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

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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Cancer Detection Using a New Hybrid Method Based on Pattern Recognition in MicroRNAs Combining particle swarm optimization algorithm and Artificial Neural Network

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BIG DATA AND COGNITIVE COMPUTING 2024年第3期8卷 33页

作者： Molaei, Sepideh Cirillo, Stefano Solimando, Giandomenico Tarbiat Modares Univ Fac Elect & Comp Engn Tehran 14115111 Iran Univ Salerno Dept Comp Sci I-84084 Salerno Italy

MicroRNAs (miRNAs) play a crucial role in cancer development, but not all miRNAs are equally significant in cancer detection. Traditional methods face challenges in effectively identifying cancer-associated miRNAs due to data complexity and volume. This study introduces a novel, feature-based technique for detecting attributes related to cancer-affecting microRNAs. It aims to enhance cancer diagnosis accuracy by identifying the most relevant miRNAs for various cancer types using a hybrid approach. In particular, we used a combination of particle swarm optimization (PSO) and artificial neural networks (ANNs) for this purpose. PSO was employed for feature selection, focusing on identifying the most informative miRNAs, while ANNs were used for recognizing patterns within the miRNA data. This hybrid method aims to overcome limitations in traditional miRNA analysis by reducing data redundancy and focusing on key genetic markers. The application of this method showed a significant improvement in the detection accuracy for various cancers, including breast and lung cancer and melanoma. Our approach demonstrated a higher precision in identifying relevant miRNAs compared to existing methods, as evidenced by the analysis of different datasets. The study concludes that the integration of PSO and ANNs provides a more efficient, cost-effective, and accurate method for cancer detection via miRNA analysis. This method can serve as a supplementary tool for cancer diagnosis and potentially aid in developing personalized cancer treatments.

关键词： cancer diagnosis neural network particle swarm optimization algorithm microRNAs

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Research on hybrid strategy particle swarm optimization algorithm and its applications

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SCIENTIFIC REPORTS 2024年第1期14卷 1-15页

作者： Yao, Jicheng Luo, Xiaonan Li, Fang Li, Ji Dou, Jundi Luo, Hongtai Guilin Univ Elect Technol Sch Comp Sci & Informat Secur Guilin 541004 Peoples R China Guilin Univ Elect Technol Ctr Appl Math Guangxi GUET Guilin 541004 Peoples R China Guilin Univ Elect Technol Natl Local Joint Engn Res Ctr Satellite Nav & Loc Guilin 541004 Peoples R China

The increasing complexity and high-dimensional nature of real-world optimization problems necessitate the development of advanced optimization algorithms. Traditional particle swarm optimization (PSO) often faces challenges such as local optima entrapment and slow convergence, limiting its effectiveness in complex tasks. This paper introduces a novel Hybrid Strategy particle swarm optimization (HSPSO) algorithm, which integrates adaptive weight adjustment, reverse learning, Cauchy mutation, and the Hook-Jeeves strategy to enhance both global and local search capabilities. HSPSO is evaluated using CEC-2005 and CEC-2014 benchmark functions, demonstrating superior performance over standard PSO, Dynamic Adaptive Inertia Weight PSO (DAIW-PSO), Hummingbird Flight patterns PSO (HBF-PSO), Butterfly optimization algorithm (BOA), Ant Colony optimization (ACO), and Firefly algorithm (FA). Experimental results show that HSPSO achieves optimal results in terms of best fitness, average fitness, and stability. Additionally, HSPSO is applied to feature selection for the UCI Arrhythmia dataset, resulting in a high-accuracy classification model that outperforms traditional methods. These findings establish HSPSO as an effective solution for complex optimization and feature selection tasks.

关键词： particle swarm optimization algorithm Hybrid strategy Adaptive weight adjustment Reverse learning strategy Cauchy mutation mechanism Hook-Jeeves strategy Feature selection

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Novel particle swarm optimization algorithm Based on President Election: Applied to a Renewable Hybrid Power System Controller

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INTERNATIONAL JOURNAL OF ENGINEERING 2021年第1期34卷 97-109页

