CN114257507A - Method for improving network information sharing level based on evolutionary game theory - Google Patents

Method for improving network information sharing level based on evolutionary game theory Download PDF

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CN114257507A
CN114257507A CN202111581845.0A CN202111581845A CN114257507A CN 114257507 A CN114257507 A CN 114257507A CN 202111581845 A CN202111581845 A CN 202111581845A CN 114257507 A CN114257507 A CN 114257507A
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CN114257507B (en
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张万鹏
詹俊
谷学强
项凤涛
苏炯铭
张煜
刘鸿福
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National University of Defense Technology
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    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
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    • H04L41/00—Arrangements for maintenance, administration or management of data switching networks, e.g. of packet switching networks
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    • H04—ELECTRIC COMMUNICATION TECHNIQUE
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    • H04L41/00—Arrangements for maintenance, administration or management of data switching networks, e.g. of packet switching networks
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    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
    • H04L41/00—Arrangements for maintenance, administration or management of data switching networks, e.g. of packet switching networks
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Abstract

The application relates to a method for improving network information sharing level based on an evolutionary game theory, which comprises the following steps: initializing the network structure, determining the network structure, the number of nodes in the network and the average d of the networkmean(ii) a Defining a revenue matrix and a game strategy of the nodes; initializing system parameters and determining times e for carrying out an evolutionary game; each node and a neighbor node play a game to obtain accumulated earnings; introducing the node degree into the strategy updating of the node, and adjusting the node income for strategy updating; each node randomly selects a neighbor node for strategy learning; updating the policy of each node in the network; judging whether the node reaches the maximum evolution times or not, and finally calculating the maximum evolution times of the networkThe final cooperation rate. By adopting the method, the node degree of the individual in the network is added into the strategy updating process, the cooperation behavior in the network is effectively promoted, and the network information sharing level is improved.

Description

Method for improving network information sharing level based on evolutionary game theory
Technical Field
The present application relates to the technical field of network cooperation evolution analysis, and in particular, to a method, an apparatus, a computer device, and a storage medium for improving a network information sharing level based on an evolutionary game theory.
Background
With the continuous development of network technology, information isolated islands of a distributed system can be broken through when the individuals share information, the operation efficiency of the system is improved, but the cost is needed to be paid when the individuals share the information, and more benefits can be obtained when the individuals do not share the information, so that cooperation dilemma can occur in the information sharing process among the individuals due to the selfishness of the individuals, and the individuals can reasonably choose not to share the information with other individuals. On the basis, a network cooperation evolution analysis technology appears, and the evolutionary game theory is a method which is commonly used in the network cooperation evolution analysis technology, overcomes individual rationality and promotes the generation of cooperation behaviors in a group.
However, in the current technology for realizing network cooperation evolution analysis through the evolutionary game theory, the cooperation level of the network is mainly improved from two aspects of optimizing the network structure and changing the strategy updating formula, and the difference of the interaction conditions of different individuals and neighbors in the network is not considered. The method causes the problems of low network information sharing efficiency and resource waste.
Disclosure of Invention
Aiming at the problems in the prior art, the invention provides a method for improving the network information sharing level based on the evolutionary game theory, which adds the node degrees of individuals in the network into the strategy updating process, effectively promotes the generation of cooperative behaviors in the network and improves the network information sharing level.
A method for improving the network information sharing level based on an evolutionary game theory is characterized by comprising the following steps:
acquiring a network to be optimized; the network to be optimized comprises a plurality of network nodes;
initializing the network to be optimized, and determining the number of network nodes and the network average degree of the network;
carrying out an evolutionary game on the network node and a neighbor node according to a predefined game strategy of the node and a node income matrix to obtain the accumulated income of the network node; the gaming policies include a cooperation policy and a traitor policy;
adjusting the accumulated benefit of the network node by using the network average degree and the node degree of the network node to obtain the adjusted accumulated benefit of the network node;
node strategy updating learning is carried out on the adjusted accumulated income of the network node and the adjusted accumulated income of the neighbor node, and the updated game strategy of the network node is obtained;
carrying out an evolutionary game on the initialized network to be optimized according to preset evolutionary game times to obtain a plurality of game strategies of the network nodes;
calculating a plurality of game strategies of the network nodes to obtain the final cooperation rate of the network to be optimized; and optimizing the network to be optimized according to the final cooperation rate.
