Fast EM-driven nature-inspired optimization of antenna input characteristics using response features and...

Area of Science:

• Electromagnetics and Antenna Design
• Computational Intelligence
• Optimization Techniques

Background:

• Designing contemporary antenna systems necessitates optimization techniques.
• Global search methods for antenna design often incur high computational costs with full-wave electromagnetic (EM) models.

Purpose of the Study:

• To introduce an innovative method for globally optimizing the reflection responses of multi-band antennas.
• To reduce the computational expense associated with traditional optimization methods in antenna design.

Main Methods:

• Utilized surrogate models based on response features to smooth the objective function landscape.
• Employed an iterative co-kriging model refinement using coarse and high-fidelity EM analysis.
• Integrated a particle swarm optimizer (PSO) for the global search routine.
• Leveraged variable-resolution simulations for accelerated optimization.

Main Results:

• Achieved efficient global optimization with an average cost of approximately ninety high-fidelity EM antenna analyses.
• Demonstrated superior performance and efficiency compared to benchmark methods.
• Reported excellent solution repeatability in verification case studies.
• Attained up to a seventy percent speedup using variable-resolution simulations.

Conclusions:

• The proposed surrogate-assisted optimization method significantly reduces computational cost for multi-band antenna design.
• The approach offers an efficient and repeatable solution for complex antenna optimization problems.
• Variable-resolution simulations provide a substantial speed advantage over single-fidelity methods.