: . 计算机工程与结果表明改进后算法寻优精度更高，收敛速度更 快。并通过七个 UCI 数据集对基于 SVMAOA 的特征选择方法进行试验，评估平均分类准确率和所选特征个 数，结果表明该算法可有效降低特征维度,实现数据分类,具有一定的工程应用价值。 关键词：算术优化算法;支持向量机; 平衡池; 特征选择 文献标志码: A 中图分类号: doi：.1002--0331 Arithmetic Optimization Algorithm assisted by Support Vector Machine and its application
TIAN Lu, LIU Sheng+ School of Management, Shanghai University Of Engineering Science, Shanghai 200000, China Abstract：Aiming at the shortcomings of arithmetic optimization algorithm, such as poor population diversity and easily into the local optimal solution, an improved Arithmetic optimization algorithm assisted by support vector machine is proposed. First of all, the concept of balance pool in the balance optimizer algorithm is proposed. The balanced pool brings together descendant and average candidate solutions generated based on four mutational strat- egies in Success-History based Adaptive DE algorithm. The strategy is used to improve the diversity of population. Secondly, the Support Vector Machine algorithm was introduced to calculate the individual retention rate by inte- grating individual fitness value and distance between individuals. SVM is used to classify the candidate solutions in the balance pool, and only the dominant candidate solutions are reserved. Then, the dominant candidate solutions are sorted according to the retention rate, and the first N individuals are reserved to the next generation to build a new balance pool. Finally, the simulation results of SVMAOA and other optimization algorithms on the benchmark function show that the improved algorithm has higher searching accuracy and faster convergence speed. The feature selection method based on SVMAOA is