In one embodiment, the method further comprises the following steps: carrying out an evolutionary game according to game strategies of the network node and the neighbor node to respectively obtain the gains of the network node and the neighbor node; calculating according to the profits of the network node and the neighbor node to obtain a network node profit matrix; and performing accumulation calculation on the income matrix to obtain the accumulated income of the network node.
In one embodiment, the method further comprises the following steps: if the node x and the node y both adopt a cooperation strategy, namely both share own information, the benefits of the node x and the node y are R; if the node x adopts a cooperation strategy and the participant nodes y all adopt a traitor strategy, namely the node x shares the information owned by the node x but the node y does not share the information owned by the node y, the income of the node x is S and the income of the node y is T; if the node x adopts a traitor strategy and the participant nodes y all adopt a cooperation strategy, namely the node x does not share the own information and the node y shares the own information, the benefit of the node x is T and the benefit of the node y is S; if both node x and node y employ traitor policies, i.e., both do not share own information, then both have P gains.
In one embodiment, the method further comprises the following steps: calculating according to the profits of the network node and the neighbor node to obtain a network node benefit matrix:
Figure BDA0003426344170000021
where node y is a neighbor node to node x.
In one embodiment, the method further comprises the following steps: the adjusted cumulative benefit of the network node is as follows:
Figure BDA0003426344170000031
wherein d isxIs the node degree of node x, dmeanIn order to be the average degree of the network,
Figure BDA0003426344170000032
accumulated earnings obtained by prisoner gaming for the node x and the neighbor nodes,
Figure BDA0003426344170000033
adjusted cumulative revenue for network node x.
In one embodiment, the method further comprises the following steps: if it is
Figure BDA0003426344170000034
The node x keeps the own strategy in the game round; if it is
Figure BDA0003426344170000035
Then node x is formulated
Figure BDA0003426344170000036
The policy of the illustrated probabilistic learning node y; where d ═ max { d ═ dx，dy}，D＝T-S，dxRepresenting the node degree of node x, dyRepresenting the node degree of node y.
In one embodiment, the method further comprises the following steps: the final cooperation rate of the network to be optimized is:
Figure BDA0003426344170000037
wherein N isjE {0,1}, which represents the game strategy adopted by the node j. When node j employs traitor policy, Nj0; when node j adopts the cooperation strategy, Nj＝1。
An apparatus for improving a level of network information sharing based on evolutionary game theory, the apparatus comprising:
the network acquisition module is used for acquiring a network to be optimized; the network to be optimized comprises a plurality of network nodes;
the game strategy construction module is used for initializing the network to be optimized and determining the number of network nodes and the network average degree of the network; carrying out an evolutionary game on the network node and a neighbor node according to a predefined game strategy of the node and a node income matrix to obtain the accumulated income of the network node; the gaming policies include a cooperation policy and a traitor policy; adjusting the accumulated benefit of the network node by using the network average degree and the node degree of the network node to obtain the adjusted accumulated benefit of the network node; node strategy updating learning is carried out on the adjusted accumulated income of the network node and the adjusted accumulated income of the neighbor node, and the updated game strategy of the network node is obtained; carrying out an evolutionary game on the initialized network to be optimized according to preset evolutionary game times to obtain a plurality of game strategies of the network nodes;
the optimization module is used for calculating a plurality of game strategies of the network nodes to obtain the final cooperation rate of the network to be optimized; and optimizing the network to be optimized according to the final cooperation rate.
