Abstract: Particle swarm optimization used for optimization selection for hyper-parameter of support vector machine classifier was designed and implemented utilizing global searching property of particle swarm optimization algorithm while the algorithm was used to solve combinatorial optimization problems. The method of individuals coding and evaluating was described in brief. The experimental statistic results demonstrate that the algorithm is effective and efficacious. In the end, some in-depth works are listed on the base of above-mentioned study.

Key words: Support vector machine, Classifier, Parameter optimization, Particle swarm optimization

摘要： 利用粒子群算法在求解组合优化问题时具有的全局搜索特性，设计并实现了支持向量机分类器中超参数的优选粒子群算法，扼要地叙述了算法实现中个体编码和适应度函数，通过在国际标准数据集上的实验验证了算法的有效性和高效性，最后列举了一些在上述工作基础上可开展的深入性工作。

关键词: 支持向量机, 分类器, 参数优化, 粒子群算法