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Micro-scale truss optimization using genetic algorithm

机译：基于遗传算法的微尺度桁架优化

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This paper describes the development of a genetic algorithm that is capable of optimizing the mass of micro-scale trusses. Belonging to the group of periodic cellular materials, micro-scale trusses are characterized by the creation of a base cell with a pattern that is repeated in space until a global structure is obtained. Investigation in this field has generally been focused on the design of base cells and their resistance once the final structure is obtained. In this project we have attempted to optimize each individual cell and in particular its elements according to the loads and boundary conditions applied to the global structure. With this objective, we defined a dichotomic search algorithm that establishes a set of cross-sectional areas suitable for the micro-scale truss, formulated the penalty coefficient for the over-sized elements, and studied the clones and rebirth process in order to avoid stagnation of the genetic algorithm. The cell elements used in this project were equal to or less than to 1 mm long, with a cross-sectional area in the order of 10 − 9 m2.
机译：本文介绍了一种能够优化微型桁架质量的遗传算法的开发。属于周期性蜂窝材料的组，微型桁架的特征在于创建基本单元，该基本单元的模式在空间上重复直到获得整体结构。一旦获得最终结构，该领域的研究通常集中在基础电池的设计及其抗性上。在此项目中，我们尝试根据应用于全局结构的载荷和边界条件来优化每个单独的单元，尤其是其单元。以此为目标，我们定义了一种二分搜索算法，该算法建立了一组适合于微尺度桁架的横截面，为超大元素制定了惩罚系数，并研究了克隆和重生过程以避免停滞。遗传算法。该项目中使用的电池单元长度等于或小于1 mm，截面积约为10 -9 m 2 。

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