Abstract: Coyote Optimization Algorithm (COA) is a novel intelligent optimization algorithm recently proposed and has great application potential,but it has some problems such as long running time and insufficient search ability.This paper proposes an improved COA,namely COA based on Information sharing and Static greed selection (ISCOA).Firstly,a new information sharing model is constructed and applied to the growth of all coyotes in the subgroup,the difference of the sharing information is larger in the early growth so as to increase the population diversity,and the one is smaller in the late growth to be beneficial to exploitation.Secondly,a new intra-group growth mode is constructed,that is to say,a new growth way is adopted in the early stage,mainly based on the information sharing model,to strengthen the growth process to improve the exploration ability,and the growth method of the original algorithm is kept in the later stage,mainly based on the guidance of the alpha wolf and the cultural trend,to strengthen the exploiting ability.Finally,the intragroup greedy algorithm of the original algorithm is changed into a sta-tic greedy algorithm to improve the stability of the algorithm,realize the parallel calculation of the objective function,and improve the running speed.A large number of experiment results on the complex functions from CEC2017 test set show that,compared with COA,ISCOA obtains the advantage of 23 and 24 of the 29 10-dimensional and 30-dimensional functions respectively,and its average running time is 86.3% and 85.7% of COA's on the 10-dimensional and 30-dimensional functions respectively,and its running time is decreased.Compared with the 7 state-of-the-art algorithms,the average ranking of ISCOA on the 10-dimensional and 30-dimensional functions are 1.48 and 1.69,ISCOA wins 17 and 18 times ranking the first,respectively,and obtains better optimization results.Experimental results on the practical engineering problem show that ISCOA has achieved the best results.These all proved that ISCOA has stronger search ability and more competitive,and that it has better application prospects.

Key words: Coyote optimization algorithm, Exploitation, Exploration, Greedy algorithm, Swarm intelligence optimization algorithm