Adaptive Feature Selection for Anomaly Detection in Vehicular Networks Using the Recruitment‐Based Optimization Algorithm

ABSTRACT Nowadays, we increasingly encounter with highly complex real‐world optimization problems across various domains, including engineering, economics, healthcare, and artificial intelligence. Finding optimal or near‐optimal solutions to these problems remains a significant challenge. In the existing body of literature, numerous stochastic‐based optimization algorithms have been proposed to address such issues. However, ensuring consistent efficiency, robustness, and convergence across diverse problem landscapes remains an important concern. This paper introduces a novel and effective optimization algorithm called the Recruitment‐Based Optimization Algorithm (RBOA), which draws inspiration from institutional recruitment and hiring process. The algorithm simulates the dynamic interactions and decision‐making mechanisms involved in the selection of internal and external candidates during the recruitment process. Balancing exploration and exploitation is essential for any optimization approach and is achieved through the modeled behaviors of these two candidate types. External candidates facilitate global exploration, while internal candidates enhance local exploitation, together ensuring a comprehensive search of the solution space. Furthermore, the proposed RBOA has been effectively applied to an intelligent attack detection framework for Vehicular Ad Hoc Networks (VANETs), where it optimizes feature selection and classification parameters to enhance detection accuracy and reduce false alarms. In real‐world validation for VANET attack detection, RBOA achieved 97.38% accuracy and a false‐positive rate of 0.031, demonstrating its practical effectiveness in securing vehicular communications. To rigorously validate its performance, numerous benchmark functions have been used to test RBOA, encompassing multimodal, unimodal, and fixed‐dimensional optimization problems. Comparative analysis with 11 well‐established optimization algorithms reveals that RBOA consistently outperforms the compared algorithms.