Multi-Strategy Enhanced Harris Hawks Optimization for Global Optimization and Deep Learning-Based Channel Estimation Problems

Harris Hawks Optimization (HHO) simulates the cooperative hunting behavior of Harris hawks and it has the advantages of fewer control parameters, simple principles, and excellent exploitation ability. However, HHO also has the disadvantages of slow convergence and easy falling into local optimality. Aiming at the above shortcomings, this paper proposes a Multi-strategy Enhanced Harris Hawks Optimization (MEHHO). Firstly, the map-compass operator and Cauchy mutation strategy are used to increase the population diversity and improve the ability of the algorithm to jump out of the local optimal. Secondly, a spiral motion strategy is introduced to improve the exploration phase to enhance search efficiency. Finally, the convergence speed and accuracy of the algorithm are improved by greedy selection to fully retain the dominant individuals. The global search capability of the proposed MEHHO is verified by 28 benchmark test functions, and then the parameters of the deep learning network used for channel estimation are optimized by using the MEHHO to verify the practicability of the MEHHO. Experimental results show that the proposed MEHHO has more advantages in solving global optimization problems and improving the accuracy of the channel estimation method based on deep learning.