Work

Optimization method for jacket platforms using random forest surrogate model

Year: 2025

Type: article

Abstract: The optimization of jacket platforms typically relies on computationally intensive finite element analysis (FEA), which is relatively time-consuming. Surrogate models are widely used in multi-domain optimization problems, while machine learning algorithms are employed to construct surrogate models, demonstrating promising applications. In this study, an optimization method that integrates machine learning surrogate models with an improved genetic algorithm (GA) is employed to enhance structural optimization efficiency. Then, the proposed method is applied to a typical jacket platform in the Bohai Sea using a seven-dimensional optimization space designed to minimize the total structural weight. A surrogate model is derived through a random forest algorithm to replace FEA. The surrogate model is combined with a real-coded GA featuring adaptive crossover and mutation for iterative optimization. Moreover, surrogate models are developed using Decision Tree and KNN methods. Evaluation results show that the random forest algorithm outperforms Decision Tree and KNN methods. The random forest surrogate model can save significant computation time while maintaining high accuracy, providing an efficient and effective alternative to the FEA optimization method.

Source: Ocean Engineering

DOI: 10.1016/j.oceaneng.2025.122620

Authors: Tianyou Jiao, Hongchang Zhen, Kevin Huang

Institutions: South China University of Technology, China Petroleum Pipeline Engineering (China)

Language: English

Topic: Advanced Multi-Objective Optimization Algorithms

Subfield: Computational Theory and Mathematics

Field: Computer Science

Domain: Physical Sciences

SDG: Industry, innovation and infrastructure

Open Access status: hybrid

Estimated APC paid: $4,150