Grey wolf optimization with momentum for function optimization

Grey wolf optimization (GWO) is one of the metaheuristics, which imitates the hierarchy structure and hunting mechanism in nature. In this paper, we propose an algorithm of grey wolf optimization with momentum (GWOM). The momentum has a movement vector of search point from a previous position to a current position. In the proposed algorithm, the momentum is only applied to wolves with the better fitness than that of the previous step. Therefore, wolves are enhanced probability for searching better positions and tend to avoid to local optima. To show the effectiveness of the proposed algorithm, we compare it to existing algorithms by test functions.