Abstract: Aiming at the problems that Harris Hawks Optimization (HHO) is easy to fall into local optimization and has slow convergence speed, an improved Harris Hawks Optimization algorithm based on elite guidance (EHHO) is proposed. Firstly, elite opposite learning is introduced, and the elite center is used as the symmetrical center for opposite learning to optimize the population structure and enhance the ability of the algorithm to jump out of local optimum. Secondly, the elite evolution strategy is introduced, and the evolution based on Gaussian random mutation is carried out with elite individuals as the main body to improve the quality of the population and improve the convergence speed of the algorithm. Finally, an adaptive mechanism is introduced to dynamically adjust the selection probability of the two evolution modes in the elite evolution strategy to improve the stability of the algorithm. To verify the effectiveness of the improved algorithm, 15 benchmark functions are selected for simulation experiments. The experimental results show that the improved algorithm has obvious improvement in optimization performance and robustness, and has certain competitiveness in optimization algorithms.

Key words: Harris hawks optimization (HHO) algorithm, elite opposite learning, elite evolution strategy, Gaussian random mutation, adaptive mechanism