Improved Monte Carlo tree search formulation with multiple root nodes for discrete sizing optimization of truss structures

TL;DR

This paper introduces an improved Monte Carlo tree search algorithm with multiple root nodes for efficient and stable discrete optimization of truss structures, reducing computational cost and handling large-scale problems.

Contribution

It presents a novel IMCTS formulation with multiple root nodes, including new update, acceleration, and terminal techniques for structural optimization.

Findings

Achieves low computational cost in finding optimal truss designs

Stably produces optimal solutions across various examples

Suitable for multi-objective and large-scale structural optimization

Abstract

This paper proposes a novel reinforcement learning (RL) algorithm using improved Monte Carlo tree search (IMCTS) formulation for discrete optimum design of truss structures. IMCTS with multiple root nodes includes update process, the best reward, accelerating technique, and terminal condition. Update process means that once a final solution is found, it is used as the initial solution for next search tree. The best reward is used in the backpropagation step. Accelerating technique is introduced by decreasing the width of search tree and reducing maximum number of iterations. The agent is trained to minimize the total structural weight under various constraints until the terminal condition is satisfied. Then, optimal solution is the minimum value of all solutions found by search trees. These numerical examples show that the agent can find optimal solution with low computational cost, stably produces an optimal design, and is suitable for multi-objective structural optimization and large-scale structures.