作者： Yahyazadeh, M. Johari, M. S. HosseinNia, S. H. Vali E Asr Univ Rafsanjan Fac Engn Rafsanjan Iran Fac 3mE Dept Precis & Microsyst Engn Mekelweg 2 NL-2628 CD Delft Netherlands

particle swarm optimization has been a popular and common met heuristic algorithm from its genesis time. However, some problems such as premature convergence, weak exploration ability and great number of iterations have been accompanied with the nature of this algorithm. Therefore, in this paper we proposed a novel classification for particles to organize them in a different way. This new method which is inspired from president election is called President Election particle swarm optimization (PEPSO). This algorithm is trying to choose useful particles and omit functionless ones at initial steps of algorithm besides considering the effects of all generated particles to get a directed and fast convergence. Some preparations are also done to escape from premature convergence. To validate the applicability of our proposed PEPSO, it is compared with the other met heuristic algorithm including GAPSO, Logistic PSO, Tent PSO, and PSO to estimate the parameters of the controller for a hybrid power system. Results verify that PEPSO has a better reaction in worst conditions in finding parameters of the controller.

关键词： Hybrid optimization algorithm Chaotic Function Hybrid Power System particle swarm optimization algorithm President Election algorithm

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Application and Improvement of the particle swarm optimization algorithm in Source-Term Estimations for Hazardous Release

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ATMOSPHERE 2023年第7期14卷 1168-1168页

作者： Lu, Jinshu Huang, Mengqing Wu, Wenfeng Wei, Yonghui Liu, Chong Zhejiang Ocean Univ Dept Naval Architecture & Maritime Zhoushan 316022 Peoples R China

Hazardous gas release can pose severe hazards to the ecological environment and public safety. The source-term estimation of hazardous gas leakage serves a crucial role in emergency response and safety management practices. Nevertheless, the precision of a forward diffusion model and atmospheric diffusion conditions have a significant impact on the performance of the method for estimating source terms. This work proposes the particle swarm optimization (PSO) algorithm coupled with the Gaussian dispersion model for estimating leakage source parameters. The method is validated using experimental cases of the prairie grass field dispersion experiment with various atmospheric stability classes. The results prove the effectiveness of this method. The effects of atmospheric diffusion conditions on estimation outcomes are also investigated. The estimated effect in extreme atmospheric diffusion conditions is not as good as in other diffusion conditions. Accordingly, the Gaussian dispersion model is improved by adding linear and polynomial correction coefficients to it for its inapplicability under extreme diffusion conditions. Finally, the PSO method coupled with improved models is adapted for the source-term parameter estimation. The findings demonstrate that the estimation performance of the PSO method coupled with improved models is significantly improved. It was also found that estimated performances of source parameters of two correction models were significantly distinct under various atmospheric stability classes. There is no single optimal model;however, the model can be selected according to practical diffusion conditions to enhance the estimated precision of source-term parameters.

关键词： source-term parameter estimation forward dispersion model particle swarm optimization algorithm gas release atmospheric diffusion conditions

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A Comprehensively Improved particle swarm optimization algorithm to Guarantee particle Activity

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RUSSIAN PHYSICS JOURNAL 2021年第5期64卷 866-875页

作者： Bi, Ya Lam, Anthony Quan, Huiqun Liu, Hui Wang, Cunfa Hubei Univ Econ Sch Business Adm Wuhan Peoples R China Huazhong Univ Sci & Technol Coll Publ Adm Wuhan Peoples R China Katholieke Univ Leuven Fac Econ & Business Leuven Belgium Wuhan Univ Technol Sch Management Wuhan Peoples R China Fujian Zhuozhi Project Investment Consulting Co L Wuhan Peoples R China

The particle swarm optimization (PSO) algorithm has certain disadvantages;for instance, the convergence viscosity of the algorithm is reduced in the post evolution phase, the optimization search efficiency is also reduced, the algorithm is easy to be inserted with a local extremum during the calculation of a complex problem of a high-dimensional multiple extremum, and the convergence thereof is low. To compensate for the PSO disadvantage, we propose a particle swarm optimization of the comprehensive improvement strategy, which is a simple particle swarm optimization with a dynamic adaptive hybridization of the extremum disturbance and the ecds-PSO algorithm. This new comprehensively improved particle swarm algorithm discards the particle velocity and reduces the PSO from the second-order to a first-order difference equation. The evolutionary process is controlled by the particle position variables only. The hybridization operation of increasing the extremum disturbance and introducing a genetic algorithm can accelerate the particles to overstep the local extremum. The mathematical derivation and the plurality of a comparative experiment provide the following information: the improved particle swarm optimization is a simple and effective optimization algorithm that can enhance the algorithm accuracy, the convergence viscosity and the ability of avoiding the local extremum, and effectively reduce the calculation complexity.

关键词： particle swarm optimization algorithm dynamic adaptive hybridization local extremum particle activity

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