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Design of BLDC Motor Using African Vulture optimization algorithm Based Trained FNN

SN Computer Science 2024年第4期5卷 385页

作者： Tripathy, Surya Narayan Kundu, Shubhasri Pradhan, Arjyadhara Samal, Padarbinda School of Electrical Engineering KIIT Deemed to be University Bhubaneswar India

Brush-less DC motors with permanent magnets are widely used in robotics, electric vehicles, and other industrial applications. Enhancing the performance of a BLDC motor can indeed be challenging due to the presence of non-linearities and complex design considerations. The present research aims to increase the effectiveness and performance of BLDC motors by using a feed-forward neural network (FNN). It is possible to quickly determine the FNN’s appropriate weight and bias settings by using the cutting-edge African Vulture Optimisation algorithm (AVOA). Combining biological inspiration, machine learning, and motor technology, it develops a distinctive and promising way to improve the performance of BLDC motors. This study also includes the sensitive analysis of controlling parameters of the AVOA to investigate its effect on the statistical variables of the fitness function. This aids in optimizing the algorithm for better outcomes. The proposed method is then contrasted with various FNN learning algorithms based on Practical swarm optimization (PSO), Gravitational Search algorithm (GSA), and PSO-GSA. Considerations like convergence rate and the capacity to steer clear of local minima are probably evaluated throughout the comparison. According to the results, AVOA-based optimization performs better than the other methods (PSO, GSA, and PSO-GSA) in terms of convergence speed and its ability to avoid becoming stuck in local minima. This suggests that the AVOA is useful for training FNNs to optimize the design parameters of BLDC motors. © The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd 2024.

关键词： Brush-less DC motors Feed-forward neural network Gravitational Search algorithm particle swarm optimization algorithm

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optimization of backpropagation neural network-based models in EDM process using particle swarm optimization and simulated annealing algorithms

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JOURNAL OF THE BRAZILIAN SOCIETY OF MECHANICAL SCIENCES AND ENGINEERING 2020年第1期42卷 1-14页

作者： Saffaran, Ali Moghaddam, Masoud Azadi Kolahan, Farhad Khayyam Univ Mashhad Dept Mech Engn Mashhad Razavi Khorasan Iran Ferdowsi Univ Mashhad Dept Mech Engn Mashhad Razavi Khorasan Iran

In the present study, artificial neural network (ANN) along with heuristic algorithms, namely particle swarm optimization (PSO) and simulated annealing (SA), has been employed to carry out the modeling and optimization procedure of electrical discharge machining (EDM) process on AISI2312 hot worked steel parts. Surface roughness (SR), tool wear rate (TWR) and material removal rate (MRR) are the process quality measures considered as process output characteristics. Determination of a process variables (pulse on and off time, current, voltage and duty factor) combination to minimize TWR and SR and maximize MRR independently (as single objective) and also simultaneously (as multi-criteria) optimization is the main objective of this study. The experimental data are gathered using Taguchi L-36 orthogonal array based on design of experiments approach. Next, the output measures are used to develop the ANN model. Furthermore, the architecture of the ANN has been modified using PSO algorithm. At the last step, in order to determine the best set of process output variables values for a desired set of process quality measures, the developed ANN model is embedded into proposed heuristic algorithms (SA and PSO) with which their derived results have been compared. It is evident that the proposed optimization procedure is quite efficient in modeling (with less than 1% error) and optimization (less than 4 and 7 percent error for single- and multi-objective optimizations, respectively) of EDM process variables.

