The Particle Swarm Optimization And Research And Application Of The Support Vector Machine

Intelligent optimization algorithm is a kind of modern optimization algorithm which is simulated the rule of nature and society. It based on mathematics and was realized by the establishment and composition of mathematical model and modern computer tools. The particle swarm optimization and the support vector machine are two important algorithm of intelligent optimization algorithm.The particle swarm optimization is a kind of optimization of swarm intelligence algorithm that was proposed by Eberhart and Kennedy. PSO was found through the feature of predatory behavior of birds and was used to solve optimization problem. For the particle optimization, each particle represents a potential solution result, each particle corresponds to a fitness value through an objective function determined the speed of particles determines in the direction and distance of particle. Velocity has dynamic adjustment with itself and the mobile experience of the other particles, so the particle achieves optimization in solution space.The support vector machine was introduced by Vapnik and co-workers in the mid-1990s are a new learning algorithm based on the statistical learning theory. SVM is mainly used to solve the problem of pattern recognition and regression. SVM was realized through practical problems were mapped to high-dimensional feature space by nonlinear mapping ,structure linear discriminant function in high dimensional space realized non-linear discriminant function of the original space, then the complexity of SVM has nothing to do with the sample dimension and solved cleverly the dimension problem.In the article, PSO and SVM were also studied. Combined the complex local searching, the Particle Swarm optimization with Complex local searching(PSOWC) was posed in this paper. The particle swarm algorithm (PSO) was used to solving the mean of function by adjusting the model of goal extremum and local extremum ofIt constructed hybrid kernel function via different features of different kernel functions. Then it proposes SVM based on hybrid kernel function and apply the model to test the heart disease dataset. The support vector regression machine was applied on Shanghai Stock Index Forecast PSO algorithm is used to optimize the penalty parameter C and the parameter of kernel function it achieved good results...