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 strategies in success-history based adaptive DE algorithm. The strategy is used to improve the diversity of population. Secondly, the support vector machine algorithm is introduced to calculate the individual retention rate by integrating 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 tested in seven UCI data sets. By evaluating the average classification accuracy and the number of selected features, it is concluded that the algorithm can effectively reduce the feature dimension and achieve data classification, which has certain engineering application value.

Key words: arithmetic optimization algorithm, support vector machine, balance pool, feature selection

摘要： 针对算术优化算法（arithmetic optimization algorithm，AOA）种群多样性较差、易陷入局部最优解等问题，提出支持向量机辅助演化的算术优化算法（arithmetic optimization algorithm assisted by support vector machine，SVMAOA）。引入平衡优化器算法中的平衡池概念，池内汇聚了基于成功历史自适应差分算法中四种突变策略生成的子代和平均候选解，以提高种群的多样性；引入支持向量机算法，依据适应度值和个体间距离计算得出的留存率将平衡池中候选解转换为训练集，并对平衡池中候选解进行分类，保留优势候选解；根据留存率对优势候选解排序，保留前[N]个个体用以构建新的平衡池；通过将SVMAOA与其他优化算法在基准函数上进行仿真实验，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实验，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