This article proposes an exponential adjustment inertia weight immune particle swarm optimization (EAIW-IPSO) to enhance the accuracy and reliability regarding the selection of shield tunneling parameter values. According to the iteration changes and the range of inertia weight in particle swarm optimization algorithm (PSO), the inertia weight is adjusted by the form of exponential function. Meanwhile, the self-regulation mechanism of the immune system is combined with the PSO. 12 benchmark functions and the realistic cases of shield tunneling parameter value selection are utilized to demonstrate the feasibility and accuracy of the proposed EAIW-IPSO algorithm. Comparison with other improved PSO indicates that EAIW-IPSO has better performance to solve unimodal and multimodal optimization problems. When solving the selection of shield tunneling parameter values, EAIW-IPSO can provide more accurate and reliable references for the realistic engineering.(2013)] have studied the selection of shield tunneling parameter values for construction. From the perspective of preventing pressure imbalance in the excavation face, Cao et al. [Cao, Shao and An (2015)] used the least squares support vector machine to construct the nonlinear relationship model between the earth pressure and the tunneling parameters first. The model training is based on the field data samples, and then PSO is applied to optimize the tunneling parameters. On the basis of ensuring the stability of the excavation surface, Li et al. [Li, Fu and Guo (2017)] carried out the orthogonal experiment of the tunneling parameters to improve the tunneling efficiency. The mathematical model between the tunneling speed and the tunneling parameters is constructed and simplified. Then, the tunneling parameters are optimized based on mathematical model. Yang et al. [Yang, Tan and Peng (2017)] studied the shield tunneling parameters in water-soaked round gravel strata. Comparison with tunneling parameters in the complex strata found that the changes of shield tunneling parameter values are small during the construction, except earth pressure. As the same times, the method calculating the value range of the earth pressure is given. Ding et al. [Ding, Wu and Zhang (2015)] use dynamic Bayesian network to optimize the tunneling parameters. First, the dynamic Bayesian network is trained based on data samples from the realistic engineering, obtaining a complete DBN optimization model. Then, the optimal ranges of the tunneling parameters are reversed based on the optimization model, and the real-time tunneling parameter optimization is performed within the optimal range. With the improvement of the quality requirement in engineering construction, there are more and more factors to be considered when solving realistic engineering problems. The traditional methods to select parameter values will be difficult to implement. Moreover, when using the intelligent optimization algorithms for parameter value selection, the requirement for algorithm performance is also higher. Hence, fr...
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