CN112350769A - Multi-domain optical network multicast route recovery method based on mixed group intelligence - Google Patents

Multi-domain optical network multicast route recovery method based on mixed group intelligence Download PDF

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CN112350769A
CN112350769A CN202011077416.5A CN202011077416A CN112350769A CN 112350769 A CN112350769 A CN 112350769A CN 202011077416 A CN202011077416 A CN 202011077416A CN 112350769 A CN112350769 A CN 112350769A
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domain
node
artificial fish
intra
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CN112350769B (en
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吴启武
刘嘉琪
姜灵芝
周阳
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Engineering University of Chinese Peoples Armed Police Force
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    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04B—TRANSMISSION
    • H04B10/00—Transmission systems employing electromagnetic waves other than radio-waves, e.g. infrared, visible or ultraviolet light, or employing corpuscular radiation, e.g. quantum communication
    • H04B10/03—Arrangements for fault recovery
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
    • H04L45/00—Routing or path finding of packets in data switching networks
    • H04L45/16—Multipoint routing
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
    • H04L45/00—Routing or path finding of packets in data switching networks
    • H04L45/28—Routing or path finding of packets in data switching networks using route fault recovery
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04Q—SELECTING
    • H04Q11/00—Selecting arrangements for multiplex systems
    • H04Q11/0001—Selecting arrangements for multiplex systems using optical switching
    • H04Q11/0062—Network aspects
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04Q—SELECTING
    • H04Q11/00—Selecting arrangements for multiplex systems
    • H04Q11/0001—Selecting arrangements for multiplex systems using optical switching
    • H04Q11/0062—Network aspects
    • H04Q2011/0079—Operation or maintenance aspects
    • H04Q2011/0081—Fault tolerance; Redundancy; Recovery; Reconfigurability

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Abstract

The invention discloses a multi-domain optical network multicast routing recovery method based on mixed group intelligence, which adopts the following steps to obtain a recovery path when the path between a source node and a destination node of the multi-domain optical network multicast routing fails: firstly, judging the position of a failed path, if the path is an intra-domain path, searching the path in the domain by using an artificial fish swarm algorithm, and determining the next advancing direction of the artificial fish at a node by using a non-cooperative game method in the path search; obtaining an intra-domain restoration path and an intra-domain optimal node; if the path is an inter-domain path, obtaining an intra-domain recovery path and an intra-domain optimal node of each domain by fusing an artificial fish model and a game theory method, uploading all intra-domain optimal nodes to a virtual optimization layer, and then searching the nodes in the optimization layer for the path; and finally obtaining the inter-domain recovery path. The invention uses the group intelligent method to distinguish different inter-domain and intra-domain faults, the obtained optical network structure is more optimized, the perception of the whole optical network to the faults is stronger, and the survivability of the network is improved.

