###### Abstract  
In recent times, Wireless Sensor Networks (WSNs) are becoming more and more popular and are making significant advances in wireless communication thanks to low-cost and low-power sensors. However, since WSN nodes are battery-powered, they lose all of their autonomy after a certain time. This energy restriction impacts the network's lifetime. Clustering can increase the lifetime of a network while also lowering energy use. Clustering will bring several similar sensors to one location for data collection and delivery to the Base Station (BS). The Cluster Head (CH) uses more energy when collecting and transferring data. The life of the WSNs can be extended, and efficient identification of CH can minimize energy consumption. Creating a routing algorithm that considers the key challenges of lowering energy usage and maximizing network lifetime is still challenging. This paper presents an energy-efficient clustering routing protocol based on a hybrid Mayfly-Aquila optimization (MFA-AOA) algorithm for solving these critical issues in WSNs. The Mayfly algorithm is employed to choose an optimal CH from a collection of nodes. The Aquila optimization algorithm identifies and selects the optimum route between CH and BS. The simulation results showed that the proposed methodology achieved better energy consumption by 10.22%, 11.26%, and 14.28%, and normalized energy by 9.56%, 11.78%, and 13.76% than the existing state-of-art approaches. A quila optimization algorithm; cluster head; mayfly; routing protocol; wireless sensor networks 20  
residual energy, area of the CH concerning alternate nodes, topological data, and last movement of the SN as a CH [7]. Most of these CH selection approaches ignore the network's requirement for complete inclusion over long periods [8]. When removing nodes from regions that are sparsely populated with SNs, the passing of an SN issued in a densely populated territory has a lower impact on network inclusion. The importance of each SN to the incorporation endeavor may be quantified using an inclusion-conscious cost metric. This cost metric considers the node's incredible energy and the inclusion outside its detecting range, measuring the node's contribution to the network's inclusion concerns [9]. In this sense, this article looked into the differences between energy-adjusted and inclusion-conscious sensor network association, with a particular focus on clustered WSNs. The traditional method is insufficient for effective CH selection. The main reason is that the node may shift from one cluster area to the other, making it difficult to choose a CH. As a result, the shortest path selection for RN is proposed to solve this problem, allowing data transfer even when the cluster zone changes [10]. Moreover, all these operations must be completed efficiently to avoid wasting the restricted sensor battery life. The sensor's life cannot be increased by providing external or extra energy since most sensors are placed in difficult-to-reach locations. A network system with too many dead nodes may become paralyzed and unable to function correctly [11]. Therefore, it is not very easy for WSNs to develop energy-balanced and energy-efficient routing algorithms. The lifespan of a network may be successfully extended by balancing the energy usage of nodes and enhancing energy efficiency. By separating nodes into many clusters, hierarchical clustering methods lengthen the network lifespan [12]. Clustering protocols attempt to identify the best CH set and rotate the function of the CH across all nodes for the optimization of node energy usage. The chosen CHs should significantly influence the clustering protocol's performance. Meta-heuristic optimization techniques are the best alternative for selecting appropriate CHs to extend the network's lifespan. There are also many interesting applications of heuristics in different technical fields such as optimization in electric systems [13] and remote sensing models [14].