Parameter optimization of the field-road trajectory segmentation model based on the chaos sensing slime mould algorithm

Field-road trajectory segmentation, which aims to automatically divide a trajectory into a sequence of field-road segments, is one of the important tasks of the agricultural machinery trajectory process. This study aims to enhance the performance of the field-road segmentation problem by introducing a novel parameter optimization strategy that relies on metaheuristic algorithms. The utilization of the metaheuristic algorithm offers precise and efficient techniques for determining parameter combinations in the field-road segmentation model. A novel enhanced optimization algorithm called the chaos sensing slime mould algorithm (CSSMA), aimed at determining out the optimal parameter combination from a large constrained solution space is proposed to further improve the segmentation performance of the model. First, to strengthen the exploration capability of the algorithm, a chaotic strategy is used to initialize the search agents. Furthermore, the CSSMA incorporates the beetle antennae search algorithm and the genetic algorithm to improve its exploitation potential. Ultimately, a proposed technique for updating control parameters in a nonlinear and dynamic manner aims to achieve a balance between exploration and exploitation. This strategy involves adaptively adjusting computing operations based on feedback from the optimization process. In order to assess the effectiveness of the CSSMA, the algorithm is compared to other metaheuristic algorithms using 23 standard benchmark functions. Furthermore, the CSSMA is utilized to address the practical implementation of the parameter optimization approach for field-road segmentation. The goal is to achieve the utmost accuracy in segmentation and compare it with the current method. Experimental results demonstrate the superior convergence and speed of CSSMA, along with its benefits in extracting optimal parameter structures. It surpasses prior techniques in handling the complexities of field-road trajectory processing problems including several models and nonlinearity features. Additionally, it offers a comprehensive solution for optimizing parameters in various field-road segmentation models.