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32nd Chinese Control And Decision Conference (CCDC)

作者： Zhou, Jia-Li Wang, Jie-Sheng Zhang, Yong-Xin Guo, Qing-Sheng Li, Hui Lu, Yi-Xuan Univ Sci & Technol Liaoning Sch Elect & Informat Engn Anshan 114044 Peoples R China

ISBN: (纸本)9781728158556

The unequal area facility layout problem (UA-FLP) is to place some objects in a specified space according to certain requirements, which is a NP-hard problem in mathematics because of the complexity of its solution, the combination explosion and the complexity of engineering system. particle swarm optimization (PSO) algorithm is a kind of swarm intelligence algorithm by simulating the predatory behavior of birds. Aiming at the minimization of material handling cost and the maximization of workshop area utilization, the optimization mathematical model of UA-FLPP is established, and it is solved by the particle swarm optimization (PSO) algorithm which simulates the design of birds' predation behavior. The improved PSO algorithm is constructed by using nonlinear inertia weight, dynamic inertia weight and other methods to solve static unequal area facility layout problem. The effectiveness of the proposed method is verified by simulation experiments.

关键词： particle swarm optimization algorithm Inertia Weight Unequal Area Facility Layout Problem

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An Improved particle swarm optimization algorithm for Bayesian Network Structure Learning via Local Information Constraint

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IEEE ACCESS 2021年 9卷 40963-40971页

作者： Liu, Kun Cui, Yani Ren, Jia Li, Peiran Hainan Univ Sch Informat & Commun Engn Haikou 570228 Hainan Peoples R China

At present, in the application of Bayesian network (BN) structure learning algorithm for structure learning, the network scale increases with the increase of number of nodes, resulting in a large scale of structure search space, which is difficult to calculate, and the existing learning algorithms are inefficient, making BN structure learning difficulty increase. To solve this problem, a BN structure optimization method based on local information is proposed. Firstly, it proposes to construct an initial network framework with local information and uses the Max-Min Parents and Children (MMPC) algorithm to construct an undirected network framework to reduce the search space. Then the particle swarm optimization (PSO) algorithm is used to strengthen the algorithm's optimization ability by constructing a new position and velocity update rule and improve the efficiency of the algorithm. Experimental results show that under the same sample data set, the algorithm can obtain a more accurate BN structure while converging quickly, which verifies the correctness and effectiveness of the algorithm.

关键词： Bayes methods Statistics Sociology Search problems particle swarm optimization Feature extraction Encoding Bayesian network structure learning local information particle swarm optimization algorithm

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optimization of Agricultural Machinery Allocation in Heilongjiang Reclamation Area Based on particle swarm optimization algorithm

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TEHNICKI VJESNIK-TECHNICAL GAZETTE 2021年第6期28卷 1885-1893页

作者： Liu, Li Chen, Tong Gao, Shijie Liu, Ye Yang, Shuguo Wang, Xinli Heilongjiang Bayi Agr Univ Coll Engn Xinyang Rd Daqing 163319 Heilongjiang Peoples R China Heilongjiang Bayi Agr Univ Coll Econ & Management Xinyang Rd Daqing 163319 Heilongjiang Peoples R China Heilongjiang Jianshan Farm Co Ltd Beidahuang Grp Nenjiang 161499 Heilongjiang Peoples R China

Aiming at the imbalance of seasonal agricultural machinery operations in different regions and the low efficiency of agricultural machinery, an experiment is proposed to use particle swarm algorithm to plan agricultural machinery paths to solve the current problems in agricultural machinery operations. Taking the harvesting of autumn soybeans at Jianshan Farm in Heilongjiang Reclamation Area as the experimental object, this paper constructs the optimization target model of the maximum net income of farm machinery households, and uses particle swarm algorithm to carry out agricultural machinery operation distribution and path planning gradually. In this paper, by introducing 0 -1 mapping, the improved algorithm adopts continuous decision variables to solve the optimization of discrete variables in agricultural machinery operations. The test results show that the particle swarm algorithm can realize the optimal allocation of agricultural machinery path, and the particle swarm algorithm is scientific and explanatory to solve the agricultural machinery allocation problem. This research can provide a scientific basis for farm agricultural machinery allocation and decision analysis.

