改进人工鱼群算法在SVM参数优化中的应用

支持向量机的参数优化一直是一个重要的研究方向。参数的好坏很大程度上决定了支持向量机的分类精度和泛化能力。针对人工鱼群算法优化支持向量机参数时，容易在后期徘徊于最优解附近、难以逼近的问题，提出了人工鱼群加速算法，使用速度参数代替人工鱼步长，从而求得最优目标并得到SVM的最优参数组合。仿真实验结果表明：该算法收敛速度快，求解数值精度高，对初值的依赖程度低，在SVM参数优化中具有更好的性能、更高的分类准确率，是一个极其有效的参数优化方法。

An improved artificial fish swarm algorithm and its application for SVM parameter optimization

Parameter optimization of support vector machines has always been an important research direction. The quality of the parameters determines the classification accuracy and generalization ability of the support vector machine (SVM) to a great extent. Given that using the artificial fish swarm algorithm for optimizing the parameters of the SVM tends to hover near the optimal solution in the late stage, we put forward an improved artificial fish swarm algorithm. It uses velocity parameters instead of the artificial fish step and obtains optimal target and the optimal parameters of the SVM. Simulation results show that the proposed method has the advantages of fast convergence speed, high numerical accuracy and low dependence on initial values, as well as better performance and higher classification accuracy in SVM parameter optimization. It is an effective parameter optimization method.