Grey Wolf Optimization Algorithm with Invasion-based Migration Operation

This paper proposed a solution to improve the grey wolf optimizer performance with integrate the invasion-based migration operation. The traditional grey wolf optimizer algorithm have three main steps of hunting, searching for prey, encircling prey and attacking prey whereas the wolves have only one pack. The wolves in our proposed algorithm have more pack and have migrated between them. The invasion-based migration operation is used when the algorithm is trapped in the local optimum. The results are evaluated by a comparative with the traditional grey wolf optimizer (GWO) algorithm, particle swarm optimization (PSO) and differential evolution (DE) algorithm on 11 well-known benchmark functions. The experimental results showed that the proposed algorithm is capable of efficiently to solving complex optimization problems.