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Predicting frost heave in soil-water systems using the generalized regression neural network optimized with particle swarm optimization algorithm

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COLD REGIONS SCIENCE AND TECHNOLOGY 2024年 226卷

作者： Cai, Honghong Wang, Chong Ma, Ziqiang Meng, Fanshuo Lin, Zhikun Ren, Junping Li, Shuangyang Lanzhou Univ Minist Educ China Key Lab Mech Disaster & Environm Western China Lanzhou 730000 Peoples R China Lanzhou Univ Sch Civil Engn & Mech Lanzhou 730000 Peoples R China China State Construct Engn Corp Aecom Consultants Lanzhou 730000 Peoples R China Chinese Acad Sci Northwest Inst Ecoenvironm & Resources State Key Lab Frozen Soil Engn Lanzhou 730000 Peoples R China

Frost heave poses a serious hazard to geotechnical engineering. However, conventional experimental and theoretical methods, which have limitations in accurately describing the deformation behavior of soils during frost heave, struggle due to the nonlinear and uncertain nature of the process. For this reason, the study leverages the advantages of the Generalized Regression Neural Network (GRNN) in handling nonlinear problems and small sample datasets. The structure of the GRNN model is further optimized using the particle swarm optimization algorithm (PSO) and K-fold Cross Validation (K). The input variables for the model include water content (W), W ), temperature (T), T ), dry density (rho), and plasticity index (Ip) I p ) under various working conditions. The frost heave rate (7) is considered as the output variable. Meanwhile, the model also considers the effects of both one-factor and two-factor interactions among the input variables on frost heave behaviors. Finally, a prediction model for 7 based on the K-PSO-GRNN is established. The results demonstrate that the K-PSO-GRNN model exhibits greater robustness and stability in predicting 7 compared to PSO-GRNN and GRNN (R2 2 = 0.94, MAE = 0.14), and the prediction residuals for 7 range from 0 to 0.4. Among these variables, W has the most significant influence on 7, followed by T , rho, and I p . Moreover, both rho and Ip p have significant interactions with T and have a notable impact on the soil's frost heave behavior. At high rho, the soil shows reduced sensitivity to frost heave in response to changes in T , while at high I p , the soil becomes more sensitive to frost heave with changes in T . 7 generally shows a positive correlation with W and rho, and a negative correlation with T . The aforementioned K-PSO-GRNN model can be utilized for predicting 7, which is valuable in forecasting non-uniform deformation hazards caused by frost heave and studying preventive measures.

关键词： Generalized regression neural network particle swarm optimization algorithm K -fold cross validation Frost heave

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Cloud computing task scheduling based on particle swarm optimization algorithm

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Procedia Computer Science 2025年 261卷 1349-1355页

作者： Yan Han Department of Science and Research Chengdu Polytechnic Chengdu 610041 China

The task scheduling of cloud computing is the key to cloud services. It is an intelligent scheduling strategy that reasonably allocates tasks in the cloud computing platform to meet users’ resource requirements. A good cloud task scheduling strategy should not only meet the needs of users, but also improve the utilization of cloud computing resources and reduce energy consumption as much as possible. However, the traditional cloud task scheduling algorithm is mainly implemented through experience and manual intervention, and its efficiency and result quality are not ideal. In this paper, a cloud computing task scheduling method based on particle swarm optimization algorithm is proposed, and the influence of this algorithm on task completion time and energy consumption in cloud computing environment is analyzed through experiments.

