A New Technique for Support Vector Machine Parameters Optimization Based on Modiefied PSO Algorithm

AbstractThis study has been inspired by golf ball movements during the game to improve particle swarm optimization. Because, all movements from the first to the last move of the golf ball are the moves made by the player to win the game. Winning this game is also a result of successful implementation of the desired moves. Therefore, the movements of the golf ball are also an optimization, and this has a meaning in the scientific world. In this sense, the movements of the particles in the PSO algorithm have been associated with the movements of the golf ball in the game. Thus, the velocities of the particles have converted to parabolically descending structure as they approach the target. Based on this feature, this meta-heuristic technique is called RDV (random descending velocity) IW PSO. In this way, the result obtained is improved thousands of times with very small movements. For the application of the proposed new technique, the inverse kinematics calculation of the 7-joint robot arm has been performed and the obtained results have been compared with the traditional PSO, some IW techniques, artificial bee colony, firefly algorithm and quantum PSO.

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Parameters Optimization and Application of SVM Based on PCA-Particle Swarm Algorithm

International Journal of Scientific Research in Science Engineering and Technology ◽

10.32628/ijsrset196431 ◽

2019 ◽

pp. 325-330 ◽

Cited By ~ 1

Author(s):

Qingmi Yang

Keyword(s):

Parameter Optimization ◽

Recognition Accuracy ◽

Recognition Rate ◽

Particle Swarm ◽

Optimal Solution ◽

Principal Component ◽

Pso Algorithm ◽

Parameters Optimization ◽

Support Vector ◽

Particle Swarm Algorithm

The parameter optimization of Support Vector Machine (SVM) has been a hot research direction. To improve the optimization rate and classification performance of SVM, the Principal Component Analysis (PCA) - Particle Swarm Optimization (PSO) algorithm was used to optimize the penalty parameters and kernel parameters of SVM. PSO which is to find the optimal solution through continuous iteration combined with PCA that eliminates linear redundancy between data, effectively enhance the generalization ability of the model, reduce the optimization time of parameters, and improve the recognition accuracy. The simulation comparison experiments on 6 UCI datasets illustrate that the excellent performance of the PCA-PSO-SVM model. The results show that the proposed algorithm has higher recognition accuracy and better recognition rate than simple PSO algorithm in the parameter optimization of SVM. It is an effective parameter optimization method.

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Pulmonary Nodule Recognition Based on Multiple Kernel Learning Support Vector Machine-PSO

Computational and Mathematical Methods in Medicine ◽

10.1155/2018/1461470 ◽

2018 ◽

Vol 2018 ◽

pp. 1-10 ◽

Cited By ~ 2

Author(s):

Yang Li ◽

Zhichuan Zhu ◽

Alin Hou ◽

Qingdong Zhao ◽

Liwei Liu ◽

...

Keyword(s):

Support Vector Machine ◽

Pulmonary Nodule ◽

Multiple Kernel Learning ◽

Pso Algorithm ◽

Support Vector ◽

Training Time ◽

Inertia Weight ◽

Fitness Value ◽

Svm Algorithm ◽

Fitness Curve

Pulmonary nodule recognition is the core module of lung CAD. The Support Vector Machine (SVM) algorithm has been widely used in pulmonary nodule recognition, and the algorithm of Multiple Kernel Learning Support Vector Machine (MKL-SVM) has achieved good results therein. Based on grid search, however, the MKL-SVM algorithm needs long optimization time in course of parameter optimization; also its identification accuracy depends on the fineness of grid. In the paper, swarm intelligence is introduced and the Particle Swarm Optimization (PSO) is combined with MKL-SVM algorithm to be MKL-SVM-PSO algorithm so as to realize global optimization of parameters rapidly. In order to obtain the global optimal solution, different inertia weights such as constant inertia weight, linear inertia weight, and nonlinear inertia weight are applied to pulmonary nodules recognition. The experimental results show that the model training time of the proposed MKL-SVM-PSO algorithm is only 1/7 of the training time of the MKL-SVM grid search algorithm, achieving better recognition effect. Moreover, Euclidean norm of normalized error vector is proposed to measure the proximity between the average fitness curve and the optimal fitness curve after convergence. Through statistical analysis of the average of 20 times operation results with different inertial weights, it can be seen that the dynamic inertial weight is superior to the constant inertia weight in the MKL-SVM-PSO algorithm. In the dynamic inertial weight algorithm, the parameter optimization time of nonlinear inertia weight is shorter; the average fitness value after convergence is much closer to the optimal fitness value, which is better than the linear inertial weight. Besides, a better nonlinear inertial weight is verified.

