基于多目标粒子群算法的船舶航速优化
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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。
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.
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.
引文
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[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..
[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..