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A Weighted Ensemble Learning algorithm Based on Diversity Using a Novel particle swarm optimization Approach

algorithmS 2020年第10期13卷 255-255页

作者： You, Gui-Rong Shiue, Yeou-Ren Yeh, Wei-Chang Chen, Xi-Li Chen, Chih-Ming Fujian Business Univ Dept Management Engn Fuzhou 350012 Peoples R China Fujian Business Univ Informat Technol Ctr Fuzhou 350012 Peoples R China Natl Tsing Hua Univ Dept Ind Engn & Management Engn Hsinchu 300 Taiwan Fujian Business Univ Dept Finance Fuzhou 350012 Peoples R China

In ensemble learning, accuracy and diversity are the main factors affecting its performance. In previous studies, diversity was regarded only as a regularization term, which does not sufficiently indicate that diversity should implicitly be treated as an accuracy factor. In this study, a two-stage weighted ensemble learning method using the particle swarm optimization (PSO) algorithm is proposed to balance the diversity and accuracy in ensemble learning. The first stage is to enhance the diversity of the individual learner, which can be achieved by manipulating the datasets and the input features via a mixed-binary PSO algorithm to search for a set of individual learners with appropriate diversity. The purpose of the second stage is to improve the accuracy of the ensemble classifier using a weighted ensemble method that considers both diversity and accuracy. The set of weighted classifier ensembles is obtained by optimization via the PSO algorithm. The experimental results on 30 UCI datasets demonstrate that the proposed algorithm outperforms other state-of-the-art baselines.

关键词： ensemble learning diversity machine learning particle swarm optimization algorithm weighted ensemble

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Research on pulse laser detection anti-interference method in smoke environment

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INFRARED PHYSICS & TECHNOLOGY 2025年 145卷

作者： Qu, Qinyang Su, Zerun Xu, Xiaobin Li, Moyuan Chen, Shanshan Ran, Yingying Tan, Zhiying Hohai Univ Coll Mech & Elect Engn Chang Zhou 213200 Peoples R China Hohai Univ Coll Informat Sci & Engn Chang Zhou 213200 Peoples R China

To address the problem of smoke medium interference on pulse laser detection signals, this study employs Monte Carlo method to precisely establish the backscattered signals model of pulsed lasers in smoke conditions, and proposes an anti-interference strategy combining particle swarm optimization (PSO) and convolutional neural network (CNN) by echo signals collected experimentally. Specifically, this study uses the Monte Carlo method and scattering phase function to simulate particle collisions and constructs a sampling model of reflected photons based on the target surface's reflective properties. Semi-analytical reception technology is used to extract the received backscattered photon signals. Finally, a CNN is built taking all the backscattered data points as the input. The parameter of the network is optimized using PSO algorithm. To verify the accuracy of the simulated backscattered echo signals and the effectiveness of the anti-interference algorithm, a laser detection experiment in a smoke environment was conducted. The experimental results show that the simulated detection back- scattered signals and the experimental backscattered signals have a high degree of consistency, with a maximum root mean square error of only 0.043. The recognition accuracy of the CNN optimized by the PSO algorithm is 96.154 %, with a computational parameter size of 193.008 KB. This study provides solid theoretical support and technical assurance for the pulse laser detection technology in the anti-interference field.

关键词： Pulse laser Smoky environment Echo model Monte Carlo method particle swarm optimization algorithm

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Comparative study on constitutive models of a near β titanium alloy TC18 during thermoplastic deformation based on machine learning

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MATERIALS TODAY COMMUNICATIONS 2025年 42卷

作者： Ding, Shaoling Gao, Shuai Jiang, Xiang Shi, Shuangxi Liang, Yaobiao Guilin Univ Technol Sch Math & Stat Guilin 541006 Peoples R China Guangxi Coll & Univ Key Lab Appl Stat Guilin 541006 Peoples R China Guilin Univ Aerosp Technol Guangxi Key Lab Special Engn Equipment & Control Guilin 541004 Peoples R China

