A novel self‐adaptive hybrid slime mould naked mole‐rat algorithm for numerical optimization and energy‐efficient wireless sensor network

Abstract

SummaryA wireless sensor network (WSN) is made up of widely spaced nodes inside the sensor field. Clustering is an effective data collection approach that lowers energy consumption by organizing nodes into groups. So, choosing the right cluster head (CH) and employing an effective routing protocol is required to prolong the lifetime of WSNs. This paper develops a novel self‐adaptive hybrid slime mould naked mole‐rat algorithm (SM‐NMRA) based on the hybridization of the slime mould algorithm (SMA) and naked mole‐rat algorithm (NMRA) to solve the above‐said problem of WSN. The new approach integrates SMA's wrap food abilities with NMRA to improve the working capabilities of the original NMRA. A new stagnation phase inspired by the grey wolf optimizer and cuckoo search algorithms has been integrated to handle the local optima stagnation problem. In addition, self‐adaptivity has been added for SM‐NMRA's parameters. Here, the performance of SM‐NMRA is evaluated for CEC 2005 and CEC 2014 numerical test suites. The statistical outcomes along with Freidman's test, Wilcoxon's rank‐sum test, and convergence profiles validate the superior performance of SM‐NMRA. The proposed clustering protocol for efficient CH selection in WSN using SM‐NMRA outperforms other state‐of‐the‐art techniques with improved network lifespan and reduced energy consumption.