Enhancement of risk-based metaheuristic flood evacuation route optimisation algorithms

Human activities have led to global warming, triggering frequent natural disasters, especially floods, which have caused significant harm to people. Evacuation is an effective measure to enhance urban resilience against flood. Research on optimising evacuation routes, particularly for pedestrian evacuation, is limited. Metaheuristic route optimisation algorithms, such as Ant Colony Optimisation (ACO), Genetic Algorithm (GA), Particle Swarm Optimisation algorithm (PSO) and Sparrow Search Algorithm (SSA), are gaining attention due to their flexibility, high computational efficiency, and adaptability to various scenarios, yet they have not been applied to flood evacuation problems. This research improves metaheuristic algorithms to optimise flood evacuation routes by revising and including the flood risk associated with the route as a part of the objective function. The improved algorithms developed in this study include Improved ACO (IACO), Improved GA (IGA), Improved PSO (IPSO) and Improved SSA (ISSA). The improved algorithms were incorporated into a flood evacuation research framework comprising flood modelling, flood hazard rating, and evacuation route optimisation and applied to the 2023 flood event in York, UK. The improved metaheuristic algorithms effectively navigate road networks and narrow streets, optimising pedestrian evacuation routes and avoiding high-risk flood zones. The IACO and IGA produced the route with the lowest risk and fewest turns, respectively. The IPSO algorithm was the most computationally efficient, generating the routes at the highest speed, while the ISSA showed the slowest speed. This generalisable framework integrates flood risk with infrastructure connectivity to optimise evacuation routes that enhance urban resilience and sustainability.