Multi-condition multi-objective optimization using deep reinforcement learning

TL;DR

This paper introduces a novel deep reinforcement learning approach for multi-condition multi-objective optimization, enabling efficient Pareto front discovery across diverse conditions, demonstrated on benchmark and airfoil problems.

Contribution

It is the first to develop a deep reinforcement learning method for multi-condition multi-objective optimization that learns correlations between conditions and solutions.

Findings

Successfully determined high-resolution Pareto fronts over condition spaces.

Achieved faster Pareto front search with fewer function evaluations.

Confirmed the importance of multi-condition optimization for robust airfoil design.

Abstract

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.