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.

It is easier to understand an algorithm when it can be seen in interactive mode. The current study implemented four algorithms to construct geometric spanners; the path-greedy, gap-greedy, Theta-graph and Yao-graph algorithms. The data structure visualization framework (http://www.cs.usfca.edu/~galles/visualization/) developed by David Galles was used. Two features were added to allow its use in spanner algorithm visualization: support point-based algorithms and export of the output to Ipe drawing software format. The interactive animations in the framework make steps of visualization beautiful and media controls are available to manage the animations. Visualization does not require extensions to be installed on the web browser. It is available at http://cs.yazd.ac.ir/cgalg/AlgsVis/.

Let (S,d) be a finite metric space, where each element p∈S has a non-negative weight w (p). We study spanners for the set S with respect to the following weighted distance function: $$\mathbf{d}_{\omega}(p,q)=\left\{\begin{array}{ll}0&\mbox{ if $p=q$,}\\ \operatorname {w}(p)+\mathbf{d}(p,q)+ \operatorname {w}(q)&\mbox{ if $p\neq q$.}\end{array}\right.$$ We present a general method for turning spanners with respect to the d-metric into spanners with respect to the d ω -metric. For any given ε>0, we can apply our method to obtain (5+ε)-spanners with a linear number of edges for three cases: points in Euclidean space ℝ d , points in spaces of bounded doubling dimension, and points on the boundary of a convex body in ℝ d where d is the geodesic distance function. We also describe an alternative method that leads to (2+ε)-spanners for weighted point points in ℝ d and for points on the boundary of a convex body in ℝ d . The number of edges in these spanners is O(nlog n). This bound on the stretch factor is nearly optimal: in any finite metric space and for any ε>0, it is possible to assign weights to the elements such that any non-complete graph has stretch factor larger than 2−ε.

Given a Euclidean graph G in $\mathbb{R}^d$ with n vertices and m edges, we consider the problem of adding an edge to G such that the stretch factor of the resulting graph is minimized. Currently, the fastest algorithm for computing the stretch factor of a graph with positive edge weights runs in $\cal{O}$$(nm+n^2 \log n)$ time, resulting in a trivial $\cal{O}$$(n^3m+n^4 \log n)$-time algorithm for computing the optimal edge. First, we show that a simple modification yields the optimal solution in $\cal{O}$$(n^4)$ time using $\cal{O}$$(n^2)$ space. To reduce the running time we consider several approximation algorithms.

Given a Euclidean graph G in Rd with n vertices and m edges we consider the problem of adding a shortcut such that the stretch factor of the resulting graph is minimized. Currently, the fastest algorithm for computing the stretch factor of a Euclidean graph runs in O(mn+n2 log n) time, resulting in a trivial O(mn3+n4 log n) time algorithm for computing the optimal shortcut. First, we show that a simple modification yields the optimal solution in O(n4) time using O(n2) space. To reduce the running times we consider several approximation algorithms. Our main result is a (2+ε)-approximation algorithm with running time O(nm+n2(log n+1/ε3d)) using O(n2) space.