## 1 Introduction
[PERSON] [47] is also based on the MDL principle, which transforms the trajectory segmentation problem into the shortest path problem of undirected graph and then uses Dijkstra algorithm to get the best segmentation, which has higher complexity. The distance index is used to measure the difference between the trajectory segments in the above methods, and [PERSON] [48; 49] defines the corner index to measure the angle difference between the trajectory segments, which represents the movement trend of the trajectory, and realizes the local forward segmentation and compression based on direction change. In view of the shortcomings of position-preserving trajectory segmentation algorithm in some scenarios, [PERSON] [50] proposed a direction-preserving trajectory segmentation simplification algorithm, which uses the maximum angle difference instead of the average angle difference to better maintain the trajectory shape to obtain a better segmentation solution. The prominent advantage of the local forward methods is that it has less computation, but the existing problems are mainly reflected in two aspects. The first is searching from the first point, which can only obtain local optimal segmentation, and the resulting lack of the overall consideration of the segmentation problem. The selection of feature points is difficult to ensure global optimization, which affects the accuracy of segmentation and reduces the similarity between the compressed trajectory and the original trajectory. The second is that there are dimensions in the distance and direction to measure the homogeneity and difference between the trajectories, which are very sensitive to the setting of threshold parameters. Considering that our application pursues high trajectory approximation accuracy in order to improve the quality of subsequent trajectory data mining, rather than the segmentation and compression speed, we prefer the global-oriented method. Therefore, can segmentation accuracy be further improved without increasing the compression rate? This problem can be solved by finding the trajectory segmentation feature points that can better represent the original trajectory, and the swarm intelligence (SI) algorithm can not only meet the need of global search, but also help to find the trajectory segmentation optimization solution quickly and accurately. Swarm intelligence, a major branch of artificial intelligence, was rendered to model the collective behavior of social swarms in nature. SI methods [51; 52; 53] have been widely used to solve global search problems, and particle swarm optimization (PSO) is the main paradigm [54; 55]. The original idea of the PSO came from [PERSON] and [PERSON]'s observation of birds looking for food by sharing knowledge. They then proposed applying this evolutionary approach to the optimization of nonlinear problems [56; 57]. Due to its simplicity, PSO has been successfully utilized in many fields, such as computer vision [58], path planning [59], signal processing [60] and other fields. The PSO variants mainly focus on parameter optimization [61; 62; 63; 64] and multi-objective search [65; 66; 67], which improve the convergence speed, stability and applicability. In summary, trajectory segmentation and compression mainly face the problem of how to balance trajectory segmentation accuracy and trajectory data compression rate. Intuitively, to improve the accuracy of trajectory segmentation, one can set more segmentation feature points, i.e., retain as many original trajectory points as possible; however, this will have negative impact on the trajectory data compression rate. Similarly, the local forward method can improve compression efficiency of trajectory segmentation, but it would sacrifice the accuracy of trajectory segmentation to a certain extent. Therefore, the basic consideration of this paper is to provide a better trajectory segmentation and compression method, which can obtain lower trajectory compression rate under certain trajectory approximation accuracy requirements.