[1]王振武,孙佳骏,尹成峰.改进粒子群算法优化的支持向量机及其应用[J].哈尔滨工程大学学报,2016,37(12):1728-1733.[doi:10.11990/jheu.201601005]
 WANG Zhenwu,SUN Jiajun,YIN Chengfeng.A support vector machine based on an improved particle swarm optimization algorithm and its application[J].hebgcdxxb,2016,37(12):1728-1733.[doi:10.11990/jheu.201601005]
点击复制

改进粒子群算法优化的支持向量机及其应用(/HTML)
分享到:

《哈尔滨工程大学学报》[ISSN:1006-6977/CN:61-1281/TN]

卷:
37
期数:
2016年12期
页码:
1728-1733
栏目:
出版日期:
2016-12-25

文章信息/Info

Title:
A support vector machine based on an improved particle swarm optimization algorithm and its application
作者:
王振武 孙佳骏 尹成峰
中国矿业大学 机电与信息工程学院, 北京 100083
Author(s):
WANG Zhenwu SUN Jiajun YIN Chengfeng
School of Mechanical Electronic & Information Engineering, China University of Mining and Technology, Beijing 100083, China
关键词:
粒子群优化算法混沌序列支持向量机遥感影像
分类号:
TP301.6
DOI:
10.11990/jheu.201601005
文献标志码:
A
摘要:
传统粒子群优化(particle swarm optimization,PSO)算法主要包含两方面问题,即易陷入局部极小和后期震荡严重,为此引入混沌序列来初始化粒子群的位置,并在简化的粒子群数学模型上从两个方面对其进行了改进。本文利用改进的PSO算法对支持向量机(support vector machine,SVM)的参数进行优化,仿真实验结果表明:与SVM、PSO-SVM以及遗传算法(genetic algorithm,GA)优化的SVM(GA-SVM)相比,改进PSO优化的SVM(IPSO-SVM)算法具有较高的分类准确率,并且与PSO-SVM算法相比,准确率提高了3%~5%,与PSO-SVM算法以及GA-SVM算法相比,IPSO-SVM的训练和泛化速度都明显提高。本文将IPSO-SVM算法应用到遥感影像的分类中,分类结果表明,与PSO-SVM算法相比,IPSO-SVM算法具有更好的分类结果。

参考文献/References:

[1] 张鑫源, 胡晓敏, 林盈. 遗传算法和粒子群优化算法的性能对比分析[J]. 计算机科学与探索, 2014, 8(1):91-102. ZHANG Xinyuan, HU Xiaomin, LIN Ying. Comparisons of genetic algorithm and particle swarm optimization[J]. Journal of frontiers of computer science and technology, 2014, 8(1):91-102.
[2] SHI Yuhui, EBERHART R C. A modified particle swarm optimizer[C]//Proceedings of The 1998 IEEE International Conference on Evolutionary Computation. Anchorage, AK, USA:IEEE, 1998:69-73.
[3] SHI Yuhui, EBERHART R C. Fuzzy adaptive particle swarm optimization[C]//Proceedings of the 2001 Congress on Evolutionary Computation. Seoul, South Korea:IEEE, 2001:101-106.
[4] 王俊伟, 汪定伟. 粒子群算法中惯性权重的实验与分析[J]. 系统工程学报, 2005, 20(2):194-198. WANG Junwei, WANG Dingwei. Experiments and analysis on inertia weight in particle swarm optimization[J]. Journal of systems engineering, 2005, 20(2):194-198.
[5] SUGANTHAN P N. Particle swarm optimizer with neighborhood operator[C]//Proceedings of the Congress on Evolutionary Computation. Washington, DC, USA, 1999:1958-1962.
[6] RATNAWEERA A, HALGAMUGE S K, WATSON H C. Self-organizing hierarchical particle swarm optimizer with time-varying acceleration coefficients[J]. IEEE transactions on evolutionary computation, 2004, 8(3):240-255.
[7] 于立君, 陈佳, 刘繁明, 等. 改进粒子群算法的PID神经网络解耦控制[J]. 智能系统学报, 2015, 10(5):699-704. YU Lijun, CHEN Jia, LIU Fanming, et al. An improved particle swarm optimization for PID neural network decoupling control[J]. CAAI transactions on intelligent systems, 2015, 10(5):699-704.
[8] KENNEDY J, MENDES R. Population structure and particle swarm performance[C]//Proceedings of Congress on Evolutionary Computation. Honolulu, HI, USA:IEEE, 2002:1671-1676.
[9] 潘峰, 陈杰, 甘明刚, 等. 粒子群优化算法模型分析[J]. 自动化学报, 2006, 32(3):368-377. PAN Feng, CHEN Jie, GAN Minggang, et al. Model analysis of particle swarm optimizer[J]. Acta automatica sinica, 2006, 32(3):368-377.
[10] HU Xiaohui, EBERHART R C. Multiobjective optimization using dynamic neighborhood particle swarm optimization[C]//Proceedings of the 2002 IEEE International Congress on Evolutionary Computation. Honolulu, HI, USA:IEEE, 2002:1677-1681.
[11] VAN DEN BERGH F, ENGELBRECHT A P. Training product unit networks using cooperative particle swarm optimisers[C]//Proceedings of IEEE International Joint Conference on Neural Networks. USA:IEEE, 2001:1-9.
[12] ANGELINE P J. Evolutionary optimization versus particle swarm optimization:philosophy and performance differences[M]//PORTO V W, SARAVANAN N, WAAGEN D, et al. Evolutionary Programming VⅡ. Berlin Heidelberg:Springer, 1998:601-610.
[13] 高鹰, 谢胜利. 基于模拟退火的粒子群优化算法[J]. 计算机工程与应用, 2004(1):47-50. GAO Ying, XIE Shengli. Particle swarm optimization algorithms based on simulated annealing[J]. Computer engineering and applications, 2004(1):47-50.
[14] 肖燕彩, 陈秀海, 朱衡君. 遗传支持向量机在电力变压器故障诊断中的应用[J]. 上海交通大学学报, 2007, 41(11):1878-1881, 1886. XIAO Yancai, CHEN Xiuhai, ZHU Hengjun. The application of genetic support vector machine in power transformer fault diagnosis[J]. Journal of Shanghai Jiaotong University, 2007, 41(11):1878-1881, 1886.
[15] 胡旺, 李志蜀. 一种更简化而高效的粒子群优化算法[J]. 软件学报, 2007, 18(4):861-868. HU Wang, LI Zhishu. A simpler and more effective particle swarm optimization algorithm[J]. Journal of software, 2007, 18(4):861-868.
[16] 郭广寒, 王志刚. 一种改进的粒子群算法[J]. 哈尔滨理工大学学报, 2010, 15(2):31-34. GUO Guanghan, WANG Zhigang. A modified particle swarm optimization[J]. Journal of Harbin university of science and technology, 2010, 15(2):31-34.

