Traffic network node selection method based on slime mold swarm intelligence

Abstract

The invention discloses a traffic network node selection method based on slime mold swarm intelligence. The method comprises: optimizing a traffic network node by adopting a slime mold swarm intelligence behavior; performing importance sorting and layout optimization on the traffic network nodes; simulating a plurality of traffic network nodes into a plurality of external food sources; simulatinga link between two nodes in a traffic network into a viscous deformation body, simulating the traffic network into a slime mold swarm protoplasm group covering all external food sources, simulating the traffic transportation into a slime mold swarm transportation in-vivo nutrient substance, and simulating a traffic network node optimization problem into a slime mold swarm foraging network optimization problem. The biomimetic method can efficiently complete node importance sorting of the multi-node traffic network and solving of the node selection problem, saves social cost, improves resource use efficiency, and simplifies the problem solving process of 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

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