Enhancing Smart City Healthcare with Hybrid Swarm Optimization: A Comparison of MFO-PSO and ACO Approaches

Optimal healthcare transportation is a key challenge of smart cities, particularly in cases of emergencies, when traffic congestion and inefficient routing cause delays. In this work, a new hybrid swarm intelligence algorithm based on Moth Flame Optimization (MFO), Particle Swarm Optimization (PSO), and Ant Colony Optimization (ACO) for optimizing real-time multi-source multi-destination (MSMD) traffic routing in healthcare services is presented. The hybrid approach dynamically adjusts vehicle paths based on traffic conditions to reduce travel time, improve traffic flow, and minimize fuel consumption. Simulation experiments based on the NetLogo platform show that the hybrid strategy saves travel time by 14%, maximizes throughput by 6.4%, and conserves energy by 7.5% compared to individual optimization approaches. This study demonstrates the potential of hybrid swarm optimization to optimize healthcare logistics and emergency response systems in smart cities. Future research will focus on further improving the optimization by exploring machine learning-based predictive routing.