In order to overcome the defects of the artificial bee colony algorithm in convergence speed, convergence precision and early maturity when dealing with complexity problems, the information entropy is introduced into the artificial bee colony algorithm. The information entropy is a measure of uncertainty, the uncertain choice of onlookers in artificial bee colony algorithm is shown by information entropy value, and the choice of onlookers is controlled by information entropy value, which it realizes self-adaptive adjustment of artificial bee colony algorithms. Through simulating the test functions and TSP problems, comparing with the artificial bee colony algorithm, the ant colony algorithm and other improved algorithms, the feasibility and effectiveness of the method proposed are shown.