Optimization design method for fixed ballast in fishing vessel based on nonlinear constraints

Abstract

With the modernization of fishing vessels, green and intelligent design has become a paramount trend. However, increased equipment weight challenges vessel stability, often necessitating fixed ballast that leads to resource redundancy and inefficiency. To address this, this paper proposes a nonlinear constraint optimization model integrating intelligent algorithms. The model aims to minimize ballast weight and intensify regional layout by establishing an accurate mapping between the ballast scheme and the vessel’s buoyancy and stability. The entire process, from model construction to solution strategy and program implementation, is elaborated. A MATLAB-VC hybrid programing toolchain, incorporating the Penalty Function Method (PFM), Genetic Algorithm (GA), and Particle Swarm Optimization (PSO), was developed to solve the problem for a 66 m purse seiner. The results demonstrate a reduction in fixed ballast weight of over 40%, significantly improving loading capacity and reducing material consumption, in line with green design principles. Crucially, a comprehensive algorithmic performance analysis reveals that intelligent algorithms, particularly PSO, are indispensable for handling complex objectives like layout concentration, providing clear guidance for algorithmic selection in practical ship design. The proposed method offers solid theoretical support and technical tools for the intelligent transformation and automated decision-making in ship design.