A computer device comprising a memory and a processor, the memory storing a computer program, the processor implementing the following steps when executing the computer program:
acquiring a network to be optimized; the network to be optimized comprises a plurality of network nodes;
initializing the network to be optimized, and determining the number of network nodes and the network average degree of the network;
carrying out an evolutionary game on the network node and a neighbor node according to a predefined game strategy of the node and a node income matrix to obtain the accumulated income of the network node; the gaming policies include a cooperation policy and a traitor policy;
adjusting the accumulated benefit of the network node by using the network average degree and the node degree of the network node to obtain the adjusted accumulated benefit of the network node;
node strategy updating learning is carried out on the adjusted accumulated income of the network node and the adjusted accumulated income of the neighbor node, and the updated game strategy of the network node is obtained;
carrying out an evolutionary game on the initialized network to be optimized according to preset evolutionary game times to obtain a plurality of game strategies of the network nodes;
calculating a plurality of game strategies of the network nodes to obtain the final cooperation rate of the network to be optimized; and optimizing the network to be optimized according to the final cooperation rate.
A computer-readable storage medium, on which a computer program is stored which, when executed by a processor, carries out the steps of:
acquiring a network to be optimized; the network to be optimized comprises a plurality of network nodes;
initializing the network to be optimized, and determining the number of network nodes and the network average degree of the network;
carrying out an evolutionary game on the network node and a neighbor node according to a predefined game strategy of the node and a node income matrix to obtain the accumulated income of the network node; the gaming policies include a cooperation policy and a traitor policy;
adjusting the accumulated benefit of the network node by using the network average degree and the node degree of the network node to obtain the adjusted accumulated benefit of the network node;
node strategy updating learning is carried out on the adjusted accumulated income of the network node and the adjusted accumulated income of the neighbor node, and the updated game strategy of the network node is obtained;
carrying out an evolutionary game on the initialized network to be optimized according to preset evolutionary game times to obtain a plurality of game strategies of the network nodes;
calculating a plurality of game strategies of the network nodes to obtain the final cooperation rate of the network to be optimized; and optimizing the network to be optimized according to the final cooperation rate.
According to the method, the device, the computer equipment and the storage medium for improving the network information sharing level based on the evolutionary game theory, the evolutionary game is carried out on the network nodes and the neighbor nodes by defining the game strategy and the node income matrix to obtain the accumulated income of the network nodes, then the network average degree and the node degree of the network nodes are utilized to adjust the accumulated income of the network nodes, the adjusted accumulated income and the adjusted accumulated income of the neighbor nodes are subjected to node strategy updating learning, and finally the final cooperation rate of the network to be optimized is obtained; and optimizing the network to be optimized according to the final cooperation rate. By the method, the difference of interaction conditions of different individuals and neighbors in the network is fully considered, cooperation behaviors generated among all nodes in the network can be effectively promoted, the network information sharing level is improved, the network cooperation efficiency among the nodes is improved, network resource waste is avoided, and therefore the requirement of people for network optimization is met.
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FIG. 1 is a diagram of an application scenario of a method for improving a network information sharing level based on an evolutionary game theory in an embodiment;
FIG. 2 is a schematic flowchart illustrating a method for improving a network information sharing level based on an evolutionary game theory according to an embodiment;
FIG. 3 is a schematic flowchart illustrating a step of increasing the level of sharing network information based on evolutionary game theory according to an embodiment;
FIG. 4 is a schematic flowchart illustrating a step of increasing the level of sharing network information based on evolutionary game theory according to an embodiment;
FIG. 5 is a block diagram of an apparatus for improving the level of network information sharing based on evolutionary game theory according to an embodiment;
FIG. 6 is a diagram illustrating an internal structure of a computer device according to an embodiment.
Detailed Description
In order to make the objects, technical solutions and advantages of the present application more apparent, the present application is described in further detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative of the present application and are not intended to limit the present application.
The method for improving the network information sharing level based on the evolutionary game theory can be applied to the application environment shown in fig. 1. The network to be optimized comprises a plurality of network nodes, each network node is communicated with each neighbor node through a wired or wireless network, wherein the network nodes can be user terminals or servers, the terminals can be but are not limited to various personal computers, notebook computers, smart phones, tablet computers and portable wearable devices, and the servers can be realized by independent servers or server clusters formed by a plurality of servers.
