Research on intelligent algorithms for amplitude optimization of wavefront shaping

TLDR

The study aims to advance binary amplitude optimization algorithms for wavefront shaping through numerical simulations. Numerical simulations are employed to evaluate genetic algorithm and particle swarm optimization approaches for binary amplitude control. A new genetic‑algorithm fitness function raises enhancement from 0.159 to 0.225, and a modified particle‑swarm algorithm achieves higher enhancement than the unmodified PSO while converging faster than the GA, providing insights for future intelligent wavefront shaping.

Abstract

This paper demonstrates further research on intelligent algorithms of binary amplitude optimization for wavefront shaping by numerical simulations. A better fitness function of the genetic algorithm (GA) has been presented after a comparative analysis of enhancement. With this new discriminant, we have achieved a relative enhancement of 0.225, which is higher than the theoretical value (0.159). In addition, we have also proposed a kind of modified particle swarm optimization algorithm (PSO), which has a higher enhancement than the unmodified PSO and a faster convergence speed than the GA. These studies provide remarkable insights into future exploration of intelligent algorithms for wavefront shaping.