# A Comprehensive Review of Metaheuristic Algorithms for Node Placement in UAV Communication Networks

## TL;DR

This paper reviews metaheuristic algorithms for optimally placing UAV nodes in communication networks to improve performance and coverage.

## Contribution

The paper provides a comprehensive and critical review of metaheuristic algorithms specifically for UAV communication network node placement.

## Key findings

- Metaheuristic algorithms are effective for solving the NP-hard node placement problem in UAV communication networks.
- Hybrid algorithms show strong performance in optimizing coverage and computational time in UAVCN scenarios.
- Current reviews often focus on terrestrial networks, leaving UAV-specific solutions underexplored.

## Abstract

Unmanned Aerial Vehicle Communication Networks (UAVCNs) have emerged as a transformative solution to enable resilient, scalable, and infrastructure-independent wireless communication in urban and remote environments. A key challenge in UAVCNs is the optimal placement of Unmanned Aerial Vehicle (UAV) nodes to maximize coverage, connectivity, and overall network performance while minimizing latency, energy consumption, and packet loss. As this node placement problem is NP-hard, numerous meta-heuristic algorithms (MHAs) have been proposed to find near-optimal solutions efficiently. Although research in this area has produced a wide range of meta-heuristic algorithmic solutions, most existing review articles focus on MANETs with terrestrial nodes, while comprehensive reviews dedicated to node placement in UAV communication networks are relatively scarce. This article presents a critical and comprehensive review of meta-heuristic algorithms for UAVCN node placement. Beyond surveying existing methods, it systematically analyzes algorithmic strengths, vulnerabilities, and future research directions, offering actionable insights for selecting effective strategies in diverse UAVCN deployment scenarios. To demonstrate practical applicability, selected hybrid algorithms are evaluated in a reproducible Python framework using computational time and coverage metrics, highlighting their ability to optimize multiple objectives and providing guidance for future UAVCN optimization studies.