Harmonizing trade data from diverse datasets with varied product categories poses a substantial challenge due to differences in trade volume representation. Discrepancies arise from distinct subcategory structures in datasets, leading to disparities in traded volume. This study focuses on devising an approach to harmonize product subcategory selection by comparing volumes across datasets. Metaheuristic techniques: Genetic Algorithm (GA), Population-based Incremental Learning (PBIL), Distribution Estimation using MRF (DEUM), and Simulated Annealing (SA) are employed to address the intricate challenge of aligning subcategory volumes across sources while ensuring the agreement of selected subcategories. Evaluation of solutions considers fitness, scalability, and technique-specific strengths and weaknesses. Multiple instances of trade data harmonization are examined to assess the applicability of these techniques in mitigating trade-volume disparities. The study provides insights into the efficacy of metaheuristic techniques addressing complexities of harmonizing the trade data with inconsistent subcategory structures across datasets. Results contribute to the understanding of effective strategies for achieving alignment in hierarchical trade data.