The hot deformation behavior of TC18 alloy has been systematically studied at the temperature of 993-1113 K and strain rate of 0.001-1 s-1. Based on the stress-strain data obtained under the above process parameters, machine learning models describing the thermoplastic constitutive relationship of the alloy were established by using random forest (RF), support vector regression (SVR), back propagation artificial neural network (BPANN) and Gaussian process regression (GPR). The hyper-parameters of these models were optimized by adaptive inertial weighted particle swarm optimization (APSO) algorithm, and the models were further evaluated by statistical analysis and cross-validation. The results show that the prediction ability of the developed four machine learning models was ranked as GPR>BPANN>RF>SVR. Then APSO was applied to four models to further enhance their prediction accuracy, and the prediction accuracy of these APSO models was ranked as APSOGPR>APSO-BPANN>APSO-RF>APSO-SVR. The developed APSO-GPR model has the highest R2 (>0.999), as well as the lowest RMSE (<1.77) and MAPE (<0.63 %) on both the training set and the testing set, demonstrating its strong predictive performance. Sixteen cross-validation tests also confirm the APSO-GPR model has high prediction accuracy.

关键词： TC18 alloy Hot deformation Flow stress Machine learning particle swarm optimization algorithm

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Vibration separation method for permanent magnet guideway irregularity measurement

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MECHANICAL SYSTEMS AND SIGNAL PROCESSING 2025年 223卷

作者： Luo, Yi Yuan, Yuhang Zeng, Peiyang Liu, Jianru Deng, Bin Deng, Zigang Southwest Jiaotong Univ Sch Mech Engn Chengdu 610031 Sichuan Peoples R China Southwest Jiaotong Univ State Key Lab Rail Transit Vehicle Syst Chengdu 610031 Sichuan Peoples R China

High-temperature superconducting (HTS) pinning maglev has the potential to achieve high operational speed while having a low maintenance cost, attracting a lot of attention. The HTS pinning maglev system realizes the levitation of the vehicle through the flux pinning characteristics between the high-temperature superconducting bulk and the magnetic field of the permanent magnet guideway (PMG). Thus, the profile of the magnetic field of the PMG can significantly affect the levitation status of the vehicle, which makes the PMG irregularity become one of the main excitation sources of the system. The magnetic induction intensity on the surface of the PMG can be used to characterize the PMG irregularity, but the non-contact dynamic measurement may produce large errors by the vibration of the measurement system during the process. In order to evaluate the PMG irregularity, the vibration component needs to be separated from the overall signal. In this paper, through the synchronous measurement of the Hall sensor and accelerometer, the vibration separation method of the PMG irregularity measurement is simplified to the optimal filter estimation. The particle swarm optimization (PSO) algorithm is used to calculate the parameters of the optimal filter based on the correlation coefficient criterion. The effectiveness of the proposed method is verified by the test. The separated signal is more accurate in both time domain and frequency domain for the characterization of PMG irregularity.

关键词： High-temperature superconducting pinning maglev Permanent magnet guideway irregularity Vibration separation Correlation coefficient particle swarm optimization algorithm

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Research on Intelligent optimization of Wellbore Trajectory in Complex Formation

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APPLIED SCIENCES-BASEL 2025年第3期15卷 1364-1364页

作者： Gu, Haipeng Yan, Tie Wu, Yang Stat Northeast Petr Univ Sch Math Daqing 163318 Heilongjiang Peoples R China Northeast Petr Univ NEPU Sanya Offshore Oil & Gas Res Inst Sanya 572000 Peoples R China

Borehole trajectory optimization is a key issue in oil and gas drilling engineering. The traditional wellbore trajectory design method faces great challenges in optimizing the trajectory length and complexity, and it is difficult to meet the actual engineering requirements. In this paper, the three-stage wellbore trajectory optimization problem is studied, and a multi-objective optimization model including two objective functions of trajectory length and trajectory complexity is constructed. In this paper, an improved multi-objective particle swarm optimization algorithm is proposed, which combines the clustering strategy to improve the diversity of solutions, and enhances the local search ability and global convergence performance of the algorithm through the elite learning strategy. In order to verify the performance of the algorithm, comparative experiments were carried out using classical multi-objective benchmark functions. The results showed that the improved algorithm is superior to the traditional method in terms of diversity and convergence of solutions. Finally, the proposed algorithm was applied to the actual three-stage wellbore trajectory optimization problem. In summary, the research results of this paper provide theoretical support and engineering practice methods for wellbore trajectory optimization, and serve as an important reference for further improving the efficiency and quality of wellbore trajectory design.

