Multi-condition multi-objective optimization using deep reinforcement learning

Abstract

A novel multi-condition multi-objective optimization method that can find Pareto front over a defined condition space is developed using deep reinforcement learning. Unlike the conventional methods which perform optimization at a single condition, the present method learns correlations between conditions and optimal solutions. The exclusive capability of the developed method is examined in solutions of a modified Kursawe benchmark problem and an airfoil shape optimization problem. The solutions include nonlinear characteristics which are difficult to be resolved using conventional optimization methods. Pareto front with high resolution over a condition space is successfully determined in both problems. Compared with multiple operations of a single-condition optimization method for multiple conditions, the present multi-condition optimization method shows a greatly accelerated search of Pareto front by reducing the required number of function evaluations. An analysis of aerodynamic performance of optimally designed airfoils confirms that multi-condition optimization is indispensable to avoid significant degradation of target performance for varying flow conditions.