Sizing optimization of truss structures is a complex computational problem, and the reinforcement learning (RL) is suitable for dealing with multimodal problems without gradient computations. In this paper, a new efficient optimization algorithm called update Monte Carlo tree search (UMCTS) is developed to obtain the appropriate design for truss structures. UMCTS is an RL-based method that combines the novel update process and Monte Carlo tree search (MCTS) with the upper confidence bound (UCB). Update process means that in each round, the optimal cross-sectional area of each member is determined by search tree, and its initial state is the final state in the previous round. In the UMCTS algorithm, an accelerator for the number of selections for member area and iteration number is introduced to reduce the computation time. Moreover, for each state, the average reward is replaced by the best reward collected on the simulation process to determine the optimal solution. The proposed optimization method is examined on some benchmark problems of planar and spatial trusses with discrete sizing variables to demonstrate the efficiency and validity. It is shown that the computation time for the proposed approach is at least ten times faster than the branch and bound (BB) method. The numerical results indicate that the proposed method stably achieves better solution than other conventional methods.

翻译：桁架结构的尺寸优化是一类复杂的计算问题，强化学习(RL)无需梯度计算即可有效处理多模态问题。本文提出一种名为更新型蒙特卡洛树搜索(UMCTS)的新型高效优化算法，用于获取桁架结构的合理设计。UMCTS是一种基于强化学习的方法，它融合了新型更新过程与基于上置信界(UCB)的蒙特卡洛树搜索(MCTS)。更新过程指每轮迭代中，各构件的优化截面面积由搜索树确定，且其初始状态为上一轮的最终状态。在UMCTS算法中，引入构件面积选择次数和迭代次数的加速器以减少计算时间。此外，对于每个状态，采用模拟过程中收集的最佳奖励替代平均奖励来确定最优解。通过在离散尺寸变量平面与空间桁架的若干基准问题上验证所提优化方法的有效性及高效性。结果表明，所提方法的计算速度至少比分支定界(BB)法快十倍。数值结果证实，该方法能稳定地获得优于其他传统方法的解。