An improved black-winged kite optimization algorithm incorporating multiple strategies

Abstract In the wake of the uninterrupted progress of science and technology, optimization algorithms are increasingly extensively applied to the field of engineering. As an emerging meta-heuristic algorithm over recent years, the optimization algorithm of the Black-winged kite algorithm (BKA) is motivated by the migration and the predatory behavior of black-winged kites, which shows remarkable advantages in solving many engineering problems. However, when dealing with some complicated issues, the algorithm has the limitation of sluggish convergence speed and unsatisfactory solution precision. In response to these limitations, this article puts forward a hybrid multi-strategy optimization algorithm (HBKA). The algorithm introduces Tent mapping in the predation stage, breaks through the original search limitation expands the search scope enhances the global exploration capacity of the algorithm accelerates the convergence speed, and strengthens the search solution potential of the algorithm to help the algorithm escape from the local optimal solution. The ablation experiments using CEC2017 and CEC2022 test functions were carried out and compared with other advanced optimization algorithms. The results show that the HBKA has outstanding optimization performance. The accuracy and scientificity of the conclusion were ensured by the Friedman test and t-test. Moreover, the application of HBKA is used in solving actual engineering issues such as gear train design, pressure vessel design, reducer design, and path planning problem, which further demonstrates the latent capacity of the algorithm for resolving practical challenges.