Grey wolf optimization (GWO) algorithm is a relatively novel optimization technique which has been shown to be competitive to other population-based algorithms.However

there is still an insufficiency in canonical GWO regarding its position update equation

which is good at exploitation but poor at exploration.Inspired by differential evolution and particle swarm optimization

the personal best information and the random selected individual from population are used to construct a modified position update equation for enhancing the exploration.Inspired by particle swarm optimization

a random adjustment strategy of control parameterais proposed.In addition

to enhance the global convergence

when producing the initial population

the chaos method is employed.Simulation experiments were conducted on the 18 high-dimensional conventional test functions.The simulation results show that the proposed algorithm provides better performance than basic GWO algorithms in the same or less number of maximum fitness function evaluation in most cases.