Mobile nodes in underwater wireless sensor networks are becoming very important as they not only enable flexible sensing areas but also entails the ability to provide means for data and energy sharing among existing static sensor nodes. In this paper, three efficient meta-heuristic evolutionary algorithms ant colony optimization, artificial bees colony and firefly algorithm, inspired by swarm intelligence are being compared with an objective to achieve the shortest path for the mobile node in traversing the complete sensing network. We transform this problem into the traveling salesman problem. It is the most famous and commonly used nondeterministic-polynomial combinatorial optimization problem in which an artificial agent is set to travel between different cities and calculate distance or time consumed to travel between these nodes or cities for best route selection. Heuristic and meta-heuristic algorithms are being used for decades to solve such type of problems. In this comparative study, an analysis of meta-heuristic algorithms for obtaining results in less processing time while searching for the optimal solution has been done. Moreover, this paper provides a classification of mentioned algorithms and highlights their characteristics. The experiment has been carried out on these algorithms by manipulating different parameters such as population and number of iteration.

水下无线传感器网络中的移动节点变得非常重要，因为它们不仅可以实现灵活的传感区域，而且还具有为现有静态传感器节点之间的数据和能量共享提供手段的能力。本文将群体智能启发的三种有效的元启发式进化蚁群优化算法，人工蜂群算法和萤火虫算法进行了比较，目的是实现移动节点遍历完整传感网络的最短路径。我们将此问题转换为旅行商问题。这是最著名和最常用的不确定性多项式组合优化问题，其中设置了人工代理以在不同城市之间旅行，并计算在这些节点或城市之间旅行所需的距离或时间以进行最佳路线选择。启发式和元启发式算法已被使用了数十年，以解决这类问题。在这项比较研究中，已经对元启发式算法进行了分析，以便在寻找最佳解决方案的同时以更少的处理时间获得结果。此外，本文提供了所提到算法的分类并突出了它们的特性。通过操纵不同的参数（例如种群和迭代次数），对这些算法进行了实验。启发式和元启发式算法已被使用了数十年，以解决这类问题。在这项比较研究中，已经对元启发式算法进行了分析，以便在寻找最佳解决方案的同时以更少的处理时间获得结果。此外，本文提供了所提到算法的分类并突出了它们的特性。通过操纵不同的参数（例如种群和迭代次数），对这些算法进行了实验。启发式和元启发式算法已被使用了数十年，以解决这类问题。在这项比较研究中，已经对元启发式算法进行了分析，以便在寻找最佳解决方案的同时以更少的处理时间获得结果。此外，本文提供了上述算法的分类并突出了它们的特性。通过操纵不同的参数（例如种群和迭代次数），对这些算法进行了实验。