Multi-condition multi-objective optimization using deep reinforcement learning
This addresses optimization challenges in domains like aerodynamics where performance must be maintained across varying conditions, representing a novel method for a known bottleneck.
The authors tackled the problem of multi-condition multi-objective optimization by developing a deep reinforcement learning method that learns correlations between conditions and optimal solutions, achieving a greatly accelerated search of Pareto fronts with reduced function evaluations compared to conventional single-condition methods.
A multi-condition multi-objective optimization method that can find Pareto front over a defined condition space is developed for the first time using deep reinforcement learning. Unlike the conventional methods which perform optimization at a single condition, the present method learns the correlations between conditions and optimal solutions. The exclusive capability of the developed method is examined in the solutions of a novel modified Kursawe benchmark problem and an airfoil shape optimization problem which include nonlinear characteristics which are difficult to resolve using conventional optimization methods. Pareto front with high resolution over a defined condition space is successfully determined in each problem. Compared with multiple operations of a single-condition optimization method for multiple conditions, the present multi-condition optimization method based on deep reinforcement learning shows a greatly accelerated search of Pareto front by reducing the number of required function evaluations. An analysis of aerodynamics performance of airfoils with optimally designed shapes confirms that multi-condition optimization is indispensable to avoid significant degradation of target performance for varying flow conditions.