Abstract: Fruit fly optimization algorithm (FOA) is a new global optimization algorithm inspired by the osphresis and vision behaviors of the fruit flies, which has been shown to have a strong capacity for solving continuous optimization problems. However, the candidate solutions of FOA could not take values those are negative, and the basic FOA is also faced with the challenges of poor diversity of the swarm and weak local search ability. To overcome these limitations synthetically, this study presents an improved FOA based on multi-strategy evolution and dynamic updating of swarm optimal information (MDFOA), aiming at well balancing the global search and local search abilities. In the proposed MDFOA, an effective candidate solution generating method and a new control parameter are introduced to improve the convergence performance. Moreover, a real-time update mechanism of the global optimal information is designed to further improve the convergence speed of the algorithm. 29 complex continuous benchmark functions are used to test the effectiveness of the proposed method. Numerical results show that the proposed MDFOA is superior to several other algorithms, such as the basic FOA, six variants of FOA, and two state-of-the-art intelligent optimization algorithms.