关键词： agricultural machinery allocation particle swarm optimization algorithm path planning

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optimization of Square-shaped Bolted Joints Based on Improved particle swarm optimization algorithm

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EKSPLOATACJA I NIEZAWODNOSC-MAINTENANCE AND RELIABILITY 2023年第3期25卷

作者： Chen, Kui Yang, Cheng Zhao, Yongsheng Niu, Peng Niu, NaNa Wu, Hongchao Beijing Univ Technol Fac Mat & Mfg Beijing Peoples R China Beijing Inst Aerosp Control Devices Beijing Peoples R China XCMG Construction Machinery Xuzhou Peoples R China

The bolted joint is widely used in heavy-duty CNC machine tools, which has huge influence on working precision and overall stiffness of CNC machine. The process parameters of group bolt assembly directly affect the stiffness of the connected parts. The dynamic model of bolted joints is established based on the fractal theory, and the overall stiffness of joint surface is calculated. In order to improve the total stiffness of bolted assembly, an improved particle swarm optimization algorithm with combination of time-varying weights and contraction factor is proposed. The input parameters are preloading of bolts, fractal dimension, roughness, and object thickness. The main goal is to maximize the global rigidity. The optimization results show that improved algorithm has better convergence, faster calculation speed, preferable results, and higher optimization performance than standard particle swarm optimization algorithm. Moreover, the global rigidity optimization is achieved.

关键词： particle swarm optimization algorithm bolt connection bolted joint fractal theory

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Software cost estimation predication using a convolutional neural network and particle swarm optimization algorithm

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SCIENTIFIC REPORTS 2024年第1期14卷 1-21页

作者： Draz, Moatasem. M. Emam, Osama Azzam, Safaa. M. Kafrelsheikh Univ Fac Comp & Informat Software Engn Dept Kafrelsheikh Egypt Helwan Univ Fac Comp & Artificial Intelligence Informat Syst Dept Helwan Egypt

Over the past decades, the software industry has expanded to include all industries. Since stakeholders tend to use it to get their work done, software houses seek to estimate the cost of the software, which includes calculating the effort, time, and resources required. Although many researchers have worked to estimate it, the prediction accuracy results are still inaccurate and unstable. Estimating it requires a lot of effort. Therefore, there is an urgent need for modern techniques that contribute to cost estimation. This paper seeks to present a model based on deep learning and machine learning techniques by combining convolutional neural networks (CNN) and the particle swarm algorithm (PSO) in the context of time series forecasting, which enables feature extraction and automatic tuning of hyperparameters, which reduces the manual effort of selecting parameters and contributes to fine-tuning. The use of PSO also enhances the robustness and generalization ability of the CNN model and its iterative nature allows for efficient discovery of hyperparameter similarity. The model was trained and tested on 13 different benchmark datasets and evaluated through six metrics: mean absolute error (MAE), mean square error (MSE), mean magnitude relative error (MMRE), root mean square error (RMSE), median magnitude relative error (MdMRE), and prediction accuracy (PRED). Comparative results reveal that the performance of the proposed model is better than other methods for all datasets and evaluation criteria. The results were very promising for predicting software cost estimation.

关键词： Software cost estimation Software effort estimation Promise database SCE Convolutional neural network particle swarm optimization algorithm PSO CNN

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Research on a hybrid model for flood probability prediction based on time convolutional network and particle swarm optimization algorithm

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SCIENTIFIC REPORTS 2025年第1期15卷 1-13页

作者： Yu, Qiying Liu, Chengshuai Li, Runxi Lu, Zhenlin Bai, Yungang Li, Wenzhong Tian, Lu Shi, Chen Xu, Yingying Cao, Biao Zhang, Jianghui Hu, Caihong Zhengzhou Univ Sch Water Conservancy & Transportat Zhengzhou 450001 Peoples R China Xinjiang Inst Water Resources & Hydropower Res Urumqi 830049 Xinjiang Peoples R China

