Adaptive quasi-opposition and dynamic switching in black-winged kite algorithm for global optimization and constrained engineering designs

The Black-winged Kite Algorithm (BKA) is a bio-inspired optimization algorithm that mimics the migratory and predatory behaviors of the black kite to find global solutions to complex optimization problems. However, BKA suffers from issues such as stagnation at local minima and slow convergence. To address these challenges, we propose two key enhancements. First, we introduce adaptive quasi opposition-based learning in both the initialization and iterative phases, enabling the algorithm to explore a broader and more diverse solution space from the outset. Second, a dynamic switching mechanism is implemented to adaptively balance exploration and exploitation throughout the search process. The resulting approach, named Adaptive Quasi Opposition and Dynamic Switching in Black-winged Kite Algorithm (AQODSBKA), is evaluated on 23 standard benchmark functions, 10 CEC2019 test functions, and 5 real-world engineering design problems. Comparative analysis, supported by statistical tests, demonstrates that AQODSBKA outperforms the original BKA and other well-established algorithms in terms of accuracy and convergence speed, while preserving the algorithm’s simplicity.