Abstract As an application of composite science in the marine industry, the present paper deals with the multi-objective optimal structural design of a superstructure composite sandwich panel based on the first-order shear deformation laminated plate theory (FSDT). Several parameters including the type of fiber, matrix and core material, the amount of reinforcement, the core, lamina and laminate thickness, the laminate arrangement (stacking sequence) and the laminate construction are considered as the design parameters. A novel Multi-Objective Niching Memetic Particle Swarm Optimization (MONMPSO) algorithm is proposed and its performance is evaluated using the well-known non-dominated sorting genetic algorithm (NSGA-II). The results show that the proposed MONMPSO algorithm has a better performance in comparison to the NSGA-II algorithm in extracting the Pareto front pattern. Based on the numerical results, many useful structural rules for designing a composite sandwich panel under the out of plane pressure and buckling load have been deduced.

中文翻译：

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采用新型MONMPSO算法的复合上部结构多目标优化结构设计

摘要 作为复合材料科学在船舶工业中的应用，本文基于一阶剪切变形层压板理论（FSDT）研究了上部结构复合夹芯板的多目标优化结构设计。包括纤维类型、基体和芯材、增强量、芯材、薄板和层压板厚度、层压板排列（堆叠顺序）和层压板结构在内的几个参数被视为设计参数。提出了一种新的多目标Niching Memetic粒子群优化（MONMPSO）算法，并使用众所周知的非支配排序遗传算法（NSGA-II）评估其性能。结果表明，与NSGA-II算法相比，所提出的MONMPSO算法在提取Pareto前沿图案方面具有更好的性能。基于数值结果，推导出了许多用于设计在面外压力和屈曲载荷下的复合夹芯板的有用结构规则。