Adaptive mutation sparrow search optimization algorithm

To address the problems that the sparrow search algorithm is prone to fall into local extremum points in the early stage and not high accuracy in the later stage of the search, an adaptive variational sparrow search algorithm (AMSSA) is proposed. Firstly, the initial population is initialized by cat mapping chaotic sequences to enhance the randomness and ergodicity of the initial population and improve the global search ability of the algorithm; Secondly, the Cauchy mutation and Tent chaos disturbance are introduced to expand the local search ability, so that the individuals caught in the local extremum can jump out of the limit and continue the search. Finally, the explorer-follower number adaptive adjustment strategy the adaptive adjustment strategy of explorer-follower number is proposed to enhance the global search ability in the early stage and the local depth mining ability in the later stage of the algorithm by using the change of the explorer and follower numbers in each stage to improve the optimization-seeking accuracy of the algorithm. Sixteen benchmark functions and the Wilcoxon test are selected for validation, and the experimental results show that the AMSSA achieves greater improvement in search accuracy, convergence speed and stability compared with other algorithms. © 2023 Beijing University of Aeronautics and Astronautics (BUAA). All rights reserved.