Hypervolume-Based Multi-Objective Optimization Method Applying Deep Reinforcement Learning to the Optimization of Turbine Blade Shape - Kazuo Yonekura; Ryusei Yamada; Shun Ogawa; Katsuyuki Suzuki (AI)
A multi-objective turbine shape optimization method based on deep reinforcement learning (DRL) is proposed. DRL-based optimization methods are useful for repeating optimization tasks that arise in applications such as the design of turbines and automotive parts. In conventional research, DRL is applied only to single-optimization tasks. In this study, a multi-objective optimization method using improvements in hypervolume is proposed. The proposed method is applied to a benchmark problem and a turbine optimization problem. It succeeded in efficiently solving the problems, and Pareto optimal solutions are obtained.