SVM性能的免疫鱼群多目标优化研究

Multi-objective optimization of an immune fish swarm algorithm to improve support vector machine performance

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.

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 non-linear 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 multl-objective optimization, the immune fish swarm algorithm was superior to both a basic artificial fish swarm algorithm and to genetic algorithms.