本文针对核函数参数选择的随意性影响支持向量机分类性能的问题,提出了一种基于离散编码的蚁群算法(C-CACO-DE)的SVM核函数优化模型.C-CACO-DE解决了连续函数优化的蚁群算法(C-ACO)求解之前必须进行预处理的问题,解决了基于网格划分策略的连续域蚁群算法(CACO-GT)在求解精度的缺点、最优解必在定义域内的等分割点问题.仿真结果验证了该方法的有效性,F1值达到了90％以上.%As to the problem that the arbitrariness selection of the kernel parameters affects the performance of SVM, this paper presents the SVM kernel function optimization model based on the discrete ACO algorithm (C-CACODE). C-CACO-DE solves the problem that the continuous ACO algorithm (C-ACO) must be pretreateds solves the precision shortcomings of the C-ACO based on a grid search strategy. The simulation result shows that the method is effective, and the Fl value reaches more than 92%.
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