An improved Multi-Objective Whale Optimization Algorithm for hydrodynamic and acoustic performance optimization of Myring-shaped underwater vehicle

This study introduces a Laplacian-enhanced Multi-Objective Whale Optimization Algorithm (LE-MOWOA) for the hydrodynamic and acoustic performance optimization of underwater vehicles with a Myring-shaped hull. In underwater vehicle design, most existing research focuses primarily on improving hydrodyn...

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Bibliographic Details
Published in:	Engineering applications of computational fluid mechanics Vol. 19; no. 1
Main Authors:	Wang, Qigan, Dong, Yu, Wu, Han, Cao, Peizhan, Zhang, Zhijun
Format:	Journal Article
Language:	English
Published:	Taylor & Francis Group 31.12.2025
Subjects:	Ffowcs Williams and Hawkings acoustic analogy Large Eddy Simulation (LES) Least squares support vector regression Multi-Objective Whale Optimization Algorithm Myring
ISSN:	1994-2060, 1997-003X
Online Access:	Get full text
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Summary:	This study introduces a Laplacian-enhanced Multi-Objective Whale Optimization Algorithm (LE-MOWOA) for the hydrodynamic and acoustic performance optimization of underwater vehicles with a Myring-shaped hull. In underwater vehicle design, most existing research focuses primarily on improving hydrodynamic performance, often overlooking noise reduction, which has adverse impacts on marine ecosystems and stealth capability. This paper incorporates four key improvements into the traditional Multi-Objective Whale Optimization Algorithm (MOWOA): Optimal Latin Hypercube Sampling (OLHS) for population initialization, nonlinear control parameters, a Laplacian crossover operator, and a random differential-Laplacian mutation strategy. These improvements enhance the algorithm’s capability in solving Multi-Objective Optimization (MOP) problems. The Algebraic Wall-Modeled Large Eddy Simulation (WMLES) S-Omega turbulence model was combined with the Ffowcs Williams and Hawkings (FW-H) acoustic analogy to simulate hydrodynamic noise, including the quadrupole noise component. The Marine Predators Algorithm (MPA) was employed to optimize the Least Squares Support Vector Regression (LSSVR) model for predicting hydrodynamic noise. LE-MOWOA was applied to optimize the Myring profile. The optimization objectives were to minimize hydrodynamic resistance and hydrodynamic noise, and to maximize hull volume. The efficiency of the proposed algorithm was evaluated using DTLZ2 and DTLZ4 benchmark functions, where it outperformed the traditional MOWOA. The optimization results suggest that LE-MOWOA efficiently balances the hydrodynamic and acoustic objectives, with superior performance compared to the initial design.
ISSN:	1994-2060 1997-003X
DOI:	10.1080/19942060.2025.2525900