## 1 Introduction
in 2017 [13], was originally designed to imitate the behavior of salps, where a leader navigates, and others are followers, to solve optimization problems. The first member is selected as the leader to find optimal solutions, and followers update their positions based on the position of their leader. The SSA method is a direct and effective swarming strategy that avoids the issue of gradient descent with the paces of followers. [PERSON] et al. [14] modified the strategies used in Salp Swarm Algorithms by integrating particle swarm optimization (PSO) and largely solved the problems of local optima and early convergence. [PERSON] et al. [15] summarized the detailed theory and applications of the SSA in different scenarios; with the advantages of efficiency and collectiveness, the accuracy of results in decision-making cases can be greatly improved. [PERSON] et al. [16] improved the general SSA by adding the updated positions of individual salps and designed a novel search algorithm strategy for local places, which improves accuracy. [PERSON] et al. [17] modified the inner mechanism of the Salp Swarm Algorithm (SSA) by altering adaptive parameters and the iteration period to maintain the balance between discovery and development. The issues of early local convergence and linear degradation can be largely resolved by hybridizing different mechanisms from other optimization methods. [PERSON] et al. [18] explored the influencing factorsof data extraction and noisy disturbance. By combining the Salp Swarm Algorithm with K-nearest neighbor methods, the authors considerably improved the classification accuracy and the algorithm's performance in less-than-truckload problems. [PERSON] et al. [19] designed a novel version of the Salp Swarm Algorithm (SSA) method, with the utilization of opposition-based learning to improve the diversity of the search space and resolve the issue of local convergence. [PERSON] et al. [20] utilized automation technology along with the Salp Swarm Algorithm (SSA) to overcome the issue of local optima in transportation and improve the robustness of the original model. Our proposed algorithm aims to provide matching services for truck drivers and cargo shippers and offer route planning services for truck drivers shipping cargo to different destinations. This paper's contributions can be summarized as follows: (1) We improved the original Salp Swarm Algorithm (SSA) by introducing the SubChain operation, resulting in our proposed SubChain Salp Swarm Algorithm (SSSA). (2) We designed an LTL matching model for a multi-objective optimization problem and verified the model's performance on data from a real logistics company. The results show that it has improved convergence speed and overcomes the issue of local convergence. (3) We fully considered the booking mechanism that matches goods that have not yet arrived at the distribution center to smoothly handle unexpected conditions and matching issues. The structure of this paper is as follows: Section 2 introduces the LTL freight matching system. Section 3 presents the standard SSA and the SubChain Salp Swarm Algorithm and demonstrates the effectiveness of the improvement through six validation functions. Section 4 explains the application of the SubChain Salp Swarm Algorithm to the planning of the LTL freight matching system and provides the results of simulation experiments. Section 5 is the conclusion of this paper.