low pressure area based on neural network

Anti-interference model of intelligent control for high-prec...

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2024 International Conference on Computer Application and Information Security, ICCAIS 2024

作者： Ren, Yan Wang, Yunjiang Department of Information and Control Engineering Shenyang Institute of Science and Technology Liaoning Shenyang 110167 China School of Mechatronics Engineering Shenyang Aerospace University Liaoning Shenyang 110136 China

ISBN: (纸本)9781510689312

As science and technology progress, the traditional pressure regulation system is being phased out, and researchers today are focusing on the study of higher precision regulation systems as a crucial task. However, there are still problems with pressure regulation in low pressure areas. In this regard, a high-precision regulation model for low-pressure zone pressure based on neural networks is proposed, which applies the neural system to pressure regulation in low-pressure zones to achieve the purpose of high-precision regulation. Neural network is a computing model that imitates the structure and function of the human brain. It can achieve complex nonlinear mapping and intelligent processing by learning large amounts of data. Applying the nervous system to the field of pressure regulation can adaptively learn the load characteristics of the pressure system, thereby improving the accuracy and stability of load prediction. Real-time monitoring and fault diagnosis can also be performed on the operating status of the pressure system, thereby improving the reliability and safety of the system. And optimize and adjust the control strategy of the pressure system, so as to improve the efficiency and energy saving of the system. Based on this, this paper proposes to combine BP algorithm and particle swarm optimization algorithm to obtain PSOB model. stability. And it can adaptively adjust network parameters and control strategies, adapt to different working conditions and environmental disturbances, and improve control flexibility and intelligence. Historical data and real-time data can also be used for learning and updating to improve control accuracy and prediction capabilities. © The Authors.

关键词： intelligent processing low pressure zone pressure regulation Neural network particle swarm algorithm

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Virtual network mapping algorithm based on load balancing multi-objective particle swarm optimization

Virtual network mapping algorithm based on load balancing mu...

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11th International Conference on Wireless Communications, Networking and Mobile Computing (WiCOM 2015)

作者： Jun Zhao Zhong Ma Wuhan Digital Engineering Institute China

ISBN: (纸本)9781510821279

Existing virtual network mapping algorithms does not consider resource consumption of intermediate node on communication path usually. Minimum resource consumption or shortest path of physical network is regarded as objective, thereby leading to bottleneck due to insufficient resource of intermediate node on communication path, and affecting performance of the whole physical network and subsequent success rate of virtual network. A virtual network mapping algorithm based on load balancing multi-objective particle swarm optimization is proposed in the paper aiming at the problem. Resource consumption of intermediate node is sufficiently considered in the algorithm, double balance of node load and link load is regarded as objective. Meanwhile, the optimal path of particle swarm optimization algorithm is adopted. Experiments show that the algorithm proposed in the paper can not only realize double balance of node load and link load, but also effectively improve request receiving success rate, overall resource load balance and long-term operation income.

关键词： Load Balancing particle swarm Optimization algorithm Virtual Network Mapping Load balancing intermediate node Link Load Telecommunication lines particle swarm optimization Virtual networks particle swarm algorithm Resource utilization physical network

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Thermal layout optimization of stacked chips based on hybrid algorithm of simulated annealing and particle swarm

Thermal layout optimization of stacked chips based on hybrid...

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International Conference on Computer Science and Network Technology (ICCSNT)

作者： Mingxiang Zang Meng Wang Xinquan Lai Huisen He School of Computer Science and Technology XiDian University Xian China School of Electronic Engineering XiDian University Xian China

ISBN: (纸本)9781467329637

As the density of chip package increases, the thermal layout optimization of stacked chips has become more and more important. The thermal layout optimization of stacked chips is studied in this paper. Firstly, a hybrid algorithm of simulated annealing and particle swarm (SA-PSO) is proposed for thermal layout optimization. Secondly, the fitness function is obtained based on thermal superposition model and heat conduction equation. Then the temperature of chips is chosen as the evaluation index and the corresponding optimization scheme is achieved. Finally, finite element analysis (FEA) software is used to evaluate the thermal field distribution of stacked chips and the result shows the temperatures of stacked chips are lowered after optimization. Thus, we can prove that the chip layout optimization algorithm proposed in this paper is effective and robust in thermal layout optimization of stacked chips.

关键词： Stacked chips Simulated annealing algorithm particle swarm algorithm Thermal layout optimization Finite element analysis particle swarm algorithm simulated annealing algorithm Computer chips Finite element method particle swarm optimization simulated annealing layouts Thermal chip package

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Distributed particle swarm Optimization Using an Average Consensus algorithm

Distributed Particle Swarm Optimization Using an Average Con...

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IEEE Annual Conference on Decision and Control

作者： Yuji Wakasa Sosuke Nakaya Graduate School of Science and Engineering Yamaguchi University 2-16-1 Tokiwadai Ube Yamaguchi 755-8611 Japan

ISBN: (纸本)9781479978878

In order to improve the efficiency of distributed systems over a network, various distributed optimization algorithms have been developed recently. In particular, for optimization problems with convex and differentiable functions, sophisticated algorithms have been proposed, motivated by energy network systems such as smart grid. As an algorithm with easier implementation and wider range of applications, this paper proposes a distributed optimization algorithm that can deal with optimization problems with nonconvex and nondifferentiable functions by combining a particle swarm optimization algorithm and an average consensus algorithm. Moreover, the convergence property of the proposed algorithm is proven under mild assumptions. Through numerical experiments, the effectiveness of the proposed algorithm is illustrated.

关键词： Optimization algorithms particle swarm optimization algorithms Smart grids Distribute Distributed Systems Convexity energy grid particle swarm algorithm DIFFERENTIABLE FUNCTIONS

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The ADBPSO-LightGBM internal threat detection framework based on hybrid data balancing

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SYSTEMS SCIENCE & CONTROL ENGINEERING 2025年第1期13卷

作者： Zheng, Jin-Jie Kan, Xiu Wu, Jian-Zhen Zhang, Zhen Gao, Xiu-Yu Shanghai Univ Engn Sci Sch Elect & Elect Engn Shanghai Peoples R China

The misuse of privileges by users can lead to significant reputational and financial losses for enterprises. To reduce the risk of information leakage, it is crucial to detect and analyze abnormal behaviours of internal employees. Firstly, based on the characteristics of internal employee behaviour, a data filter strategy based on user behaviour is proposed. Then, a data balancing strategy based on the concept of hybrid sampling is introduced. Moreover, to further construct the behaviour model, an improved particle swarm optimization algorithm based on adaptive delay and genetic factors is proposed, and it is used to search for the optimal parameters of LightGBM. Experimental results demonstrate that the proposed method is highly effective in detecting internal threats.

关键词： Internal threat feature selection data balancing high-dimensional clustering particle swarm algorithm threat detection

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