Practical Topology Optimization with Deep Reinforcement Learning and Genetic Algorithms

Despite the rapid adoption of Large Language Models across many domains, identifying artificial intelligence applications that deliver clear, high-impact value in industrial engineering remains a significant challenge. This work addresses that gap by presenting a comprehensive topology optimization framework tailored for engineering design problems, integrating deep reinforcement learning (DRL), genetic algorithms (GA), and finite element methods (FEM). The proposed framework makes several key contributions. First, it demonstrates substantial performance improvements by achieving a 12×12 grid resolution in two-dimensional topology optimization using a purely DRL-based approach, enabled by carefully designed smart reward shaping strategies. Second, it introduces a hybrid DRL–GA methodology that leverages the complementary strengths of learning-based exploration and evolutionary optimization, resulting in consistently improved solutions compared to standalone DRL. Third, the framework addresses dimensionality scaling challenges by proposing a novel “pseudo-3D” Euclidean space formulation based on a multi-objective optimization strategy. Through a systematic 2D deconstruction approach, this enables effective optimization at a 15×15 grid resolution while mitigating the computational burden of full 3D simulations. Finally, the practical relevance of the framework is validated through an applied engineering case study: the design and optimization of a rocket nozzle. This application highlights the framework’s potential for real-world, high-value industrial use cases.