  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.
