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). The algorithm adopts an adaptive update mechanism, inspired by particle swarm optimization, incorporating the individual’s historical best position during the optimization process, and dynamically adjusts the weights of the global best and individual best positions through an adaptive strategy to avoid getting trapped in local optima; 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 are selected for simulation experiments, compared to other traditional and improved swarm intelligence optimization algorithms, IWOA demonstrates superior convergence accuracy and speed, validating its effectiveness; and IWOA is applied to two engineering design problems, welding beam and pressure vessel design, compared with WOA, the economic cost is saved by 3.94% and 5.58%, respectively, verifying the effectiveness of the algorithm.