Aiming at the shortcomings of slow convergence rate and poor local search ability of particle swarm optimization algorithms, an adaptive multi-strategy particle swarm optimization algorithm is proposed. Each particle has four behavioral evolution strategies in the algorithm. In the iteration process, the evolutionary behavior of the particles is determined by calculating the immediate value, the future value and the comprehensive reward of each evolutionary strategy, and the strategy behavioral mutation algorithm is proposed to improve the individual search speed or to avoid falling into the local optimal solution. Comparison of the results of the proposed algorithm with the other 7 swarm intelligence evolutionary algorithms for the classical benchmark function show that the algorithm has better accuracy and convergence speed, especially suitable for some high-dimensional optimization problems.