船舶避碰的粒子群-遗传(PSO-GA)的混合优化算法研究  

Research on hybrid optimization algorithm of particle swarmgenetic(PSO-GA)for ship collision avoidance

作　　者：周凤杰[1] ZHOU Feng-jie(Chongqing Youth Vocational and Technical College,Chongqing 400712,China)

机构地区：[1]重庆青年职业技术学院,重庆400712

出　　处：《船舶力学》2021年第7期909-916,共8页Journal of Ship Mechanics

基　　金：重庆青年职业技术学院院级科研项目(CQY2016JXZ03);重庆青年职业技术学院2019年院级课题项目(CQY2019KY)。

摘　　要：随着海运航线的愈加繁忙,船舶碰撞事故时有发生,为避免船舶发生碰撞,船舶避碰决策研究已成为目前研究的热点。本文在既往研究的基础上,综合考虑船舶避碰的经济性及安全性要求,基于粒子群算法、遗传算法与非线性编程理论,建立了船舶避碰路径规划的优化模型,并通过具体案例进行仿真分析。仿真结果显示,粒子群遗传混合优化算法的收敛速度较快,船舶避碰的优化路径能够同时满足经济性及安全性要求,算法的有效性及运算效率均有了显著提高。With the increasing busyness of shipping routes,ship collision accidents occur from time to time,in order to avoid ship collisions,research on ship collision avoidance decision-making has become a hot issue currently.On the basis of the aforementioned research,comprehensively considering the economic and safety requirements of ship collision avoidance,and on the basis of particle swarm algorithm,genetic algorithm and nonlinear programming theory,an optimization model for ship collision avoidance path planning is established,and the specific case is simulated.The simulation results show that the particle swarm genetic hybrid optimization algorithm has a faster convergence rate,and the optimized path of ship collision avoidance can meet the economic and safety requirements at the same time,also the effectiveness and efficiency of the algorithm have been significantly improved.

关 键 词：船舶避碰 粒子群算法 遗传算法 混合算法 路径优化 

分 类 号：U675.96[交通运输工程—船舶及航道工程]