Abstract: The Harris Hawk Optimization algorithm (HHO) is a recently proposed meta-heuristic algorithm that simulates biological population predation scheduling in the original hawk algorithm design. A Multi-strategy improved Harris Hawk Optimization algorithm (MHHO) is proposed to address the shortcomings of the Harris Hawk Optimization algorithm such as insufficient exploitation capability, decreasing population diversity, and easily falling into local optimality. Firstly, a chaotic local search strategy is introduced into the Harris Hawk to improve the exploitation ability of the algorithm. The advantages of chaotic mapping are exploited to find better individuals by performing local search around the current individual. Secondly, to enhance the population diversity, an elite alternative pooling strategy is proposed. In addition, the distribution estimation strategy is used to improve the convergence efficiency of the algorithm by sampling the dominant population information to better guide the direction of population evolution. Experimental tests on CEC2017 demonstrate that the improved algorithm achieves a balance between convergence speed and global search ability. Finally, the practicality of the improved algorithm is demonstrated by applying it to solve engineering constrained problems.

Key words: harris hawk optimization algorithm (HHO), distribution estimation strategy, chaotic local search, engineering constraints problem