Multi-Objective Optimization of Marine Winch Based on Surrogate Model and MOGA

Abstract

This study proposes a multi-objective optimization framework for electric winches in fiber-reinforced plastic (FRP) fishing vessels to address critical limitations of conventional designs, including excessive weight, material inefficiency, and performance redundancy. By integrating surrogate modeling techniques with a multi-objective genetic algorithm (MOGA), we have developed a systematic approach that encompasses parametric modeling, finite element analysis under extreme operational conditions, and multi-fidelity performance evaluation. Through a 10-t electric winch case study, the methodology’s effectiveness is demonstrated via parametric characterization of structural integrity, stiffness behavior, and mass distribution. The comparative analysis identified optimal surrogate models for predicting key performance metrics, which enabled the construction of a robust multi-objective optimization model. The MOGA-derived Pareto solutions produced a design configuration achieving 7.86% mass reduction, 2.01% safety factor improvement, and 23.97% deformation mitigation. Verification analysis confirmed the optimization scheme’s reliability in balancing conflicting design requirements. This research establishes a generalized framework for marine deck machinery modernization, particularly addressing the structural compatibility challenges in FRP vessel retrofitting. The proposed methodology demonstrates significant potential for facilitating sustainable upgrades of fishing vessel equipment through systematic performance optimization.