This paper adopts a multi-objective particle swarm optimization (MOPSO) algorithm to optimize the drawing parameters inversely according to the target MOFs, to realize fast and precise fabrication. This paper proposes the improved adaptive large-scale cooperative coevolution (IALSCC) algorithm to obtain the strain sensors deployment on iFEM, and the method includes the initialization strategy, adaptive region partitioning strategy, and gbest selection and particle updating strategies. The microstructured optical fibers (MOFs) fabrication process involves repetitive mapping of the drawing parameters, which is time-consuming, laborious and inefficient, and has become a fundamental obstacle restricting the current design of many MOFs with high excellent performances from being. Abstract: The diverse applications of mode-locked fiber lasers (MLFLs) raise various demands on the output of the laser, including the pulse duration, energy, and shape. Simulation is an excellent method to guide the design and construction of an MLFL for on-demand laser output. Traditional. This paper presents a comprehensive review of AI-enhanced OFS technologies, encompassing both localized sensors such as fiber Bragg gratings (FBG), Fabry–Perot (FP) interferometers, and Mach–Zehnder interferometers (MZI), and distributed sensing systems based on Rayleigh, Brillouin, and Raman. Inverse design of few-mode fiber by Neural Network for weak-coupling optimization Z. He, "Inverse design of few-mode fiber by Neural Network for weak-coupling optimization," in Optical Fiber Communication Conference (OFC) 2020, OSA Technical.