摘要
由于遗传算法(GAs)的随机性,其直接应用于拓扑优化问题时会产生棋盘格现象。因此,研究小组提出了一种新的拓扑优化方法,它在传统的双向进化结构优化算法(BESO)的基础上融合了非支配排序遗传算法(NSGA-Ⅱ),并利用NSGA-Ⅱ算法的全局进化机制,改善BESO算法的全局最优可搜索性。优化目标为结构刚度最大化,数值算例验证了算法的有效性与稳定性。在相同约束条件下与传统BESO算法比较,发现研究小组提出的方法可以提高结构刚度,证明了其优越性,并且其适用于复杂的工况条件,对工程结构的优化具有指导意义。
Because of the randomness of Genetic Algorithms(GAs), the checkerboard phenomenon will occur when it is directly applied to topology optimization problems. Therefore, the research group proposed a new topology optimization method, which integrates the Non-dominated Sorting Genetic Algorithm(NSGA-Ⅱ) on the basis of the traditional Bi-directional Evolutionary Structural Optimization algorithm(BESO), and utilizes the global evolution of the NSGA-Ⅱ algorithm mechanism to improve the global optimal searchability of the BESO algorithm. The optimization objective is to maximize the stiffness of the structure. The numerical example verifies the validity and stability of the algorithm. Compared with the traditional BESO algorithm under the same constraint conditions, it is found that the method proposed by the research group can improve the structural stiffness, which proves its superiority, and it can be applied to complex working conditions, which has guiding significance for the optimization of engineering structures.
作者
齐峻
武星
Qi Jun;Wu Xing(School of Construction Machinery,Chang’an University,Shaanxi Xi’an 710064)
关键词
刚度
非支配排序遗传算法
双向进化结构优化
stiffness
Non-dominated Sorting Genetic Algorithm
Bi-directional Evolutionary Structural Optimization
