Improved many-objective particle swarm optimization based welding sequence optimization research

Lei Dong, Shimin Gu, Jianwei Dong, Qiukai Ji and Jinfeng Liu

PLOS ONE, 2026, vol. 21, issue 3, 1-23

Abstract: Welding sequence optimization (WSO) for ship components is a complex, multi-objective, and nonlinear challenge. Traditional methods relying heavily on engineer experience often lead to inadequate decisions, resulting in excessive deformation, residual stress, and even cracking. To address this, we propose a systematic WSO method for ship structural parts that integrates both process and geometric constraints. The optimization objectives are formally defined through objective functions quantifying structural deformation and residual stress. For solving this high-dimensional problem, an Improved Many-Objective Particle Swarm Optimization (IMaOPSO) algorithm is developed. IMaOPSO enhances the classical PSO by incorporating an adaptive fuzzy dominance relation to improve selection pressure and a perturbation term guided by elite solutions to maintain population diversity. This ensures rapid convergence to a well-distributed set of high-quality solutions. Simulation analysis of different welding sequences is conducted based on the SYSWELD software platform. A case study on a ship deck structure demonstrates that IMaOPSO outperforms several established algorithms (NSGA-II, SPEA2, SMPSO) in convergence speed and stability. The optimal sequence identified reduces average deformation by 32.6% to 62.2% compared to other methods, confirming the proposed method’s significant practical engineering value for improving welding quality and efficiency in shipbuilding.

Date: 2026
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Persistent link: https://EconPapers.repec.org/RePEc:plo:pone00:0343554

DOI: 10.1371/journal.pone.0343554

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