Particle Swarm Optimization for Parameter Optimization of Support Vector Machine Model

2009 ◽  
Author(s):  
Ning Lu ◽  
Jianzhong Zhou ◽  
Yaoyao He ◽  
Ying Liu
Keyword(s):  
Support Vector Machine ◽  
Particle Swarm Optimization ◽  
Parameter Optimization ◽  
Support Vector Machine Model ◽  
Particle Swarm ◽  
Support Vector ◽  
Machine Model ◽  
Swarm Optimization

scholarly journals A novel deformation forecasting method utilizing comprehensive observation data

2018 ◽  
Vol 10 (9) ◽  
pp. 168781401879633 ◽  
Author(s):  
Sunwen Du ◽  
Yao Li
Keyword(s):  
Support Vector Machine ◽  
Particle Swarm Optimization ◽  
Mean Square Error ◽  
Support Vector Machine Model ◽  
Particle Swarm ◽  
Support Vector ◽  
Mean Square ◽  
Machine Model ◽  
Swarm Optimization ◽  
Forecasting Method

Mine disasters often happen unpredictably and it is necessary to find an effective deformation forecasting method. A model between deformation data and the factors data that affected deformation is built in this study. The factors contain hydro-geological factors and meteorological factors. Their relationship presents a complex nonlinear relationship which cannot be solved by ordinary methods such as multiple linear regression. With the development of artificial intelligence algorithm, Artificial Neural Network, Support Vector Machine, and Extreme Learning Machine come to the fore. Support Vector Machine could establish a deformation prediction model perfectly in the condition that there is less input data and output data. The deformation forecast model that uses quantum-behaved particle swarm optimization algorithm is selected to optimize the Support Vector Machine. The optimum configuration of Support Vector Machine model needs to be determined by two parameters, that is, normalized mean square error and correlation coefficient (R). Quantum-behaved particle swarm optimization could determine the optimal parameter values by minimizing normalized mean square error. It investigates the application effect of the proposed quantum-behaved particle swarm optimization–Support Vector Machine model by comparing their performances of popular forecasting models, such as Support Vector Machine, GA-Support Vector Machine, and particle swarm optimization–Support Vector Machine models. The results show that the proposed model has better performances in mine slope surface deformation and is superior to its rivals.

scholarly journals A Hybrid Global Local Adaptive Particle Swarm Optimization-Based Support Vector Machine Model for Human Facial Authentication

2020 ◽  
Vol 55 (1) ◽  
Author(s):  
Salam Allawi Hussein ◽  
Alyaa Abduljawad Mahmood ◽  
Mohammed Iqbal Dohan
Keyword(s):  
Support Vector Machine ◽  
Particle Swarm Optimization ◽  
Support Vector Machine Model ◽  
Particle Swarm ◽  
Support Vector ◽  
Machine Model ◽  
Swarm Optimization ◽  
Adaptive Particle Swarm Optimization ◽  
Inertia Weight ◽  
Proposed Model

A new facial authentication model called global local adaptive particle swarm optimization-based support vector machine, was proposed in this paper. The proposed model aimed to solve the problem of finding the preeminent parameters of support vector machine in order to come out with a powerful human facial authentication technique. The conventional particle swarm optimization algorithm was utilized with support vector machine to explore the preeminent parameters of support vector machine. However, the particle swarm optimization support vector machine model has some limitations in selecting the velocity coefficient and inertia weight. One of the best approaches, which is used to solve the velocity coefficient problem, is adaptive acceleration particle swarm optimization. Also, the global-local best inertia weight is used efficiently for selecting the inertia weight. Therefore, the global local adaptive particle swarm optimization-based support vector machine model was proposed based on combining adaptive acceleration particle swarm optimization, global-local best inertia weight, and support vector machine. The proposed model used the principal component analysis approach for feature extraction, as well as global local adaptive particle swarm optimization for finding the preeminent parameters of support vector machine. In the experiments, two datasets (YALEB and CASIAV5) were used, and the suggested model was compared with particle swarm optimization support vector machine and adaptive acceleration particle swarm optimization support vector machine methods. The comparison was via accuracy, computational time, and optimal parameters of support vector machine. Our model can be used for security applications and apply for human facial authentication.

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