关键词： wellbore trajectory multi-objective optimization particle swarm optimization algorithm elite learning strategies

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Virtual network mapping algorithm based on load balancing multi-objective particle swarm optimization

Virtual network mapping algorithm based on load balancing mu...

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11th International Conference on Wireless Communications, Networking and Mobile Computing (WiCOM 2015)

作者： Jun Zhao Zhong Ma Wuhan Digital Engineering Institute China

ISBN: (纸本)9781510821279

Existing virtual network mapping algorithms does not consider resource consumption of intermediate node on communication path usually. Minimum resource consumption or shortest path of physical network is regarded as objective, thereby leading to bottleneck due to insufficient resource of intermediate node on communication path, and affecting performance of the whole physical network and subsequent success rate of virtual network. A virtual network mapping algorithm based on load balancing multi-objective particle swarm optimization is proposed in the paper aiming at the problem. Resource consumption of intermediate node is sufficiently considered in the algorithm, double balance of node load and link load is regarded as objective. Meanwhile, the optimal path of particle swarm optimization algorithm is adopted. Experiments show that the algorithm proposed in the paper can not only realize double balance of node load and link load, but also effectively improve request receiving success rate, overall resource load balance and long-term operation income.

关键词： Load Balancing particle swarm optimization algorithm Virtual Network Mapping Load balancing intermediate node Link Load Telecommunication lines particle swarm optimization Virtual networks particle swarm algorithm Resource utilization physical network

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Joint Multiuser And Inter-symbol Interference Suppression In CDMA Systems Using particle swarm optimization algorithms

Joint Multiuser And Inter-symbol Interference Suppression In...

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21st Iranian Conference on Electrical Engineering (ICEE)

作者： Arani, Atefeh Haji Jamali Azmi, Paeiz Tarbiat Modares Univ Tehran Iran

ISBN: (纸本)9781467356343

Using particle swarm optimization (PSO) has become a common heuristic technique in many fields of engineering. In this paper, we apply PSO to solve Joint Multiuser and Inter-symbol Interference (ISI) Suppression problems in the code-division multiple-access (CDMA) systems over multipath Rayleigh fading channel, to reduce the computational complexity. In the proposed method, conventional detector (CD) is used as the first stage to initialize the PSO algorithm and time-varying acceleration coefficients (TVAC) are used in PSO algorithm. The simulation results show that the performance of PSO-based MUD with TVAC is promising and outperform the CD.

关键词： Code division multiple access particle swarm optimization algorithm multiple access interference Inter-symbol Interference multiuser detection

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A Hybrid Genetic and particle swarm optimization algorithms for Dynamic Facility Layout Problem with Multiple Transporters 15

A Hybrid Genetic and Particle Swarm Optimization Algorithms ...

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15th Iran International Industrial Engineering Conference (IIIEC)

作者： Pourhassan, Mohammad Reza Raissi, Sadigh Islamic Azad Univ South Tehran Branch Sch Ind Engn Tehran Iran

ISBN: (纸本)9781728117751

Nowadays, manufacturing plants should be agile to changes their production mix plan based on dynamic demands. Here, layout design significantly could impact on manufacturing efficiency. When the flows of materials between departments embed variability during the planning horizon, this problem is known as the dynamic facility layout problem (DFLP). This paper extends such problem with considering multiple transporters, which commonly are used for transportation tasks among facilities. Hence, we extended the classical DFLP objective function in such a way that could encounter total combined rearrangement, material handling and transporting costs. Firstly, the relevant mathematical model is presented and then hybrid metaheuristic algorithms based on particle swarm optimization (PSO) and genetic algorithm (GA) presented to solve such problem efficiently. To achieve reliable results, a Taguchi's design of experiments is applied to calibrate initial parameters. Also, a few small-sized problems are solved using the CPLEX software. Analysis of the results shows that the proposed hybrid PSO algorithms have good solution quality according to the objective function and CPU time rather than hybrid GA and proved the effectiveness of this algorithm on the set of test problems.

