Hierarchical Wireless Sensor Networks (WSNs) have got vital application domains in modern era especially in monitoring and tracking of events, and without human intervention. In WSN, sensor nodes are characterized to have short life span due to continuous sensing and consequently the battery drains quickly. Under heavy traffic condition, sensors in close proximity to sink die quickly and initiate energy-hole problem. Thus, optimal usage of available energy is a key challenge in WSN assisted applications. A precise clustering and optimal path selection from sensor nodes to sink has become extremely important to preserve energy. Keeping this in view, the paper presents a Multi-Objective Based Clustering and Sailfish Optimizer (SFO) guided routing method to sustain energy efficiency in WSNs. In it the Cluster Head (CH) is selected, based on effective fitness function which is formulated from multiple objectives. It helps to minimize energy consumption and reduces number of dead sensor nodes. After CH selection, SFO is used to select an optimal path to sink node for data transmission. The proposed approach is analytically analyzed and results are compared with the similar existing approaches namely, Grey wolf optimization (GWO), Genetic algorithm (GA), Ant Lion optimization (ALO), and Particle Swarm Optimization (PSO) in terms of energy consumption, throughput, packet delivery ratio, and network lifetime. The simulation results show that proposed method has performed 21.9% and 24.4% better in terms of energy consumption and number of alive sensor nodes respectively when compared to GWO. Further, it shows significantly better results than other optimization-based approaches.

分层无线传感器网络（WSN）在现代时代已成为至关重要的应用领域，尤其是在事件的监视和跟踪中，并且无需人工干预。在WSN中，由于连续感应，传感器节点的使用寿命很短，因此电池消耗很快。在交通繁忙的情况下，靠近水槽的传感器会迅速死亡，并引发能量孔问题。因此，在WSN辅助应用中，可利用能量的最佳利用是一项关键挑战。从传感器节点到接收器的精确群集和最佳路径选择对于保持能量至关重要。考虑到这一点，本文提出了一种基于多目标的聚类和旗鱼优化器（SFO）指导的路由方法，以维持WSN中的能源效率。在其中选择簇头（CH），基于有效的适应性功能，该功能由多个目标组成。它有助于最大程度地减少能耗，并减少传感器失效节点的数量。在选择CH之后，SFO用于选择接收节点以进行数据传输的最佳路径。对提出的方法进行了分析分析，并将结果与​​现有的类似方法进行了比较，例如，灰狼优化（GWO），遗传算法（GA），蚁狮优化（ALO）和粒子群优化（PSO）的能耗，吞吐量，数据包传递率和网络寿命。仿真结果表明，与GWO相比，该方法在能耗和存活传感器节点数量上分别提高了21.9％和24.4％。此外，与其他基于优化的方法相比，它显示出明显更好的结果。