The efficient management of energy resources remains a critical challenge in the operation of Wireless Sensor Networks (WSNs), where clustering is widely adopted to reduce energy consumption and extend network lifetime. A key factor in clustering efficacy is the optimal selection of cluster heads (CHs), as poor selection leads to excessive energy consumption due to disproportionate data transmission burdens. This study proposes a novel Hybrid Mutation Albatross Optimization Algorithm with Differential Evaluation (HMAOA-DE) for CH selection to address this issue. The proposed method is inspired by the natural aging behavior and efficient foraging patterns of albatrosses, integrating hybrid mutation strategies and differential evaluation to enhance the optimization process. The HMAOA-DE aims to minimize overall energy consumption, maintain reliable data transmission, and ensure balanced network coverage by effectively managing the trade-off between exploration and exploitation. HMAOA-DE is a hybrid algorithm inspired by albatross foraging behavior, designed for efficient cluster head (CH) selection. It combines Differential Evolution (DE) for global exploration with Albatross Optimization Algorithm (AOA) for local exploitation. This integration enhances the balance between exploration and exploitation in the search process. The approach leads to improved energy efficiency and extended network lifetime in wireless sensor networks. Simulation results demonstrate that the HMAOA-DE significantly outperforms conventional clustering approaches regarding energy efficiency, network lifetime, and stability. These findings establish the proposed algorithm as a promising candidate for deployment in real-time, energy-constrained WSN scenarios. Future work will refine the algorithm’s adaptability under dynamic network conditions and integrate real-world sensor deployment constraints.
