  Multi-objective evolutionary algorithms (MOEAs) are widely used to solve
multi-objective optimization problems. The algorithms rely on setting
appropriate parameters to find good solutions. However, this parameter tuning
could be very computationally expensive in solving non-trial (combinatorial)
optimization problems. This paper proposes a framework that integrates MOEAs
with adaptive parameter control using Deep Reinforcement Learning (DRL). The
DRL policy is trained to adaptively set the values that dictate the intensity
and probability of mutation for solutions during optimization. We test the
proposed approach with a simple benchmark problem and a real-world, complex
warehouse design and control problem. The experimental results demonstrate the
advantages of our method in terms of solution quality and computation time to
reach good solutions. In addition, we show the learned policy is transferable,
i.e., the policy trained on a simple benchmark problem can be directly applied
to solve the complex warehouse optimization problem, effectively, without the
need for retraining.
