[1]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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Multiobjective optimization of an immune fish swarm algorithm to improve support vector machine performance

CAAI Transactions on Intelligent Systems[ISSN 1673-4785/CN 23-1538/TP] Volume: 5 Number of periods: 2010 2 Page number: 144-149 Column: 学术论文—人工智能基础 Public date: 2010-04-25
Title:
Multiobjective optimization of an immune fish swarm algorithm to improve support vector machine performance
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
Keywords:
support vector machines;  multiobjective optimization;  Pareto approximate solution set;  immune fish swarm algorithm
CLC:
TP183
DOI:
-
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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