Multi group sparrow search algorithm based on K-means clustering

A K-means multi-group sparrow search algorithm (KSSA) based on K-means clustering is proposed in order to improve the convergence speed of the sparrow search algorithm (SSA) in single population search, which causes redundancy in its convergence speed and makes it simple to ignore the flaw that the high-quality solution falls into local optimization. Firstly, the multi-population mechanism is introduced into SSA to weaken the convergence ability of a single population and reduce the probability of falling into local optimization. Secondly, in order to boost the effectiveness of early search, the sub-population is divided, the differences between the sub-populations are increased, and the members of the sub-population are forced to concentrate on searching within a certain area Then, the weighted center of gravity communication strategy is used to improve the quality of population communication, reduce the interference of its own population, and reduce the risk of all sub populations falling into local optimization due to a sub population falling into local optimization. Finally, dynamic reverse learning is introduced into vigilant to enhance their back feeding behavior and improve the defects of slow convergence speed and insufficient convergence accuracy caused by the increase of factor population. Through the test function simulation experiment, it is proved that KSSA has better optimization performance than SSA and other algorithms. © 2024 Beijing University of Aeronautics and Astronautics (BUAA). All rights reserved.