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Design of Zero-Differential Steering Controller for Tracked Vehicles With Hydraulic-Mechanical Transmission Based on particle swarm optimization algorithm

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IEEE ACCESS 2023年 11卷 32187-32200页

作者： Jia, Weijian Liu, Xixia Zhang, Chuanqing Qiu, Mianhao Tan, Yongying Yu, Zhang Army Acad Armored Forces Beijing 100072 Peoples R China Beijing Special Engn & Design Inst Beijing 100028 Peoples R China

The handling stability of tracked vehicles not only affects the handling convenience of drivers but is also an important part of vehicle active safety. A zero-differential steering controller for tracked vehicles with hydraulic-mechanical transmissions was designed in this paper. First, the working principle of the steer-by-wire systems of tracked vehicles was analyzed, and the vehicle speed calculation model was established. Then, the steering dynamics model of the tracked vehicle was established based on the shear stress model. Finally, based on the particle swarm optimization algorithm, the established tracked vehicle steering dynamics model was iteratively solved, and the optimal yaw rate gain K-r was calculated in real time and used for vehicle steering control. The steering control simulation model of tracked vehicles was established in MATLAB/Simulink, and the control effect of the designed steering controller was verified by the simulation. The control effect was evaluated by the comprehensive evaluation index of the handling stability. The simulation results showed that the steering controller based on the particle swarm optimization algorithm effectively improved the handling stability of the tracked vehicle and reduced the burden on the driver.

关键词： Mathematical models Stability criteria Wheels Vehicles Hydraulic systems Gears Engines particle swarm optimization algorithm steer-by-wire system tracked vehicle zero-differential steering controller

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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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Evaluation of multi-objective inverse heat conduction problem based on particle swarm optimization algorithm, normal distribution and finite element method

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INTERNATIONAL JOURNAL OF HEAT AND MASS TRANSFER 2018年第PartA期127卷 1114-1127页

作者： Wang, Xiaowei Li, Huiping He, Lianfang Li, Zhichao Shandong Univ Sci & Technol Sch Mat Sci & Engn 579 Qianwangang Rd Qingdao 266590 Shandong Peoples R China

The boundary heat transfer coefficient (BHTC) and thermal conductivity are the most essential thermal physical parameters, which have a significant effect on the calculation accuracy of physical fields in the numerical simulation. To simultaneously estimate the temperature-dependent BHTC and thermal conductivity by solving the inverse heat conduction problem (IHCP), a hybrid method (ZPSO) based on particle swarm optimization algorithm (PSO), normal distribution method and finite element method (FEM) is presented, where normal distribution method representing the transient solution space is used to improve the convergence speed. A two-dimensional direct heat conduction problem (DHCP) with the temperature-dependent thermal-physical parameters is simulated based on FEM. Some temperature curves at certain position of FEM model are attained in the simulation. The temperature-dependent BHTC and thermal conductivity are simultaneously evaluated by PSO-FEM and ZPSO-FEM according to the temperature curves attained in DHCP. Comparing the temperature-dependent thermal-physical parameters attained in the IHCP to that used in the DHCP, the results show that the BHTC and thermal conductivity evaluated by the ZPSO are well consistent with those used in the DHCP. The evaluation results show that the convergence of the hybrid method is well, and the convergence speed is accelerated by the ZPSO. (C) 2018 Elsevier Ltd. All rights reserved.

关键词： Inverse heat conduction Multi-objective problem particle swarm optimization algorithm Finite element method

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QSAR modeling of peptide biological activity by coupling support vector machine with particle swarm optimization algorithm and genetic algorithm

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JOURNAL OF MOLECULAR GRAPHICS & MODELLING 2010年第2期29卷 188-196页

作者： Zhou, Xuan Li, Zhanchao Dai, Zong Zou, Xiaoyong Sun Yat Sen Univ Sch Chem & Chem Engn Guangzhou 510275 Guangdong Peoples R China Guangdong Pharmaceut Univ Sch Pharm Guangzhou 510006 Guangdong Peoples R China

A novel method coupling particle swarm optimization algorithm (PSO) and genetic algorithm (GA) was proposed to optimize simultaneously the kernel parameters of support vector machine (SVM) and determine the optimized features subset. By coupling GA with PSO, the particles produced in each generation in PSO algorithm were processed by crossover and mutation of GA, and then the particles could keep diversity to escape from local optima and find the global optima quickly and accurately. In order to evaluate the proposed method, four peptide datasets were employed for the investigation of quantitative structure-activity relationship (QSAR). The structural and physicochemical features of peptides from amino acid sequences were used to represent peptides for QSAR. The correlation coefficients (R) of training set of the four datasets were 1.0000, 0.9508, 1.0000, 0.9995, the R of test set of the four datasets were 0.9922, 0.9687, 0.9022, 0.7404, respectively. The root-mean-square errors (RMSEs) of training set of the four datasets were 0.0000, 0.0986, 0.0000, 0.0203, the RMSEs of test set of the four datasets were 0.2522, 0.2782, 0.9625, 0.2928, respectively. A protein dataset, which consists of 277 proteins, was also employed to evaluate the current method for predicting protein structural class, and the good results of overall success rate were obtained. The results indicated that the proposed method might hold a high potential to become a useful tool in peptide QSAR and protein prediction research. (C) 2010 Elsevier Inc. All rights reserved.

