Ensuring stability and extending network lifetime in Wireless Sensor Networks (WSNs) achieved through significantly reduced energy consumption is considered as a potential challenge. The selection of Cluster Head (CH) during the process of clustering is determined to be highly complicated in spite of its role in facilitating efficient and balanced energy consumption in the network. In this paper, Hybrid Stochastic Ranking and Opposite Differential Evolution enhanced Firefly Algorithm (HSRODE-FFA)-based clustering protocol is proposed for handling the issues of location-based CH selection approaches that select duplicate nodes with increased computation and poor selection accuracy. This HSRODE-FFA clustering scheme includes the process of sampling for selecting the CHs from among the sensor nodes that exist in the sample population and address the problems introduced by different locations of nodes and CHs. It is proposed as an attempt to improve stability and lifetime of WSNs based on the merits of Stochastic Firefly Ranking (SFR) that enhances the exploration capability of Firefly Algorithm (FFA). The hybridization of the enhanced FFA with Opposition Differential Evolution (ODE) aids in speeding and ensuring optimal exploitation in the selection of CHs. The proposed HSRODE-FFA thereby maintains a balance between the rate of exploitation and exploration for deriving mutual benefit of rapid and potential selection of CHs from the sampling population. The experimental results of the proposed HSRODE-FFA scheme confirm an enhanced stability period and network lifetime of 16.21% and 13.86% respectively in contrast to the benchmarked Harmony Search and Firefly Algorithm-based Cluster Head Selection (HSFFA-CHS), Krill Herd Optimization and Genetic Algorithm-based Cluster Head Selection (KHOGA-CHS), Particle Swarm Optimization with Energy Centers Searching-based Cluster Head Selection (PSO-ECS-CHS) and Spider Monkey Optimization-based Cluster Head Selection (SMO-CHS) schemes.

通过显着降低能耗来确保无线传感器网络（WSN）的稳定性并延长网络寿命是一项潜在的挑战。尽管群集头（CH）在促进网络中高效而平衡的能耗中发挥了作用，但在群集过程中对群集头（CH）的选择被确定为非常复杂。本文提出了一种基于混合随机排序和对立差分进化增强萤火虫算法（HSRODE-FFA）的聚类协议，以解决基于位置的CH选择方法的问题，该方法选择重复节点，且计算量大，选择精度差。此HSRODE-FFA群集方案包括采样过程，用于从样本总体中存在的传感器节点中选择CH，并解决由节点和CH的不同位置引起的问题。提出了基于随机萤火虫等级（SFR）的优点来提高无线传感器网络的稳定性和寿命的尝试，该优点提高了萤火虫算法（FFA）的探索能力。增强型FFA与反对派差异进化（ODE）的杂交有助于加快CH的选择速度并确保其最佳利用。拟议的HSRODE-FFA从而在开采率和勘探率之间保持平衡，以从采样人群中快速和潜在地选择CHs互惠互利。