Abstract
Black-Breasted Lapwing Algorithm (BBLA), a novel metaheuristic optimization method inspired by the adaptive defensive and foraging strategies of the black-breasted lapwing in nature, is presented in this paper.The bird's behavior-such as deceptive zigzag flights, broken-wing displays, unpredictable movements, and cooperative alerting-is translated into a mathematical framework that balances global exploration and local exploitation without relying on additional control parameters.The algorithm begins with population-based initialization to ensure diverse coverage of the solution space, followed by an exploration phase that mimics the lapwing's erratic manoeuvrers and group alert signals to escape local optima, and an exploitation phase modelled on its refined defensive tactics for accurate local search and convergence.BBLA's performance is rigorously assessed on a suite of standard benchmark functions and several constrained engineering design problems, including tension/compression spring, welded beam, and pressure vessel design.Comparative studies with nine advanced metaheuristics demonstrate that BBLA consistently achieves superior results in terms of mean, median, standard deviation, and rank, while also producing stable distributions with minimal variance, as confirmed by boxplot analyses.These findings highlight BBLA's robustness, reliability, and capacity to address nonlinear, multimodal, and high-dimensional optimization challenges, making it a promising tool for real-world engineering applications.