关键词： Dynamic facility layout particle swarm optimization algorithm Genetic algorithm Taguchi design of experiments

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Integrated Control Strategy of Bearingless Switched Reluctance Motor Based on Active Disturbance Rejection Control

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IEEE TRANSACTIONS ON INDUSTRIAL ELECTRONICS 2025年

作者： Wang, Xuan Wang, Xilian Huang, Zhuliang Wang, Baohua Yang, Ruikang Beijing Jiaotong Univ Coll Elect Engn Beijing 100044 Peoples R China

Traditional proportion integration differentiation (PID) controller is difficult to solve the contradiction between response speed and overshoot, and its anti-interference ability is limited. To solve the problem, an integrated control strategy of bearingless switched reluctance motor based on active disturbance rejection control (ADRC) is proposed. A first-order speed ADRC controller and a second-order displacement ADRC controller are designed for direct instantaneous torque control and direct instantaneous force control system, respectively. With the integral time-weighted absolute error (ITAE) as the optimization objective, the particle swarm optimization algorithm is used to tune the main parameters of the ADRC controllers, overcoming the drawbacks of adjusting parameters manually, such as complex operation and time consumption. Simulations and experiments are carried out. The results demonstrate that the control strategy has superior dynamic and static response performance in both speed and displacement control and has good application prospects.

关键词： Control systems Rotors Torque Windings Force Motors Centralized control Switched reluctance motors Stator windings particle swarm optimization Active disturbance rejection control (ADRC) bearingless switched reluctance motor (BSRM) particle swarm optimization algorithm sharing suspension windings

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Design of impact ultrasonic penetrator and optimization of impact efficiency

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ADVANCES IN SPACE RESEARCH 2025年第8期75卷 6070-6088页

作者： Wang, Yinchao Yu, Ziming Yin, Zihao Zhang, Weiwei Zu, Lin Tao, Guanghong Yu, Suyang Shenyang Aerosp Univ Sch Mechatron Engn Shenyang 110136 Peoples R China Shenyang Aerosp Univ Key Lab Rapid Dev & Mfg Technol Aircraft Minist Educ Shenyang 110136 Peoples R China Harbin Inst Technol State Key Lab Robot & Syst Harbin 150001 Peoples R China Deep Space Explorat Lab Hefei 230026 Peoples R China

This paper proposes, optimizes, and experimentally investigates a thread-clamped ultrasonic penetrator that uses a spring to induce forced vibration in the transducer, thereby enhancing drilling efficiency. The penetrator consists mainly of a transducer, a free mass, an elastic energy storage unit, a drilling tool, and a housing. The elastic energy storage unit is attached to the transducer's flange, permitting limited axial movement. When excited by a sinusoidal signal at a specific frequency, the transducer's front end generates high-frequency longitudinal vibrations that impact the free mass. Upon colliding with the drilling tool and the transducer subsequently, the elastic energy storage unit absorbs and utilizes this energy, optimizing the energy transfer process. This study designs the penetrator's structure, analyzes the motion curves of each component, and derives the kinetic energy curve of the drilling tool. A novel particle swarm optimization algorithm is employed to optimize the key parameters of the penetrator, verifying the optimization effect. The prototype was fabricated, and its vibration and output characteristics were tested. The results from rigorous testing clearly demonstrate a significant improvement in the penetrator's drilling efficiency after meticulous structural and parameter optimization. Both simulation and experimental results confirm the feasibility of the penetrator. (c) 2025 Published by Elsevier B.V. on behalf of COSPAR.

关键词： Piezoelectric actuator Kinetic optimization Energy amplification particle swarm optimization algorithm

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