Enhanced binary particle swarm optimization for mitigating pandemic spread through passenger air traffic management

• An enhanced binary particle swarm optimization (BPSO) metaheuristic is proposed (EBPSO). • EBPSO incorporates novel mechanisms to promote exploration of the solution space and a robust strategy for managing infeasible solutions. • A rigorous comparative analysis is conducted to evaluate the performance of EBPSO against both the original BPSO and several established state-of-the-art metaheuristics utilizing three benchmark datasets of a constrained problem. • The effectiveness of the proposed enhanced metaheuristic is demonstrated in the context of the pandemic importation risk reduction problem. 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.

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Publication Details

Journal
Knowledge-Based Systems
Published
2025-09-10
DOI
https://doi.org/10.1016/j.knosys.2025.114430
Primary Topic
Aviation Industry Analysis and Trends
Type
article
Field-Weighted Citation Impact
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article

Enhanced binary particle swarm optimization for mitigating pandemic spread through passenger air traffic management

Alfonso Mateos, Antonio Jiménez Martín, Gabriel A. Peña
Knowledge-Based Systems
Aviation Industry Analysis and Trends
article

Enhanced binary particle swarm optimization for mitigating pandemic spread through passenger air traffic management

Alfonso Mateos, Antonio Jiménez Martín, Gabriel A. Peña
article en

Abstract

• An enhanced binary particle swarm optimization (BPSO) metaheuristic is proposed (EBPSO). • EBPSO incorporates novel mechanisms to promote exploration of the solution space and a robust strategy for managing infeasible solutions. • A rigorous comparative analysis is conducted to evaluate the performance of EBPSO against both the original BPSO and several established state-of-the-art metaheuristics utilizing three benchmark datasets of a constrained problem. • The effectiveness of the proposed enhanced metaheuristic is demonstrated in the context of the pandemic importation risk reduction problem. 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.

Knowledge-Based SystemsVol. 329
Universidad Politécnica de Madrid (ES)
Openalex Percentile: Top 61%
Aviation Industry Analysis and Trends
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