| Citation: | SONG L Q，CHEN W J，CHEN W H，et al. Improvement and application of hybrid strategy-based sparrow search algorithm[J]. Journal of Beijing University of Aeronautics and Astronautics，2023，49（8）：2187-2199 （in Chinese） doi: 10.13700/j.bh.1001-5965.2021.0629
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Aiming at solving the problems in the original sparrow search algorithm (SSA), such as low search accuracy, weak global search ability, slow convergence speed and easy tendency to fall into local optimum, a hybrid strategy-based sparrow search algorithm (HSSA) is proposed. First, an improved Circle chaotic map was used to initialize the population and increase the diversity of the population. Then, the salp swarm algorithm was integrated into the search formula of the discoverers to enhance its global search ability and scope in the early stage of iteration, and an adaptive step size factor was introduced into the search formula of the participants to improve the local search ability and convergence speed of the algorithm. Next, the mirror selection mechanism was applied to boost the individual quality after each iteration, thereby improving the search accuracy and speed of the algorithm. Finally, a simulated annealing mechanism was added to the location update, thus enabling the algorithm effectively to jump out of local optimum. The test results of eight functions show that the HSSA has better optimization performance than SSA. By combining the improved algorithm and the extreme learning machine, the classification and prediction accuracy of human surface electromyogram signal data increased from 80.17% to 90.87%, which proves the feasibility and good performance of the improved algorithm.
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