This study tackles a complex binary multi-objective optimization problem focused on minimizing the risk of pandemic importation through strategic passenger 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 preliminary decision support system designed to assist decision-makers in the parametrization of the problem and quantify their preferences, thereby facilitating the derivation of a compromise solution via a binary particle swarm optimization (BPSO) metaheuristic. The standard BPSO is prone to particles getting trapped in local optima instead of searching for new solution and does not handle infeasible solutions properly. To overcome these inherent 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 established 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.
This study tackles a complex binary multi-objective optimization problem focused on minimizing the risk of pandemic importation through strategic passenger 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 preliminary decision support system designed to assist decision-makers in the parametrization of the problem and quantify their preferences, thereby facilitating the derivation of a compromise solution via a binary particle swarm optimization (BPSO) metaheuristic. The standard BPSO is prone to particles getting trapped in local optima instead of searching for new solution and does not handle infeasible solutions properly. To overcome these inherent 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 established 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. Read More
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