Multi-condition Multi-objective Airfoil Shape Optimisation Using Deep Reinforcement Learning Compared to Genetic Algorithms

Dasun Shalila Balasooriya, Alan Blair, Craig A. Wheeler, Stephan K. Chalup

Abstract: This study investigates the potential of Deep Reinforcement Learning (DRL) in the context of Multi-Condition Multi-Objective airfoil shape optimisation by benchmarking a customised DRL algorithm, namely Single-Step Proximal Policy Optimisation, against NSGA-II, a conventional genetic algorithm. We illustrate the capability of the DRL algorithm to effectively optimise across a continuous multi-condition plane, eliminating the need to discretise it into discrete points, a practice commonly employed in conventional Genetic Algorithms. We further demonstrate that the DRL algorithm achieves hypervolume averages and convergence rates that are competitive when compared to NSGA-II. Analysis of Deep Neural Networks extracted from the training phase of the DRL algorithm indicates that almost complete knowledge of the Pareto front is retained by the network, which can be utilised to accelerate the discovery of the Pareto front in similar optimisation tasks via transfer learning.