Multi-Objective Optimization Design of Cylindrical FPSO Mooring System Based on KAN Surrogate Model and NSGA-III Algorithm

Abstract

Cylindrical floating production storage and offloading (FPSO) units are advancing into deeper waters. Overcoming the severe challenges posed by complex deepwater environments requires the design of mooring systems that balance economic efficiency and mooring performance. This paper proposes an innovative optimization method for cylindrical FPSO mooring systems, combining the Kolmogorov–Arnold Network (KAN) with the Non-dominated Sorting Genetic Algorithm III (NSGA-III). Configuration samples are generated within predefined design variable ranges using Latin Hypercube Sampling (LHS), followed by time-domain global response simulations using OrcaFlex (version 11.3) software. A KAN surrogate model is constructed to predict the dynamic responses of the mooring system. Finally, the NSGA-III algorithm is employed for multi-objective optimization to obtain the Pareto optimal set, aiming to minimize mooring costs, maximum tension, fatigue damage, and platform offset. The results demonstrate that, compared to traditional optimization methods, the combination of KAN and NSGA-III exhibits superior prediction accuracy and generalization capabilities. The optimized configurations significantly outperform the original design in both mooring performance and economic costs. Specifically, the most economical scheme reduces mooring costs by 20.73%, the minimum tension scheme decreases mooring line tension by 46.75%, and the minimum displacement scheme reduces platform offset by 30.88%.

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