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A physically based constitutive model for 41CrS4 steel based on particle swarm optimization algorithm

MATERIALS RESEARCH EXPRESS 2024年第10期11卷 106514页

作者： Wei, Xiaoxiao Tan, Fan Yang, Peipei Pan, Hongchen State Grid Gansu Elect Power Res Inst Lanzhou 730070 Peoples R China Shanghai Jiao Tong Univ Sch Naval Architecture Ocean & Civil Engn Shanghai 200240 Peoples R China Lanzhou Univ Technol Sch Mat Sci & Engn Lanzhou 730050 Peoples R China State Grid Lanzhou Elect Power Supply Co Lanzhou 730050 Peoples R China Evergrande New Energy Automot R&D Inst Global Head Mat Engn Dept Shanghai 201600 Peoples R China

Utilizing the Gleeble-3500 thermal simulation apparatus, a thermal compression assay was performed on 41CrS4 steel within the temperature range of 900 degrees C to 1200 degrees C, featuring a strain rate of 0.01 to 5 s-1, to derive its flow stress curve. The evaluation of the Arrhenius equation parameters was adeptly carried out by deploying a sophisticated particle swarm optimization algorithm. Through rigorous analysis, the correlation coefficient and the mean absolute deviation were calculated to quantify the alignment between the predictive accuracy of the developed model and the empirical data. The findings demonstrate the ability of the particle swarm optimization algorithm to significantly enhance the precision of the constitutive model. This augmented level of accuracy substantively increases the model's utility and reliability for simulations of high-temperature material forming processes.

关键词： 41CrS4 steel high-temperature constitutive model particle swarm optimization algorithm

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An Improved particle swarm optimization algorithm for Parameters Optimizing of Feedforward Neural Networks 14

An Improved Particle Swarm Optimization Algorithm for Parame...

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14th International Conference on Advanced Computational Intelligence

作者： Zhang, Xiaoping Yang, Tianhang Wang, Li He, Zhonghe Liu, Shida North China Univ Technol Sch Elect & Control Engn Beijing Peoples R China

ISBN: (数字)9781665470452

ISBN: (纸本)9781665470469;9781665470452

Deep learning is an important branch of neural networks, which has high accuracy in classification and regression problems, and has been widely used. However, its performance is greatly affected by the parameters. In this paper, an improved particle swarm algorithm named as PSO-C is proposed to automatically train the parameters of the feedforward neural networks. In the proposed algorithm, the curiosity factor is introduced to divide the particles into two categories with different curiosity characteristics so as to improve the exploration ability and information mining ability of the particle swarms. At the same time, a chaotic factor is also introduced to avoid the local optimum problem during the neural network's training. The simulation results show that the PSO-C has better optimization effect on the whole.

关键词： particle swarm optimization algorithm curiosity deep learning artificial neural network parameter optimization

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Accelerometer calibration based on improved particle swarm optimization algorithm of support vector machine

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SENSORS AND ACTUATORS A-PHYSICAL 2024年 369卷

作者： Zhao, Xin Ji, Yong-xiang Ning, Xiao-lei China HuaYin Ordnance Test Ctr Huayin 714200 Peoples R China

Aiming at the problem that the deterministic errors caused by non-orthogonal installation, calibration factor, zero bias and other factors in production and in the use of accelerometers need to be calibrated by high-precision instruments, support vector machine regression is used to process the original data output by the accelerometer, and the processed data of each axis are used to establish a parameter calibration model without reference datum through the relationship between the output value of each axis of accelerometer, gravity acceleration and coaxial reversal in the paper. Then, the adaptive mutation rate is used to dynamically adjust the number of reverse learning particles, and the particles of particle swarm optimization algorithm are selected and adjusted according to the reverse learning, which solves the problems that particle swarm optimization algorithm tends to fall into localoptimum and the convergence speed is slow, through which a fast, accurate and simple calibration can be realized, and the performance of particle swarm optimization algorithm is improved. The calibration experiment shows that the improved particle swarm optimization algorithm has higher accuracy and faster convergence speed than the particle swarm optimization algorithm, and the calibration parameter accuracy is higher than that of the least square method, which does not need the datum of each axis. The calibration model proposed in this paper can realize a benchmark-free calibration outside the laboratory. At the same time, the improved particle swarm optimization algorithm can obtain calibration parameters with higher accuracy and faster speed in the rapid calibration, which provides the idea of a new model for accelerometer calibration and expands the application environment of accelerometer.

关键词： Calibration Accelerometer Reverse learning particle swarm optimization algorithm

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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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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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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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Bi-level programming model for military supply chain coordination with contracts based on particle swarm optimization algorithm

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JOURNAL OF COMPUTATIONAL METHODS IN SCIENCES AND ENGINEERING 2025年第4期25卷 2964-2975页

作者： Shen, Yachen Xiang, Huachun Li, Jing Air Force Engn Univ Sch Equipment Management & Unmanned Aerial Vehicle Xian Peoples R China Xian Eurasia Univ Sch Accounting & Finance Dongyi Rd Xian 710065 Shaanxi Peoples R China

A scientific and reasonable model for military supply chain coordination with contracts can effectively reduce the overall cost of the supply chain, increase the profits of supply chain members, achieve supply chain coordination, and balance the interests of all relevant parties. Aiming at the military supply chain coordination with contracts under normal order demand, this paper first explains the concept and main characteristics of the military supply chain, defines the concept of supply chain coordination, analyzes the supply chain coordination mechanism, and puts forward the hypotheses of modeling. Based on the bi-level programming theory, the minimum total cost of military enterprises and maximum profit of manufacturers are further taken as the objectives of the upper and lower levels, and such factors as cost of means of production, ordering cost of parts, inventory cost, cost of late delivery and penalty cost are taken into account in this paper. On this basis, a bi-level programming model for military supply chain coordination with contracts is built. The model is then solved by hierarchical particle swarm optimization (HPSO), and specific operation steps are given. Finally, the model is verified in terms of rationality and feasibility through case analysis.

关键词： military supply chain coordination with contracts bi-level programming particle swarm optimization algorithm

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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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