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In order to solve the disadvantage of the traditional whale optimization algorithm, which is slow convergence and easy to fall into local optimum, this paper proposed a mixed strategy based whale optimization algorithm. Firstly, it introduced the nonlinear adjustment strategy to modify the convergence factor, balanced the exploration and exploitation capability and accelerated the convergence speed. Then, it introduced an adaptive weighted coefficient into the position update formula of whales to improve the search precision of the algorithm. Finally, it combined the limit threshold idea of artificial bee colony algorithm to effectively jump out of the local optimum and prevent premature convergence. The results show that the proposed algorithm has better search precision and convergence speed through experiments on different dimensions of 14 benchmark functions. [ABSTRACT FROM AUTHOR]