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Volume 13 | Issue 9 | Year 2026 | Article Id. IJECE-V13I9P102 | DOI : https://doi.org/10.14445/23488549/IJECE-V13I9P102

A Hybrid Genetic Reinforcement Learning Technique for Intelligent Cluster Head Selection in WSNs

Ruchi Sharma, Sachin Patel

Received Revised Accepted Published
25 May 2026 06 Aug 2026 11 Aug 2026 29 Sep 2026

Citation :

Ruchi Sharma, Sachin Patel, "A Hybrid Genetic Reinforcement Learning Technique for Intelligent Cluster Head Selection in WSNs," International Journal of Electronics and Communication Engineering, vol. 13, no. 9, pp. 12-22, 2026. Crossref, https://doi.org/10.14445/23488549/IJECE-V13I9P102

Abstract

In Wireless Sensor Networks (WSNs), selection of Cluster Head (CH) in view of the optimum performance is a difficult task in view of constraints of energy resources, dynamic energy state of the network and dynamic data communication requirements, etc. Here, optimization of these can make better solutions. A Hybrid RL-GA Algorithm is developed to give the optimum solution for this, by combining a Genetic Algorithm (GA) with Q-learning. Optimization of both how CHs are selected and how the data is routed afterward was performed. Genetic Algorithm founds initial global structure, individual nodes and Cluster Heads take over using Q learning. This allows them to make adaptive routing decisions. Network continuously learns the most energy-efficient paths, and it recovers much faster from link failures. The GA provide a strong foundation, and Q-learning handles the day-to-day network stability. With simulations, our Hybrid RL-GA protocol outperformed the standalone RL method used in the latest research works. The total network lifetime is higher by roughly 50% than standalone RL.

Keywords

Wireless network, Cluster Head (CH) selection, Genetic Algorithm (GA), Reinforcement Learning (RL), Network lifetime.

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