关键词: 算术优化算法, 支持向量机, 平衡池, 特征选择

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