Enhanced Binary Particle Swarm Optimization for Mitigating Pandemic Spread through Passenger Air Traffic Management

This study tackles a complex binary multi-objective optimization problem fo
cused on minimizing the risk of pandemic importation through strategic pas
senger air traffic management. The approach involves determining whether
 international connections to destination airports within a specified country
 should be activated or deactivated over a defined time frame, considering
 epidemiological, economic, and socio-political impacts. We introduce a pre
liminary decision support system designed to assist decision-makers in the
 parametrization of the problem and quantify their preferences, thereby facil
itating the derivation of a compromise solution via a binary particle swarm
 optimization (BPSO) metaheuristic. The standard BPSO is prone to par
ticles getting trapped in local optima instead of searching for new solution
 and does not handle infeasible solutions properly. To overcome these inher
ent limitations, we propose an enhanced version of the BPSO metaheuristic.
 This enhanced algorithm incorporates novel mechanisms to promote solution
 space exploration and a robust strategy for managing infeasible solutions.
 A rigorous comparative analysis is conducted to evaluate the performance
 of the enhanced BPSO against both the original BPSO and several estab
lished state-of-the-art metaheuristics utilizing three benchmark datasets of
 a constrained problem. Finally, the effectiveness of the proposed enhanced
 metaheuristic is demonstrated in the context of the pandemic importation
 risk reduction problem, where it outperforms the original BPSO.