Abstract
The Red-Billed Blue Magpie Optimization (RBMO) algorithm is an emerging metaheuristic with strong potential applications in solving function optimization and various engineering problems, but it is often hampered by limitations such as premature convergence and an imbalanced exploration-exploitation mechanism. To overcome these deficiencies, an Improved Red-Billed Blue Magpie Optimization (IRBMO) algorithm is introduced in this paper. The IRBMO integrates three synergistic strategies within a multi-population cooperative framework: (1) an enhanced RBMO search with elite guidance to accelerate convergence; (2) an adaptive differential evolution operator to bolster local search and escape local optima; and (3) a mechanism for boosting global exploration and enhancing population diversity through quasi-opposition-based learning. The performance of IRBMO was rigorously evaluated on 26 classical benchmark functions and several real-world engineering design problems. As demonstrated by the experimental results, IRBMO significantly exceeds the performance of the original RBMO and other leading algorithms across the metrics of solution accuracy, convergence speed, and stability.