Efficient hybrid enhanced genetic algorithm and ant colony system model for rerouting multimedia message in multiple node–link failures within wireless network

Summary Failure‐resilient wireless networks have attracted the interest of the research community and have been an open area of concern in the studies of wireless network in recent years. Accordingly, different research on restoration techniques have been carried out to proffer solution to the network component failures. In the same vein, this article proposes a hybridized enhanced genetic algorithm and ant colony system (EGAACS) survivability model, which can instantly resolve node–link failure problems, thus improving the quality of service of the wireless network. The EGAACS is a hybrid model that combines the principles of the enhanced genetic algorithm (EGA) and ant colony system (ACS) models to form a capacity efficiency solution that outperforms the ACS and EGA models in terms of path cost and transmission delay. The resilience of this proposed EGAACS model is verified for different wireless network sizes (20, 30, 40, and 50 node networks). Simulation results show that the proposed EGAACS model generates the best close to optimal paths in terms of the path cost and transmission delay in comparison to the EGA and ACS models. In fact, the performance of the proposed EGAACS model is more conspicuous as the size of the network increases. More importantly, the proposed EGAACS model is suitable for real‐time wireless network as it exhibits moderate computational time complexity. Failure‐resilient wireless networks have attracted the interest of the research community and have been an open area of concern in the studies of wireless network in recent years. Accordingly, different research on restoration techniques have been carried out to proffer solution to the network component failures. In the same vein, this article proposes the EGAACS survivability model, which can instantly resolve node–link failure problems, thus improving the quality of service of the wireless network in terms of the path cost and transmission delay. Additionally, this proposed EGAACS model is suitable for real‐time wireless network as it exhibits moderate computational time complexity.