In an embodiment, as shown in fig. 2, a method for improving a network information sharing level based on an evolutionary game theory is provided, which is described by taking an application scenario in which the method is applied to a user terminal as a network node as an example, and specifically includes the following steps:
step 202, acquiring a network to be optimized, wherein the network to be optimized comprises network nodes formed by a plurality of user terminals; it is worth to be noted that, the network to be optimized may be a user relationship network formed by the connection between users in the internet, each user in the user relationship network corresponds to a user terminal, the user terminal is a network node, the purpose of the method of the present invention is to improve the network information sharing level between the network nodes;
step 204, initializing the network to be optimized, wherein the number of nodes in the network is 1000, and the average degree d of the networkmean＝4；
Step 206, performing an evolutionary game on the network node and the neighbor nodes according to a game strategy of the predefined node and a node income matrix, wherein in the game strategy of the network node, 50% of the nodes are randomly selected to adopt a cooperation strategy, the other 50% of the nodes adopt a traitor strategy, and after the evolutionary game is completed, a cumulative income P of the network node is obtainedxy；
Step 208, utilizing the network average dmeanAnd a node degree d of the network nodexAccumulated profit P for the network nodexyAdjusting to obtain the adjusted accumulated income Pa of the network nodex_adjust；
Step 210, adjusting the accumulated profit Pa of the network nodex_adjustCarrying out node strategy updating learning on the adjusted accumulated earnings of the neighbor nodes to obtain an updated game strategy of the network node;
step 212, carrying out an evolutionary game on the initialized network to be optimized according to preset evolutionary game times to obtain a plurality of game strategies of the network node;
step 214, calculating a plurality of game strategies of the network nodes to obtain the final cooperation rate of the network to be optimized
Figure BDA0003426344170000071
According to the final cooperation rateAnd optimizing the network to be optimized.
In the method for improving the network information sharing level based on the evolutionary game theory, a game strategy and a node income matrix are defined to carry out an evolutionary game on a network node and a neighbor node to obtain the cumulative income of the network node, the network average degree and the node degree of the network node are used for adjusting the cumulative income of the network node, the adjusted cumulative income and the adjusted cumulative income of the neighbor node are subjected to node strategy updating learning, and finally the final cooperation rate of a network to be optimized is obtained; and optimizing the network to be optimized according to the final cooperation rate. By the method, the difference of interaction conditions of different individuals and neighbors in the network is fully considered, cooperation behaviors generated among all nodes in the network can be effectively promoted, the network information sharing level is improved, the network cooperation efficiency among the nodes is improved, network resource waste is avoided, and therefore the requirement of people for network optimization is met.
In an embodiment, as shown in fig. 3, an evolutionary game is performed according to game strategies of the network node and the neighboring node, so as to obtain profits of the network node and the neighboring node respectively; calculating according to the profits of the network node and the neighbor node to obtain a network node profit matrix:
Figure BDA0003426344170000072
(1) if the node x and the node y of the two participants adopt a cooperation strategy, namely the two participants share own information, the gains of the two participants are both 1;
(2) if the participant node x adopts a cooperation strategy and the participant nodes y all adopt a traitor strategy, namely the node x shares the information owned by the node x but the node y does not share the information owned by the node y, the benefit of the node x is-1 and the benefit of the node y is 1.2;
(3) if the participant node x adopts a traitor strategy and the participant nodes y all adopt a cooperation strategy, namely the node x does not share the own information and the node y shares the own information, the benefit of the node x is 1.2 and the benefit of the node y is-1;
(4) if both participant nodes x and y employ traitor policies, i.e., neither node shares its own information, then both gains are 0;
in one embodiment, the network average d is used, as shown in FIG. 4meanAnd a node degree d of the network nodexAccumulated profit P for the network nodexyAdjusting to obtain the adjusted accumulated income Pa of the network nodex_adjustThe method specifically comprises the following steps:
Figure BDA0003426344170000081
wherein d isxIs the node degree of node x, dmeanIn order to be the average degree of the network,
Figure BDA0003426344170000082
accumulated earnings obtained by prisoner gaming for the node x and the neighbor nodes,
Figure BDA0003426344170000083
adjusted cumulative revenue for network node x.
