Algorithmic optimization of figure-of-9 fiber lasers via particle swarm methods

Figure-of-9 fiber lasers (F9FL) represent a significant advancement in the field of ultrafast photonics, offering improved environmental stability, enhanced self-starting behavior, and efficient pulse shaping. However, optimizing the performance of F9FL remains a challenging task due to the complex interplay between nonlinear dynamics, dispersion management, and gain saturation effects. This study presents a systematic approach to optimize the operating conditions of an F9FL using an advanced two-particle swarm optimization (PSO) algorithm. The optimization process aims to maximize pulse energy, improve temporal stability, and enhance mode-locking efficiency by tuning critical laser parameters such as fiber lengths, gain coefficients, and phase shift conditions. A comprehensive numerical model incorporating bidirectional pulse propagation and nonlinear effects is employed to evaluate performance metrics under realistic constraints. Simulation results demonstrate that the proposed PSO-based strategy effectively identifies optimal parameter sets that lead to high-energy single pulses with stable temporal profiles. The findings offer practical guidelines for experimental implementation and highlight the potential of evolutionary algorithms for accelerating fiber laser design and optimization in advanced nonlinear optical systems.