Red-Billed Blue Magpie Optimization Algorithm-Based Aquila Optimizer: Numerical Optimization, Engineering Problem, and Cybersecurity Intrusion Prediction

A hybrid metaheuristic methodology that combines the Red-billed Blue Magpie Optimization (RBMO) algorithm with the Aquila Optimizer (AO) is introduced in this work as the RBMOAO method. The novel algorithm addresses a critical shortcoming of the standard AO: its exploration-to-exploitation ratio across different optimization stages is inefficient, yielding premature convergence and low diversity within the population. This is achieved by using RBMO’s Group-Based Directional Perturbation (GDP) and its dynamic convergence factor (CF) as part of the methodology. The early stages of the optimization process are characterized by a grouping methodology to maintain population diversity through coordinated exploration across subgroups of varying sizes using GDP. Later iterations are characterized by a CF-guided updating process that increases the resolution of the search for the best areas, thereby improving convergence precision without sacrificing solution quality. Empirical testing of the proposed methodology using the CEC 2015 and CEC 2020 test sets demonstrated RBMOAO’s superior performance compared to other metaheuristics, outperforming other optimizers in 73.33% of CEC 2015 functions and 80% of CEC 2020 functions, with statistical significance in the increased precision and robustness of solutions across all problem types. Additionally, the RBMOAO methodology demonstrated outstanding performance in constrained engineering design problems. In addition to optimization, an RBMOAO-optimized ensemble architecture was implemented to predict cybersecurity intrusion threats, achieving an accuracy of 89.6%. Through the dynamic calibration of the base learner weights via metaheuristic search, the RBMOAO ensemble achieved the top ranking. These results illustrate the wide range of applications of the RBMOAO methodology and provide support for its deployment in the context of high-stakes predictive analytics.