An Improved NSGA-II Algorithm for Multi-Objective Optimization of Irregular Polygon Patch Antennas

Area of Science:

• Electromagnetics and Antenna Design
• Computational Intelligence and Optimization Algorithms
• Microwave Engineering

Background:

• Multi-objective optimization of antenna designs is crucial for achieving desired performance characteristics.
• Existing optimization algorithms may face challenges in convergence speed and avoiding local optima for complex antenna geometries like irregular polygon patch antennas (IPPAs).
• The X-band frequency range requires antennas with specific bandwidth and size constraints.

Purpose of the Study:

• To present an enhanced Non-dominated Sorting Genetic Algorithm II (NSGA-II) for optimizing irregular polygon patch antennas (IPPAs).
• To improve convergence efficiency and the quality of the Pareto front in multi-objective antenna design.
• To simultaneously minimize antenna volume and maximize operational bandwidth within the X-band.

Main Methods:

• Integration of adaptive mechanisms for dynamic adjustment of crossover and mutation rates in the NSGA-II algorithm.
• Incorporation of a simulated annealing-inspired acceptance criterion to enhance evolutionary robustness and avoid local optima.
• Utilizing High-Frequency Structure Simulator (HFSS) co-simulation with detailed electromagnetic models for design and optimization.

Main Results:

• The optimized IPPA achieved a compact volume of 2807.6 mm³.
• A significant operational bandwidth of 2.7 GHz was obtained for the X-band antenna.
• Experimental validation confirmed the accuracy and reliability of the simulation-based optimization results.

Conclusions:

• The improved NSGA-II algorithm effectively addresses complex multi-objective antenna design challenges.
• The proposed method demonstrates superior performance in terms of convergence and Pareto front quality for IPPAs.
• The optimized antennas show significant potential for advanced broadband applications requiring compact and wide-bandwidth solutions.