Abstract Thrust allocation is a key procedure in the dynamic position system (DPS) of marine vessels. The present work aims to study the characteristics of dynamic optimization in thrust allocation. A two-phase analysis process is proposed. In phase-I, the model for thrust allocation is generated from the viewpoint of multiobjective optimization. Fuel consumption and tear-and-wear on the thrusters are chosen as objectives. Multiobjective feasibility enhanced particle swarm optimization algorithm (MOFEPSO) is applied to find the Pareto set of this problem. An additional decision-making procedure, the technique of order preference by similarity to ideal solution (TOPSIS) is utilized to choose the final compromise solution in phase-II. The self-organizing map (SOM) technique is undertaken to mine the Pareto data set. A Remote Operated Vehicle (ROV) example is provided for illustrating the above analysis process. The effects of relative importance between objectives upon characteristics of thrust allocation are studied. The trajectories of decision variables and objectives are examined through the SOM method. Results from numerical examples demonstrate that the multiobjective optimization method together with decision-making skills can extend the application of optimization in the thrust allocation field. The findings in this work add to the understanding of relationships among several aspects of DPS.

中文翻译：

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船舶应用推力分配的动态多目标优化

摘要 推力分配是船舶动态定位系统（DPS）中的一个关键过程。目前的工作旨在研究推力分配中的动态优化特性。提出了一个两阶段分析过程。在第一阶段，从多目标优化的角度生成推力分配模型。选择燃料消耗和推进器上的撕裂和磨损作为目标。应用多目标可行性增强粒子群优化算法（MOFEPSO）来寻找该问题的帕累托集。另一个决策程序，即通过与理想解决方案相似的顺序偏好技术 (TOPSIS) 用于选择阶段 II 中的最终折衷解决方案。自组织映射 (SOM) 技术用于挖掘帕累托数据集。提供远程操作车辆 (ROV) 示例以说明上述分析过程。研究了目标之间的相对重要性对推力分配特性的影响。通过 SOM 方法检查决策变量和目标的轨迹。数值算例结果表明，多目标优化方法与决策技能相结合，可以扩展优化在推力分配领域的应用。这项工作的发现增加了对 DPS 几个方面之间关系的理解。数值算例结果表明，多目标优化方法与决策技能相结合，可以扩展优化在推力分配领域的应用。这项工作的发现增加了对 DPS 几个方面之间关系的理解。数值算例结果表明，多目标优化方法与决策技能相结合，可以扩展优化在推力分配领域的应用。这项工作的发现增加了对 DPS 几个方面之间关系的理解。