Abstract: Grey wolf optimizer (GWO) is a novel intelligent optimization algorithm which has proposed resently and it has such merits as fast convergence speed,high optimization precision,but easily entraps in local optima.The differential evolution (DE) algorithm has strong global search ability,but its local search ability is poor and its performance is sensitive to the parameters.To take advantage of the merits of GWO and DE and overcome their defects in dealing with function optimization problems,a hybrid optimization algorithm based on grey wolf optimization and differential evolution (GWODE) was proposed.First,the optima-inclinded operator embedded GWO is utilized which is benefit to improving the optimization precision and convergence rate of the algorithm in a shorter search process.Then,an adaptive differential strategy,which can automatically adjust the value of the parameters,is employed to further improve the optimization performance of the algorithm for complex optimization functions.Thus,a hybrid algorithm with high performance is obtained and it’s more efficient to solve various function optimization problems.The optimization results on 12 benchmark functions show that the new hybrid optimization algorithm has higher search precision,better optimal performance and stronger applicability,and it’s more suitable for solving a variety of optimization problems,compared with the standard GWO,ACS,DMPSO and SinDE.

Key words: Optimization algorithm,Hybrid optimization algorithm,Grey wolf optimization algorithm,Differential evolution,Function optimization