Aiming at the shortcomings of the basic Grey Wolf Optimization (GWO) algorithm, such as insufficient population diversity and easy to fall into local optimum, an improved grey wolf optimization (ISIAGWO) algorithm based on information sharing search strategy is proposed from the perspectives of chaos initialization and information sharing among populations. Firstly, the Iterative chaotic mapping is used to initialize the population to ensure the diversity, and the adaptive dynamic operator is introduced to increase the weight of outstanding individuals; Secondly, the information sharing search strategy is used to update the population to effectively avoid the algorithm falling into local optimum; Thirdly, eight benchmark functions are tested for optimization and the proposed algorithm is compared with other advanced swarm intelligence algorithms.The experimental results show that ISIAGWO algorithm has significantly improved the accuracy and robustness of the solution; Finally, ISIAGWO algorithm is applied to solve the classic traveling salesman problem, so as to prove the practicability of the algorithm.