关键词： Electrical discharge machining Taguchi technique Design of experiments Artificial neural network Simulated annealing algorithm particle swarm optimization algorithm

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Color quantization with particle swarm optimization and artificial ants

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SOFT COMPUTING 2020年第6期24卷 4545-4573页

作者： Perez-Delgado, Maria-Luisa Univ Salamanca Escuela Politecn Super Zamora Av Requejo 33 Zamora 49022 Spain

This article describes a color quantization algorithm that combines two swarm-based methods: particle swarm optimization and artificial ants. The proposed method is based on a previous method that solves the quantization problem by combining the particle swarm optimization algorithm with the K-means algorithm. K-means is a popular clustering method that has been applied to solve a variety of problems, including the color quantization problem. Nevertheless, it is a time-consuming method, which makes combining the particle swarm optimization algorithm and K-means less suitable than other color quantization techniques. The proposed method, however, discards the K-means algorithm and applies the Ant-tree for color quantization algorithm in order to reduce execution time. This article shows that the new method outperforms the original one, since it requires less time to obtain higher quality images. In addition, the images produced are also of better quality than those produced by other well-known color quantization methods, such as Neuquant, Octree, Median-cut, Variance-based, Binary splitting and Wu's methods.

关键词： Color quantization Artificial ants Ant-tree algorithm particle swarm optimization algorithm Clustering

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Research on Intelligent Selection of Hydraulic Support for Gently Inclined Comprehensive Mining Face Based on PSO-BP Neural Network 5

Research on Intelligent Selection of Hydraulic Support for G...

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5th International Conference on Mechatronics Technology and Intelligent Manufacturing (ICMTIM)

作者： Wu, Yue Li, Guoping Zhang, Zhiwei He, Longlong Xian Univ Sci & Technol Sch Mech Engn Xian Shaanxi Peoples R China

ISBN: (纸本)9798350363272;9798350363265

As an important part of coal mining mechanization, the reliability of hydraulic support is an important guarantee for the hydraulic support system of the comprehensive mining face, and the fast, accurate, and intelligent selection of hydraulic support determines its reliability. To realize the intelligent selection of hydraulic support for the slow inclined comprehensive mining face, and to improve the efficiency and accuracy of the selection of the hydraulic support, a hydraulic support selection method based on the PSO-BP neural network is proposed in this paper. A relationship model between geological parameters and hydraulic support technical parameters is established, and the model is trained by using hydraulic support selection case data as samples. Based on the training results, the model can be used to input new geological parameters to intelligently recommend the selection plan of hydraulic support technical parameters. The actual selection scheme of a coal mine is analyzed as a case study, and the results are compared with the traditional BP neural network selection results. The results indicate that the PSO-BP neural network can efficiently generate the hydraulic support technical parameter selection program. It outperforms the traditional BP neural network in terms of computational accuracy and convergence speed. This provides a reliable reference for intelligent hydraulic support selection.

关键词： hydraulic bracket particle swarm optimization algorithm BP neural network intelligent selection

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Credit Value Based Autonomous Vehicles Joint Task Offloading optimization

Credit Value Based Autonomous Vehicles Joint Task Offloading...

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2024 IEEE International Conference on Cognitive Computing and Complex Data

作者： Yang, Yongyi Li, Taoshen Ge, Zhihui Nanning Univ Sch Informat Engn Naning Peoples R China Guangxi Univ Sch Comp Elect & Informat Naning Peoples R China

ISBN: (纸本)9798350352900;9798350352894

To address the issue of selfish behavior demonstrated by autonomous vehicles in vehicle-infrastructure cooperation, where vehicles having spare computing resources are disinclined to share them, we propose a vehicle selection-assisted computing scheme based on a credit value incentive mechanism. This scheme is aimed at resolving the reluctance of autonomous vehicles to supply their spare computing resources while also facilitating low-performance vehicles to participate effectively in task computing. Additionally, considering the optimization problem of delay and energy consumption during offloading and computing tasks for autonomous vehicles, we formulate a mathematical problem that integrates weighted sums of delay and energy consumption. To handle this problem, we propose a double populations adaptive particle swarm optimization algorithm (DPAPSO) based on the traditional particle swarm optimization algorithm (PSO). The DPAPSO forms two populations of particles in accordance with our model and utilizes different updating strategies for each population. Experimental results show that the proposed DPAPSO outperforms the traditional particle swarm optimization algorithm in terms of optimization effects within this model. Furthermore, it presents superior performance and stability compared to both the simulated annealing algorithm (SA) and the genetic algorithm (GA).

