基于Pareto蚁群算法的船舶风险规避路径优化
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摘要
船舶在海上航行时,一直面临着海上运输风险的威胁,为了降低海上运输风险同时考虑船舶经济效益,本文建立了以运输风险最小和航行成本最小的双目标路径优化模型,实现船舶风险规避.运用栅格法构建环境模型,为相应的栅格路径赋予航行成本和运输风险,并设计了一种基于Pareto最优解集和NSGA小生境方法的多目标蚁群算法.以印度洋海域的2条航线为案例,以经典单目标蚁群算法为对比,验证了模型和算法的有效性.结果表明,该模型和算法在解决船舶风险规避路径优化问题上具有良好的效果,能为决策者制定船舶海上运输风险规避路径提供决策参考.
When ships are sailing at sea, they are always facing the threat of maritime transportation risk. In order to reduce the risk in maritime transportation while taking into account the economic benefits of the ship, a biobjective path optimization model is established. The two objectives of the model are the minimization of the cost of navigation and the total risk of the ship route. Grid method is used to construct the environment, and the cost of navigation and the risk in maritime transportation are assigned to the corresponding raster path. A multi-objective ant colony algorithm based on Pareto optimal solution set and NSGA niche method is designed. As a case study,the proposed model and algorithm are applied for optimizing the ship routes in the area of Indian Ocean. The classic single-target ant colony algorithm is also used as the comparison object to verify the validity of algorithm.The results show that the model and algorithm have a good effect in solving the ship risk aversion path optimization problem, and can provide decision-making reference for decision makers to formulate the marine shipping route.
When ships are sailing at sea, they are always facing the threat of maritime transportation risk. In order to reduce the risk in maritime transportation while taking into account the economic benefits of the ship, a biobjective path optimization model is established. The two objectives of the model are the minimization of the cost of navigation and the total risk of the ship route. Grid method is used to construct the environment, and the cost of navigation and the risk in maritime transportation are assigned to the corresponding raster path. A multi-objective ant colony algorithm based on Pareto optimal solution set and NSGA niche method is designed. As a case study,the proposed model and algorithm are applied for optimizing the ship routes in the area of Indian Ocean. The classic single-target ant colony algorithm is also used as the comparison object to verify the validity of algorithm.The results show that the model and algorithm have a good effect in solving the ship risk aversion path optimization problem, and can provide decision-making reference for decision makers to formulate the marine shipping route.
引文
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[3]VENETI A,MAKRYGIORGOS A,KONSTANTOPOULOS C,et al.Minimizing the fuel consumption and the risk in maritime transportation:a bi-objective weather routing approach[J].Computers&Operations Research,2017(88):220-236.
[4]LIU Y,BUCKNALL R.Path planning algorithm for unmanned surface vehicle formations in a practical maritime environment[J].Ocean Engineering,2015(97):126-144.
[5]MA Y,HU M,YAN X.Multi-objective path planning for unmanned surface vehicle with currents effects[J].Isa Transactions,2018(75):137-156.
[6]PAULO VITOR SILVESTRIN,MARCUS RITT.An iterated tabu search for the multi-compartment vehicle routing problem[J].Computers and Operations Research,2017(81):192-202.
[7]KANCHANA SETHANAN,WORAYANEUNGMATCHA.Multi-objective particle swarm optimization for mechanical harvester route planning of sugarcane field operations[J].European Journal of Operational Research,2016,252(3):969-984.
[8]BIN YU,ZHONG ZHEN YANG.An ant colony optimization model:The period vehicle routing problem with time windows[J].Transportation Research Part E,2010,47(2):166-181.
[9]盖文妹,蒋仲安,邓云峰,等.重大事故救灾路线双目标优化模型及算法[J].北京科技大学学报,2014,36(4):535-542.[GAI W M,JIANG Z A,DENG Y F,et al.Bi-objective optimization model and algorithm of rescue routes during major accident time[J].Journal of University of Science and Technology Beijing,2014,36(4):535-542.]
[10]CURRENT J,REVELIE C,COHON J.An interactive approach to identify the best compromise solution for two objective shortest path problem[J].Computers&Operatioms Research,1990,17(2):187-198.
[11]NING SHI,SHAORUI ZHOU,FAN WANG,et al.The multi-criteria constrained shortest path problem[J].Transportation Research Part E,2017(101):13-29.
[12]LUIS A,MONCAYO-MARTíNEZ,GUSTAVO RECIO.Bi-criterion optimisation for configuring an assembly supply chain using pareto ant colony meta-heuristic[J].Journal of Manufacturing Systems,2014,33(1):1818-195.
[13]ARIYASINGHA I D I D,FERNANDO T G I.Performance analysis of the multi-objective ant colony optimization algorithms for the traveling salesman problem[J].Swarm and Evolutionary Computation,2015(23):11-26.
[14]DORIGO M,GAMBARDELLA L M.Ant colony system:A cooperative learning approach to the traveling salesman problem[J].IEEE Transactions on Evolutionary Computation,1997,1(1):53-66.