基于果蝇优化算法的支持向量机参数优化在船舶操纵预报中的应用

FOA-Based SVM Parameter Optimization and Its Application in Ship Manoeuvring Prediction

应用果蝇优化算法对船舶操纵运动预报的ε-支持向量机(ε-SVM)的参数进行优化,建立船舶操纵运动预报黑箱模型,并用所建立的模型对Z形试验进行预报.通过预报结果与仿真试验结果对比,验证了该优化算法的有效性.研究结果表明,所设计的参数寻优方法具有算法设置简单、调整参数少以及不易陷入局部极小值等优点.

The fruit fly optimization algorithm （FOA） was applied to optimize the parameters of ε-support vector machine （ε-SVM）. By using theε-SVM, a black-box model for predicting the ship manoeuvring mo- tion was established and applied to predict the zig zag tests. The validity of the proposed algorithm was verified by comparing the predicted results with the simulation data. It is shown that the proposed algo- rithm is simple, convenient and effective.