Abstract:  To address the problems that the population diversity of the sparrow search algorithm （SSA） decreases in the late iteration and easily falls into local optimum， a multi-strategy based improved sparrow search algorithm （MUSSA） is proposed. Firstly， MUSSA uses opposition-based learning and iterative strategy to enhance population diversity. According to the forgotten decline strategy， the number of populations using the reverse iteration strategy is gradually reduced， the loss of useless search is reduced， and the convergence speed of the algorithm is accelerated. Then， the adaptive weight spiral search strategy and reference frame mechanism are introduced to modify the discoverer update formula， further expand the search range of individuals and enhance the global search capability of the algorithm. Finally， direction factor and non-static selection strategy are introduced into follower renewal strategy to enhance local mining excavation. The simulation results of 13 benchmark test functions show that MUSSA has better optimization performance than SSA， HHO， WOA and AO.

Key words: Key words： sparrow search algorithm, opposition-based learning, Iterative mapping, forgetting curve, spiral strategy, adaptive weighting