关键词： Quantitative structure-activity relationship particle swarm optimization algorithm Genetic algorithm Support vector machine Peptide

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Performance of A Circular Crossed-Dipole Array for SDMA Configuration Adopting Directivity and Polarization Control Using particle swarm optimization algorithm

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INTERNATIONAL JOURNAL OF RF AND MICROWAVE COMPUTER-AIDED ENGINEERING 2009年第1期19卷 50-59页

作者： Mahmoud, K. R. Bansal, R. El-Adawy, M. I. Zainud-Deen, S. H. Ibrahem, Sabry M. M. Helwan Univ Fac Engn Dept Elect Commun & Comp Cairo Egypt Univ Connecticut Dept Elect & Comp Engn Storrs CT USA Menoufia Univ Fac Elect Engn Dept Elect & Elect Commun Engn Menoufia Egypt

In this article, the performance of a circular crossed-dipole array (CCDA) for space division multiple access (SDMA) configurations adopting directivity and polarization control is presented. The array consists of 12 dual-polarized elements uniformly distributed in a circular configuration;each dual-polarized element (crossed-dipole) consists of two half-wave dipoles in a +/- 45 degrees slant configuration. The modified particle swarm optimization and moment of method (MPSO-MOM) algorithm is used to calculate the complex weightings of the array elements in a mutual coupling environment for beamforming synthesis. In addition, the performance of the adaptive array using discrete feedings (1-bit amplitude and 4-bit phase shifters or only 4-bit phase shifters) is studied. (C) 2008 Wiley Periodicals, Inc. Int J RF bind Microwave CAE 19: 50-59. 2009.

关键词： smart antennas adaptive beamforming crossed-dipole antenna uniform circular arrays method of moments particle swarm optimization algorithm

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Trajectory planning of a redundant planar manipulator based on joint classification and particle swarm optimization algorithm

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MULTIBODY SYSTEM DYNAMICS 2020年第1期50卷 25-43页

作者： Yu, Luchuan Wang, Kaiqiang Zhang, Qinhe Zhang, Jianhua Shandong Univ Minist Educ Peoples Republ China Key Lab High Efficiency & Clean Mech Mfg Jinan Shandong Peoples R China Shandong Univ Natl Demonstrat Ctr Expt Mech Engn Educ Jinan Shandong Peoples R China Shandong Univ Sch Mech Engn Jinan Shandong Peoples R China

This paper presents a general method for the trajectory planning of the redundant planar manipulator. The mathematical relation between joint space and Cartesian space in a two-dimensional space is first derived. The joint classification is employed to obtain the solutions of corresponding joints. It divides joints into class I redundant joints, class II redundant joints, and nonredundant joints. The new application of knot points in the quintic B-spline curve is introduced to generate inverse solutions of class I redundant joints. Examples show that the number and distribution of knot points have a large effect on their solutions. Moreover, the particle swarm optimization algorithm is extended to generate solutions of class II redundant joints. It also optimizes the initial trajectories of joints and end-effector. Finally, solutions of nonredundant joints can be generated by the derived relation between the joint space and Cartesian space. The proposed methodology is confirmed by a case study. Under the same conditions, results show that the solution obtained by the extended method is not only better than that obtained by the particle swarm optimization algorithm but also closer to the global optimal solution.

关键词： Joint classification Redundant planar manipulator Quintic B-spline curve particle swarm optimization algorithm

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Prediction and modeling of mechanical properties in fiber reinforced self-compacting concrete using particle swarm optimization algorithm and artificial neural network

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CONSTRUCTION AND BUILDING MATERIALS 2016年 119卷 277-287页

作者： Mashhadban, Hadi Kutanaei, Saman Soleimani Sayarinejad, Mohammad Ali Saroyeh Univ Dept Civil Engn POB 404 Sari Iran Islamic Azad Univ Qaemshahr Branch Young Researchers & Elite Club Qaemshahr Iran Islamic Azad Univ Dept Civil Engn Qaemshahr Iran

