Antenna S-parameter optimization based on golden sine mechanism based honey badger algorithm with tent chaos

Area of Science:

• Electromagnetics and Antenna Engineering
• Computational Intelligence
• Optimization Algorithms

Background:

• Metaheuristic optimization algorithms, like the Honey Badger Algorithm (HBA), are valuable but often suffer from premature convergence and limited population diversity.
• Antenna S-parameter optimization is crucial for device performance, yet existing methods face challenges in efficiency and accuracy.

Purpose of the Study:

• To introduce a novel optimization method, the Golden Sine mechanism-based Honey Badger Algorithm with Tent chaos (GST-HBA), for enhancing antenna S-parameter optimization.
• To address the limitations of the standard HBA, specifically premature convergence and lack of population diversity.

Main Methods:

• The proposed GST-HBA integrates the Golden Sine mechanism and Tent chaos into the Honey Badger Algorithm framework.
• The algorithm's efficacy was evaluated using 20 standard benchmark functions and subsequently applied to 8 antenna S-parameter functions.
• Performance was benchmarked against other established optimization algorithms.

Main Results:

• The GST-HBA demonstrated significant improvements in convergence speed and population diversity compared to the standard HBA.
• Testing on benchmark functions and antenna S-parameter functions confirmed the algorithm's robustness and effectiveness.
• Comparative analysis showed the GST-HBA outperformed existing optimization algorithms in antenna S-parameter optimization tasks.

Conclusions:

• The GST-HBA offers a superior approach for antenna S-parameter optimization, balancing exploration and exploitation effectively.
• The integration of Golden Sine and Tent chaos significantly enhances the HBA's performance, providing a powerful tool for antenna design and other complex optimization problems.