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This study introduces an artificial intelligence (AI) approach for multi-objective optimization (MO) in antenna design, significantly reducing computational costs. The AI method achieves substantial savings, making advanced antenna optimization more accessible.

Keywords:
Antenna engineeringArtificial intelligence, variable-fidelity simulationsArtificial intelligence-based designMachine learningNeural networksSurrogate modeling

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Area of Science:

  • Electrical Engineering
  • Computational Electromagnetics
  • Antenna Theory

Background:

  • Multi-objective optimization (MO) in antenna design is computationally intensive due to electromagnetic (EM) simulations.
  • Conventional algorithms are often prohibitive for complex antenna optimization tasks.
  • Surrogate modeling and soft computing offer potential cost reduction strategies.

Purpose of the Study:

  • To introduce an innovative artificial intelligence (AI)-based approach for antenna multi-objective optimization (MO).
  • To reduce the computational expenses associated with traditional MO methods in antenna design.
  • To enhance the efficiency and feasibility of antenna optimization, especially under computational budget constraints.

Main Methods:

  • Utilized a machine learning (ML) procedure employing artificial neural network (ANN) models for antenna MO.
  • Integrated Pareto ranking and multi-objective evolutionary algorithms to generate infill vectors.
  • Employed multi-resolution electromagnetic simulations and iterative metamodel refinement.
  • Incorporated full-wave simulation results for all infill points to refine the surrogate model.

Main Results:

  • The AI-based approach demonstrated significant cost reduction, averaging approximately 200 high-fidelity EM analyses.
  • Achieved a 40% relative speedup through variable-fidelity modeling and nearly 90% savings compared to a one-shot approach.
  • Validated the methodology on four planar antenna designs, including broadband monopoles and a quasi-Yagi antenna.
  • Comparative experiments confirmed the framework's reliability without compromising computational efficiency.

Conclusions:

  • The proposed AI-driven methodology offers a feasible and efficient alternative for antenna multi-objective optimization.
  • This approach effectively addresses the critical constraint of computational budget in antenna design.
  • The integration of ML, surrogate modeling, and multi-resolution simulations significantly enhances optimization processes.