关键词： particle swarm optimization algorithm Cloud computing Task scheduling Cloud technology

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A Novel Dynamic Clustering Method by Integrating Marine Predators algorithm and particle swarm optimization algorithm

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IEEE ACCESS 2021年 9卷 3557-3569页

作者： Wang, N. Wang, J. S. Zhu, L. F. Wang, H. Y. Wang, G. Nanjing Univ Aeronaut & Astronaut Coll Econ & Management Nanjing 211106 Peoples R China Univ Sci & Technol Liaoning Sch Elect & Informat Engn Anshan 114051 Liaoning Peoples R China

Data clustering is the process of identifying natural groupings or clusters based on a certain similarity measure in muti-dimensional data. Aiming at the dynamic clustering problem where the number of clusters cannot be determined in advance, a hybrid dynamic clustering method based on the marine predators algorithm (MPA) and particle swarm optimization (PSO) algorithm was proposed. The position update strategy of the PSO algorithm was used to make up for the lack of MPA in global searching. The fixed-length coding strategy with the real number coding method was used to deal with the variable length clustering optimization problem, and the unfeasible solution processing strategy and the penalty function strategy are adopted to improve the performance of the algorithm and achieve simultaneous optimization of the number of clusters and cluster centers. The proposed MPA-PSO algorithm with PSO algorithm, MPA, Differential Evolution (DE) algorithm, Spotted Hyena Optimizer (SHO), Lightning Searching algorithm (LSA) and Equilibrium Optimizer (EO) are adopted to carry out the clustering simulation experiments on four artificial data sets and six real data sets (Iris, Wine, Wisconsin breast cancer, Vowel, Seeds, and Wdbc) in UCI databases. Three performance indicators (the number of clusters, ARI and Accuracy) are used to evaluate the clustering results. The experimental results show that the proposed method can not only successfully find the correct number of clusters, but also obtain stable results for most test problems.

关键词： Clustering algorithms Indexes Heuristic algorithms optimization particle swarm optimization Encoding Clustering methods Dynamic clustering marine predator algorithm particle swarm optimization algorithm

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particle swarm optimization algorithm with Variety Inertia Weights to Solve Unequal Area Facility Layout Problem 32

Particle Swarm Optimization Algorithm with Variety Inertia W...

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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 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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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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optimization Management of Storage Location in Stereoscopic Warehouse by Integrating Genetic algorithm and particle swarm optimization algorithm

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JOURNAL OF APPLIED MATHEMATICS 2024年第1期2024卷

作者： Zhang, Shuhong Zheng, Xianghui Xu, Fan Wang, Suzhen Zhang, Qixia Cao, Yuan Anhui Sanlian Univ Fac Engn Hefei 230601 Peoples R China Hefei Coll Finance & Econ Dept Artificial Intelligence Hefei 230601 Peoples R China Anhui Honglu Steel Struct Grp Co Ltd Operat Dept Hefei 230601 Peoples R China Hefei Coll Finance & Econ Dept Bldg & Mapping Hefei 230601 Peoples R China

The management of a three-dimensional warehouse is a key part of the upper computer monitoring and management system for three-dimensional warehouses. Currently, there are problems such as unreasonable planning and inability to respond to real-time demands. Therefore, further improvement is needed to optimize the management of storage locations. The main purpose of the research is to achieve better storage stability and improve storage and retrieval efficiency. First, the study constructed a multiobjective mathematical model based on the weight, frequency of use, and category of the goods. Three objective functions were constructed. Therefore, the operational efficiency of the stacker crane and the turnover rate of items were improved. Meanwhile, the overall stability of the shelves was ensured, and the management efficiency of the warehouse was improved. At the same time, the study introduced the GA-PSO algorithm to solve the mathematical model and optimize the goods location planning. These results confirmed that the proposed algorithm had significantly lower iteration times than traditional particle swarm optimization in different warehouse sizes and types of goods. The iteration required to reach the optimal value in Situation 1 was only 80, which was 90 fewer than PSO. Meanwhile, in Situation 2, the optimization results of the proposed algorithm in four objective functions were as high as 42.94%, 26.03%, 30.72%, and 46.15%, respectively, which increased by 1.20%, 8.04%, 5.61%, and 7.38% compared to PSO. The proposed algorithm can achieve more efficient and intelligent warehouse management, improving the efficiency and accuracy of logistics operations. It is significant for logistics industry development and enterprise competitiveness enhancement.

关键词： automated stereoscopic warehouse GA-PSO genetic algorithm monitoring and management system particle swarm optimization algorithm

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