基于IFOA算法的SVM参数优化及其应用

为了提高果蝇优化算法的种群多样性和果蝇搜索的遍历性,有效提高算法的收敛精度,提出一种改进的果蝇算法(Improving fruit fly optimization algorithm, IFOA),仿真实验表明, IFOA算法保持了搜索过程中的搜索尺度变化,平衡了算法的全局与局部搜索能力。在此基础上,为了改善支持向量机模型参数选择的随机性和盲目性,提高模式分类的准确率,提出并建立了一种IFOA-SVM模式分类模型。该方法将IFOA算法引入到支持向量机模型参数优化中,建立性能最优的支持向量机模型。应用该模型对UCI机器学习数据库中wine数据集进行模式分类研究,通过算法对比分析,结果表明：提出的改进果蝇优化算法在收敛速度和寻优效率上均有一定的提高,依此而建立的IFOA-SVM模式分类模型具有较准确的分类准确率,从而也验证了该模式分类方法在wine数据集分类应用中的有效性。

SVM Parameters Optimizing Based on Improved Diminishing Step Fruit Fly Optimization Algorithm and Its Application

In order to advance population diversity and ergodic property for fruit fly optimization algorithm, enhance its convergence precision effectively, an algorithm named improving fruit fly optimization algorithm (abbreviated as IFOA) is proposed in this paper. The simulation experiment shows that this algorithm maintains changing in scale and balances the overall and local searching capability. In order to improve the randomness and blindness in choosing SVM model parameter artificially, enhancing accuracy for pattern classification at the same time. A method using IFOA in the field of SVM model parameter optimization is put forward and established. In this method, IFOA is applied into penalty factor and kernel function parameters optimization for SVM, with which the optimal model parameters will be chosen and the optimal SVM model can be established. This model is used in pattern classification research for wine data in UCI machine study database, different algorithms were used for comparison, the result shows that, the improved FOA has a fast speed in convergence and high efficiency in optimization, a better classification accuracy could be reached for IFOA-SVM model. The effectiveness for IFOA in wine database classification is proved thereby.