Wireless Sensor Networks (WSNs) consist of a large number of spatially distributed sensor nodes connected through the wireless medium to monitor and record the physical information from the environment. The nodes of WSN are battery powered, so after a certain period it loose entire energy. This energy constraint affects the lifetime of the network. The objective of this study is to minimize the overall energy consumption and to maximize the network lifetime. At present, clustering and routing algorithms are widely used in WSNs to enhance the network lifetime. In this study, the Butterfly Optimization Algorithm (BOA) is employed to choose an optimal cluster head from a group of nodes. The cluster head selection is optimized by the residual energy of the nodes, distance to the neighbors, distance to the base station, node degree and node centrality. The route between the cluster head and the base station is identified by using Ant Colony Optimization (ACO), it selects the optimal route based on the distance, residual energy and node degree. The performance measures of this proposed methodology are analyzed in terms of alive nodes, dead nodes, energy consumption and data packets received by the BS. The outputs of the proposed methodology are compared with traditional approaches LEACH, DEEC and compared with some existing methods FUCHAR, CRHS, BERA, CPSO, ALOC and FLION. For example, the alive nodes of the proposed methodology are 200 at 1500 iterations which is higher compared to the CRHS and BERA methods.

无线传感器网络（WSN）由通过无线介质连接的大量空间分布的传感器节点组成，以监视和记录来自环境的物理信息。WSN的节点由电池供电，因此在一定时间后它将失去全部能量。这种能量限制会影响网络的寿命。这项研究的目的是最大程度地减少总能耗并最大程度地延长网络寿命。目前，在WSN中广泛使用集群和路由算法来延长网络寿命。在这项研究中，采用蝴蝶优化算法（BOA）从一组节点中选择一个最佳的簇头。通过节点的剩余能量，到邻居的距离，到基站的距离，节点度和节点中心度来优化簇头选择。集群头与基站之间的路由通过蚁群优化（ACO）进行识别，并根据距离，剩余能量和节点度来选择最优路由。从BS接收到的活动节点，死节点，能耗和数据分组方面分析了该提出的方法的性能度量。所提出方法的输出与传统方法LEACH，DEEC进行了比较，并与一些现有方法FUCHAR，CRHS，BERA，CPSO，ALOC和FLION进行了比较。例如，所提出的方法的活动节点在1500次迭代时为200个，这比CRHS和BERA方法更高。剩余能量和节点度。从基站收到的活动节点，死节点，能耗和数据包方面分析了该方法的性能指标。所提出方法的输出与传统方法LEACH，DEEC进行了比较，并与一些现有方法FUCHAR，CRHS，BERA，CPSO，ALOC和FLION进行了比较。例如，所提出的方法的活跃节点在1500次迭代时为200个，这比CRHS和BERA方法更高。剩余能量和节点度。从基站收到的活动节点，死节点，能耗和数据包方面分析了该方法的性能指标。所提出方法的输出与传统方法LEACH，DEEC进行了比较，并与一些现有方法FUCHAR，CRHS，BERA，CPSO，ALOC和FLION进行了比较。例如，所提出的方法的活跃节点在1500次迭代时为200个，这比CRHS和BERA方法更高。