Abstract
This paper proposed the Revamped Black-winged Kite Algorithm (RBKA), a newly developed optimization intelligence method to boost the performance of classic Black-winged Kite Algorithm (BKA). It employs three revolutionary tactics to enhance its efficiency. Initially, the technique uses a logistic map for population initialization, swapping random generation to enhance global search effectiveness and fast convergence. Secondly, a novel search strategy is devised, incorporating chaotic perturbation factor-based attack behaviour and Brownian motion-based migratory behaviour to find an ideal balance between exploration and exploitation. An opposition-based learning (OBL) technique is utilized to tackle stagnation in local optima and augment the algorithm's capacity to identify global solutions. The effectiveness and stability of RBKA are systematically evaluated using established benchmark functions, such as CEC2005, CEC2020, and CEC2022. Additionally, the method is utilized in fifteen constraint optimization problems from the CEC2011 test suite and six complex engineering design problems, demonstrating its versatility and efficacy. The comparative statistical evaluation demonstrates that RBKA outperforms the other intelligence algorithms in terms of convergence speediness, stability, and overall effectiveness, positioning it as a robust and adaptable solution for complex optimization problems.