基于多目标粒子群算法的船舶航速优化

doi:10.16182/j.issn1004731x.joss.17-0111

系统仿真学报 ›› 2019, Vol. 31 ›› Issue (4): 787-794.doi: 10.16182/j.issn1004731x.joss.17-0111

基于多目标粒子群算法的船舶航速优化

张进峰^1,2,3, 杨涛宁¹, 马伟皓¹

1. 武汉理工大学航运学院,湖北武汉 430063;
2. 国家水运安全工程技术研究中心,湖北武汉 430063;
3. 内河航运技术湖北省重点实验室,湖北武汉 430063

收稿日期:2017-03-14 修回日期:2017-07-12 出版日期:2019-04-08 发布日期:2019-11-20
作者简介:张进峰(1980-),男,安徽无为,博士,副教授,研究方向为船舶气象导航和航线优化等。
基金资助:
国家重点研发计划(2018YFC1407404), 2016年国家级大学生创新创业训练计划(20161049712004)

Ship Speed Optimization Based on Multi-objective Particle Swarm Algorithm

Zhang Jinfeng^1,2,3, Yang Taoning¹, Ma Weihao¹

1. School of Navigation, Wuhan University of Technology, Wuhan 430063, China;
2. National Engineering Research Center for Water Transport Safety, Wuhan 430063, China;
3. Hubei Inland Shipping Technology Key Laboratory, Wuhan 430063, China

Received:2017-03-14 Revised:2017-07-12 Online:2019-04-08 Published:2019-11-20

摘要/Abstract

摘要： 在航运低迷阶段航速优化对降低营运成本具有重要的现实意义,针对降低营运成本和减少船舶排放两个目标互相冲突的问题,建立了实际风浪影响下的船舶航速多目标优化模型,利用多目标粒子群算法求解Pareto最优解集,结合改进的TOPSIS算法在Pareto最优解集中权衡筛选出最优折中航速,选定一条沿海运营航线为例进行仿真和验证,结果表明船舶在该最优航速下航行的营运成本和排放与实测数据较为一致,优化模型能有效降低排放并控制营运成本,验证了求解算法的有效性。

关键词: 航速优化, 多目标粒子群算法, TOPSIS算法, 营运成本, 排放

Abstract: Ship speed optimization is an effective means to reduce operational costs in the downturn of shipping. To deal with the conflict between reducing the operating cost and reducing the ship emissions, the multi-objective ship speed optimization model is proposed based on the influence of the actual wind and wave. The MOPSO algorithm is introduced to solve the Pareto optimal solution set, and the compromise speed is an effective tradeoff based on the improved TOPSIS algorithm. The operational shipping route is selected as an example to simulate and verify the model. The results show that the operating costs and ship emissions at the optimal speed are consistent with the measured data. The optimization model can effectively reduce the emission and control the operation cost, and the algorithm is proved to be effective.

Key words: ship speed optimization, multi-objective particle swarm optimization (MOPSO), TOPSIS algorithm, cost, emission

中图分类号:

U697.1

张进峰, 杨涛宁, 马伟皓. 基于多目标粒子群算法的船舶航速优化[J]. 系统仿真学报, 2019, 31(4): 787-794.

Zhang Jinfeng, Yang Taoning, Ma Weihao. Ship Speed Optimization Based on Multi-objective Particle Swarm Algorithm[J]. Journal of System Simulation, 2019, 31(4): 787-794.

参考文献

[1] Smith T W P, Jalkanen J P, Anderson B A, et al. Third IMO GHG Study 2014 (Executive Summary and Final Report)[R]. London: International Maritime Organization, 2015.
[2] 魏应三, 王永生. 船舶航速优化原理研究[J]. 中国造船, 2008, 49(增1): 75-82.
Wei Yingsan, Wang Yongsheng.Ship speed optimization research[J]. Shipbuilding of China (S1000-4882), 2008, 49(S1): 75-82.
[3] Norlund E K, Gribkovskaia I.Reducing emissions through speed optimization In supply vessel operations[J]. Transportation Research Part D: Transport and Environment (S1361-9209), 2013, 23(6): 105-113.
[4] 李铮, 潘晓萌. 船舶航速优化算法研究[J]. 舰船科学技术, 2016, 38(12):7-9.
Li Zheng, Pan Xiaomeng.Research on optimization algorithm of ship Speed[J]. Ship Science and Technology (S1672-7646), 2016, 38(12): 7-9.
[5] 魏照坤, 谢新连, 魏明. 风浪影响下的集装箱班轮航速优化[J]. 交通运输系统工程与信息, 2016, 16(3): 154-160.
Wei Zhaokun, Xie Xinlian, Wei Ming.The container liner under the influence of the wind speed of optimization[J]. Journal of Transportation Systems Engineering and Information Technology (S1009-6744), 2016, 16(3): 154-160.
[6] 陈前昆, 严新平, 尹奇志, 等. 基于EEOI的内河船舶航速优化研究[J]. 交通信息与安全, 2014, 32(4): 87-91.
Cheng Qiankun, Yan Xinping, Yi Qizhi, et al.Speed Optimization for Inland River Ships Based on EEOI[J]. Journal of Transport Information and Safety, 2014, 32(4): 87-91.
[7] 李学强, 刘海林. 一种新的求解复杂多目标优化问题的进化算法[J]. 系统仿真学报, 2011, 23(9): 1860-1899.
Li Xueqiang, Liu Hailin.New Evolutionary Algorithm for Complicated Multiobjective Optimization[J]. Journal of System Simulation, 2011, 23(9): 1860-1865.
[8] 黄松, 田娜, 纪志成. 基于自适应变异概率粒子群优化算法的研究[J]. 系统仿真学报, 2016, 28(4): 874-879.
Huang Song, Tian Na, Ji Zhicheng.Study of Modified Particle Swarm Optimization Algorithm Based on Adaptive Mutation Probability[J]. Journal of System Simulation, 2016, 28(4): 874-879.
[9] 刘淳安. 一种求解动态多目标优化问题的粒子群算法[J]. 系统仿真学报, 2011, 23(2): 288-293.
Liu Chunan, Particle Swarm Algorithm for Solving Dynamic Multi-objective Optimization Problems[J]. Journal of System Simulation, 2011, 23(2): 288-293.
[10] Hu X H, Eberhart R C.Multi-objective optimization using dynamic neighborhood particle swarm optimization[C]. Proceedings of IEEE Congress on Evolutionary Computation. USA, Honolulu: IEEE, 2002: 1677-1681.
[11] Hwang C L, Yoon K S.Multiple attribute decision making[M]. Berlin: Spring-Verlag, 1981.
[12] Lindstad H.Assessment of profit, cost and emissions by varying speed as a function of sea conditions and freight market[J]. Transportation Research Part D (S1361-9209), 2013, 19: 5-12.

基于多目标粒子群算法的船舶航速优化

Ship Speed Optimization Based on Multi-objective Particle Swarm Algorithm

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