Wireless Sensor Networks (WSNs) have left an indelible mark on the lives of all by aiding in various sectors such as agriculture, education, manufacturing, monitoring of the environment, etc. Nevertheless, because of the wireless existence, the sensor node batteries cannot be replaced when deployed in a remote or unattended area. Several researches are therefore documented to extend the node's survival time. While cluster-based routing has contributed significantly to address this issue, there is still room for improvement in the choice of the cluster head (CH) by integrating critical parameters. Furthermore, primarily the focus had been on either the selection of CH or the data transmission among the nodes. The meta-heuristic methods are the promising approach to acquire the optimal network performance. In this paper, the ‘CH selection’ and ‘sink mobility-based data transmission’, both are optimized through a hybrid approach that consider the genetic algorithm (GA) and particle swarm optimization (PSO) algorithm respectively for each task. The robust behavior of GA helps in the optimized the CH selection, whereas, PSO helps in finding the optimized route for sink mobility. It is observed through the simulation analysis and results statistics that the proposed GAPSO-H (GA and PSO based hybrid) method outperform the state-of-art algorithms at various levels of performance metrics.

无线传感器网络（WSN）通过协助农业，教育，制造，环境监测等各个部门，在所有人的生命中留下了不可磨灭的烙印。然而，由于存在无线技术，传感器节点电池无法部署在偏远或无人值守的区域时替换。因此，有数项研究记录在案，以延长节点的生存时间。尽管基于群集的路由为解决此问题做出了巨大贡献，但通过集成关键参数在群集头（CH）的选择上仍有改进的空间。此外，主要关注点是CH的选择或节点之间的数据传输。元启发式方法是获得最佳网络性能的有前途的方法。在本文中，“ CH选择”和“基于接收器移动性的数据传输”均通过混合方法进行了优化，这两种混合方法分别针对每个任务考虑了遗传算法（GA）和粒子群优化（PSO）算法。GA的鲁棒性有助于优化CH的选择，而PSO则有助于找到优化的汇宿移动路径。通过仿真分析和结果统计可以发现，在各种性能指标级别上，所提出的GAPSO-H（基于GA和PSO的混合）方法均优于最新算法。PSO有助于找到针对接收器移动性的最佳路由。通过仿真分析和结果统计可以发现，在各种性能指标级别上，所提出的GAPSO-H（基于GA和PSO的混合）方法均优于最新算法。PSO有助于找到针对接收器移动性的最佳路由。通过仿真分析和结果统计可以发现，在各种性能指标级别上，所提出的GAPSO-H（基于GA和PSO的混合）方法均优于最新算法。