In Underwater Acoustic Sensor Network (UWASN), node redeployment strategy is utilized to handle the reliable network coverage. The sensors deployed in the underwater are used to intellect the area and collected information is moved to the sink node. Node redeployment strategy is essential for the nodes which are placed outside the monitoring area in UWASN. In this paper, the node redeployment strategy is performed based on the hybrid Emperor Penguin Optimization (EPO) algorithm with Particle Search Algorithm (PSO) for better underwater acoustic communication and the proposed method is named as HEPSO. This hybridization is performed to reduce node failure rate and network energy consumption rate by optimally place the sensor nodes in underwater acoustic communication. The stability of the network topology is guaranteed by this algorithm and it enhances the node redeployment strategy by calculating the fitness function for each and every node. The implementation of the proposed algorithm is carried out by the MATLAB platform. The performance parameters like network coverage rate, network connectivity rate, network lifetime, number of nodes outside monitored space and total movement distance of nodes are evaluated and related with current methods like NRBSCT (Node Redeployment Based on Stratified Connected Tree) and MRNR (Moving Redundancy Nodes Redeployment) strategy.

在水下声传感器网络（UWASN）中，节点重新部署策略用于处理可靠的网络覆盖范围。部署在水下的传感器用于对区域进行智能化，并将收集到的信息移至水槽节点。节点重新部署策略对于放置在UWASN监视区域之外的节点至关重要。本文基于混合的帝王企鹅优化（EPO）算法与粒子搜索算法（PSO）进行节点重新部署策略，以实现更好的水下声通信，该方法被称为HEPSO。通过将传感器节点最佳地放置在水下声通信中，执行此混合操作以降低节点故障率和网络能耗率。该算法保证了网络拓扑的稳定性，并通过计算每个节点的适应度函数来增强节点重新部署策略。该算法的实现是在MATLAB平台上进行的。评估性能参数，如网络覆盖率，网络连接率，网络寿命，受监视空间之外的节点数以及节点的总移动距离，并与诸如NRBSCT（基于分层连接树的节点重新部署）和MRNR（移动冗余）之类的当前方法相关联节点重新部署）策略。