An Improved Red-Billed Blue Magpie Algorithm and Its Application to Constrained Optimization Problems

The Red-Billed Blue Magpie Optimization (RBMO) algorithm is a metaheuristic method inspired by the foraging behavior of red-billed blue magpies. However, the conventional RBMO often suffers from premature convergence and performance degradation when solving high-dimensional constrained optimization problems due to its over-reliance on population mean vectors. To address these limitations, this study proposes an Improved Red-Billed Blue Magpie Optimization (IRBMO) algorithm through a multi-strategy fusion framework. IRBMO enhances population diversity through Logistic-Tent chaotic mapping, coordinates global and local search capabilities via a dynamic balance factor, and integrates a dual-mode perturbation mechanism that synergizes Jacobi curve strategies with Lévy flight strategies to balance exploration and exploitation. To validate IRBMO's efficacy, comprehensive comparisons with 16 algorithms were conducted on the CEC-2017 (30D, 50D, 100D) and CEC-2022 (10D, 20D) benchmark suites. Subsequently, IRBMO was rigorously evaluated against ten additional competing algorithms across four constrained engineering design problems to validate its practical effectiveness and robustness in real-world optimization scenarios. Finally, IRBMO was applied to 3D UAV path planning, successfully avoiding hazardous zones while outperforming 15 alternative algorithms. Experimental results confirm that IRBMO exhibits statistically significant improvements in robustness, convergence accuracy, and speed compared to classical RBMO and other peers, offering an efficient solution for complex optimization challenges.