Wireless Sensor Network (WSN) is a resource constraint network that utilizes more energy for transmitting and receiving the data. Hence energy efficiency is the vital issue faced by the WSN. Besides the packet routing process consumes more energy than the other processes. Moreover, the working of WSN is based on the battery life span of sensor nodes. Thus the constrained energy source affects the life span of the network battery. To tackle this issue, we proposed a novel method known as the

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... id Improved Whale optimization-based Artificial Ecosystem optimization method (HIWAEO). This enhances the energy efficiency of the WSN and thereby improves the routing of the network. The energy-efficient WSN can be obtained by selecting optimal cluster head (CH) and forward nodes. To select the optimal CH the proposed method estimates the fitness function which includes node degree, space between the sensor nodes and space between the CH and base station (BS), residual energy, and node centrality. This estimated fitness function arranges the sensor nodes based on their increased energy and distance from the BS and the best node is chosen as the CH. Henceforth to obtain the routing efficiency the forward nodes are selected based on their residual energy and distance. The performance of the proposed method is analyzed with the other existing approaches for three conditions of BS alignment and concluded that our proposed method outperforms all the other approaches.