Intelligence system is a field of computer science that designs and studies efficient computational methods for solving problem. The purpose of present study is to investigate the effects of fibers on the performance of self compacting concrete (SCC). In this experiment study, 9 concrete mixtures containing two types of fibers (polyphenylene sulfide: 0.1, 0.2, 0.3 and 0.4% by volume and steel: 0.1, 0.2, 0.3 and 0.4% by volume) and unreinforced samples have been tested and compared. Fresh, mechanical and durability properties and ultrasonic pulse velocity of all SCC mixtures were evaluated. Then this experimental data was used to train the feed forward artificial neural network type. Finally the trained ANN (artificial neural network) and PSOA (particle swarm optimization algorithm) are used to generate a polynomial model for predicting SCC properties. The obtained results showed that the mechanical properties can be significantly improved by fiber reinforcement and workability of the SCC decreases with increasing fiber content. Moreover, steel fibers have better performance with relation to mechanical properties than polyphenylene sulfide fibers. In addition, PSOA integrated with the ANN is a flexible and accurate method in prediction of mechanical properties of fiber reinforced SCC properties. (C) 2016 Elsevier Ltd. All rights reserved.

关键词： Fiber Self compacting concrete Artificial neural network particle swarm optimization algorithm

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An application of particle swarm optimization algorithm to clustering analysis

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SOFT COMPUTING 2011年第3期15卷 533-542页

作者： Kuo, R. J. Wang, M. J. Huang, T. W. Natl Taiwan Univ Sci & Technol Dept Ind Management Taipei 106 Taiwan Natl Taipei Univ Technol Dept Ind Engn & Management Taipei 106 Taiwan

particle swarm optimization algorithm (PSOA), which maintains a population of particles, where each particle represents a potential solution to an optimization problem, is a population-based stochastic search process. This study intends to integrate PSOA with K-means to cluster data. It is shown that PSOA can be employed to find the centroids of a user-specified number of clusters. The proposed PSOA is evaluated using four data sets, and compared to the performance of some other PSOA-based methods and K-means method. Computational results show that the proposed method has much potential. A real-world problem for order clustering also illustrates that the proposed method is quite promising.

关键词： Clustering analysis particle swarm optimization algorithm K-means

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Hybrid particle swarm optimization algorithm for text feature selection problems

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NEURAL COMPUTING & APPLICATIONS 2024年第13期36卷 7471-7489页

作者： Nachaoui, Mourad Lakouam, Issam Hafidi, Imad Natl Sch Appl Sci ENSA Khouribga 25000 Morocco Univ Sultan Moulay Slimane Fac Sci & Tech Equipe Math & Interact Beni Mellal Morocco

Feature selection (FS) is a crucial preprocessing step that aims to eliminate irrelevant and redundant features, reduce the dimensionality of the feature space, and enhance clustering efficiency and effectiveness. FS is categorized as NP-Hard due to the high number of existing solutions. Various metaheuristic methods have been developed to address the FS problem, yielding promising results. Particularly, particle swarm optimization (PSO), an evolutionary computing (EC) approach guided by swarm intelligence, has gained widespread adoption owing to its implementation simplicity and potential for global search. This paper analyzes several variants of PSO algorithms and introduces a new FS method called HPSO. The proposed approach utilizes an asynchronously adaptive inertia weight and an improved constriction factor. Additionally, it incorporates a chaotic map and a MAD fitness function with a feature count penalty to tackle the clustering FS problem. The efficiency of the developed method is evaluated against the genetic algorithm (GA) and well-known variants of PSO algorithms, including PSOs with fixed inertia weights, PSOs with improved inertia weights, PSOs with fixed constriction factors, PSOs with improved constriction factors, PSOs with adaptive inertia weights, and PSO's includes advanced learning exemplars and sophisticated structure topologies. This paper assesses two different reference text data sets, Reuters-21578 and Webkb. In comparison with competitive methods, the proposed HPSO method achieves higher clustering precision and selects a more informative feature set.

关键词： Text feature selection particle swarm optimization algorithm Genetic algorithm Constriction factor Chaotic map K-mean text clustering algorithm

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A Hybrid algorithm Based on Gravitational Search and particle swarm optimization algorithm to Solve Function optimization Problems

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ENGINEERING LETTERS 2017年第1期25卷 22-29页

作者： Wang, Jie-Sheng Song, Jiang-Di Univ Sci & Technol Liaoning Sch Elect & Informat Engn Anshan 114051 Peoples R China Univ Sci & Technol Liaoning Natl Financial Secur & Syst Equipment Engn Res Ct Anshan Peoples R China

Gravitational search algorithm (GSA) is a swarm intelligence heuristic optimization algorithm based on the law of gravitation. Aiming at the disadvantage of poor local search ability and slow convergence speed in standard GSA, four improved GSA-PSO hybrid algorithm are proposed by introducing a small constant updating strategy in order to enhance the update ability of velocity, acceleration factor and the optimal individual location, where PSO strategy was used to optimize the position and velocity of the GSA. Through simulation experiments on typical test functions to verify its performance, the simulation results show that the optimal setup of GSA parameters can improve the convergence rate of the algorithm and improve the accuracy of the solution.

关键词： gravitational search algorithm particle swarm optimization algorithm function optimization

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