Energy efficiency has become a primary issue in wireless sensor networks (WSN). The sensor networks are powered by battery and thus they turn out to be dead after a particular interval. Hence, enhancing the data dissipation in energy efficient manner remains to be more challenging for increasing the life span of sensor devices. It has been already proved that the clustering method could improve or enhance the life span of WSNs. In the clustering model, the selection of cluster head (CH) in each cluster regards as the capable method for energy efficient routing, which minimizes the transmission delay in WSN. However, the main problem dealt with the selection of optimal CH that makes the network service prompt. Till now, more research works have been processing on solving this issue by considering different constraints. Under this scenario, this paper attempts to develop a new clustering model with optimal cluster head selection by considering four major criteria like energy, delay, distance, and security. Further, for selecting the optimal CHs, this paper proposes a new hybrid algorithm that hybridizes the concept of dragon fly and firefly algorithm algorithms, termed fire fly replaced position update in dragonfly. Finally, the performance of the proposed work is carried out by comparing with other conventional models in terms of number of alive nodes, network energy, delay and risk probability.

能源效率已成为无线传感器网络（WSN）中的主要问题。传感器网络由电池供电，因此在特定的时间间隔后它们会失效。因此，以节能的方式增强数据耗散对于增加传感器装置的寿命仍然是更具挑战性的。已经证明，聚类方法可以改善或延长无线传感器网络的寿命。在群集模型中，每个群集中群集头（CH）的选择被视为实现节能路由的有效方法，从而使WSN中的传输延迟最小。但是，主要问题涉及使网络服务提示的最佳CH的选择。到现在为止，通过考虑不同的约束条件，更多的研究工作正在着手解决这个问题。在这种情况下，本文尝试通过考虑能量，延迟，距离和安全性这四个主要标准，开发一种具有最佳簇头选择的新簇模型。此外，为选择最佳的CH，本文提出了一种新的混合算法，将蜻蜓和萤火虫算法算法的概念进行了混合，称为蜻蜓中的萤火虫替换位置更新。最后，通过在活动节点数，网络能量，延迟和风险概率方面与其他常规模型进行比较，来完成所提出工作的性能。本文提出了一种新的混合算法，将蜻蜓和萤火虫算法算法的概念进行了混合，称为蜻蜓中的萤火虫替换位置更新。最后，通过在活动节点数，网络能量，延迟和风险概率方面与其他常规模型进行比较，来完成所提出工作的性能。本文提出了一种新的混合算法，将蜻蜓和萤火虫算法算法的概念进行了混合，称为蜻蜓中的萤火虫替换位置更新。最后，通过在活动节点数，网络能量，延迟和风险概率方面与其他常规模型进行比较，来完成所提出工作的性能。