An improved grey wolf algorithm for global optimization

This paper presents a novel improved grey wolf optimization (IGWO) to avoid local minimization and premature convergence. In the proposed IGWO algorithm, a nonlinear control parameter based on cosine function is presented to ensure a faster convergence rate of late iteration. Genetic algorithm is introduced into the IGWO to avoid premature convergence and trapping in local minima, in which the probability of cross-over and mutation is dynamically adjusted according to the swarm's fitness value. The new approach is compared against the original GWO and GA on a set of well-known benchmark test function. Experimental results show that the presented algorithm is superior to the other two comparative approaches.