|Table of Contents|

Twin support vector regression based on grey wolf optimization algorithm

《南京理工大学学报》(自然科学版)[ISSN:1005-9830/CN:32-1397/N]

Issue:
2020年02期
Page:
202-208
Research Field:
Publishing date:

Info

Title:
Twin support vector regression based on grey wolf optimization algorithm
Author(s):
Shen GeliangGu BinjiePan Feng
Key Laboratory of Advanced Process Control for Light Industry,Ministry of Education, Jiangnan University,Wuxi 214122,China
Keywords:
twin support vector regression grey wolf optimization algorithm parameter optimization model selection
PACS:
TP181
DOI:
10.14177/j.cnki.32-1397n.2020.44.02.011
Abstract:
In order to solve the problem of parameter optimization for the twin support vector regression(TSVR),a twin support vector regression based on the grey wolf optimization(GWOTSVR)algorithm is proposed here. First,the root mean square error(RMSE)and the mean absolute error(MAE)are selected as the fitness function of the GWO. By resorting to the global optimization capability of the GWO algorithm,the positions of the wolves generated in the target range are used to represent different parameter values of TSVR. Finally,the optimal parameters are obtained by utilizing the position upgrade mechanism of the GWO within finite iterations. The experimental results show that the algorithm in this paper can find suitable parameters,has better prediction performance and costs much less optimization time than the state-of-the-art algorithms.

References:

[1] Jayadeva,Khemchandani R,Chandra S,et al. Twin support vector machines for pattern classification[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence(S0162-8828),2007,29(5):905-910.
[2]Yang Hongying,Wang Xiangyang,Niu Panpan,et al. Image denoising using nonsubsampled shearlet transform and twin support vector machines[J]. Neural Networks(S0893-6080),2014:152-165.
[3]Yan Rui,Ye Qiaolin,Zhang Linyan,et al. A feature selection method for projection twin support vector machine[J]. Neural Processing Letters(S1370-4621),2018,47(1):21-38.
[4]He Jun,Zheng Shihui. Intrusion detection model with twin support vector machines[J]. Journal of Shanghai Jiaotong University(Science)(S1007-1172),2014,19(4):448-454.
[5]张前进,王华东. 基于核典型相关分析和支持向量机的语音情感识别模型[J]. 南京理工大学学报,2017,41(2):191-197.
Zhang Qianjin,Wang Huadong. Voice emotion model based on kernel canonical correlation analysis and support vector machine[J]. Journal of Nanjing Univer-sity of Science and Technology,2017,41(2):191-197.
[6]Huang Huajuan,Wei Xiuxi,ZhouYongquan,et al. Twin support vector machines:asurvey[J]. Neurocomputing(S0925-2312),2018,300:34-43.
[7]Khemchandani R,Jayadeva,Chandra S,et al. Optimal kernel selection in twin support vector machines[J]. Optimization Letters(S1862-4472),2009,3(1):77-88.
[8]Ding Shifei,Yu Junzhao,Huang Huajuan,et al. Twin support vector machines based on particle swarm optimization[J]. Journal of Computers(S1991-1599),2013,8(9):2296-2303.
[9]Ding Shifei,Zhang Xiekai,Yu Junzhao,et al. Twin support vector machines based on fruit fly optimization algorithm[J]. International Journal of Machine Learning and Cybernetics(S1868-8071),2016,7(2):193-203.
[10]Mirjalili S,Mirjalili S M,Lewis A,et al. Grey wolf optimizer[J]. Advances in Engineering Software(S0965-9978),2014,69:46-61.
[11]Mirjalili S,Saremi S,Mirjalili S M,et al. Multi-objective grey wolf optimizer:A novel algorithm for multi-criterion optimization[J]. Expert Systems with Applications(S0957-4174),2016,47:106-119.
[12]Zhang Xinming,Kang Qiang,Cheng Jinfeng,et al. A novel hybrid algorithm based on biogeography-based optimization and grey wolf optimizer[J]. Applied Soft Computing(S1568-4946),2018,67:197-214.
[13]Joshi H,Arora S. Enhanced grey wolf optimization algorithm for global optimization[J]. Fundamenta Informaticae(S0169-2968),2017,153(3):235-264.
[14]Yang Zhi,Liu Cungen. A hybrid multi-objective gray wolf optimization algorithm for a fuzzy blocking flow shop scheduling problem[J]. Advances in Mechanical Engineering,2018,10(3):1-13.
[15]姜天华. 混合灰狼优化算法求解柔性作业车间调度问题[J]. 控制与决策,2018,33(3):503-508.
Jiang Tianhua. Mixed grey wolf optimization algorithm for solving flexible job shop scheduling problem[J]. Control and Decision,2018,33(3):503-508.
[16]Joshi H,Arora S. Enhanced grey wolf optimization algorithm for global optimization[J]. Fundamenta Informaticae,2017,153(3):235-264.
[17]Ding Shifei,Zhang Nan,Zhang Xiekai,et al. Twin support vector machine:Theory,algorithm and applications[J]. Neural Computing and Applications(S0941-0643),2017,28(11):3119-3130.
[18]张捍东,陶刘送. 粒子群优化BP算法在液压系统故障诊断中应用[J]. 系统仿真学报,2016,28(5):1186-1190.
Zhang Handong,Tao Liusong. Particle swarm optimization BP algorithm is applied in fault diagnosis of hydraulic system[J]. Journal of System Simulation,2016,28(5):1186-1190.
[19]陈飞跃,徐震浩,顾幸生. 基于离散布谷鸟搜索算法的带阻塞有差速混合流水车间调度[J]. 华东理工大学学报(自然科学版),2017,43(3):425-435.
Chen Feiyue,Xu Zhenhao,Gu Xingsheng. Mixed flow shop scheduling with blocking and differential based on discrete Cuckoo search algorithm[J]. Journal of East China University of Science and Technology,2017,43(3):425-435.
[20]刘皓,胡明昕,朱一亨,等. 基于遗传算法和支持向量回归的锂电池健康状态预测[J]. 南京理工大学学报,2018,42(3):329-334.
Liu hao,Hu Mingxin,Zhu Yiheng,et al. Prediction of health status of lithium battery based on genetic algorithm and support vector regression[J]. Journal of Nanjing University of Science and Technology,2018,42(3):329-334.
[21]汤可宗. 遗传算法与粒子群优化算法的改进及应用研究[D]. 南京:南京理工大学计算机科学与工程学院,2011.
[22]李树江,赵晨,苏锡辉,等. 基于遗传算法优化PID控制器参数的环境测试舱温湿度控制[J]. 南京理工大学学报,2017,41(4):511-518.
Li Shujiang,Zhao Chen,Su Xihui,et al. Temperature and humidity control of environmental test cabin based on genetic algorithm optimizing PID controller parameters[J]. Journal of Nanjing University of Science and Technology,2017,41(4):511-518.
[23]顾键萍,张明敏,王梅亮. 基于改进遗传算法的路径选择算法及仿真实现[J]. 系统仿真学报,2016,28(8):1805-1811.
Gu Jianping,Zhang Mingmin,Wang Meiliang. Path selection algorithm and simulation implementation based on improved genetic algorithm[J]. Journal of System Simulation,2016,28(8):1805-1811.

Memo

Memo:
-
Last Update: 2020-04-20