Genetic Algorithm Synthesis for Linear Antenna Array Pattern

In wireless communication and radar systems, pattern synthesis for linear antenna arrays represents a critical challenge. Traditional methods like Fourier transforms or analytical optimization often struggle with complex constraints, while Genetic Algorithms (GA) serve as effective alternatives due to their global search capabilities.

The genetic algorithm optimizes excitation amplitude or phase distributions of antenna arrays by simulating biological evolution processes. This implementation employs integer encoding, representing each antenna element's excitation parameters as chromosomal genes to facilitate discrete adjustments (such as quantized attenuator or phase shifter values). The cross-generational competitive selection strategy further improves convergence—offspring populations compete not only with contemporary individuals but also compare with elite parents, preventing premature convergence while maintaining diversity.

The optimization objective typically minimizes pattern sidelobe levels or achieves specific beam shapes. The fitness function must comprehensively consider metrics like main lobe width and null depth. Compared to local optimization methods like gradient descent, genetic algorithms handle nonlinear, multi-peak problems more flexibly, particularly suitable for non-ideal arrays commonly encountered in engineering (such as arrays with element mutual coupling effects).

This method demonstrates strong scalability—subsequent enhancements can incorporate hybrid strategies (like simulated annealing) to improve convergence speed, or design distributed parallel genetic algorithms for large-scale array configurations. Implementation typically involves population initialization, fitness evaluation, selection operators (tournament/roulette wheel), crossover (single/multi-point), and mutation operations with adaptive probabilities.