| Citation: | WANG Z K，HUANG X Y，ZHU D L，et al. Learning sparrow search algorithm of hybrids boundary processing mechanisms[J]. Journal of Beijing University of Aeronautics and Astronautics，2024，50（1）：286-298 （in Chinese） doi: 10.13700/j.bh.1001-5965.2022.0195
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A learning sparrow search algorithm called HSSA that combines a boundary processing mechanism is proposed in order to address the sparrow search algorithm's (SSA) insufficient population distribution during the initialization stage and the optimization process' insufficient interference from local optimal solutions. Using the Piecewise map initializes the population, which improves the population distribution. In order for the position updates of the following generation to be guided by the optimal solution data of each generation, the follower and vigilante are updated individually using the sorted pairing learning and competitive learning procedures. According to the optimization characteristics of different stages, a multi-strategy boundary processing mechanism is formulated. While preserving the population size, it provides a more reasonable search location for individuals beyond the boundary. After 12 simulation experiments of reference functions, the stability of HSSA in convergence speed and the efficiency of optimization are proved by means of ablation experiment and Wilcoxon rank sum test.
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