Optimized Optical Fiber Routing and Bragg Grating Placement via Customized Genetic Algorithm with LLM-Assisted Parameter Tuning

In quasi-distributed monitoring systems using Fiber Bragg Grating sensors, unfavorable sensor placement and fiber routing may lead to insufficient coverage, unnecessary data redundancy, or infeasible cabling. To address these issues, this study proposes an integrated optimization framework that jointly considers sensor configurations and fiber routing. The framework adopts a two-stage strategy. First, an improved multi-objective genetic algorithm, leveraging dynamic chromosome encoding and customized genetic operators, is employed to optimize sensor layouts along the Pareto front, regarding modal orthogonality, strain energy coverage, and the amount of sensors. Second, a tailored traveling salesman problem is formulated and solved to obtain the shortest feasible fiber length under the minimum bending radius constraint. Furthermore, benefiting from large language models, a novel parameter tuning mechanism is introduced to reduce reliance on human decisions and enhance global search capability. More broadly, the proposed approach provides an efficient, scalable, and engineering-practical solution framework for the automated design of quasi-distributed sensing networks.