相似文献/References:

[1]秦洪德,石丽丽.一种新型的被动启发式粒子群优化算法[J].哈尔滨工程大学学报,2010,(10):0.
 QIN Hong-De,SHI Li-Li.A new passive heuristic particle swarm optimization algorithm[J].hebgcdxxb,2010,(12):0.
[2]谢业海,林孝工,赵大威,等.基于粒子群优化算法的海浪方向谱估计[J].哈尔滨工程大学学报,2012,(12):1504.[doi:10.3969/j.issn.1006-7043.201202018]
 XIE Yehai,LIN Xiaogong,ZHAO Dawei,et al.Directional wave spectrum estimation based on particle swarm optimization algorithm[J].hebgcdxxb,2012,(12):1504.[doi:10.3969/j.issn.1006-7043.201202018]
[3]严浙平,邓超,赵玉飞,等.无人水下航行器近海底空间路径规划方法[J].哈尔滨工程大学学报,2014,(03):307.[doi:10.3969/j.issn.10067043.201303043]
 YAN Zheping,DENG Chao,ZHAO Yufei,et al.Path planning method for UUV near sea bottom[J].hebgcdxxb,2014,(12):307.[doi:10.3969/j.issn.10067043.201303043]
[4]王皓,高立群,欧阳海滨.多种群随机差分粒子群优化算法及其应用[J].哈尔滨工程大学学报,2017,38(04):652.[doi:10.11990/jheu.201512017]
 WANG Hao,GAO Liqun,OUYANG Haibin.Multi-population random differential particle swarm optimization and its application[J].hebgcdxxb,2017,38(12):652.[doi:10.11990/jheu.201512017]
[5]许爱东,李昊飞,程乐峰,等.PCA-PSO-ELM配网供电可靠性预测模型[J].哈尔滨工程大学学报,2018,39(06):1116.[doi:10.11990/jheu.201611088]
 XU Aidong,LI Haofei,CHENG Lefeng,et al.Prediction model for power supply reliability of distribution network using PCA-PSO-ELM[J].hebgcdxxb,2018,39(12):1116.[doi:10.11990/jheu.201611088]

备注/Memo

备注/Memo:
收稿日期:2016-01-05
基金项目:国家自然科学基金项目(61302157);国家高技术研究发展计划重大专项(2012AA12A308);核设施退役及放射性废物治理科研项目(FZ1402-08);北京市高等学校青年英才计划(YETP0939);中央高校基本科研业务费项目(2009QJ-11).
作者简介:王振武(1978-),男,副教授.
通讯作者:王振武,E-mail:wangzhenwu@126.com.
更新日期/Last Update: 2017-01-06