Then, node strategy updating learning is carried out on the adjusted accumulated earnings of the network nodes and the adjusted accumulated earnings of the neighbor nodes, and updated game strategies of the network nodes are obtained, and the method specifically comprises the following steps:
if it is
Figure BDA0003426344170000084
The node x keeps the own strategy in the game round;
if it is
Figure BDA0003426344170000085
Then node x is formulated
Figure BDA0003426344170000086
The policy of the illustrated probabilistic learning node y; it is composed ofWhere d ═ max { d ═ d >x，dy}，D＝T-S，dxRepresenting the node degree of node x, dyRepresenting the node degree of node y.
Finally, calculating a plurality of game strategies of the network nodes to obtain the final cooperation rate of the network to be optimized, which specifically comprises the following steps:
Figure BDA0003426344170000087
wherein N isjE {0,1}, which represents the game strategy adopted by the node j. When node j employs traitor policy, Nj0; when node j adopts the cooperation strategy, Nj＝1。
It should be understood that although the various steps in the flow charts of fig. 2-4 are shown in order as indicated by the arrows, the steps are not necessarily performed in order as indicated by the arrows. The steps are not performed in the exact order shown and described, and may be performed in other orders, unless explicitly stated otherwise. Moreover, at least some of the steps in fig. 2-4 may include multiple sub-steps or multiple stages that are not necessarily performed at the same time, but may be performed at different times, and the order of performance of the sub-steps or stages is not necessarily sequential, but may be performed in turn or alternating with other steps or at least some of the sub-steps or stages of other steps.
In one embodiment, as shown in fig. 5, there is provided an apparatus for improving a network information sharing level based on an evolutionary game theory, including: network acquisition module, game strategy construction module and optimization module, wherein:
a network obtaining module 501, configured to obtain a network to be optimized; the network to be optimized comprises a plurality of network nodes;
a game strategy construction module 502, configured to initialize the network to be optimized, and determine the number of network nodes and the network average degree of the network; carrying out an evolutionary game on the network node and a neighbor node according to a predefined game strategy of the node and a node income matrix to obtain the accumulated income of the network node; the gaming policies include a cooperation policy and a traitor policy; adjusting the accumulated benefit of the network node by using the network average degree and the node degree of the network node to obtain the adjusted accumulated benefit of the network node; node strategy updating learning is carried out on the adjusted accumulated income of the network node and the adjusted accumulated income of the neighbor node, and the updated game strategy of the network node is obtained; carrying out an evolutionary game on the initialized network to be optimized according to preset evolutionary game times to obtain a plurality of game strategies of the network nodes;
the optimization module 503 is configured to calculate a plurality of game strategies of the network node to obtain a final cooperation rate of the network to be optimized; and optimizing the network to be optimized according to the final cooperation rate.
For specific limitation of the device for improving the network information sharing level based on the evolutionary game theory, reference may be made to the above limitation on a method for improving the network information sharing level based on the evolutionary game theory, and details are not described here. All or part of each module in the device for improving the network information sharing level based on the evolutionary game theory can be realized by software, hardware and a combination thereof. The modules can be embedded in a hardware form or independent from a processor in the computer device, and can also be stored in a memory in the computer device in a software form, so that the processor can call and execute operations corresponding to the modules.
In one embodiment, a computer device is provided, which may be a terminal, and its internal structure diagram may be as shown in fig. 6. The computer device includes a processor, a memory, a network interface, a display screen, and an input device connected by a system bus. Wherein the processor of the computer device is configured to provide computing and control capabilities. The memory of the computer device comprises a nonvolatile storage medium and an internal memory. The non-volatile storage medium stores an operating system and a computer program. The internal memory provides an environment for the operation of an operating system and computer programs in the non-volatile storage medium. The network interface of the computer device is used for communicating with an external terminal through a network connection. The computer program is executed by a processor to implement a method for improving the level of network information sharing based on evolutionary game theory. The display screen of the computer equipment can be a liquid crystal display screen or an electronic ink display screen, and the input device of the computer equipment can be a touch layer covered on the display screen, a key, a track ball or a touch pad arranged on the shell of the computer equipment, an external keyboard, a touch pad or a mouse and the like.
