A comparative study of combined surrogate models and evolutionary algorithms for ship resistance optimization

Jin Feng; Shuxia Ye; Yongwei Zhang; Liang Qi; Yujie Shen

doi:10.1117/12.3030217

5 June 2024 A comparative study of combined surrogate models and evolutionary algorithms for ship resistance optimization

Jin Feng, Shuxia Ye, Yongwei Zhang, Liang Qi, Yujie Shen

Author Affiliations +

Proceedings Volume 13163, Fourth International Conference on Mechanical, Electronics, and Electrical and Automation Control (METMS 2024); 1316328 (2024) https://doi.org/10.1117/12.3030217
Event: International Conference on Mechanical, Electronics, and Electrical and Automation Control (METMS 2024), 2024, Xi'an, China

Abstract

The optimal parameterization of ship resistance poses a complex engineering problem involving issues such as solution space sampling, to address this problem, this study presents a solution by comparing six optimization frameworks formed by two surrogate models and three evolutionary algorithms. The proposed solution involves the DE-RBF combination. Building upon an existing sample set, surrogate models, including Radial Basis Function (RBF) neural network and Kriging model, were employed to establish mathematical relationships between input design variables and resistance performance. Using these surrogate models as a foundation, the study applied Particle Swarm Optimization (PSO), Differential Evolution (DE), and Artificial Bee Colony (ABC) to optimize water resistance. Experimental results confirm the significant optimization effect of the surrogate model-evolutionary algorithm framework for ship resistance. Among them, the DE-RBF combination exhibits superior performance in finding the optimal parameter combination for ship resistance performance. Numerical experiments demonstrate that this frame effectively reduces ship resistance, with a 56% reduction compared to the initial resistance value of the original ship model.

(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Jin Feng, Shuxia Ye, Yongwei Zhang, Liang Qi, and Yujie Shen "A comparative study of combined surrogate models and evolutionary algorithms for ship resistance optimization", Proc. SPIE 13163, Fourth International Conference on Mechanical, Electronics, and Electrical and Automation Control (METMS 2024), 1316328 (5 June 2024); https://doi.org/10.1117/12.3030217

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