Due to the unique nature of underwater acoustic communication, data collection from the Underwater Acoustic Sensor Networks (UASNs) is a challenging problem. It has been reported that data collection from the UASNs with the assistance of the autonomous underwater vehicles (AUVs) will be more convenient. The AUV needs to schedule a tour to contact all sensors once, which is a variant of the Traveling Salesman Problem. A hybrid optimization algorithm is proposed for the solution of the problem. The algorithm combines the quantum-behaved particle swarm optimization and improved ant colony optimization algorithms. It is an algorithm with quadratic complexity, which can yield approximate but satisfactory results for the problem. Simulation experiments are carried out to demonstrate the efficiency of the algorithm. Compared to the Self-Organizing Map based (SOM-based) algorithm, it not only plans a shorter tour, but also shortens the distance from the sensor to its closest waypoint. Therefore, the algorithm can reduce the energy required for data transmission since the communication distance drops, and the service life of the sensor can be extended.

中文翻译：

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基于优化算法的水声传感器网络数据采集

由于水声通信的独特性，从水声传感器网络 (UASN) 收集数据是一个具有挑战性的问题。据报道，在自主水下航行器（AUV）的帮助下从 UASN 收集数据将更加方便。AUV 需要安排一次巡视以接触所有传感器，这是旅行商问题的一个变体。针对该问题提出了一种混合优化算法。该算法结合了量子行为粒子群优化和改进的蚁群优化算法。它是一种具有二次复杂度的算法，对于该问题可以产生近似但令人满意的结果。仿真实验验证了算法的有效性。与基于自组织地图（SOM-based）的算法相比，它不仅规划了更短的行程，而且缩短了从传感器到其最近航点的距离。因此，该算法可以减少由于通信距离下降而导致数据传输所需的能量，延长传感器的使用寿命。