Comparative Analysis of Metaheuristics Techniques for Trade Data Harmonization

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    Abstract

    The harmonization of trade data from two datasets containing different and distinct categories poses a challenging real-world problem. To address this issue, we model it as an optimization problem and investigate the effectiveness of various metaheuristic techniques in achieving optimal or near-optimal solutions. Particularly, we analyze the performance of Genetic Algorithm (GA), Population-based Incremental Learning (PBIL), DEUM, and Simulated Annealing (SA) in terms of best fitness, scalability, and their respective strengths and weaknesses. We explore multiple instances of the trade data harmonisation problem of different sizes to assess the applicability of these techniques in mitigating trade volume disparities. By examining the outcomes, our research offers valuable insights into the suitability of metaheuristic techniques for this problem.

    Original languageBritish English
    Title of host publicationProceedings of the 15th International Joint Conference on Computational Intelligence, IJCCI 2023
    EditorsNiki van Stein, Francesco Marcelloni, H. K. Lam, Marie Cottrell, Joaquim Filipe
    Pages206-213
    Number of pages8
    ISBN (Electronic)9789897586743
    DOIs
    StatePublished - 2023
    Event15th International Joint Conference on Computational Intelligence, IJCCI 2023 - Hybrid, Rome, Italy
    Duration: 13 Nov 202315 Nov 2023

    Publication series

    NameInternational Joint Conference on Computational Intelligence
    ISSN (Electronic)2184-3236

    Conference

    Conference15th International Joint Conference on Computational Intelligence, IJCCI 2023
    Country/TerritoryItaly
    CityHybrid, Rome
    Period13/11/2315/11/23

    Keywords

    • Distribution Estimation Using MRF
    • Genetic Algorithm
    • Population-Based Incremental Learning
    • Simulated Annealing
    • Trade Data Harmonisation

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