Abstract: To address the limitations of traditional algorithms in the multiobjective optimization of immersed tunnel segment lengths, an improved algorithm based on nondominated sorting whale optimization algorithm (NDSWOA) is proposed. First, the factors influencing construction duration, cost, and structural safety associated with segment length in immersed tunnels are analyzed, and a multiobjective optimization model is established. Second, the NDSWOA is designed and implemented by integrating nondominated sorting mechanism into the whale optimization algorithm to enhance global search capability and solution diversity. Finally, the NDSWOA is applied to optimize segment length in a case study of the Yanjiang immersed tunnel in Shenzhen, China. The results are compared with those obtained using traditional optimization algorithms such as NSGA-Ⅱ and MOEA/D. The experimental results reveal the following: (1) The NDSWOA demonstrates faster convergence speed and superior solution quality in solving the immersed tunnel segment length optimization problem, effectively generating a well-distributed Pareto front. (2) Compared with NSGA-Ⅱ and MOEA/D, the NDSWOA achieves better performance in optimizing construction duration, cost, and structural safety, suggesting an 80 m segment length as an optimal trade-off among the optimization objectives.

Key words: immersed tunnel, segment length optimization, nondominated sorting, whale optimization algorithm, multiobjective optimization