盾构掘进参数的高精度预测与协同优化是保障隧道施工安全、提升掘进效能的关键。为破解掘进效率、能源消耗与运行稳定性难以协同优化的工程难题，以深大城际铁路工程为背景，选取刀盘贯入度、掘进比能与刀盘总功率作为关键优化目标，通过Relief算法筛选出10项关键施工参数作为输入，构建基于黄金正余弦改进粒子群算法优化的随机森林回归预测模型（GDPSO-RF）；进一步将GDPSO-RF作为适应度函数，融合NSGA-II、MOEA/D与RVEA 3种算法组成混合多目标优化框架，全面探索解空间并生成覆盖多类工况的Pareto前沿解集，最终结合变异系数-灰色评价法遴选出最佳施工参数组合。结果表明： 1）GDPSO-RF模型在刀盘贯入度、掘进比能与刀盘总功率的预测中表现优异，测试集R²分别达0.927 8、0.900 9与0.943 7； 2）NSGA-II、MOEA/D与RVEA 3种优化算法在目标空间中展现出明显的区域互补性，为不同施工需求提供了丰富多样的参数选择； 3）所获理想参数组合在工程应用中成效显著，与优化前均值相比，最优参数组合刀盘贯入度提升18.7%，掘进比能下降21.2%，刀盘总功率降低12.1%。

Accurate prediction and coordinated optimization of shield tunneling parameters are crucial for ensuring construction safety and enhancing tunneling performance. To address the engineering challenge of simultaneously optimizing tunneling efficiency, energy consumption, and operational stability, this study, based on the Shenzhen-Dongguan Intercity Railway project, selects cutterhead penetration rate, tunneling specific energy, and total cutterhead power as key optimization objectives. Ten critical construction parameters are screened as inputs using the Relief algorithm, and a regression prediction model based on random forest optimized by a golden sine-cosine improved particle swarm optimization algorithm (GDPSO-RF) is developed. Furthermore, the GDPSO-RF model is utilized as a fitness function and integrated with three algorithms—non-dominated sorting genetic algorithm(NSGA)-Ⅱ, multi-objective evolutionary algorithm based on decomposition (MOEA/D), and reference vector guided evolutionary algorithm(RVEA)—to form a hybrid multi-objective optimization framework. This framework comprehensively explores the solution space and generates a Pareto front solution set covering multiple working conditions. Finally, the optimal construction parameter combination is selected by combining the coefficient of variation-grey evaluation method. The results demonstrate that: (1) The GDPSORF model performs excellently in predicting cutterhead penetration rate, tunneling specific energy, and total cutterhead power, withR2values of 0.927 8, 0.900 9, and 0.943 7, respectively, on the test set. (2) The three optimization algorithms—NSGA-Ⅱ, MOEA/D, and RVEA—exhibit significant regional complementarity in the objective space, providing diverse parameter choices for different construction requirements. (3) The obtained optimal parameter combination shows remarkable effectiveness in engineering applications. Compared to the pre-optimization average values, the optimal parameters increase the cutterhead penetration rate by 18.7%, reduce tunneling specific energy by 21.2%, and decrease total cutterhead power by 12.1%.