基于遗传算法与粒子群算法的支持向量机参数选择

支持向量机(SVM)参数的选择是评价SVM性能的一个很重要的因素。SVM在解决小样本、非线性等问题中起到的效果是很好的。但是,该方法的缺点是在解决大样本数据集时消耗时间长,且易陷入局部最优解。为了降低SVM在这方面的不足,本文提出了遗传算法和粒子群算法相结合(PSOGA)对参数进行优化求解,并将该算法建立的模型应用到实验中。仿真结果说明该方法避免了陷入局部解,提高了收敛速度并缩短了优化时间,是一个很有效的方法。

Parameter Selection of Support Vector Machines Based on the Fusion of Genetic Algorithm and the Particle Swarm Optimization

Parameter selection is a very important factor to evaluating the performance of Support Vector Machine(SVM).SVM is helpful to solve the small sample,nonlinear problems,but is time-consuming in solving large sample data sets and easy to fall into local optimal solution.Therefore,in order to reduce this shortage,this paper proposes to combine the genetic algorithm and the particle swarm optimizations to optimize parameter selection.Besides,we apply the model algorithm to the artificial experiment.The result shows that our proposal is a very efficient method,it can avoid falling into the partial solution and improve the convergence rate to shorten the optimization time.