Abstract Design optimization of moderately thick hexagonal honeycomb sandwich plate has been investigated via employing an improved multi-objective particle swarm optimization with genetic algorithm (MOPSOGA). Based on the first-order shear deformation theory (FSDT), governing equations of the plate are obtained. The equations are solved analytically. Total weight and maximum deflection of the plate under static gravity loads are considered to be objective functions of the problem. Core height, faces thickness, cell walls thickness, vertical and inclined cell wall length and the angle between inclined cell wall and horizontal line are set to be design variables of the problem. The geometrical and failure constrains are chosen to have desirable performance and stability of the sandwich plate. In the used multi-objective optimization technique, the optimum velocity parameter, inertia weight and acceleration coefficients for next iteration of the MOPSO are obtained by employing the genetic algorithm via minimizing generational distance between the sets of dominated and non-dominated particles in the previous iteration. Efficiency and accuracy of the proposed solution procedure are demonstrated and effects of different parameters on design optimization of the plate are studied. Also, TOPSIS multi-criteria decision-making method has been selected to report appreciate results from the Pareto-front curve of the MOPSOGA.

中文翻译：

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基于遗传算法（MOPSOGA）的改进多目标粒子群优化中厚六角蜂窝夹层板的设计优化

摘要 采用改进的遗传算法多目标粒子群优化算法（MOPSOGA）对中厚六角蜂窝夹层板的设计优化进行了研究。基于一阶剪切变形理论（FSDT），得到了板的控制方程。方程被解析求解。在静态重力载荷下板的总重量和最大挠度被认为是问题的目标函数。核心高度、面厚度、单元壁厚度、垂直和倾斜单元壁长度以及倾斜单元壁与水平线之间的角度被设置为问题的设计变量。选择几何和破坏约束以具有所需的夹层板性能和稳定性。在使用的多目标优化技术中，MOPSO 下一次迭代的最佳速度参数、惯性权重和加速度系数是通过最小化前一次迭代中支配和非支配粒子集之间的代距，采用遗传算法获得的。证明了所提出的求解程序的效率和准确性，并研究了不同参数对板设计优化的影响。此外，还选择了 TOPSIS 多标准决策方法来报告来自 MOPSOGA 的帕累托前沿曲线的欣赏结果。证明了所提出的求解程序的效率和准确性，并研究了不同参数对板设计优化的影响。此外，还选择了 TOPSIS 多标准决策方法来报告来自 MOPSOGA 的帕累托前沿曲线的欣赏结果。证明了所提出的求解程序的效率和准确性，并研究了不同参数对板设计优化的影响。此外，还选择了 TOPSIS 多标准决策方法来报告来自 MOPSOGA 的帕累托前沿曲线的欣赏结果。