The rapid development of LiDAR technology has made the accuracy and stability of waveform decomposition critical factors limiting its practical application. Traditional waveform decomposition optimization algorithms face challenges such as sensitivity to initial conditions and insufficient fitting stability, which hinder the in-depth application and widespread use of full-waveform LiDAR across various fields. This paper focuses on optimizing the waveform decomposition algorithm for full-waveform LiDAR. The primary objective is to address key issues, such as sensitivity to initial values and poor fitting stability, present in traditional algorithms, thereby significantly enhancing the accuracy and stability of waveform decomposition.Regarding the research methodology, a decomposition model based on the Gaussian function is initially constructed. For the collected LiDAR echo data, a multi-step preprocessing procedure is applied. A wavelet denoising algorithm is employed to eliminate background noise, and the five-point cubic smoothing method is used to enhance the smoothness of the data. Building on this, the Gaussian inflection point method is implemented to provide a preliminary estimation of the initial parameter values. Following this, the parameter optimization process begins. This paper introduces the Differential Evolution and Levenberg-Marquardt (DELM) optimization algorithm for waveform decomposition. The Differential Evolution (DE) algorithm is first used for preliminary optimization. After determining the population size and dimensionality, the DE algorithm randomly generates individuals in the decision space, and through iterative processes such as mutation, crossover, and selection, it converges toward the optimal solution. Subsequently, the Levenberg-Marquardt (LM) algorithm is applied for secondary optimization of the DE algorithm results. The LM algorithm calculates the iteration step size based on critical components such as the objective function, Jacobian matrix, and damping factor, accurately updating the parameters, and decides whether to accept the update based on predefined criteria. This iterative process continues until the termination condition is met. The entire process is guided by the objective function, which minimizes the gap between the fitted and the collected waveforms.To collect abundant echo data, a fixed plate and target plates with different reflectivity are positioned at specific distances in front of the LiDAR, and the position and reflectivity of the target plates are adjusted. Additionally, to simulate a more complex and realistic application scenario, multiple obstacles are introduced between the fixed plate and the target plate, successfully collecting multi-target composite echo waveform data.The experimental results indicate that, in terms of fitting accuracy, the DELM algorithm demonstrates superior performance, far exceeding that of both the LM and DE algorithms. In most cases, the fitting correlation coefficient surpasses 0.97, and for selected representative echo waveform datasets, the fitting evaluation index exceeds 0.99. This strongly supports the ability of the DELM algorithm to accurately restore the waveform. In terms of stability, the standard deviation of the fitting correlation coefficient for the DELM algorithm is exemplary. Regardless of varying reflectivity or distance conditions, the DELM algorithm consistently achieves stable, high-quality waveform fitting. With respect to ranging accuracy, the DELM algorithm achieves an accuracy of 70 mm, which can be further reduced to 40 mm under optimal conditions. In contrast, the LM algorithm achieves an accuracy of 1 cm, and the DE algorithm achieves 90 mm, highlighting the significant advantage of the DELM algorithm.The proposed DELM optimization algorithm effectively addresses the inherent limitations of traditional algorithms, achieving a breakthrough in both fitting accuracy and stability. Fully validated through rigorous experimentation in both simple and complex environments, this algorithm offers substantial improvements in the ranging performance of LiDAR.