Improved Harris Eagle Optimization Algorithm Based on Adaptive Water Wave

Aiming at the shortcomings of Harris Eagle optimization algorithm such as weak ability to jump out of local optimum and slow convergence speed, a Harris Eagle optimization algorithm based on dynamic adaptive water waved was proposed. Firstly, Tent chaotic sequence and refraction reverse learning are introduced to increase the population diversity of initialized individuals and improve the population quality; Secondly, the dynamic adaptive factor of water waved are introduced into the position updating formula for the global exploration stage to enhance its global optimization ability; Then, the adaptive search operator is introduced to the local development stage. With the increase in the number of iterations, the weight of prey position is adjusted adaptively, and the Cauchy mutation is used to make it still have strong search ability in the late iteration. Finally, the simulation results in 2 benchmark functions shows that IHHO has better global exploration and local development ability, shortens the average time of the original algorithm, and improves the performance of the algorithm.