Fracture performance prediction of polyvinyl alcohol fiber-reinforced cementitious composites containing nano-SiO2 using least-squares support vector machine optimized with quantum-behaved particle swarm optimization algorithm

Theoretical and Applied Fracture Mechanics ◽

10.1016/j.tafmec.2021.103074 ◽

2021 ◽

pp. 103074

Author(s):

Tingyu Liu ◽

Peng Zhang ◽

Guo Cui ◽

Xiaodong Yue

Keyword(s):

Support Vector Machine ◽

Particle Swarm Optimization ◽

Polyvinyl Alcohol ◽

Performance Prediction ◽

Particle Swarm Optimization Algorithm ◽

Support Vector ◽

Swarm Optimization ◽

Polyvinyl Alcohol Fiber ◽

Fracture Performance ◽

Fiber Reinforced Cementitious Composites

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Shape optimization of GFRP elastic gridshells by the weighted Lagrange ε-twin support vector machine and multi-objective particle swarm optimization algorithm considering structural weight

10.1016/j.istruc.2021.05.077 ◽

2021 ◽

Vol 33 ◽

pp. 2066-2084

Author(s):

Soheila Kookalani ◽

Bin Cheng ◽

Sheng Xiang

Keyword(s):

Support Vector Machine ◽

Particle Swarm Optimization ◽

Shape Optimization ◽

Optimization Algorithm ◽

Particle Swarm Optimization Algorithm ◽

Particle Swarm ◽

Twin Support Vector Machine ◽

Support Vector ◽

Swarm Optimization ◽

Multi Objective

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Fault diagnosis of sensor by chaos particle swarm optimization algorithm and support vector machine

Expert Systems with Applications ◽

10.1016/j.eswa.2011.02.043 ◽

2011 ◽

Vol 38 (8) ◽

pp. 9908-9912 ◽

Author(s):

Zhao Chenglin ◽

Sun Xuebin ◽

Sun Songlin ◽

Jiang Ting

Keyword(s):

Support Vector Machine ◽

Particle Swarm Optimization ◽

Fault Diagnosis ◽

Optimization Algorithm ◽

Particle Swarm Optimization Algorithm ◽

Particle Swarm ◽

Support Vector ◽

Swarm Optimization

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Image reconstruction for an electrical capacitance tomography system based on a least-squares support vector machine and a self-adaptive particle swarm optimization algorithm

Measurement Science and Technology ◽

10.1088/0957-0233/22/10/104008 ◽

2011 ◽

Vol 22 (10) ◽

pp. 104008 ◽

Author(s):

Xia Chen ◽

Hong-li Hu ◽

Fei Liu ◽

Xiang Xiang Gao

Keyword(s):

Support Vector Machine ◽

Particle Swarm Optimization ◽

Optimization Algorithm ◽

Particle Swarm Optimization Algorithm ◽

Support Vector ◽

Electrical Capacitance Tomography ◽

Electrical Capacitance ◽

Swarm Optimization ◽

Tomography System ◽

Capacitance Tomography

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Vehicle Fuel Consumption Prediction Based on Least Squares Support Vector Machine Optimized by Improved Particle Swarm Optimization Algorithm

Lecture Notes in Electrical Engineering - Proceedings of the 19th Asia Pacific Automotive Engineering Conference & SAE-China Congress 2017: Selected Papers ◽

10.1007/978-981-10-8506-2_75 ◽

2018 ◽

pp. 1109-1118

Author(s):

Yu Liu ◽

Mengliang Li ◽

Yueyun Xu ◽

Guo Jinwei

Keyword(s):

Support Vector Machine ◽

Particle Swarm Optimization ◽

Least Squares ◽

Fuel Consumption ◽

Optimization Algorithm ◽

Particle Swarm Optimization Algorithm ◽

Support Vector ◽

Swarm Optimization ◽

Improved Particle Swarm Optimization ◽

Consumption Prediction

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

Journal of Molecular Graphics and Modelling ◽

10.1016/j.jmgm.2010.06.002 ◽

2010 ◽

Vol 29 (2) ◽

pp. 188-196 ◽

Author(s):

Xuan Zhou ◽

Zhanchao Li ◽

Zong Dai ◽

Xiaoyong Zou

Keyword(s):

Genetic Algorithm ◽

Support Vector Machine ◽

Biological Activity ◽

Particle Swarm Optimization ◽

Optimization Algorithm ◽

Particle Swarm Optimization Algorithm ◽

Support Vector ◽

Qsar Modeling ◽

Swarm Optimization ◽

Coupling Support

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Blasting vibration velocity prediction based on least squares support vector machine with particle swarm optimization algorithm

Geosystem Engineering ◽

10.1080/12269328.2019.1607570 ◽

2019 ◽

Vol 22 (5) ◽

pp. 279-288

Author(s):

Qing Yuan ◽

Shihong Zhai ◽

Li Wu ◽

Peishuai Chen ◽

Yuchun Zhou ◽

...

