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Inorder to improve the slow convergence, low convergence accuracy and easy to fall into local optimality of whale optimization algorithm (WOA), the improved whale optimization algorithm (Immune memory-a Weight-Time varying search factor Improved Whale Optimization Algorithm, IWTWOA) is proposed. The application of nonlinear convergence factors, cooperative inertial weights and time-varying independent search probabilities improves the WOA iterative model, balancing the global exploration and local search capabilities of the algorithm, effectively avoiding the problem of local optimization. The memory mechanism of immune algorithm improve the algorithm convergence speed. Selected 15 test functions for performance testing, the results show that the IWTWOA has improved stability, calculation accuracy and convergence speed. Finally, applied it to the path planning problem, and obtained the better planning.