The Whale Optimization Algorithm (WOA) is a highly competitive and efficient swarm intelligence optimization algorithm. In comparison to other intelligent optimization algorithms, WOA offers a simple structure, fewer parameters, and robust optimization capabilities. However, the conventional WOA exhibits slow convergence and falls into local optima easily. To address these issues, this paper proposes an Improved Whale Optimization Algorithm (IWOA), which adopts an adaptive update mechanism, introduces individual historical optimal positions during the optimization process, and dynamically adjusts the weights of global historical optimal positions and individual historical optimal positions through adaptive strategies; at the same time, through neighborhood search strategy, neighborhood updates are carried out around the global historical optimal position in the later stage of iteration to improve the algorithm"s optimization ability. 16 typical benchmark test functions and 8 composite functions from the CEC2014 test set were selected for simulation experiments to verify the effectiveness of IWOA; and IWOA was applied to two engineering design problems, welding beam and pressure vessel design. Compared with WOA, the economic cost was saved by 3.94% and 5.58%, respectively, verifying the effectiveness of the algorithm.