In view of the shortcomings of the fruit fly optimization algorithm(FOA), such as slow convergence speed, low accuracy, easy to fall into local optimum, and the candidate solutions of the algorithm cannot take negative values, an improved fruit fly optimization algorithm(IFOA) for solving constrained optimization problems is proposed. Taking advantage of the best memory information of individual history and group global history, a multi-strategy hybrid co-evolutionary search mechanism is constructed, which can effectively balance the global exploration and local exploitation of the IFOA, and the premature convergence of the algorithm can also be better avoided. By introducing a real-time dynamic update mechanism and a local depth search strategy, the convergence speed and precision of the IFOA are further improved. The 13 benchmark problems and 2 engineering optimization problems are used to test the feasibility and effectiveness of the proposed method. Numerical results show that the proposed IFOA has obvious advantages such as stronger global search ability, better stability, faster convergence speed and higher convergence accuracy and so on, which can be used to effectively solve complex constrained optimization problems.