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Support Vector Machine Optimization Based on Bacterial Foraging Algorithm and Applied in Fault Diagnosis

Advanced Materials Research ◽

10.4028/www.scientific.net/amr.216.153 ◽

2011 ◽

Vol 216 ◽

pp. 153-157

Author(s):

D.L. Yang ◽

Xue Jun Li ◽

K. Wang ◽

Ling Li Jiang

Keyword(s):

Support Vector Machine ◽

Fault Diagnosis ◽

Parameter Optimization ◽

Rolling Bearing ◽

Optimization Method ◽

Parameters Optimization ◽

Support Vector ◽

Bearing Fault Diagnosis ◽

Bacterial Foraging ◽

Bacterial Foraging Algorithm

The parameter optimization is the key to study of support vector machine (SVM). With strong global search capability of bacterial foraging algorithm(BFA), the optimization method—support vector machine parameters optimization based on bacterial foraging algorithm was proposed, which can achieve the dynamic optimization of the parametersCandγ,and overcomes the problem of inefficiency for selecting reasonable parameters according to the experience in the traditional fault diagnosis. Compared with other methods, the BFA is simpler and easier for programming, and the optimization SVM model become smaller. The rolling bearing fault diagnosis results show that bacterial foraging algorithm is suitable for support vector machine parameter optimization.

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A new technique for color based image segmentation using support vector machines

2014 International Conference on Medical Imaging, m-Health and Emerging Communication Systems (MedCom) ◽

10.1109/medcom.2014.7006001 ◽

2014 ◽

Author(s):

S. V. S. Prasad ◽

T. Satya Savithri ◽

Iyyanki V. Murali Krishna

Keyword(s):

Image Segmentation ◽

Support Vector Machines ◽

New Technique ◽

Support Vector ◽

Vector Machines ◽

A New Technique

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Crude Oil Production Predictive Model Based on Support Vector Machine and Parameters Optimization Algorithm

Advances in Intelligent and Soft Computing - Software Engineering and Knowledge Engineering: Theory and Practice ◽

10.1007/978-3-642-29455-6_88 ◽

2012 ◽

pp. 637-643 ◽

Cited By ~ 1

Author(s):

Zhou Xiao-lin ◽

Wu Hai-wei

Keyword(s):

Support Vector Machine ◽

Crude Oil ◽

Predictive Model ◽

Optimization Algorithm ◽

Oil Production ◽

Parameters Optimization ◽

Support Vector ◽

Model Based ◽

Crude Oil Production

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A Novel Hybrid Model for Solar Radiation Forecasting Using Support Vector Machine and Bee Colony Optimization Algorithm: Review and Case Study

Journal of Solar Energy Engineering ◽

10.1115/1.4047852 ◽

2020 ◽

Vol 143 (2) ◽

Author(s):

Mawloud Guermoui ◽

Kacem Gairaa ◽

John Boland ◽

Toufik Arrif

Keyword(s):

Support Vector Machine ◽

Solar Radiation ◽

Least Squares ◽

Correlation Coefficient ◽

Cuckoo Search ◽

Measured Data ◽

Parameters Optimization ◽

Support Vector ◽

Bee Colony ◽

Svm Model

Abstract This article proposes a new hybrid least squares-support vector machine and artificial bee colony algorithm (ABC-LS-SVM) for multi-hour ahead forecasting of global solar radiation (GHI) data. The framework performs on training the least squares-support vector machine (LS-SVM) model by means of the ABC algorithm using the measured data. ABC is developed for free parameters optimization for the LS-SVM model in a search space so as to boost the forecasting performance. The developed ABC-LS-SVM approach is verified on an hourly scale on a database of five years of measurements. The measured data were collected from 2013 to 2017 at the Applied Research Unit for Renewable Energy (URAER) in Ghardaia, south of Algeria. Several combinations of input data have been tested to model the desired output. Forecasting results of 12 h ahead GHI with the ABC-LS-SVM model led to the root-mean-square error (RMSE) equal to 116.22 Wh/m2, Correlation coefficient r = 94.3%. With the classical LS-SVM, the RMSE error equals to 117.73 Wh/m2 and correlation coefficient r = 92.42%; for cuckoo search algorithm combined with LS-SVM, the RMSE = 116.89 Wh/m2 and r = 93.78%. The results achieved reveal that the proposed hybridization scheme provides a more accurate performance compared to cuckoo search-LS-SVM and the stand-alone LS-SVM.

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