Hybrid Stochastic Ranking and Opposite Differential Evolution-Based Enhanced Firefly Optimization Algorithm for Extending Network Lifetime Through Efficient Clustering in WSNs

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