基于多种群遗传算法的钢框架结构优化设计

Optimization design of steel frame structure based on multi-population genetic algorithm

传统的基于力学分析软件的结构设计方法存在效率低下、依靠专家经验等局限性,采用智能算法能实现高效的结构自动优化设计。然而,由于随机搜索特征,优化结果和收敛性高度依赖于算法的参数设置,需要通过试算来确定其合理取值,该方法会造成优化效率低、计算量大等问题。引入多种群协作和信息共享机制来改善此类问题,并研究其在结构优化设计中的适用性。利用MSC.Marc软件建立钢框架结构有限元模型,采用底部剪力法将地震作用等效为水平荷载施加到结构上,搭建有限元软件与智能算法的自动优化过程,以结构的总体材料用量最低为目标,考虑了层间位移角、应力比、构件稳定性和宽厚比等多种约束条件,以遗传算法为基础,通过适应度尺度变换、基于方向的交叉算子、非均匀变异算子、自适应概率、精英保留策略、重复项替代机制、基于约束的策略对其进行改进,引入多种群思想,对比多种算法优化结果的差异。结果表明:基于多种群的遗传算法能有效改善优化结果对算法参数的依赖性,提高结构优化设计的效率。

The traditional structural design method based on mechanical analysis software has some limitations,such as low efficiency and expert experience reliance.The efficient automatic structural optimization design can be achieved by using intelligent algorithms.However,due to the random search feature,the optimization result and convergence are highly dependent on the parameter settings of the algorithm whose reasonable values need to be determined by the trial-and-error procedure.It results in inefficient optimization and substantial computational cost.Therefore,this paper introduces the multi-population collaboration and information sharing mechanism to improve such problems and its applicability in the structural optimization design is studied.The finite element model of a steel frame is built by MSC.Marc and the equivalent horizontal load from earthquake obtained by base shear method is exerted on the structure.The automatic optimization process is established based on finite element software and the intelligent algorithm with the aim of mininizing the total material cost of the structure.Multiple structural constraints are considered including the inter-story drift ratio,the stress ratio,and the stability and width-thickness ratio of the component.Several strategies are used to improve the performance of the genetic algorithm,such as the fitness scaling,the direction-based crossover operator,the non-uniform mutation operator,the adaptive probability,the elite strategy,the duplicate substitution mechanism,and the constraint-based strategy.Then the multi-population mechanism is introduced to such an algorithm.The results of different algorithms are compared with each other,which shows that the multi-population genetic algorithm can improve the dependence of optimization results on algorithm parameters and the efficiency of structural optimization design.