摘要
针对原始萤火虫算法(Firefly Algorithm,FA)易陷入局部最优、求解精度低,双支持向量机(Twin Support Vector Machine,TWSVM)参数选择困难的问题,提出基于改进萤火虫算法(DEFA)的双支持向量机模型(DEFA-TWSVM).首先,对原始萤火虫算法进行改进,得到DEFA算法:在萤火虫位置更新公式中结合动态惯性权重,自适应地调整步长控制因子来快速搜索全局和局部最优解,对每次移动后的萤火虫群融入差分进化算法(Differential Evolution,DE)策略,保证种群迭代多样性,通过基准测试函数的仿真结果表明改进后的算法全局寻优能力强,不易陷入局部最优.其次,利用DEFA算法优化TWSVM的参数.最后,在UCI数据集进行测试,得到DEFA-TWSVM和其他模型的分类准确率.通过比较发现:DEFA算法可以在训练过程中自动确定TWSVM参数,解决了TWSVM参数选择盲目的问题,平均分类准确率相较其他模型提高了2到5个百分点.
In view of the problems of the original Firefly Algorithm(FA),which is easy to fall into local optimization,low solution accuracy and difficult parameter selection of twin support vector machine(TWSVM),a dual support vector machine model(DEFA-TWSVM)based on improved firefly algorithm(DEFA)is proposed.Firstly,the original firefly algorithm is improved to obtain DEFA algorithm.In the firefly position update formula,dynamic inertia weight was combined,and the step size control factor was adjusted adaptively to quickly search for global and local optimal solutions.Differential Evolution(DE)strategy was applied to the firefly population after each movement to ensure the iterative diversity of the population.The simulation results of benchmark test function show that the improved algorithm has strong global optimization ability and is not easy to fall into local optimization.Secondly,DEFA algorithm was used to optimize the parameters of TWSVM.Finally,the classification accuracy of DEFA-TWSVM and other models is obtained by testing in UCI data set.By comparison,it is found that DEFA algorithm can automatically determine TWSVM parameters in the training process,which solves the problem of blind TWSVM parameter selection,and the average classification accuracy is increased by 2 to 5 percentage points compared with other models.
作者
顾佳鑫
贺兴时
杨新社
GU Jiaxin;HE Xingshi;YANG Xinshe(College of Science,Xi′an polytechnic University,Xi′an 710600,China;College of Science and Technology,Middlesex University,London NW44BT 701,UK)
出处
《微电子学与计算机》
2022年第11期11-18,共8页
Microelectronics & Computer
基金
国家自然科学基金(12101477)
陕西省自然科学基础研究计划(2020JQ-831)。
关键词
双支持向量机
参数选择
萤火虫算法
动态惯性权重
差分进化算法
自适应步长
twin support vector machine
parameter selection
firefly algorithm
dynamic inertia weight
differential evolution algorithm
adaptive step size
