Guest Editorial Special Issue on Deep Reinforcement Learning for Optimization: Methods and Application

Optimization is an old research topic, which widely exists in many engineering problems. Till now loads of methods have been proposed to handle complex optimization problems, amongst which evolutionary algorithms have attracted a great deal of attentions due to their robustness to the underlying problem characteristics. However, evolutionary algorithms which simulate the evolution of nature is an iterative optimizer, causing high computational effort to approximate the optima. That is, such methods may not be applicable on online or real-time optimization.