In this paper, a variant of Grey Wolf Optimizer (GWO) that uses reinforcement learning principles combined with neural networks to enhance the performance is proposed. The aim is to overcome, by reinforced learning, the common challenges of setting the right parameters for the algorithm. In GWO, a single parameter is used to control the exploration/exploitation rate which influences the perform...

Received Dec 24, 2016 Revised Apr 26, 2017 Accepted Jun 14, 2017 The augment of ecological shield and the progressive exhaustion of traditional fossil energy sources have increased the interests in integrating renewable energy sources into existing power system. Wind power is becoming worldwide a significant component of the power generation portfolio. Profuse literatures have been reported for...

Exploration and exploitation are two essential components for any optimization algorithm. Much exploration leads to oscillation and premature convergence while too much exploitation slows down the optimization algorithm and the optimizer may be stuck in local minima. Therefore, balancing the rates of exploration and exploitation at the optimization lifetime is a challenge. This study evaluates ...