Abstract: China's offshore wind power generation enter the era of affordable grid connection， and all component designs in the industry face the challenge of optimizing design. Bend restrictors are widely used in wind cable protection， and their bending stiffness and Mises peak stress are key indicators in structural design. At present， the optimization design of bend restrictors is mostly based on experience and finite element analysis iteration， which has low efficiency and is difficult to achieve multiobjective optimization. The paper proposes a multi-objective optimization method based on neural networks for the optimization of marine cable of wind farm bend restrictors to address this issue. Firstly， construct an RBF neural network proxy model using samples obtained from orthogonal experimental design and finite element analysis within the given design domain. Furthermore， the non-dominated sorting genetic algorithm Ⅱ （NSGA-Ⅱ） is used for multi-objective optimization of the bend restrictor， and the Pareto optimal solution set is obtained. A feasible multi-objective optimization method is provided for the structural design of the bend restrictor.

Key words: marine cable of wind farm, bend restrictor, radial basis neural network, genetic algorithm, multi-objective optimization, pareto-optimal set