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Hou et al. (2017) successfully applied the self-organizing innuendo network to the route planning problem and the solution of the vehicle distribution area problem, using a one-dimensional ring network topology to solve the vehicle routing problem. Du et al. (2018) discussed two types of demand paths and adopted the simulated annealing algorithm, which saved the computational complexity of the algorithm and improved the applicability of the two-stage simulated annealing algorithm. Li et al. (2016) transformed the traditional free annealing algorithm into a directional annealing with direction orientation, transformed the traditional free annealing algorithm into a directional annealing algorithm by providing some commonsense knowledge for the search program, and proved that the algorithm improved the efficiency with an example. Safeer et al. (2014) studied the vehicle routing optimization problem considering the uncertainty of vehicle travel time and customer service time, proposed an incomplete undirected graph representation of the physical delivery network composed of two types of nodes, the distribution center and the customer, and established a physical delivery network, a fuzzy programming model for vehicle path optimization, which solves the problem by embedding the Floyd algorithm in the predator search algorithm. He (2020) transformed the traditional free annealing algorithm into directional annealing with direction orientation, transformed the traditional free annealing algorithm into a directional annealing algorithm by providing some common-sense knowledge for the search procedure, and proved that the algorithm improved the efficiency with an example. Moncayo–Martínez et al. (2016) proposed an ACA with the characteristic of mutation in the genetic algorithm, so as to change the problem of the slow convergence speed of the. Utamima et al. (2019) deeply studied the ant colony system model for optimization problems in continuous space. S. Q. Liu et al. (2012) proposed a model that solves the assignment problem and can be used to solve the graph coloring problem. Ting et al. (2013) analyzed and studied the transportation route optimization problem of distribution centers from the perspectives of direct delivery and distribution transportation. Considering the shortest distribution route and the minimum cost, a vehicle route optimization model was established, and the ACA was used to solve the problem. Feng (2020) also introduced a genetic algorithm into the ACA and adopted a new coding method: coding the distance between the distribution center and the customer, constructing a fitness function, and designing a new improved ACA. C. Qi (2013) used the activity-based costing method to improve the model of the vehicle routing optimization problem to study the book routing problem and used the ACA to solve the model.
The research of these methods has not fully integrated the characteristics of the actual distribution and transportation network. Most researchers regard the distribution and transportation network as a fully connected graph, that is, the basis for thinking that any two points can be directly reached. The model and algorithm design are carried out on the above, the physical delivery path optimization problem is not combined with the connectivity of the distribution and transportation network, and the solution to the optimal result may be one-sided to a certain extent. In the physical delivery problem, at present, the application research of ACA is gradually emerging. ACA has strong potential in the problem of combinatorial optimization, and it is worthy of in-depth study in the optimization of the physical delivery problem. The improvement and application of the ACA can be divided into three main aspects: First is to improve the structure and rules of the existing ACA to improve the performance of the algorithm; second is to integrate other algorithms and combine the advantages of various algorithms, establishing a hybrid intelligent algorithm; third is to expand the application field of ACA. This subject is mainly to improve the ACA and apply it to an actual physical delivery model.