End fire linear antenna array synthesis using differential evolution inspired Adaptive Naked Mole Rat Algorithm

Linear antenna arrays (LAAs) play a critical role in smart system communication applications such as the Internet of Things (IoT), mobile communication and beamforming. However, minimizing secondary lobes while maintaining a low beamwidth remains challenging. This study presents an enhanced synthesis methodology for LAAs using the Adaptive Naked Mole Rat Algorithm (ANMRA). ANMRA, inspired by mole-rat mating habits, improves exploration and exploitation capabilities for directive LAA applications. The performance of ANMRA is assessed using the CEC 2019 benchmark test functions, a widely adopted standard for statistical evaluation in optimization algorithms. The proposed methodology results are also benchmarked against state-of-the-art algorithms, including the Salp Swarm Algorithm (SSA), Cuckoo Search (CS), Artificial Hummingbird Algorithm (AHOA), Chimp Optimization Algorithm (ChOA), and Naked Mole Rat Algorithm (NMRA). The results demonstrate that ANMRA achieves superior performance among the benchmarked algorithms by successfully minimizing secondary lobes and obtaining a narrow beamwidth. The ANMRA controlled design achieves the lowest Side Lobe Level (SLL) of − 37.08 dB and the smallest beamwidth of 74.68°. The statistical assessment using the benchmark test functions further confirms the effectiveness of ANMRA. By optimizing antenna element magnitude and placement control, ANMRA enables precise primary lobe placement, grating lobe elimination, and high directivity in LAAs. This research contributes to advancing smart system communication technologies, particularly in the context of IoT and beamforming applications, by providing an enhanced synthesis methodology for LAAs that offers improved performance in terms of secondary lobe reduction and beamwidth optimization.