基于改进麻雀搜索优化支持向量机的渔船捕捞方式识别  

Fishing Type Identification of Marine Fishing Vessels Based on Support Vector Machine Optimized by Improved Sparrow Search Algorithm

作　　者：单晓英 任迎春 SHAN Xiao-ying;REN Ying-chun(Pinghu Normal College,Jiaxing University,Pinghu,Zhejiang 314200,China;College of Data Science,Jiaxing University,Jiaxing,Zhejiang 314001,China)

机构地区：[1]嘉兴学院平湖师范学院,浙江平湖314200 [2]嘉兴学院数据科学学院,浙江嘉兴314001

出　　处：《计算机科学》2022年第S01期211-216,222,共7页Computer Science

基　　金：浙江省教育厅科研项目(Y202044497);浙江省自然科学基金(LQ20F020027)。

摘　　要：准确识别渔船的捕捞方式对监测近海渔船的捕捞行为和维护海洋生态平衡具有重要意义。为保护海洋环境,提高渔船的监管效率,提出了一种基于改进麻雀搜索算法(Improved Sparrow Search Algorithm,ISSA)优化支持向量机(Support Vector Machine,SVM)的渔船捕捞方式识别模型。首先引入t分布变异算子对种群进行优化选择,提高了原麻雀搜索算法的全局搜索能力和局部开发能力;其次修订原麻雀算法中警戒者的位置更新公式,进一步提高了算法的收敛速度;最后用ISSA优化SVM的核函数参数和惩罚项系数,建立渔船捕捞方式识别模型。在3546艘渔船上的实验结果表明,与原支持向量机、粒子群优化支持向量机、灰狼算法优化支持向量机和麻雀搜索算法优化支持向量机相比,文中提出的基于改进麻雀搜索优化支持向量机的渔船捕捞方式识别模型的准确率更高,而且具有更快的收敛速度。The identification of fishing type has significance for monitoring the fishing activities of motor vessels and maintaining the marine ecological balance.To protect the marine environment and improve the supervision efficiency of fishing vessels,a fi-shing type identification algorithm based on support vector machine optimized by the improved sparrow search algorithm(ISSA-SVM)is proposed.First,the t-distribution mutation operator is introduced to optimize the population selection,which improves the global search ability and local development ability of the original SSA.Second,the position update formula of the spectators of SSA is modified to further improve the convergence speed of the algorithm.Finally,the fishing type identification model ISSA-SVM is constructed by using ISSA to optimize the parameters of SVM.The experimental results on 3546 fishing vessels show that compared with SVM,PSO-SVM,GWO-SVM and SSA-SVM,the fishing type identification model of ISSA-SVM proposed in this paper has higher accuracy and faster convergence speed.

关 键 词：捕捞方式识别 麻雀搜索算法 T分布 适应度值 支持向量机 

分 类 号：TP391[自动化与计算机技术—计算机应用技术]