果蝇耦合均匀设计算法及其优化SVM参数

支持向量机的参数选择仍无系统的理论指导,且参数优化一直是支持向量机的一个重要研究方向。传统果蝇优化算法能够较快寻得一个较优的近似最优解,随后在该解的邻域继续迭代而造成寻优时间的严重增加。针对该问题构建了果蝇优化算法与均匀设计相耦合的果蝇耦合均匀设计算法,并将其用于支持向量机的参数优化。该算法首先利用果蝇优化算法并行寻优以快速得到所研究问题的一个较优近似最优解,然后跳转执行均匀设计的局部寻优,以获得一个更优的近似最优解。数值实验结果表明:该算法具有较快的寻优效率和较高的分类精度,验证了其在支持向量机参数优化中的有效性和可行性。

A fruit fly coupled uniform design algorithm for optimizing SVM parameters

Parameter optimization of support vector machines (SVMs) is an important research direction, however, there is a lack of systematic theoretical guidance for SVM parameter selection. The fruit fly optimization algorithm (FOA) can find a better approximate optimal solution quickly and then iterates in the solution neighborhood, but the search time is prolonged. We therefore propose a fruit fly coupled uniform design algorithm (FFUD), which couples the FOA and the uniform design method (UD) to solve the problem of SVM parameter optimization. The proposed algorithm can quickly gain an approximate optimal solution to the problem via the parallel optimization performance of the FOA, and then jumps out of the FOA and continues to perform the local optimization searching through the UD method to obtain a much better approximate optimal solution. Experimental results show that the proposed algorithm has better searching efficiency and higher classification accuracy, and it is effective and feasible in the SVM parameter optimization.