基于混沌更新策略的蜂群算法在SVM参数优化中的应用

针对支持向量机的参数寻优缺乏数学理论指导,传统人工蜂群算法易陷入长期停滞的不足,而混沌搜索算法具有很好的随机性和遍历性,提出了基于混沌更新策略人工蜂群支持向量机参数选择模型(IABC-SVM)。该模型利用混沌搜索对侦察蜂搜索方式进行改进,有效提高蜂群算法搜索效率。以UCI标准数据库中的数据进行数值实验,采用ACO-SVM、PSO-SVM、ABC-SVM作为对比模型,实验表明了IABC在SVM参数优化中的可行性和有效性,具有较高的预测准确率和较好的算法稳定性。

Application of artificial bee colony based on chaos update strategy in support vector machine parameter optimization

There is little mathematical theory guidance for the parameter optimization of support vector machines (SVMs), and the traditional artificial bee colony (ABC) is easy to fall into the long term stagnation. Since the chaotic search algorithm has good randomicity and ergodicity, we propose a parameter optimization model based on the ABC algorithm with the chaos update strategy (IABC SVM) to solve this problem. This model uses the chaotic search algorithm to improve the searching way of reconnaissance peak, and improve the ABC’s searching efficiency. We evaluate the proposed algorithm on the public data sets from University of California Irvine (UCI), and compare it with the ACO SVM, PSO SVM, and ABC SVM models. Experimental results show that the IABC algorithm is feasible and effective for optimizing SVM parameters, and has higher prediction accuracy and better stability.