Keyword(s):

Support Vector Machine ◽

Particle Swarm Optimization ◽

Least Squares ◽

Optimization Algorithm ◽

Particle Swarm Optimization Algorithm ◽

Vibration Velocity ◽

Support Vector ◽

Blasting Vibration ◽

Swarm Optimization ◽

Velocity Prediction

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A short-term load forecasting approach based on support vector machine with adaptive particle swarm optimization algorithm

2009 Chinese Control and Decision Conference ◽

10.1109/ccdc.2009.5192275 ◽

2009 ◽

Author(s):

Huang Yue ◽

Li Dan ◽

Gao Liqun ◽

Wang Hongyuan

Keyword(s):

Support Vector Machine ◽

Particle Swarm Optimization ◽

Optimization Algorithm ◽

Particle Swarm Optimization Algorithm ◽

Load Forecasting ◽

Support Vector ◽

Swarm Optimization ◽

Adaptive Particle Swarm Optimization ◽

Short Term Load Forecasting

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A Combination of Modified Particle Swarm Optimization Algorithm and Support Vector Machine for Pattern Classification

2009 Third International Symposium on Intelligent Information Technology Application ◽

10.1109/iita.2009.149 ◽

2009 ◽

Author(s):

Zhiming Liu ◽

Cheng Wang ◽

Shanzhen Yi

Keyword(s):

Support Vector Machine ◽

Particle Swarm Optimization ◽

Pattern Classification ◽

Optimization Algorithm ◽

Particle Swarm Optimization Algorithm ◽

Particle Swarm ◽

Support Vector ◽

Swarm Optimization ◽

Modified Particle Swarm Optimization

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A reliability forecasting method for distribution systems based on support vector machine with chaotic particle swarm optimization algorithm

2013 48th International Universities' Power Engineering Conference (UPEC) ◽

10.1109/upec.2013.6714983 ◽

2013 ◽

Author(s):

Z. Y. Li ◽

Z. Y. Xu ◽

H. C. Ye ◽

Z. Q. Wang

Keyword(s):

Support Vector Machine ◽

Particle Swarm Optimization ◽

Optimization Algorithm ◽

Distribution Systems ◽

Particle Swarm Optimization Algorithm ◽

Particle Swarm ◽

Support Vector ◽

Swarm Optimization ◽

Forecasting Method ◽

Chaotic Particle Swarm Optimization

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Parameter Selection of a Support Vector Machine, Based on a Chaotic Particle Swarm Optimization Algorithm

Cybernetics and Information Technologies ◽

10.1515/cait-2015-0047 ◽

2015 ◽

Vol 15 (3) ◽

pp. 140-149 ◽

Author(s):

Huang Dong ◽

Gao Jian

Keyword(s):

Support Vector Machine ◽

Particle Swarm Optimization ◽

Particle Swarm Optimization Algorithm ◽

Particle Swarm ◽

Parameter Selection ◽

Support Vector ◽

Optimal Parameters ◽

Swarm Optimization ◽

Chaotic Particle Swarm Optimization ◽

Abstract This paper proposes a SVM (Support Vector Machine) parameter selection based on CPSO (Chaotic Particle Swarm Optimization), in order to determine the optimal parameters of the support vector machine quickly and efficiently. SVMs are new methods being developed, based on statistical learning theory. Training a SVM can be formulated as a quadratic programming problem. The parameter selection of SVMs must be done before solving the QP (Quadratic Programming) problem. The PSO (Particle Swarm Optimization) algorithm is applied in the course of SVM parameter selection. Due to the sensitivity and frequency of the initial value of the chaotic motion, the PSO algorithm is also applied to improve the particle swarm optimization, so as to improve the global search ability of the particles. The simulation results show that the improved CPSO can find more easily the global optimum and reduce the number of iterations, which also makes the search for a group of optimal parameters of SVM quicker and more efficient.

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