A hybrid genetic algorithm (HGA) is proposed in this paper to optimize the placement of sensors installed on Nanjing Yangtze River Bridge (NYRB) for structural health monitoring (SHM). The proposed HGA is the combination of genetic algorithm (GA) and simulated annealing (SA) which can enhance the local search ability and avoid the premature convergence in GA. Firstly, the ability of global convergence of the HGA is proved by a classical testing function, Schaffer function. Secondly, the objective function for optimization is defined based on the displacement mode shapes. Given the number of sensors, the HGA is used to find the optimal placement of sensors in NYRB according to the objective function. The results show that the proposed HGA is robust and can converge to global optimum. Finally, the optimal sensor placement on NYRB for is determined.
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