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Optimization of fishing vessels using a Multi-Objective Genetic Algorithm

机译:使用多目标遗传算法的渔船优化

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A fishing boat hull is used as an example of how hull form optimization can be accomplished using a Multi-Objective Genetic Algorithm (MOGA). The particular MOGA developed during this study allows automatic selection of a few Pareto Optimal results for examination by the designers while searching the complete Pareto Front. The optimization uses three performance indices for resistance, seakeeping and stability to modify the hull shape to obtain optimal hull offsets as well as optimal values for the principal parameters of length, beam and draft. The modification of the 148/1-B fishing boat hull, the parent hull form of the istanbul Technical University (ITU) series of fishing boats, is presented by first fixing the principal parameters and allowing the hull offsets to change, and secondly by simultaneously allowing variation of both the principal parameters and the hull offsets. Improvements in all three objectives were found. For further research the methodology can be modified to allow for the addition of other performance objectives, such as cost or specific mission objectives, as well as the use of enhanced performance prediction solvers. In addition, one or more hulls could be evaluated by experiment to validate the results of using this particular optimization approach.
机译:以渔船船体为例,说明如何使用多目标遗传算法(MOGA)实现船体形状优化。在此研究过程中开发的特定MOGA可以自动选择一些帕累托最优结果,以供设计师在搜索整个帕累托前沿时进行检查。该优化使用了三个性能指标,分别是阻力,航海性能和稳定性,以修改船体形状以获得最佳船体偏移以及长度,横梁和吃水深度的主要参数的最佳值。 148 / 1-B渔船船体的修改是伊斯坦布尔技术大学(ITU)系列渔船的父船体形式,其方法是首先确定主要参数并允许船体偏移发生变化,其次是同时允许主要参数和船体偏移的变化。发现所有三个目标都有改进。为了进一步研究,可以修改方法,以允许添加其他性能目标,例如成本或特定任务目标,以及使用增强的性能预测求解器。此外,可以通过实验评估一个或多个船体,以验证使用此特定优化方法的结果。

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