Those skilled in the art will appreciate that the architecture shown in fig. 5 is merely a block diagram of some of the structures associated with the disclosed aspects and is not intended to limit the computing devices to which the disclosed aspects apply, as particular computing devices may include more or less components than those shown, or may combine certain components, or have a different arrangement of components.
In an embodiment, a computer device is provided, comprising a memory storing a computer program and a processor implementing the steps of the method in the above embodiments when the processor executes the computer program.
In an embodiment, a computer storage medium is provided, on which a computer program is stored, which, when being executed by a processor, carries out the steps of the method in the above-mentioned embodiments.
It will be understood by those skilled in the art that all or part of the processes of the methods of the embodiments described above can be implemented by hardware instructions of a computer program, which can be stored in a non-volatile computer-readable storage medium, and when executed, can include the processes of the embodiments of the methods described above. Any reference to memory, storage, database, or other medium used in the embodiments provided herein may include non-volatile and/or volatile memory, among others. Non-volatile memory can include read-only memory (ROM), Programmable ROM (PROM), Electrically Programmable ROM (EPROM), Electrically Erasable Programmable ROM (EEPROM), or flash memory. Volatile memory can include Random Access Memory (RAM) or external cache memory. By way of illustration and not limitation, RAM is available in a variety of forms such as Static RAM (SRAM), Dynamic RAM (DRAM), Synchronous DRAM (SDRAM), Double Data Rate SDRAM (DDRSDRAM), Enhanced SDRAM (ESDRAM), Synchronous Link DRAM (SLDRAM), Rambus Direct RAM (RDRAM), direct bus dynamic RAM (DRDRAM), and memory bus dynamic RAM (RDRAM).
The technical features of the above embodiments can be arbitrarily combined, and for the sake of brevity, all possible combinations of the technical features in the above embodiments are not described, but should be considered as the scope of the present specification as long as there is no contradiction between the combinations of the technical features.
The above-mentioned embodiments only express several embodiments of the present application, and the description thereof is more specific and detailed, but not construed as limiting the scope of the invention. It should be noted that, for a person skilled in the art, several variations and modifications can be made without departing from the concept of the present application, which falls within the scope of protection of the present application. Therefore, the protection scope of the present patent shall be subject to the appended claims.

Claims (10)

1. A method for improving the network information sharing level based on an evolutionary game theory is characterized by comprising the following steps:
acquiring a network to be optimized; the network to be optimized comprises a plurality of network nodes;
initializing the network to be optimized, and determining the number of network nodes and the network average degree of the network;
carrying out an evolutionary game on the network node and a neighbor node according to a predefined game strategy of the node and a node income matrix to obtain the accumulated income of the network node; the gaming policies include a cooperation policy and a traitor policy;
adjusting the accumulated benefit of the network node by using the network average degree and the node degree of the network node to obtain the adjusted accumulated benefit of the network node;
node strategy updating learning is carried out on the adjusted accumulated income of the network node and the adjusted accumulated income of the neighbor node, and the updated game strategy of the network node is obtained;
carrying out an evolutionary game on the initialized network to be optimized according to preset evolutionary game times to obtain a plurality of game strategies of the network nodes;
calculating a plurality of game strategies of the network nodes to obtain the final cooperation rate of the network to be optimized; and optimizing the network to be optimized according to the final cooperation rate.
2. The method of claim 1, wherein evolutionary gaming is performed on the network node and neighbor nodes according to a predefined gaming strategy of the node and a node profit matrix to obtain a cumulative profit of the network node, and the method comprises:
carrying out an evolutionary game according to game strategies of the network node and the neighbor node to respectively obtain the gains of the network node and the neighbor node;
calculating according to the profits of the network node and the neighbor node to obtain a network node profit matrix;
and performing accumulation calculation on the income matrix to obtain the accumulated income of the network node.
