Decomposition-based multiobjective optimization for multipass cell design aided by particle swarm optimization and the K-means algorithm.

We proposed a method to intelligently design two-spherical-mirror-based multipass cells (MPCs) and optimize multiple objectives simultaneously. By integrating the K-means algorithm into the particle swarm optimization (PSO) algorithm, an efficient method is developed to optimize MPC configurations possessing characteristics of both long optical path lengths (OPLs) and circle patterns. We built and tested an MPC with four concentric circle patterns, which achieved an OPL of 54.1 m in a volume of 273.1 cm. We demonstrated the stability and detection precision of the developed gas sensor. Continuous measurement of methane in ambient laboratory air was realized, with a detection precision of 8 ppb and an averaging time of 13 s. The combination of K-means and PSO algorithms is effective in optimizing MPCs with multiple objectives, which makes it suitable for designing versatile MPCs satisfying various requirements of field applications, including pollution and greenhouse gas emission monitoring and high-sensitivity measurements of other trace gases.