文档介绍：: .
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