Alpha guided grey wolf optimizer and its application in two stage operational amplifier design

Grey Wolf Optimizer (GWO) is recently developed heuristics inspired from the leadership hierarchy and hunting mechanism of grey wolves in nature. It is a successful algorithm which has high performance in unknown and challenging search spaces. But in the original GWO, the evolutionary information of the population has not been fully utilized. In order to improve the convergence speed and convergence accuracy of the GWO, a alpha guided GWO (AgGWO) algorithm is proposed, which the evolving process of the population is guided by the update direction of alpha (best individual). The simulation experiment with other three algorithms shows that the proposed algorithm has faster convergence speed and convergence accuracy, which indicates the AgGWO algorithm is an efficient approach for solving global optimization problems. Finally, it is found that AgGWO could also be well applied in two stage operational amplifier design.