An Intelligent Ship Equipment Layout Design Method Based on the AEPSO Algorithm and Multiple Optimization Strategies

Abstract

 To address the limitations in ship equipment layout design (SELD), such as excessive reliance on expert experience, poor adaptability of traditional optimization algorithms, and susceptibility to local optima, this study proposes an intelligent layout method that integrates an adaptive enhanced particle swarm optimization (AEPSO) algorithm with multiple adaptive strategies. The approach introduces a dynamic parameter adjustment mechanism to improve global search performance, while incorporating particle scouting and mutation elimination strategies to enhance local exploitation and global exploration capabilities, thereby improving both solution quality and diversity. The layout model comprehensively considers equipment interrelations, zone feasibility, spatial constraints, and engineering rules, and employs a multi-objective evaluation function covering key indicators such as collaboration, space utilization, and layout stability. Finally, the method is validated through a real-world ship engine room case study. Experimental results demonstrate that the proposed method outperforms the standard PSO in convergence speed, optimization accuracy, and adaptability, with improvements of 3.5% and 12.6% in accuracy and speed, respectively. Compared to expert-designed layouts, the proposed method yields over 6.9% improvement, validating its effectiveness and practical value in addressing complex layout challenges. This method holds promise as an intelligent optimization solution for preliminary ship design.