ABSTRACT
Harris Hawks Optimization (HHO) is a Swarm Intelligence (SI) algorithm that is inspired by the cooperative behavior and hunting style of Harris Hawks in the nature. Researchers' interest in HHO is increasing day by day because it has global search capability, fast convergence speed and strong robustness. On the other hand, Emergency Vehicle Dispatching (EVD) is a complex task that requires exponential time to choose the right emergency vehicles to deploy, especially during pandemics like COVID-19. Therefore, in this work we propose to model the EVD problem as a multi-objective optimization problem where a potential solution is an allocation of patients to ambulances and the objective is to minimize the travelling cost while maximizing early treatment of critical patients. We also propose to use HHO to determine the best allocation within a reasonable amount of time. We evaluate our proposed HHO for EVD using 2 synthetic datasets. We compare the results of the proposed approach with those obtained using a modified version of Particle Swarm Optimization (PSO). The experimental analysis shows that the proposed multi-objective HHO for EVD is very competitive and gives a substantial improvement over the enhanced PSO algorithm in terms of performance. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.