The optimal location and the number of sensors in the wireless sensor network were found efficiently and accurately using an improved genetic algorithm (IGA) and applied to the dynamic detection of bridges. First, we optimized the conventional genetic algorithm (GA) by considering the optimization characteristics of multiple sensors. IGA improves the drawbacks of the conventional GA, such as slow convergence and the tendency to fall into local optima when applied to large structures. This improvement increases the convergence speed and ensures an adequate search for the optimal value. To achieve the optimal arrangement, classical optimization criteria including the acceptable independence, model confidence, and model strain energy criteria are embedded into the IGA as fitness functions. Through the simulation analysis of a bridge model, we demonstrate that the IGA outperforms the conventional GA in terms of searching ability, computational efficiency, reliability, and other relevant metrics. Moreover, the IGA significantly outperforms the classical sequence method in the searching ability.

On the basis of the ant colony routing algorithm, we propose an improved energy-efficient routing algorithm based on power-saving ant colony optimization (PSACO). Taking into account the residual energy of wireless sensors as a parameter, the proposed algorithm ensures the route between the source and destination nodes to be optimal more efficiently and quickly, and finds an optimal solution that prolongs the network's lifetime as long as possible. In improving the previous ant colony routing algorithm, an advanced bionic intelligent algorithm is integrated as it is known for its excellent distribution and the on-demand energy-saving mechanism. The proposed algorithm selects the best path to balance the network load and achieve positive feedback using distributed computing. The test of the proposed algorithm for measuring the vibration characteristics of a bridge validates that the improved ant colony routing algorithm is energy-efficient and robust, and shows excellent network load balancing with positive feedback. Therefore, the improved ant colony routing algorithm proves its superiority in wireless sensor network routing.

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