An Optimized Fitness-Based Enhanced ant Colony Optimization for Optimal Road Selection in Intelligent Heterogeneous Vehicular ad-hocNetworks

In Intelligent Heterogeneous Vehicular Ad Hoc Networks (IH-VANETs), long urban roads with a high density of vehicles and a maximum number of road signals increase unpredictable delays in terms of long travel times and heavy traffic congestion. These unpredictable delays are exacerbated by the rapid increase in vehicle density and irregular traffic flow on roads with high traffic signals. To address this gap, an optimized fitness-based enhanced ant colony optimization (OF-EACO) for IH-VANETs is proposed. OF-EACO aims to find optimal, uncongested short roads with low vehicle density and fewer traffic signals, thereby providing shorter travel times for vehicles without traffic congestion and unpredictable delays. To achieve this goal, the novel road fitness function of the proposed OF-EACO assigns a high road fitness score to roads according to short length, low vehicle density, and low signal count to support quick travel of vehicles between two ends without delay and traffic congestion. OF-EACO's roulette wheel takes the road fitness scores of available roads as input and outputs the optimal road. The optimal road is rich in all aspects and is intended to reduce travel time through short and un crowded roads. A network simulator is used to simulate the proposed OF-EACO, existing vehicular multi-hop routing algorithm with intelligent transportation system (VMR-ITS), and improved distance-based ant colony optimization routing (IDBACOR).Simulation results of the proposed OF-EACO indicated that, due to the use of optimal roads, it was able to achieve significant improvements in terms of vehicle travel cost, road establishment time, convergence speed, road traffic congestion overhead, routing overhead, Computational overhead, Computational Complexity, Actual Wall Time Analysis, and Energy Consumption compared to VMR-ITS and IDBACOR.