Routing in Wireless Sensor Networks Using Clustering Through Combining Whale Optimization Algorithm and Genetic Algorithm

作     者：Zhao, Guoliang Meng, Xianmeng 

作者机构：Yancheng Polytech Coll Sch Intelligent Mfg Yancheng Jiangsu Peoples R China Hefei Univ Technol Sch Comp Sci & Informat Engn Hefei Anhui Peoples R China 

出 版 物：《INTERNATIONAL JOURNAL OF COMMUNICATION SYSTEMS》 (Int J Commun Syst)

年 卷 期：2025年第38卷第3期

核心收录：

学科分类：0810[工学-信息与通信工程] 0808[工学-电气工程] 0809[工学-电子科学与技术（可授工学、理学学位）] 08[工学] 

基　　金：Anhui University Provincial Natural Science Research Project of China [KJ2021A1164] 

主　　题：clustering genetic algorithm whale optimization algorithm wireless sensor networks 

摘      要：The development of numerous wireless sensor network (WSN) applications has sparked considerable interest in the use of these networks across various fields. These networks, which do not require infrastructure and are self-organizing, can be rapidly deployed in most locations to collect information about environmental phenomena and transmit it to relevant hubs for appropriate action in emergency situations. Sensor nodes (SNs) in WSNs function as both sensors and relay nodes in relation to one another. As energy in these networks is limited, the nodes are supplied with only a specific amount of power. Because these networks are often located in difficult and remote areas, node batteries cannot be recharged or replaced. As a result, energy conservation is one of the most pressing concerns in these networks. Consequently, this study proposes a novel optimization technique for clustering WSNs, combining the whale optimization method and the genetic algorithm. In this work, information is transferred between cluster heads (CHs) and the sink using a combination of whale optimization and evolutionary algorithms, focusing on reducing intracluster distances and energy consumption in cluster members (CMs), while achieving near-optimal routing. The implementation results demonstrate that the proposed technique outperforms previous methods in terms of energy consumption, efficiency, delivery rate, and packet transmission latency, considering the evolutionary capabilities of both the whale optimization algorithm and the genetic algorithm.