<p>This study introduces the Birds-of-Paradise Search (BPS) algorithm, a novel nature-inspired metaheuristic optimization approach modeled after the foraging behavior and social dynamics of birds-of-paradise in tropical rainforests. The BPS algorithm simulates the birds’ search patterns, including their local and long-distance movements around trees, their tendency to gather in promising areas, and a custom movement control mechanism that combines a linear time-dependent factor with uniformly distributed randomness to adaptively adjust movement magnitude over iterations. This mechanism enables an effective balance between exploration and exploitation phases. BPS is evaluated against a comprehensive set of mathematical benchmark functions, including those from CEC-2014 and CEC-2020, and is compared with seven widely recognized metaheuristic algorithms including SDO, MOA, CDDO, FBI, BES, WOA, and TLBO. BPS consistently outperformed these algorithms in solving benchmark functions, as confirmed through statistical analysis with the Wilcoxon and Friedman rank tests. BPS ranks first, with Friedman rank values of 4.71 and 3.39 for CEC-2014 and CEC-2020, respectively. Furthermore, BPS was applied to engineering design problems, such as welded beam, pressure vessel design, and three-bar truss design where it achieved superior performance with fewer objective function evaluations. These results suggest that BPS is a highly effective algorithm for solving complex optimization problems.</p>