Hybrid PSO-ACO Algorithm for Routing Optimization in Ultra-Dense 5G MANET Networks

Abstract

This paper presents a hybrid metaheuristic routing algorithm integrating Particle Swarm Optimization (PSO) with Ant Colony Optimization (ACO) for mobile ad-hoc networks (MANETs) in the context of 5G communications. The proposed algorithm employs a two-phase architecture in which PSO initializes candidate paths using a greedy nearest-neighbor strategy and ACO subsequently optimizes solutions through pheromone memory with adaptive evaporation. A comprehensive simulation study in NS-3.36.1 analyzes performance across three density regimes (50, 100, and 200 nodes) within a $500 \times 500 \mathrm{~m}$ area. Results demonstrate that in ultra-dense networks (200 nodes) the hybrid algorithm achieves higher packet delivery ratio (71.5% vs 64.3%), higher throughput (343.2 kbps vs 280.8 kbps), and substantially lower performance variability (CV 26.3% vs 46.7%) compared to standard AODV.