[1]刘 胜,李高云,江 娜.SVM性能的免疫鱼群多目标优化研究[J].智能系统学报,2010,5(2):144-149.
　LIU Sheng,LI Gao-yun,JIANG Na.Multiobjective optimization of an immune fish swarm algorithm to improve support vector machine performance[J].CAAI Transactions on Intelligent Systems,2010,5(2):144-149.
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SVM性能的免疫鱼群多目标优化研究

《智能系统学报》[ISSN 1673-4785/CN 23-1538/TP] 卷: 5 期数: 2010年第2期 页码: 144-149 栏目: 学术论文—人工智能基础 出版日期: 2010-04-25
Title:
Multiobjective optimization of an immune fish swarm algorithm to improve support vector machine performance
文章编号:
1673-4785(2010)02-0144-06
作者:
刘 胜1，李高云1,2，江 娜1
1.哈尔滨工程大学 自动化学院，黑龙江 哈尔滨 150001；
2.中国船舶重工集团公司第七O七研究所九江分部，江西 九江 332007
Author(s):
LIU Sheng1， LI Gao-yun1,2， JIANG Na1
1.College of Automation,Harbin Engineering University, Harbin 150001,China;
2.Jiujiang Branch of 707 Research Institute,China Shipbuilding Industry Corporation, Jiujiang 332007,China
关键词:
支持向量机;  多目标优化;  Pareto近似解集;  免疫鱼群算法
Keywords:
support vector machines;  multiobjective optimization;  Pareto approximate solution set;  immune fish swarm algorithm
分类号:
TP183
文献标志码:
A
摘要:
SVM算法的训练精度和训练速度是衡量其性能的2个重要指标.以这2个指标为目标变量建立SVM性能多目标优化问题的数学模型，采用直接对多个目标同时进行优化的方法求得问题的Pareto近似解集.在求解Pareto近似解集时，将免疫原理中的浓度机制引入基本鱼群算法中，形成一种改进的免疫鱼群算法.以非线性动态系统仿真数据为样本数据，并采用改进的免疫鱼群算法求解SVM性能多目标优化问题的Pareto近似解集.仿真结果表明，在解决多目标优化问题时，免疫鱼群算法相对于基本鱼群算法和遗传算法具有更好的优越性.
Abstract:
Accuracy and speed when training a support vector machine (SVM) algorithm provides critical measurements of the algorithm’s performance. To optimize performance, a mathematical model of multiobjective optimization with improvements in these two parameters as goals was established. A Pareto approximate solution set was obtained by optimizing multiple targets simultaneously. In the process of finding the Pareto approximate solution set, a concentration mechanism from an immune algorithm was introduced into the basic artificial fish swarm algorithm. This produced significant improvements and resulted in the proposed immune fish swarm algorithm. Taking the nonlinear dynamic system simulation data as sample data, a Pareto approximate solution set of multiobjective optimization of SVM performance was obtained using the improved algorithm. Simulation results showed that, for solving multiobjective optimization, the immune fish swarm algorithm was superior to both a basic artificial fish swarm algorithm and to genetic algorithms. 
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备注/Memo

收稿日期：2009-03-24.
基金项目：黑龙江省自然科学基金资助项目（A200419）.
通信作者：刘 胜.E-mail:liu.sch@163.com.
作者简介：
刘 胜，男，1957年生，教授，博士生导师，黑龙江省教学名师，黑龙江省重点一级学科“控制科学与工程”学科负责人.兼任教育部工程研究中心“船舶控制工程研究中心”主任，中国造船学会仪器仪表学术委员会副主任，黑龙江省自动化学会副理事长.主要研究方向为智能控制、鲁棒控制、船舶航行与姿态控制.目前承担国家“973”计划项目，国防基础研究基金项目、国防预研项目4项，省部级项目6项.曾获黑龙江省优秀教学工作者、中国船舶工业总公司优秀青年科技工作者、获省部级科学技术奖7项、获省教学成果奖一等奖2项，二等奖2项、获省教育科学研究成果一等奖4项、获省部级自然科学技术学术成果奖8项.发表学术论文150余篇，被SCI、EI、ISTP检索70余篇，出版学术著作3部.
李高云，男，1981年生，博士研究生，主要研究方向为智能控制、故障诊断与容错控制、船舶航行与姿态控制.参与科研项目3项，获黑龙江省科学技术二等奖1项，黑龙江省高校科学技术一等奖1项，发表学术论文近10篇，被EI、ISTP检索4篇.
江 娜，女，1981年生，博士研究生，主要研究方向为故障诊断与故障预报.参与科研项目2项，发表学术论文近10篇，被EI、ISTP检索5篇.

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