基于改进粒子群-模拟退火算法的集装箱船结构优化方法

A Structural Optimization Method for Container Ships Based on Improved Particle Swarm-Simulated Annealing Algorithm

  • 摘要: 为了研究集装箱船典型结构的多目标优化问题,本文首先建立了船舶结构构件库,实现了优化变量的离散化。其次,提出了一种动态调整优化参数的改进PSO-SA算法(IPSO-SA)。 该方法实现了快速寻找单目标优化问题的全局最优解。然后,通过引入Pareto支配,提出了一种用于多目标优化的MOPSO-SA算法,以集装箱船平行中体结构优化为例,利用本文建立的构件库构件结构参数化模型,并使用提出的优化算法进行优化,不仅实现了减重26.051%,还能够实现在结构重量减少14.111%的同时,降低重心垂向高度12.951%。研究成果不仅改善了原有设计方案的经济性,而且显著改善了集装箱船的稳性。

     

    Abstract: To investigate the multi-objective optimization problem of typical container ship structures, this paper first established a ship structural component library to achieve the discretization of optimization variables. Secondly, an improved PSO-SA algorithm (IPSO-SA) with dynamically adjusted optimization parameters was proposed. This method enables rapid identification of the global optimal solution for single-objective optimization problems. Then, by introducing Pareto dominance, a MOPSO-SA algorithm for multi-objective optimization was proposed. Taking the parallel middle body structure optimization of a container ship as an example, the parametric model of the structural components was constructed using the established component library, and the proposed optimization algorithm was applied. This not only achieved a 26.051% reduction in weight but also realized a 14.111% reduction in structural weight while lowering the vertical center of gravity by 12.951%. The research results not only improved the economic efficiency of the original design but also significantly enhanced the stability of the container ship.

     

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