## 1 Introduction
### Research Status and Significance at Home and Abroad  
In view of the problem of logistics center site selection, domestic and foreign research is mainly divided into two aspects; one is to solve this problem through mathematical and physical methods [1; 2; 3; 4] and methods such as the analytic hierarchy method, center of gravity method, and dynamic programming method are proposed, which have made breakthrough progress in solving the problem of logistics distribution center site selection, such as [1] using the optimal worst criterion decision-making method to optimize the location of logistics center; ref. [2] combines the Gauss-Kruger projection inversion algorithm with the center of gravity method to optimize the location of distribution centers between four cities in Zhejiang Province; in [3], the K-Means algorithm and center of gravity method are used to select the location of logistics centers; ref. [4] uses analytic hierarchy to solve the problem of logistics center site selection. This type of method adopts mathematical or physical methods, although the algorithm structure is relatively simple, but the algorithm steps are more, the accuracy of the results is slightly poor, there is a certain error, and it is less involved in the current relevant research in this field. Another aspect is to use various intelligent optimization algorithms to solve the problem of logistics site selection-path optimization. Among them, most scholars use heuristic algorithms or meta-heuristics or hybrid heuristics [5; 6; 7; 8; 9; 10; 11; 12; 13; 14; 15], such as cuckoo search algorithm, gray wolf optimization algorithm, ant colony algorithm, genetic algorithm, simulated annealing algorithm, differential evolution strategy algorithm, etc., which are suitable for medium-scale and large-scale problems. The authors of [5] use the longhorn beetle search-rainfall algorithm for the site selection of logistics center; in [6], the differential evolution strategy is used to improve the cuckoo search algorithm to improve the accuracy of logistics center site selection. The authors of [7] use gray wolf optimization algorithm to optimize this problem; ref. [8] improved the whale optimization algorithm to study this problem;ref. [9] uses the improved ant colony algorithm to optimize logistics and distribution routes. The authors of [10] use the optimization of logistics distribution route using a genetic algorithm; simulated annealing algorithm and simulated annealing technology are proposed in [11]; ref. [12] uses particle swarm optimization for logistics center site selection; ref. [13] proposes an adaptive particle swarm optimization algorithm with nonlinear inertia weights and time-varying acceleration coefficients to study the distribution problem to control the balance of global search and local search for the distribution center position problem; ref. [14] introduces ant colony optimization algorithm for clustering and ant colony optimization algorithm for finding the shortest path and assigns customers and distribution centers to the model, respectively; ref. [15] adopts the improved bat algorithm, reasonably sets the parameters and selection operators in the algorithm, and solves the model to improve the prediction accuracy of logistics center site selection. Although the use of intelligent algorithms can effectively improve the speed of solving the site selection problem, the problem is that intelligent algorithms are prone to fall into convergence stagnation caused by local optimization in the optimization process, which is the problem faced by intelligent algorithms in the research of this problem. In addition, some researchers have studied the site selection problem through machine learning methods [16; 17; 18], but this type of method to solve the problem requires a large amount of data to train the model, resulting in too high complexity, and the time required far exceeds the use of intelligent algorithms, the learning time is too long, and even the purpose of learning cannot be achieved.