关键词： Vehicle-infrastructure cooperation Autonomous vehicles Task offloading particle swarm optimization algorithm

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SA-PSO Approach for Optimizing QoS Management in SDN Service Chains

SA-PSO Approach for Optimizing QoS Management in SDN Service...

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2024 IEEE Cyber Science and Technology Congress

作者： Ji, Wen Lin, Chenhan Liu, Jiang Xu, Hongyang Xu, Liangjie Hu, Jun State Grid Fujian Elect Power Co Ltd Informat & Commun Branch Fuzhou Peoples R China Anhui Jiyuan Software Co Ltd Syst Integrat Business Unit Hefei Peoples R China

ISBN: (纸本)9798331532109;9798331532093

In recent years, the development of software-defined networking (SDN) and network function virtualization (NFV) has provided greater flexibility and programmability for network services. As a novel network architecture, Service Function Chaining (SFC) offers operators more possibilities in deploying and managing network service functions. However, current research often overlooks user Quality of Service (QoS) requirements, focusing primarily on operator interests. This paper addresses this gap by proposing a novel approach that integrates multidimensional QoS parameters and combines Simulated Annealing (SA) and particle swarm optimization (PSO) algorithms to optimize QoS service management in SDN/NFV environments. Specifically, we devise a hybrid optimization strategy based on SA and PSO to achieve efficient resource allocation and service chain scheduling, aiming to enhance the overall QoS experience for users. Experimental results demonstrate significant improvements in network service delivery efficiency and user satisfaction, validating the effectiveness and practicality of the proposed approach.

关键词： Quality of Service Software-defined Networking Network Function Virtualization Service Function Chaining Simulated Annealing algorithm particle swarm optimization algorithm

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Dynamics modeling and placement optimization of multi-robot system for the transportation of flexible structures 19

Dynamics modeling and placement optimization of multi-robot ...

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IEEE 19th Conference on Industrial Electronics and Applications (ICIEA)

作者： Zhang, Yinbo Jia, Shiyuan Chen, Gang Wang, Yifan Beijing Univ Posts & Telecommun Sch Automat Beijing Peoples R China Beijing Univ Posts & Telecommun Sch Artificial Intelligence Beijing Peoples R China

ISBN: (纸本)9798350360875;9798350360868

Space robots have been considered as vehicles for the assembly of large spacecraft. Multi-robots offer the advantage of transporting the modular structure to the target position. This paper investigates the dynamics of a multi-robot connected to a flexible structure rigidly by manipulators, along with the optimization of end-effector placement on the flexible structure. The assumed mode method is employed to describe the vibration of the flexible structure, while the dynamics are modeled using the Kane method. A comprehensive performance index is proposed based on the model, incorporating energy consumption and vibration amplitude. The placement of end-effectors is optimized using the particle swarm optimization (PSO) algorithm. Various cases involving two transportation directions and two magnitudes of vibration amplitude metrics are investigated. Numerical simulations are conducted to demonstrate the feasibility of the optimized placement.

关键词： Dynamics modeling Placement optimization Space multi-robots Flexible structure particle swarm optimization algorithm

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EV Charging Scheduling for Flattening Load Profile Based on Improved particle swarm 36

EV Charging Scheduling for Flattening Load Profile Based on ...

