CAJ | 학술논문

为了解决支持向量机（SVM）参数优化的问题，提出一种改进的基于社会力模型群智能优化算法SFSO（Swarm Optimiza-tion algorithm based on Social Force Model）的SVM参数优化方法。SFSO通过期望力和排斥力使算法在全局搜索和局部搜索中能够较好的平衡，利用SFSO特有的搜索机制对SVM的惩罚因子和径向基函数进行优化，提高SVM的分类性能。通过对几个benchmark函数和常用的UCI数据集进行测试表明：改进后的SFSO算法不仅对于求解函数优化问题具有较强的鲁棒性和较高的求解精度，而且经改进SFSO算法优化后的SVM具有更快的收敛速度和更高的分类准确率。
위료해결지지향량궤（SVM）삼수우화적문제，제출일충개진적기우사회력모형군지능우화산법SFSO（Swarm Optimiza-tion algorithm based on Social Force Model）적SVM삼수우화방법。SFSO통과기망력화배척력사산법재전국수색화국부수색중능구교호적평형，이용SFSO특유적수색궤제대SVM적징벌인자화경향기함수진행우화，제고SVM적분류성능。통과대궤개benchmark함수화상용적UCI수거집진행측시표명：개진후적SFSO산법불부대우구해함수우화문제구유교강적로봉성화교고적구해정도，이차경개진SFSO산법우화후적SVM구유경쾌적수렴속도화경고적분류준학솔。
In order to solve the problem of SVM parameters optimisation,we proposed an improved SVM parameters optimisation method, which is based on swarm intelligence optimisation algorithm of social force model (SFSO).SFSO enables the algorithm to be able in well balance in both global and local searches through expectation force and exclusion force.We use special search mechanism of SFSO to optimise the penalty factor of SVM and the radial basis function,thus improve the classification performance of SVM.It is demonstrated through tests on several benchmark functions and common UCI datasets that the improved SFSO algorithm has stronger robustness and higher precision in solving function optimisation problems.Moreover,the SVM optimised by the improved SFSO has faster convergence speed and higher classification accuracy.