Support Vector Machine Optimized Using the Improved Fish Swarm Optimization Algorithm and Its Application to Face Recognition

Support vector machine (SVM) is always used for face recognition. However, kernel function selection is a key problem for SVM. This paper tries to make some contributions to this problem with focus on optimizing the parameters in the selected kernel function to improve the accuracy of classification and recognition of SVM. Firstly, an improved artificial fish swarm optimization algorithm (IAFSA) is proposed to optimize the parameters in SVM. In the improved version of artificial fish swarm optimization algorithm, the visual distance and the step size of artificial fish are adjusted adaptively. In the early stage of convergence, artificial fish are widely distributed, and the visual distance and step size take larger values to accelerate the convergence of the algorithm. In the later stage of convergence, artificial fish gathered gradually, and the visual distance and the step size were given small values to prevent oscillation. Then the optimized SVM is used to recognize face images. Simultaneously, in order to improve the accuracy rate of face recognition, an improved local binary pattern (ILBP) is proposed to extract features of face images. Numerical results show the advantage of our new algorithm over a range of existing algorithms.

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Face Recognition Based on SVM Optimized by the Improved Bacterial Foraging Optimization Algorithm

International Journal of Pattern Recognition and Artificial Intelligence ◽

10.1142/s021800141956007x ◽

2019 ◽

Vol 33 (07) ◽

pp. 1956007

Author(s):

Zhigao Zeng ◽

Lianghua Guan ◽

Wenqiu Zhu ◽

Jing Dong ◽

Jun Li

Keyword(s):

Face Recognition ◽

Kernel Function ◽

Optimization Algorithm ◽

Support Vector ◽

Step Size ◽

Bacterial Foraging Optimization ◽

Bacterial Foraging ◽

Function Selection ◽

Dispersal Probability ◽

Bacterial Foraging Optimization Algorithm

Support vector machine (SVM) is always used for face recognition. However, kernel function selection (kernel selection and its parameters selection) is a key problem for SVMs, and it is difficult. This paper tries to make some contributions to this problem with focus on optimizing the parameters in the selected kernel function. Bacterial foraging optimization algorithm, inspired by the social foraging behavior of Escherichia coli, has been widely accepted as a global optimization algorithm of current interest for distributed optimization and control. Therefore, we proposed to optimize the parameters in SVM by an improved bacterial foraging optimization algorithm (IBFOA). In the improved version of bacterial foraging optimization algorithm, a dynamical elimination-dispersal probability in the elimination-dispersal step and a dynamical step size in the chemotactic step are used to improve the performance of bacterial foraging optimization algorithm. Then the optimized SVM is used for face recognition. Simultaneously, an improved local binary pattern is proposed to extract features of face images in this paper to improve the accuracy rate of face recognition. Numerical results show the advantage of our algorithm over a range of existing algorithms.

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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

Structures ◽

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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Pressure Prediction of Coal Slurry Transportation Pipeline Based on Particle Swarm Optimization Kernel Function Extreme Learning Machine

Mathematical Problems in Engineering ◽

10.1155/2015/542182 ◽

2015 ◽

Vol 2015 ◽

pp. 1-7

Author(s):

Xue-cun Yang ◽

Xiao-ru Yan ◽

Chun-feng Song

Keyword(s):

Support Vector Machine ◽

Prediction Model ◽

Extreme Learning Machine ◽

Kernel Function ◽

Particle Swarm ◽

Support Vector ◽

Particle Swarm Algorithm ◽

Coal Slurry ◽

Swarm Optimization ◽

Learning Machine

For coal slurry pipeline blockage prediction problem, through the analysis of actual scene, it is determined that the pressure prediction from each measuring point is the premise of pipeline blockage prediction. Kernel function of support vector machine is introduced into extreme learning machine, the parameters are optimized by particle swarm algorithm, and blockage prediction method based on particle swarm optimization kernel function extreme learning machine (PSOKELM) is put forward. The actual test data from HuangLing coal gangue power plant are used for simulation experiments and compared with support vector machine prediction model optimized by particle swarm algorithm (PSOSVM) and kernel function extreme learning machine prediction model (KELM). The results prove that mean square error (MSE) for the prediction model based on PSOKELM is 0.0038 and the correlation coefficient is 0.9955, which is superior to prediction model based on PSOSVM in speed and accuracy and superior to KELM prediction model in accuracy.

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