Grey Wolf Optimizer based on Nonlinear Adjustment Control Parameter

Grey wolf optimizer (GWO) is a relatively novel stochastic optimization technique which has bee shown to be competitive to other methods. However, the control parameter a of GWO is decreased from 2 to 0 over the course of iterations. Inspired by particle swarm optimization (PSO), a novel nonlinear adjustment strategy of control parameter a is designed to enhance the performance of GWO algorithm. In addition, to enhance the global convergence of GWO algorithm, when generating the initial population, opposition-based learning strategy is employed. Simulation results show that the proposed algorithm is able to provide very competitive results compared to other algorithms.