Authors:
Himadri Khargharia
;
Sid Shakya
and
Dymitr Ruta
Affiliation:
EBTIC, Khalifa University, Abu Dhabi, U.A.E.
Keyword(s):
Trade Data Harmonisation, Non-Dominated Sorting Genetic Algorithm II, Genetic Algorithm, Population-Based Incremental Learning, Distribution Estimation Using MRF and Simulated Annealing.
Abstract:
Aligning trade data from disparate sources poses challenges due to volume disparities and category naming variations. This study aims to harmonize subcategories from a secondary dataset with those of a primary dataset, focusing on aligning the number and combined volumes of subcategories. We employ a multi-objective optimization approach using Non-dominated Sorting Genetic Algorithm II (NSGA-II) to facilitate trade-off assessments and decision-making via Pareto fronts. NSGA-II’s performance is compared with single-objective optimization techniques, including Genetic Algorithm (GA), Population-based Incremental Learning (PBIL), Distribution Estimation using Markov Random Field (DEUM), and Simulated Annealing (SA). The comparative analysis highlights NSGA-II’s efficacy in managing trade data complexities and achieving optimal solutions, demonstrating the effectiveness of meta-heuristic approaches in this context.