Abstract
An improved black-winged kite algorithm with multiple strategies (BKAIM) is proposed in this paper to address two critical limitations in the original black-winged kite optimization algorithm (BKA): the restricted search capability caused by the low-quality initial population and the reduced population diversity resulting from blind following behavior during the migration phase. Our enhancement implements three strategic modifications across different algorithm stages. During initialization, an opposition-based learning strategy was incorporated to generate a higher-quality initial population. For the migration phase, a differential mutation strategy was integrated to facilitate information exchange among population members, mitigate the tendency of blind leader-following behavior, enhance convergence precision, and achieve an optimal balance between exploration and exploitation capabilities. Regarding boundary handling, the conventional absorption boundary method was replaced with a random boundary approach to increase population diversity and subsequently improve the algorithm's search capabilities. Comprehensive testing was conducted on four benchmark function sets (CEC2017, CEC2019, CEC2021, and CEC2022) to validate the effectiveness of the improved algorithm. Detailed convergence analysis and Wilcoxon rank-sum test comparisons with other algorithms demonstrated BKAIM's superior convergence performance and robustness. Furthermore, the support vector machine (SVM) model was optimized by BKAIM for grade identification of Dendrobium huoshanense based on near-infrared spectral data, thereby confirming its effectiveness in practical applications.