An Improved Dual Hybrid Algorithm for Efficient Resource Utilization in mm-Communication Using ABC-Firefly and Levenberg’s Algorithm

This paper presents an improved mm-wave communication system by incorporating Artificial Bee Colony (ABC) algorithm for feature vector selection and using the firefly algorithm for hyperparameter tuning. The proposed system achieves better performance compared to existing systems, as evidenced by the lower Bit Error Rate (BER), higher throughput, and improved classification metrics. The ABC algorithm enhances feature vector selection, enabling the system to effectively identify and utilize relevant features. The firefly algorithm optimizes the system's parameters, leading to enhanced communication throughput. Experimental results demonstrate the superiority of the proposed system, highlighting the importance of hyperparameter tuning and swarm intelligence algorithms in mm-wave communication. Experiment results demonstrate the superiority of the proposed system in terms of both qualitative and quantitative parameters. For qualitative parameters analysis, the proposed work has been evaluated in terms of throughput and BER, where the proposed system outperforms by increment in throughput with 10.31% and lower BER over existing system. In terms of quantitative analysis, the proposed algorithm outperforms with the accuracy of 92.03%, precision of 96.30%, Recall of 98.96% and F-measure of 97.61% in comparison to existing work.