Performance optimization of hard rock tunnel boring machine using multi-objective evolutionary algorithm

Abstract

The hard rock Tunnel Boring Machine (TBM) is a complex engineering equipment coupled with multiple sub-systems for underground tunnel excavation in complex geological environments. Resetting the operational and structural parameters of TBM according to different geological conditions usually requires engineers to spend a lot of time dealing with the interaction between various subsystems, which is a tedious and time-consuming job. To facilitate setting the operational and structural parameters of TBM, we present a constrained multi-objective optimization model and its solving method in this paper. To be specific, three performance indices, i.e. minimizing the system construction period, construction energy consumption and construction cost of TBM, are firstly considered as the three objectives in the proposed model. Secondly, two push and pull search (PPS) based algorithms, including PPS-MOEA/D and PPS-KnEA, are suggested to solve the formulated constrained multi-objective optimization problem. Finally, to verify the performance of the developed method, the presented method is compared with several popular constrained multi-objective evolutionary algorithms by tackling the established optimization model. The experimental results reveal that the presented method has the best performance among the comparison algorithms, and the overall performance of the algorithm with PPS is better than other algorithms without PPS, which indicates the superiority of PPS framework in solving practical optimization problems.