Key node set determination method based on swarm intelligence

Abstract

The invention relates to the technical field of computer network optimization, and discloses a key node set determination method based on swarm intelligence, which comprises the following steps of: 1, preprocessing a target network and converting the target network into a matrix to obtain an adjacent matrix of the target network; 2, using an ant colony algorithm to simulate the diffusion of influence, selecting a fixed node to carry out the influence diffusion under the ant colony algorithm, and solving the most suitable propagation parameters of the ant colony algorithm in the network; 3, carrying out tracing traversal on random nodes on the target network through an ant colony algorithm, and obtaining a key path; and 4, performing dynamic path planning on the obtained key path to obtain a sequence similarity to obtain a key node. Compared with the prior art, the method has the advantages that the colony intelligence of ants is combined with a complex network, the influence diffusion is carried out by simulating the foraging path of the ants, the key path is found through the path of the ant colony moving in the weighted network, and then the key node is obtained by combining the dynamic path planning.

Description

technical field [0001] The invention relates to the technical field of computer network optimization, in particular to a method for determining a key node set based on swarm intelligence. Background technique [0002] The influence maximization problem solves the problem of how to measure the importance of nodes in the network. One of its classic applications is viral marketing, which is to sell products through word of mouth. The problem of influence maximization is to find a limited number of nodes in the network, that is, seed nodes, so that the influence of seed nodes can cover the entire network as much as possible. How to select these seed nodes is the key to the problem of maximizing influence. [0003] In many studies in the past, researchers only focused on the topology of nodes in the network, or some traditional heuristic algorithms. However, using the traditional heuristic algorithm to determine the key nodes has the following problems: generally, only the loca...

AI Technical Summary

Problems solved by technology

However, using the traditional heuristic algorithm to determine key nodes has the following problems: In general, only the local optimal solution of the optimization problem can be found, and the solution process is also very dependent on the initial parameters

[0004] The traditional heuristic algorithm mentioned above ignores the bionic swarm intelligence algorithm. Animal grouping is a common biological phenomenon, such as ants, fish, wolves, bees, etc. The survival methods of these creatures in different environments solve problems for us. Provides ideas, so that the optimization of many highly complex problems can be perfectly solved from these biological intelligence phenomena

Images