SVM参数优化的AFMC算法

支持向量机的参数选择仍未有系统的理论指导,其优化选择一直是支持向量机的一个重要研究方向。考虑到人工鱼群算法优化支持向量机参数往往易陷入最优参数组合微小邻域的问题,构造了用于支持向量机参数优化的AFMC算法。该算法前期利用鱼群算法较好的并行寻优性能,能快速寻得问题的近似最优解,而后利用MonteCarlo法进行局部寻优,以实现快速、有效地获取强近优解。数值实验结果表明,该算法具有较好的分类性能和较快的寻优速度,验证了在支持向量机参数寻优中的有效性和可行性。

AFMC algorithm for SVM parameter optimization

Support vector machine (SVM)parameter optimization selection is an important research direction, but there is still no systematic theory to guide the selection of the SVM parameters.Since optimizing the SVM parameters by the artificial fish swarm algorithm tends to fall into the small neighborhood of the approximate optimal solution,we design the AFMC algorithm for the SVM parameter optimization.At the early stage,we use the better parallel optimization performance of the fish swarm algorithm to quickly gain the approximate optimal solution.Then we use the MonteCarlo algorithm for local searching to achieve a quick and effective strong approximate optimal solution.The numerical experiments show that the proposed algorithm has better classification performance and faster searching speed,and it is effective and feasible in the SVM parameter optimization.