Grey Wolf Optimizer (GWO) and Artificial Bee Colony (ABC) are two popular and efficient intelligent optimization algorithms. GWO has some features such as strong exploitation but weak exploration. ABC has other ones such as strong global search ability but slow convergence. In order to realize their complementary advantages

a hybrid GWO with ABC (HGWOA) was proposed. Firstly

a static greedy algorithm was used to replace the dynamic greedy algorithm in the employed bee phase to enhance the exploration ability

and a new search method was created to make up for the lost convergence quality. Secondly

the scout bee phase which affects the convergence speed was removed

and an opposition learning strategy was embedded into the employed bee phase to keep the algorithm from falling into the local optima. Finally

in order to balance the exploration ability of the employed bee phase

GWO was added to the onlooker bee phase to strengthen the exploitation and improve the optimization efficiency. Experimental results on many function and clustering optimization problems show that compared with state-of-the-art methods

HGWOA has better optimization performance and stronger universality and it can solve clustering optimization problems more efficiently.