Machine Learning-Guided Population Initialization for High-Dimensional Structural Optimization of Very Large Crude Oil Carriers

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ID: 314529
2026
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Abstract
Abstract This study presents a two-step optimization framework that combines a machine learning (ML)-based prediction model with metaheuristic optimization for the structural design of very large crude oil carriers. The framework aims to minimize the midship cross-sectional area while satisfying the Common Structural Rules (CSR) and associated strength constraints. In the proposed approach, an ML-based prediction model generates a well-informed initial population, which is subsequently refined through metaheuristic optimization. The ML model is trained using 20,000 datasets derived from accumulated engineering design records. The effectiveness of the framework is demonstrated through a high-dimensional optimization problem involving more than 200 design variables. Numerical results show that conventional random or uniform initialization often leads to poor convergence, whereas ML-based initialization significantly improves convergence behavior and computational efficiency. The proposed method also exhibits robust performance for extrapolated design cases beyond the training data, indicating its potential for practical large-scale ship structural design.
Reference Key
openalex_W7162009125 Use this key to autocite in the manuscript while using SciMatic Manuscript Manager or Thesis Manager
Authors Choongki Kim, M. Y. Choi, Gunwoo Noh
Journal journal of computational design and engineering
Year 2026
DOI
10.1093/jcde/qwag051
URL
https://doi.org/10.1093/jcde/qwag051
Keywords Keywords not found

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