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AHME For THz Graphene-Based MIMO Antenna Optimization: Integrating Harris Hawks and Firefly Algorithm |
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PP: 949-958 |
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doi:10.18576/amis/200408
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Author(s) |
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Hamza A. Mashagba,
Hamza Abu Owida,
Esraa Abu Elsoud,
Asokan Vasudevan,
Mohammad Faleh Ahmmad Hunitie,
Azlan B. Abd Aziz,
Hizamel M. Hizan,
Suleiman Ibrahim Mohammad,
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Abstract |
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| For terahertz (THz) multiple-input multiple-output (MIMO) antenna array optimization to support sixth-generation (6G) wireless communication systems, the goal is to simultaneously maximize conflicting objectives including gain, isolation, bandwidth, and efficiency while minimizing the envelope correlation coefficient. Single-objective methods struggle with the highly nonlinear, multimodal fitness landscapes generated by large-scale graphene-based antennas. In this paper, we propose an Adaptive Hybrid Meta- heuristic Ensemble (AHME) that integrates Harris Hawks Optimization (HHO) for global exploration with Firefly Algorithm (FA) for local intensification, employing a dynamic switching mechanism based on population diversity and convergence rate metrics. An 8-element graphene-based THz MIMO antenna array operating across 2–8 THz is optimized using a comprehensive multi-objective fitness function incorporating five weighted performance metrics. Extensive comparative experiments against six state-of-the-art meta- heuristic algorithms demonstrate AHME’s superior performance. The optimized design achieves peak gain of 15.87 dBi at 4.2 THz, isolation better than −37.4 dB across all element pairs, aggregate bandwidth of 5.23 THz, radiation efficiency of 96.8%, ECC below 0.00008, and diversity gain of 9.9997 dB. AHME converges 34.7% faster than standard HHO and 42.3% faster than PSO, achieving 8.9% better fitness values than the second-best algorithm. Statistical validation over 30 independent runs confirms variance 67% smaller than PSO. |
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