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Matching optimization of ship engine propeller and net for the trawler based on genetic algorithm

机译：基于遗传算法的拖网渔船船用推进器与网的匹配优化。

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Matching performance of ship engine propeller and net has a significant impact on propulsion efficiency for the trawler. In this paper, an improved genetic algorithm (GA) based on the particle swarm algorithm (PSO) is developed for matching optimization of ship engine propeller and net. Based on ship theory, the matching performance of ship engine propeller and net is analyzed. Considering the angular speed, picth ratio and disk ratio of propeller, a mathematical model is constructed in which the open-water propeller efficiency is taken as the objective function for matching optimization of ship engine propeller and net. The improved GA is presented to solve it, in which the PSO operator is introduced to the GA for the diversity of populations. The effectiveness of the approach is illustrated by a matching optimization example of ship engine propeller and net for the trawler.
机译：船舶发动机螺旋桨和渔网的匹配性能对拖网渔船的推进效率有重要影响。本文提出了一种基于粒子群算法（PSO）的改进遗传算法（GA），用于舰船螺旋桨与渔网的匹配优化。基于船舶理论，分析了船舶发动机螺旋桨与渔网的匹配性能。考虑到螺旋桨的角速度，皮比和圆盘比，建立了一个数学模型，以开水螺旋桨效率为目标函数，对船舶发动机的螺旋桨和渔网进行了优化匹配。提出了改进的遗传算法来解决该问题，其中将PSO运算符引入遗传算法以实现种群的多样性。船舶发动机螺旋桨和拖网渔网的匹配优化示例说明了该方法的有效性。

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