Accurate flood forecasting in advance is crucial for planning and implementing watershed flood prevention measures. This study developed the PSO-TCN-Bootstrap flood forecasting model for the Tailan River Basin in Xinjiang by integrating the particle swarm optimisation (PSO) algorithm, temporal convolutional network (TCN), and Bootstrap probability sampling method. Evaluated on 50 historical flood events from 1960 to 2014 using observed rainfall-runoff data, the model showed, under the same lead time conditions, a higher Nash efficiency coefficient, along with lower root mean square and relative peak errors in flood forecasting. These results highlight the PSO-TCN-Bootstrap model's superior applicability and robustness for the Tailan River Basin. However, when the lead time exceeds 5 h, the model's relative peak error remains above 20%. Future work will focus on integrating flood generation mechanisms and enhancing machine learning models' generalisability in flood forecasting. These findings provide a scientific foundation for flood management strategies in the Tailan River Basin.

关键词： Flood forecasting Machine learning Temporal convolutional neural network particle swarm optimization algorithm Bootstrap probability sampling algorithm PSO-TCN model Tailan River Basin

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An optimized particle swarm optimization algorithm for turbine torque and generator load parameter identification

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Procedia Computer Science 2024年 241卷 391-396页

作者： Zhangbin Yang Xiaokun Li Xiang Liao Daixiao Peng Hang Cai Xuemin He China Three Gorges Construction Engineering Corporation Chengdu 610095 P.R. China Three Gorges Intelligent Industrial Control Technology Co. Ltd Wuhan 430070 P.R. China

Hydraulic turbine control system is a complex system with strong nonlinearity and multiple variables. Therefore, in order to better control the turbine system, it is necessary to obtain the parameters of key components. Aiming at the limitation of the traditional particle swarm optimization algorithm in global search ability, mutation operator and dynamic inertia weight coefficient are introduced to enhance the search ability of the algorithm. In addition, in order to further improve the global search performance of the algorithm, this paper combines the optimized particle swarm optimization algorithm with genetic algorithm to form a hybrid parameter identification algorithm. The hybrid algorithm not only uses the fast convergence of particle swarm optimization (PSO), but also uses the global search advantage of genetic algorithm (GA) to realize efficient and accurate identification of turbine torque and load parameters. Through MATLAB2021a/Simulink simulation experiments, the application effect of the algorithm in the identification of turbine torque and generator load parameters is verified. The simulation results show that the optimized particle swarm optimization algorithm has significant advantages in the accuracy and robustness of parameter identification, and the identified parameters have a high degree of fitting with the actual measured torque and speed. This study not only provides a new optimization strategy for the parameter identification of turbine regulation system, but also provides an effective intelligent algorithm solution for the parameter identification of nonlinear system.

关键词： particle swarm optimization algorithm Parameter identification Turbine torque Genetic algorithm Dynamic inertia weight Mutation operator

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Sequence Transfer-Based particle swarm optimization algorithm for Irregular Packing Problems

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IEEE ACCESS 2021年 9卷 131223-131235页

作者： Fang, Jie Rao, Yunqing Liu, Pan Zhao, Xusheng Huazhong Univ Sci & Technol State Key Lab Digital Mfg Equipment & Technol Wuhan 430074 Peoples R China

The two-dimensional (2D) irregular packing problem is a classical optimization problem with NP-hard characteristics and has high computational complexity. To date, packing problems have generally been solved by artificial experience and heuristic algorithms. However, these algorithms are not highly efficient and the excellent cases cannot be preserved, which both time and economic costs are high. Inspire by transfer learning and considering the characteristics of 2D irregular packing problems, we propose a sequence transfer-based particle swarm optimization algorithm (ST-PSO) to solve the multi-constraint packing problem. A piece-matching strategy based on an improved shape context algorithm, and a piece-sequencing generation strategy for transferring the packing sequence are developed for particle swarm optimization(PSO) initialization. In the process of PSO, an adaptive adjustment strategy is used with an improved positioning strategy to adjust the packing position of the pieces. The results indicate that this method can robustly, quickly, and efficiently achieve the packing of 2D irregular pieces. Compared with the data prior to transfer, the ST-PSO can inherit and transfer the historical packing sequence in less time and retain or exceed the actual packing data onto the samples. This algorithm could be applied to industrial applications to reduce waste, packing time, and production costs.

