Metaheuristic optimization in AHP–TOPSIS pairwise matrices for decision support on preventing low birth weight in adolescent pregnancies

Abstract

Low Birth Weight (LBW), defined as a birth weight below 2,500 grams, remains a major public health concern in developing countries due to its association with increased neonatal morbidity and mortality. Decision-support systems based on the Analytic Hierarchy Process (AHP) and Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) have been widely applied in healthcare prioritization; however, their reliability may be compromised by inconsistencies in expert-generated pairwise comparison matrices. Despite the growing use of metaheuristic optimization techniques, limited studies have compared the effectiveness of Genetic Algorithm (GA) and Particle Swarm Optimization (PSO) in improving AHP–TOPSIS weighting consistency for LBW prevention. This study aims to compare the performance of GA and PSO for optimizing pairwise comparison matrices within a hybrid AHP–TOPSIS framework for adolescent-pregnancy intervention prioritization. The study utilized maternal and child health data obtained from the Indramayu District Health Office in 2025. Criterion weights were determined using AHP, while intervention alternatives were ranked using TOPSIS. GA and PSO were applied to minimize matrix inconsistency through hyperparameter-optimized search processes. Performance was evaluated using Consistency Ratio (CR), Hamming Distance, Euclidean Distance, Kendall’s Tau, and computational time. The results showed that both GA and PSO successfully improved matrix consistency and generated alternative feature-weight distributions while maintaining moderate agreement with the baseline ranking structure. GA achieved higher ranking stability (Kendall’s Tau = 0.7197) and lower computational time, whereas PSO produced greater weight redistribution. These findings demonstrate that metaheuristic optimization can enhance the robustness and consistency of AHP–TOPSIS-based weighting schemes, providing a more reliable ranking-based decision-support mechanism for LBW prevention and intervention prioritization.