| Citation: | ZHANG L，SHEN J Y，HU C L，et al. Learning Harris Hawks optimization algorithm with signal-to-noise ratio[J]. Journal of Beijing University of Aeronautics and Astronautics，2025，51（7）：2360-2373 （in Chinese） doi: 10.13700/j.bh.1001-5965.2023.0433
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Aiming at the problem of insufficient population learning and adaptability of the Harris hawks optimization (HHO) algorithm, this paper proposes a learning Harris hawks optimization based on the signal-to-noise ratio（SLHHO）algorithm. By using the signal-to-noise ratio as a metric to assess individual position information, the algorithm creates a coordinated learning strategy that can more realistically update the positions of individuals within the population. It then reworks the escape distance to enhance the algorithm’s capacity for adaptation and optimization seeking. With 12 benchmark functions as the standard, the proposed algorithm was tested for its performance with the variants of the Harris hawk algorithm and other algorithms, and compared and analyzed in the evaluation indexes such as time complexity, diversity, exploration and development, etc. The results show that SLHHO algorithm is highly competitive and feasible, and finally, its practicability is verified in the pressure vessel design problem.
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