关键词： Heuristic algorithms Task analysis Licenses Transfer learning Shape Feature extraction optimization Irregular pieces multiple constraints particle swarm optimization algorithm sequence transfer two-dimensional packing optimization

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Equipping Seasonal Exponential Smoothing Models with particle swarm optimization algorithm for Electricity Consumption Forecasting

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ENERGIES 2021年第13期14卷 4036页

作者： Deng, Changrui Zhang, Xiaoyuan Huang, Yanmei Bao, Yukun Jiangxi Univ Engn Ctr Big Data Analyt Xinyu 338029 Peoples R China Huazhong Univ Sci & Technol Sch Management Ctr Modern Informat Management Wuhan 430074 Peoples R China

Electricity consumption forecasting plays an important role in investment planning of electricity infrastructure, and in electricity production/generation and distribution. Accurate electricity consumption prediction over the mid/long term is of great interest to both practitioners and academics. Considering that monthly electricity consumption series usually show an obvious seasonal variation due to their inherent nature subject to temperature during the year, in this paper, seasonal exponential smoothing (SES) models were employed as the modeling technique, and the particle swarm optimization (PSO) algorithm was applied to find a set of near-optimal smoothing parameters. Quantitative and comprehensive assessments were performed with two real-world electricity consumption datasets on the basis of prediction accuracy and computational cost. The experimental results indicated that (1) whether the accuracy measure or the elapsed time was considered, the PSO performed better than grid search (GS) or genetic algorithm (GA);(2) the proposed PSO-based SES model with a non-trend component and additive seasonality term significantly outperformed other competitors for the majority of prediction horizons, which indicates that the model could be a promising alternative for electricity consumption forecasting.

关键词： electricity consumption forecasting seasonal exponential smoothing models particle swarm optimization algorithm grid search method genetic algorithm

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Application of the particle swarm optimization algorithm-back propagation neural network algorithm introducing new parameter terms in the application field of industrial design

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RESULTS IN ENGINEERING 2024年 21卷

作者： Tian, Yuan Chang, Yu North China Inst Aerosp Engn Sch Art & Design Langfang 065000 Peoples R China Tianjin Univ Commerce Sch Arts Tianjin 300134 Peoples R China

With the development of the modern industry, to continuously use large precision instruments, the circuits protecting method through circuit design has gradually been promoted. And this method can prevent the excessive accumulation of electric heat from causing fires. The voltage at the starting point of power transmission is constant. However, during the transportation process, some lines have low resistance values, which can generate large instantaneous currents. To address this issue, this study conducted simulation experiments on selfcollected Circuit dataset based on particle swarm optimization and backpropagation neural networks. This study first introduced new parameter term learning factors into backpropagation neural networks. Then they were imported into the support vector machine, and the nonlinear variables were mapped to high plane, and the optimal hyperplane was established. Then the traditional circuit design method was improved, 40 Resistor were connected in parallel and connected to the experimental circuit in series with the rheostat. Finally, the algorithm was introduced into Circuit dataset collected in this experiment, and its protective effect on the circuit was compared with the other three algorithms. Under the protection of this design, the working times of four algorithms were 0.28, 0.42, 0.38, and 0.43 s, respectively. Their phase displacements were 0.19, 0.26, 0.36, and 0.41, respectively. The circuit design method proposed in this study can effectively address circuit faults. And the fusion algorithm can disconnect the circuit at the fastest speed and significantly reduce excitation current intensity, and it is suitable for circuit design in the field of industrial design.

关键词： Adaptive learning Support vector machine particle swarm optimization algorithm Voltage sag Circuit protection design

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