ABSTRACT Autonomous vehicles (AVs), including drones, rely on sensors, machine learning algorithms, and large datasets for perception, decision‐making, and control. However, the high dimensionality of these datasets increases computational load and hampers real‐time performance. In Unmanned Aerial Vehicle (UAV) systems, feature selection is critical for reducing complexity and enhancing processing efficiency, thereby enabling faster and more accurate decision‐making. In this study, we enhance the Harris Hawks Optimization (HHO) algorithm by introducing a novel ambush‐based operator to regulate selection pressure, resulting in an improved variant named AMHHO. The effectiveness of AMHHO is validated using IEEE CEC2019 benchmark functions and compared against several well‐known optimization algorithms. To further evaluate its robustness, ablation studies and sensitivity analyses are conducted to identify the most efficient AMHHO variants. Furthermore, a binary version of AMHHO (BAMHHO) is applied to ten high‐dimensional datasets and the UAV‐IDS‐2020 dataset for feature selection and classification tasks. BAMHHO is assessed based on classification accuracy, fitness value, feature selection ratio, and computation time, demonstrating superior performance across multiple datasets and outperforming state‐of‐the‐art methods. To rigorously evaluate the statistical significance of its results, Wilcoxon Signed‐Rank test is applied to compare BAMHHO with other well‐known algorithms, confirming the statistical superiority of BAMHHO. In conclusion, BAMHHO not only achieves effective performance on high‐dimensional datasets but also achieves 100% classification accuracy on the UAV‐IDS‐2020 dataset, all while maintaining an optimal balance between feature reduction and computational efficiency. These findings confirm BAMHHO's effectiveness in handling high‐dimensional data and highlight its potential for application in UAV‐based intrusion detection systems.

In recent years, the Internet of Things (IoT) developments have made it one of the most important technologies. The exponential growth of data and increasing the number of latency-sensitive applications has necessitated a new approach to support these applications. The emerging fog computing architecture has partially addressed the issue of latency and other limitations of the IoT-based cloud computing paradigm. In order to achieve high-quality services and high system performance, an appropriate and efficient task scheduling method is needed, in addition, the energy consumption of computing devices should be considered. In this article, a constraint bi-objective optimization problem is designed to minimize the servers’ energy consumption and overall response time simultaneously. Then, to solve this problem, by introducing a recombination operator and modifying NSGA-II, a directed non-dominated sorting genetic algorithm, called D-NSGA-II is proposed. This algorithm can control the selection pressure of agents, and balance the exploration and exploitation abilities of the algorithm using this new operator. To evaluate the performance of this algorithm, it is compared with well-known meta-heuristic algorithms. The experimental results demonstrate the D-NSGA-II has better performance than other algorithms. It can also respond to all requests before their deadline.