3. The method of claim 2, wherein performing an evolutionary game according to the game strategies of the network node and the neighboring node to obtain the earnings of the network node and the neighboring node respectively comprises:
if the node x and the node y both adopt a cooperation strategy, namely both share own information, the benefits of the node x and the node y are R;
if the node x adopts a cooperation strategy and the participant nodes y all adopt a traitor strategy, namely the node x shares the information owned by the node x but the node y does not share the information owned by the node y, the income of the node x is S and the income of the node y is T;
if the node x adopts a traitor strategy and the participant nodes y all adopt a cooperation strategy, namely the node x does not share the own information and the node y shares the own information, the benefit of the node x is T and the benefit of the node y is S;
if both node x and node y employ traitor policies, i.e., both do not share own information, then both have P gains.
4. The method of claim 3, wherein calculating the network node revenue matrix based on the revenue of the network node and the neighboring nodes comprises:
Figure FDA0003426344160000021
where node y is a neighbor node to node x.
5. The method of claim 4, wherein adjusting the accumulated profit of the network node by using the network average degree and the node degree of the network node to obtain the adjusted accumulated profit of the network node comprises:
Figure FDA0003426344160000022
wherein d isxIs the node degree of node x, dmeanIn order to be the average degree of the network,
Figure FDA0003426344160000023
accumulated earnings obtained by prisoner gaming for the node x and the neighbor nodes,
Figure FDA0003426344160000024
adjusted cumulative revenue for network node x.
6. The method of claim 5, wherein performing node policy update learning on the adjusted cumulative benefit of the network node and the adjusted cumulative benefit of the neighboring node to obtain an updated game policy of the network node, comprises:
if it is
Figure FDA0003426344160000025
The node x keeps the own strategy in the game round;
if it is
Figure FDA0003426344160000026
Then node x is formulated
Figure FDA0003426344160000027
The policy of the illustrated probabilistic learning node y; wherein d is＞＝max{dx，dy}，D＝T-S，dxRepresenting the node degree of node x, dyRepresenting the node degree of node y.
7. The method of claim 6, wherein calculating a plurality of gaming policies for the network nodes to obtain a final cooperation rate of the network to be optimized comprises:
Figure FDA0003426344160000031
wherein N isjE {0,1}, which represents the game strategy adopted by the node j. When node j employs traitor policy, Nj0; when node j adopts the cooperation strategy, Nj＝1。
8. An apparatus for improving network information sharing level based on evolutionary game theory, the apparatus comprising:
the network acquisition module is used for acquiring a network to be optimized; the network to be optimized comprises a plurality of network nodes;
the game strategy construction module is used for initializing the network to be optimized and determining the number of network nodes and the network average degree of the network; carrying out an evolutionary game on the network node and a neighbor node according to a predefined game strategy of the node and a node income matrix to obtain the accumulated income of the network node; the gaming policies include a cooperation policy and a traitor policy; adjusting the accumulated benefit of the network node by using the network average degree and the node degree of the network node to obtain the adjusted accumulated benefit of the network node; node strategy updating learning is carried out on the adjusted accumulated income of the network node and the adjusted accumulated income of the neighbor node, and the updated game strategy of the network node is obtained; carrying out an evolutionary game on the initialized network to be optimized according to preset evolutionary game times to obtain a plurality of game strategies of the network nodes;
the optimization module is used for calculating a plurality of game strategies of the network nodes to obtain the final cooperation rate of the network to be optimized; and optimizing the network to be optimized according to the final cooperation rate.
9. A computer device comprising a memory and a processor, the memory storing a computer program, wherein the processor implements the steps of the method of any one of claims 1 to 7 when executing the computer program.
10. A computer-readable storage medium, on which a computer program is stored, which, when being executed by a processor, carries out the steps of the method of any one of claims 1 to 7.
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