A novel grey wolf optimizer for global optimization problems
Wen Long, Songjin Xu · 2016
Grey wolf optimizer (GWO) is a recently proposed intelligent optimization method inspired by hunting behavior of grey wolves. In GWO algorithm, the parameter of a⃗ is decreased from 2 to 0 to balance exploitation and exploration, respectively. A novel time-varying parameter of a⃗ decreasing linearly is used to enhance the performance of GWO algorithm. In order to enhance the global convergence, when generating the initial population, the good-point-set method is employed. The simulation results tested on 10 standard unconstrained functions demonstrate that the proposed method has fine solution quality and convergence performance comparing to standard GWO method and performs superior to the other intelligent optimization method in most functions.