A multi-strategy improved black-winged kite algorithm and its engineering applications

Abstract

The black-winged kite algorithm (BKA) draws inspiration from the predatory behavior of black-winged kite population. To address its shortcomings, including insufficient exploitation capability, declining population diversity, and susceptibility to local optima, a multi-strategy improved black-winged kite algorithm (MSBKA) is proposed. First, chaotic mapping is introduced in the initialization phase to generate sequences that broadly cover the solution space. Second, a dynamic probability mechanism is employed during the exploitation phase to enhance population diversity and prevent premature convergence. MSBKA incorporates Lévy flight to expand global search capabilities, utilizes differential mutation to strengthen local exploitation, and adopts a reflection boundary-handling strategy to resolve boundary violation issues. Furthermore, the golden sine strategy is integrated, which offers significant advantages in accelerating convergence and avoiding local optima. The algorithm is evaluated on 12 benchmark functions, demonstrating that it achieves a better balance among convergence, robustness, and global search capability. Finally, its applications to engineering problems validate the practical utility of the proposed method.