TITLE:
Optimizing Mobile Payment Systems Using the Tabu Search Algorithm: Towards Increased Security and Efficiency
AUTHORS:
Dieudonné Nijimbere, Vincent Mbonigaba
KEYWORDS:
Max-Mean Dispersion, Mobile Payments, Combinatorial Optimization, Network Resilience, Metaheuristics, Reliability, Quality of Service (QoS), Load Balancing, Stochastic Modeling, Markov Chains
JOURNAL NAME:
Open Journal of Applied Sciences,
Vol.16 No.4,
April
28,
2026
ABSTRACT: This work focuses on optimizing resource and transaction dispersion in mobile payment systems based on the Max-Mean Dispersion problem. The objective is to maximize the average distance between selected elements (nodes, channels, or servers) in order to avoid load concentration and reduce the risk of saturation. Several approaches are compared, including exact methods, heuristics, metaheuristics, and stochastic models based on Markov chains. The study shows that exact methods guarantee an optimal solution but remain limited for large network sizes. Fast heuristics offer good performance but with sometimes suboptimal dispersion. Metaheuristic approaches, such as GRASP, simulated annealing, or genetic algorithms, offer a good compromise between quality and computation time. Stochastic modeling, on the other hand, allows for the analysis of temporal variability in system states. The integration of Max-Mean Dispersion in mobile payment networks improves robustness, flow distribution, fault tolerance, and quality of service. Simulation results show that controlled dispersion reduces dependence on certain critical nodes and improves overall availability.