Application of improved ant colony algorithm in support vector machine

REN Yu-yan1; BAO Jie2; WANG Hong-rui3

doi:10.13873/j.1000-97872011.08.030

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Application of improved ant colony algorithm in support vector machine

更新时间：2025-09-11

- Application of improved ant colony algorithm in support vector machine
- Vol. 30, Issue 8, Pages: 144-146(2011)
- 作者机构：
  
  1. 燕山大学里仁学院
  2. 国家发展和改革委员会国家投资项目评审中心
  3. 河北大学
- 作者简介：
- 基金信息：
- DOI：10.13873/j.1000-97872011.08.030
  CLC： TP18
- Published：2011
- 稿件说明：
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[1]任玉艳,鲍洁,王洪瑞.改进蚁群算法在支持向量机中的应用[J].传感器与微系统,2011,30(08):144-146.

REN Yu-yan1, BAO Jie2, WANG Hong-rui3. Application of improved ant colony algorithm in support vector machine[J]. 2011, 30(8): 144-146.
[1]任玉艳,鲍洁,王洪瑞.改进蚁群算法在支持向量机中的应用[J].传感器与微系统,2011,30(08):144-146. DOI： 10.13873/j.1000-97872011.08.030.

REN Yu-yan1, BAO Jie2, WANG Hong-rui3. Application of improved ant colony algorithm in support vector machine[J]. 2011, 30(8): 144-146. DOI： 10.13873/j.1000-97872011.08.030.

摘要

提出一种改进的蚁群算法（ACA）来优化支持向量机（SVM）训练参数。该改进算法建立于每只蚂蚁只根据参数β在其前次迭代的最优解附近搜索

可快速减少搜索范围。参数β的提出可以保证蚁群快速地达到最优解。仿真结果表明:使用该方法优化SVM参数可有效避免陷入局部极值

提高收敛速度。

Abstract

A new method is proposed to optimize SVM based on improved ant colony algorithm（ACA）.The proposed algorithm is based on each ant searches only around the best solution of the previous iteration with β

which can reduce search space fast.β is proposed for improving ACO’s solution performance to reach global optimum fairly quickly.Simulation results indicate that optimizing SVM with this method can avoid the local extremes effectively and improve the convergence speed.

关键词

Keywords

references

基于蚁群算法的SVM模型选择研究 [J]. 倪丽萍,倪志伟,李锋刚,潘永刚. 计算机技术与发展 . 2007(09)

基于GA参数优化的在线学习SVM算法及其在气象预测中的应用 [D]. 吴春连. 吉林大学 2007

数据挖掘中的新方法[M]. 科学出版社 , 邓乃扬,田英杰著, 2004

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