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36th Chinese Control and Decision Conference (CCDC)

作者： Zhang, Zhipeng Li, Xiaohui Mao, Shanli Cai, Bin He, Jie Zhu, Jialu Wuhan Univ Sci & Technol Sch Informat Sci & Engn Wuhan Peoples R China Wuhan Netizen Technol Co Ltd Res Ctr Wuhan Peoples R China Shenglong Elect Grp Co Ltd Res Ctr Wuhan Peoples R China

ISBN: (纸本)9798350387780;9798350387797

The load profile can reflect the change of the grid load over time. By connecting electric vehicles to the grid for charging and discharging, the goal of peak shaving and valley filling is realized, thus flattening the load profile and improving the stability of grid operation. Aiming at this characteristic, a method for optimal scheduling of electric vehicle charging based on an improved particle swarm algorithm is proposed in this paper. First, the proposed method constructs the electric vehicle charging scenario by Monte Carlo. Then, the improved particle swarm algorithm is used to solve the optimal charging and discharging quantity of electric vehicles in each period to minimize the load variance. By comparing the improved particle swarm algorithm with other intelligent algorithms on the benchmark function test set, the improved particle swarm algorithm is verified that it has superiority in convergence ability and optimization accuracy. Simulation results show that the scheduling of electric vehicle charging using the improved particle swarm algorithm reduces the peak valley difference of the power grid by 6.31% and the load variance by 74432kW(2) compared with the classical particle swarm algorithm.

关键词： load profile electric vehicles Monte Carlo particle swarm optimization algorithm load variance

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Trajectory optimization algorithm Based on Time-Optimal Cooperative Six-Axis Robot 8

Trajectory Optimization Algorithm Based on Time-Optimal Coop...

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8th International Conference on Robotics, Control and Automation (ICRCA)

作者： He, Chunlai Liu, Yunfei Liu, Jinglong Wang, Weijun Sun, Wei Liu, Shujian Cheng, Shuo China Elect Technol Robot Co Ltd Robot Engn Ctr Shanghai Peoples R China

ISBN: (纸本)9798350344738;9798350344721

The trajectory optimization problem of cooperative six-axis robot based on time optimization is to optimize the trajectory of the robot under the premise of satisfying the kinematic constraints, dynamic constraints and task constraints of the robot, so that the time of movement in the working process is the shortest. After completing the kinematics solution of the six-axis cooperative robot, based on the 5-order B-spline interpolation trajectory, the particle swarm optimization algorithm is used to optimize the time interval between the trajectory nodes, and the particle swarm optimization algorithm is used to solve the robot trajectory with the lowest energy consumption. Finally, a suitable experiment is designed to prove the feasibility of the optimization scheme.

关键词： quintic B-spline time optimal trajectory planning particle swarm optimization algorithm robot obstacle avoidance

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Modified Pi Sigma artificial neural networks for forecasting

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GRANULAR COMPUTING 2023年第1期8卷 131-135页

作者： Egrioglu, Erol Bas, Eren Giresun Univ Fac Arts & Sci Dept Stat TR-28200 Giresun Turkey

Pi Sigma artificial neural networks are a type of high-order neural network used in time series forecasting problems. In the Pi Sigma artificial neural networks, the weights between the hidden layer and the output layer are taken as constant and one, and the biases as constant and zero. Although this feature of the Pi Sigma artificial neural networks enables it to work with fewer parameters, it can also be seen as an obstacle to obtaining better forecasting performance. In this study, unlike classical Pi Sigma artificial neural networks, a modified Pi Sigma artificial neural network is proposed by taking the weights and biases as variables between the hidden layer and the output layer of the network. Thus, direct processing of the information coming to the output layer is prevented and the information coming to the output layer is weighted using different weights and bias values. The process of optimizing all the weights and bias values between the input and hidden layer, the hidden layer, and the output layer of the network is carried out together with the particle swarm optimization method. The proposed modified Pi Sigma artificial neural networks are compared with some other artificial neural networks in the literature by analyzing much well-known time series. As a result of the applications, it is seen that the forecasting performance of the modified Pi Sigma artificial neural networks is better than both the classical Pi Sigma artificial neural networks and many other artificial neural networks.

关键词： Pi Sigma artificial neural networks particle swarm optimization algorithm Forecasting

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