A traffic network node selection method based on slime mold swarm intelligence

Abstract

A transportation network node selection method based on slime mold swarm intelligence. The method uses the slime mold swarm intelligence behavior to optimize the transportation network nodes, sorts the importance of the transportation network nodes and optimizes the layout, and simulates multiple transportation network nodes into Multiple external food sources, the link between two nodes in the transportation network is simulated as a sticky deformation body, the transportation network is simulated as a slime mold population covering all external food sources, and the transportation is simulated as a slime mold population To transport nutrients in the body, the node optimization problem of the transportation network is simulated as the optimization problem of the slime mold group foraging network. This bionic method can efficiently complete the node importance ranking and node selection problem solving of multi-node traffic network, save social costs, improve resource utilization efficiency, and simplify the problem-solving process for researchers.

Description

technical field [0001] The invention belongs to the field of computer bionics, in particular to a traffic network node selection method based on slime mold swarm intelligence. Background technique [0002] The transportation network planning and design problem can be naturalized as the classic traveling salesman problem (Traveling Salesman Problem, TSP), which is also translated as the salesman problem and the salesman problem. Merrill M. Flood was the first to use a mathematical model to study the TSP problem. In the 1930s, he solved a school bus route planning problem mathematically for the first time. The problem can be described as, assuming that a traveling salesman wants to visit n cities, the walking path must traverse each city once, and finally return to the original departure city, the traveling salesman can choose the optimal total route. The traveling salesman problem has been proposed for a hundred years. Scientists at home and abroad tend to accept the traveli...

AI Technical Summary

Problems solved by technology

The traveling salesman problem has been proposed for a hundred years. Scientists at home and abroad tend to accept the traveling salesman problem as an NP-complete problem (NP-Complete or NPC) or NP-hard problem (NP-Hard or NPH). There is no effective algorithm, and It is considered that many instances with many nodes (such as traffic networks) cannot be solved using exact algorithms, only approximate optimization algorithms can be used

Third, the accuracy of solving problems by cultivating slime mold is not high enough. Basically, petri dishes are used as maps and oatmeal particles are used as nodes. The space of the petri dish is limited and the oatmeal is large in size, coupled with the influence of the culture medium, it is obvious that Limits the accuracy of the solution

Fourth, cultivating slime molds also needs to occupy valuable space, time and manpower, supporting complex environmental regulation equipment, nutrition and water supply, growth environment control before the experiment, experimental design, data processing and post-experiment cleanup all require cost and professional operation

[0009] With the wide application of computers and the research of computer bionic algorithms, it is possible to use computer bionic methods based on the foraging behavior of slime mold groups to solve the problem of node selection in transportation networks, but so far, there are still no such methods in the market or in various literatures. Discovery of a transportation network node selection method based on slime mold swarm intelligence

Images