Optimizing FBG sensor layout of tunnel monitoring using improved multi-objective snow ablation optimizer based on radial basis function

In order to improve the optimization criterion and optimization algorithm, this study introduced the objective function of monitoring performance combining the reconstruction error of strain based on Radial Basis Function and the sensor deployment cost, and proposed the improved multi-objective snow ablation optimizer (IMOSAO) with external archive and Tent mapping. Firstly, initial solutions with uniform distribution were generated by Tent mapping and decimal coding. Then, they were updated by the dual-population mechanism balancing exploration and exploitation. Secondly, the Pareto frontiers of sensor layouts in the external archive were obtained after stop updating. The effectiveness of the improved parts of IMOSAO were validated in ablation studies, numerical and physical experiments. The optimized layouts achieved promising results with the cost reduction of 39 %, average accuracy above 95.72 %, minimum accuracy 84.61 % and least R² 0.9961. Finally, the algorithm reduced operation time and offered superior convergence compared with others. These findings underscore its significant potential in optimizing sensor layout of tunnel monitoring and provide a new view for other measurement sensors.