Network reconfiguration algorithm based on game theory and genetic algorithm

A technology of network reconstruction and genetic algorithm, which is applied in computing, genetic models, special data processing applications, etc., can solve the problems of high time complexity, reduce time complexity, and time-consuming reconstruction of large networks, and achieve fewer nodes and higher efficiency high effect

Inactive Publication Date: 2015-02-04
XIDIAN UNIV
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Problems solved by technology

[0005] In the past few years, the problem of network reconstruction has received more and more attention. Most of the existing algorithms are based on the compressed sensing algorithm, which uses the sparseness of the network to reconstruct the network with the compressed sensing model. For sparse network effects Very good, but at the same time, the disadvantage is the constraint

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  • Network reconfiguration algorithm based on game theory and genetic algorithm
  • Network reconfiguration algorithm based on game theory and genetic algorithm
  • Network reconfiguration algorithm based on game theory and genetic algorithm

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Embodiment Construction

[0045] The software operation environment of the present invention is MicrosoftVisualC++6.0, and the specific steps of implementation refer to figure 1 with figure 2 , the present invention is based on the network reconstruction algorithm of game and genetic algorithm, comprises the following steps:

[0046] (1) First randomly initialize A 0-1 matrix matrix[N][N][A] of N*N, A=100;

[0047] (2) Under the prisoner’s dilemma game, initialize the game strategy state[N] of N nodes, and then calculate the node income and total income payoff_real[N+1] of the actual network under the game strategy state[N], and A The income payoff[N+1][A] of a matrix matrix[N][N][A];

[0048] The revenue of the computing node is calculated by the following formula:

[0049] F ij = S i T P S j G i = ...

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Abstract

The invention belongs to the technical field of a complicated network, and particularly discloses a network reconfiguration algorithm based on a game theory and a genetic algorithm. The algorithm comprises the main realization steps: firstly, as for a network with N nodes, A 0-1 matrixes are initialized randomly, and a game theory strategy is initialized; secondly, according to the known node actual earnings values, node earnings values of the A matrixes are calculated, and the total earnings value of each node is calculated; thirdly, a population is updated according to the genetic algorithm, and T generations are iterated to obtain A new matrixes; and finally, according to improvement on a reconfiguration algorithm on a compressed sensing network, the algorithm is used for reconstruction on a single node until the earnings values of all nodes are equal to the actual earnings values, and an actual network is obtained. Reconstruction of a network with many nodes and a large degree can be totally correct, and the time is excessively fast.

Description

technical field [0001] The invention belongs to the technical field of complex networks, relates to complex network reconstruction and data mining technology, in particular to a network reconstruction algorithm based on game and genetic algorithm. Background technique [0002] Complex network is a form of abstraction of complex systems in the real world. There are many complex networks of this type in the real world, such as friend relationship networks in social networks, power grids, the World Wide Web, neural networks in biological networks, and metabolism. network and so on. In the real-world network, we abstract the independent individuals in the system into nodes in the network, and abstract a relationship between individuals in the system that is formed naturally or artificially according to certain rules into edges between nodes. [0003] Since the publication of two articles on small-world networks and scale-free networks in "Nature" and "Science" in 1998 and 1999,...

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Application Information

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IPC IPC(8): G06N3/12G06F17/30
Inventor 吴建设焦李成张晓博尚荣华马文萍马晶晶王爽戚玉涛
Owner XIDIAN UNIV
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