Transactions on Machine Intelligence 2821-1693 8 3 2025 09 01 Multi-Objective Genetic Algorithm-Based Clustering for Energy Distribution and Lifetime Maximization in Wireless Sensor Networks 151 160 244863 10.22034/tmi.2025.244863 EN A. Aliabadian Assistant Professor, Department of Electrical and Biomedical Engineering, Shomal University 0000-0002-8646-7968 Journal Article 2025 06 10 Wireless Sensor Networks (WSNs) consist of numerous resource-constrained sensor nodes that collaboratively monitor physical or environmental conditions and transmit the collected data to a base station. Due to the limited battery capacity of sensor nodes, energy efficiency remains one of the most critical challenges affecting network lifetime and overall performance. Clustering has been widely adopted as an effective approach for reducing communication overhead, balancing energy consumption, and improving network scalability. However, traditional clustering protocols such as LEACH often suffer from inefficient cluster-head selection and uneven energy distribution among sensor nodes. To address these limitations, this paper proposes a clustering methodology based on a Multi-Objective Genetic Algorithm (MOGA) that simultaneously optimizes cluster-head selection, intra-cluster communication distance, and energy utilization. The proposed approach aims to achieve balanced energy consumption while extending network lifetime and maintaining communication reliability. Extensive simulations were conducted and the obtained results were compared with conventional clustering techniques. Performance evaluation demonstrates that the proposed method significantly reduces energy consumption, improves cluster stability, and increases overall network efficiency. The results indicate that the proposed optimization framework provides a robust and effective solution for energy-aware clustering in WSN environments.