A hybrid genetic slime mould algorithm for parameter optimization of field-road trajectory segmentation models

•A generalized parameter optimization solution based on metaheuristic algorithms for the filed-road trajectory segmentation model is proposed.•A novel hybrid optimization algorithm, aiming at finding out the optimal parameter combination for field-road segmentation from a huge constrained solution space, is first proposed.•Segmentation experiments with agricultural machinery trajectory datasets reveal the advantages over previous methods. Field-road trajectory segmentation (FRTS) is a critical step in the processing of agricultural machinery trajectory data. This study presents a generalized optimization framework based on metaheuristic algorithms (MAs) to increase the accuracy of the field-road trajectory segmentation model. The MA optimization process is used in this framework to precisely and quickly identify the parameters of the FRTS model. It is difficult to solve the parameter optimization problem with basic metaheuristic algorithms without falling into local optima due to their insufficient performance. This study therefore combines a genetic algorithm (GA) with a slime mould algorithm (SMA) to propose a novel enhanced hybrid algorithm (GASMA); the algorithm has superior global search capability due to the implicit parallelism of the GA, and the oscillation concentration mechanism of the SMA is used to enhance the algorithm's local search capability. To maintain the balance between the two capacities, a nonlinear parameter management technique is developed that adaptively modifies the algorithm's computational process based on the fitness distribution deviation of the population. Experiments were conducted on real agricultural trajectory datasets with various sample frequencies, and the proposed algorithm was compared with existing methods to validate its efficiency. According to the experimental data, the optimized model produced better results. The proposed approach provides an automatic and accurate method for determining the optimal parameter configurations of FRTS model instances, where the parameter optimization solution is not confined to a single specified procedure and can be addressed by a variety of metaheuristic algorithms.