Description

Multi-domain optical network multicast route recovery method based on mixed group intelligence
Technical Field
The invention belongs to the technical field of multicast route recovery, and relates to a multi-domain optical network multicast route recovery method based on mixed group intelligence.
Background
With the continuous development of optical networks towards high speed and transparency and the continuous increase of the scale of optical networks, intelligent optical networks with multilayer and multi-domain characteristics begin to be widely used, and the survivability of the intelligent optical networks draws more and more attention. Due to the fact that the attack has the characteristic of diffusion propagation in the transparent optical network, the transmission of the optical network along an optical path is deepened and accumulated continuously, the quality of an optical signal is reduced rapidly, and the increase of the signal error rate and the occurrence of faults in the optical network are caused. The survivability mechanism of the optical network can be divided into protection and recovery, wherein the recovery mechanism utilizes route search after a fault occurs to quickly establish a new recovery path so as to achieve the aim of keeping the optical network smooth, and compared with the protection mechanism, the recovery mechanism does not need to reserve redundant resources for the network and has important significance for ensuring the survivability of the branch network.
At present, the research on the recovery of optical networks by scholars at home and abroad mainly focuses on unicast routing of single-domain intelligent optical networks and multi-domain intelligent optical networks, and the methods cannot be directly applied to multi-domain optical networks based on distributed PCEs. The patent with publication number CN110086710A discloses a multi-domain optical network multicast route recovery method based on n-person non-cooperative game, which only considers the selection of each node to the route and ignores the overall recovery performance of the network.
Disclosure of Invention
In order to solve the defects in the prior art, the invention provides a mixed group intelligence-based multi-domain optical network multicast routing recovery method, which solves the problems of long recovery time and weak fault perception of the conventional recovery method.
In order to solve the technical problems, the invention adopts the following technical scheme:
a multi-domain optical network multicast routing recovery method based on hybrid swarm intelligence is characterized in that when a path between a source node and a destination node of a multi-domain optical network multicast routing fails, the recovery path is obtained by adopting the following steps:
step 1, judging the position of a failed path, and if the path is an intra-domain path, executing step 2; if the path is an inter-domain path, executing step 3;
step 2, calculating the toxin concentration value of the artificial fish of the current node by using an artificial fish swarm algorithm, determining the next selected behavior of the artificial fish of the current node by adopting a non-cooperative game method, and executing the selected behavior of the artificial fish to obtain a new node; the current node is a source node or a new node;
repeating the process of the step 2 until the destination node is reached; comparing the toxin concentration values of all the nodes, taking the node with the minimum toxin concentration value as an optimal node, and forming an intra-domain restoration path by the path formed by connecting all the nodes;
and 3, obtaining an intra-domain recovery path and an intra-domain optimal node of each domain by using the method in the step 2, forming an optimal node set by all the intra-domain optimal nodes, and then performing path search on the nodes in the optimal node set to finally obtain an inter-domain recovery path.
Preferably, the step 2 specifically comprises the following steps:
step 2.1, placing artificial fish at a source node to form an initial fish school, and initializing the initial fish school;
step 2.2, calculating the toxin concentration value of each artificial fish current node of the initial fish school; comparing the concentration values of the toxins, and assigning the states of the artificial fish with the minimum concentration value and the minimum concentration value to a bulletin board;
step 2.3, calculating the utility function U of each artificial fish of the current node or the updated node in the step 2.5 by using the formula (1), and selecting the utility function UiTaking the behavior with smaller value as the next step behavior corresponding to the artificial fish;
U＝{U1,U2,…Ui,…,Un} (1)
Ui＝αD+βNf+γ·σ (2)
in the formula of UiRepresenting the utility function corresponding to the ith behavior of the artificial fish at the current node, wherein N is the number of the behaviors corresponding to the artificial fish, D is the error rate of the current node, and N is the bit error rate of the current nodefThe number of the artificial fish in the current visual field range is shown, sigma is a crowding factor of the artificial fish, alpha, beta and gamma are control variables, alpha is more than or equal to 0 and less than or equal to 1, beta is more than or equal to 0 and less than or equal to 1, alpha + beta is 1, and gamma is the toxin concentration value of the current node;
step 2.4, executing the behavior selected by the artificial fish, and updating the current position information of the artificial fish to obtain an updated node;
step 2.5, calculating the toxin concentration value of each artificial fish of the updated node; comparing the toxin concentration value with the toxin concentration value on the bulletin board, if the toxin concentration value is less than the toxin concentration value on the bulletin board, updating the toxin concentration value and the state on the bulletin board by using the toxin concentration value and the state of the bulletin board, otherwise, keeping the state of the bulletin board unchanged;
step 2.6, repeating the step 2.3 to the step 2.5 until the destination node is reached; and obtaining a node with the minimum toxin concentration value, taking the node with the minimum toxin concentration value as an optimal node, and forming an intra-domain restoration path by connecting the nodes selected by the artificial fish.
Preferably, in step 3, a drosophila optimization method is used to perform path search on the nodes in the optimization layer.
Further, the method also comprises the step 4: judging the number of shortest paths in the restoration paths obtained in the step 2 or the step 3, and if one shortest path exists, outputting the shortest path as the restoration path; if two or more shortest paths exist, selecting a path with less nodes as a recovery path and outputting the path; if the number of the paths with fewer nodes is more than one, the paths can be used as recovery paths and any one path is output.
Compared with the prior art, the invention has the beneficial effects that:
the method optimizes the selection of inter-domain and intra-domain paths through a swarm intelligence optimization method, and can quickly generate a more reliable recovery path. Experimental results and analysis show that the method has good convergence rate and shorter recovery time, and reduces the blocking rate under the malicious node. The whole optical network has stronger fault perception, and the survivability of the network is improved.
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FIG. 1 is a flow chart of the method of the present invention.
Fig. 2 shows a multi-domain optical network structure according to embodiments 1 and 2 of the present invention.
Fig. 3 shows a multicast tree according to embodiments 1 and 2 of the present invention.
Fig. 4 is a schematic diagram of the intra-domain search real search direction in embodiment 1 of the present invention.
Fig. 5 shows the intra-domain restoration path finally obtained in embodiment 1 of the present invention.
Fig. 6 is node information of three domains in the optimization layer in embodiment 2 of the present invention.
Fig. 7 is a diagram of an inter-domain restoration path finally obtained in embodiment 2 of the present invention.
Fig. 8 is a network topology of a single domain generated by the script generator NSG2 in a simulation experiment.
Figure 9 is a network topology of a distributed multi-domain optical network.
FIG. 10 is a comparison of the convergence of the method of the present invention and other prior art methods.
Fig. 11 is a comparison of recovery times for the method of the present invention and other two prior art methods.
Fig. 12 shows the blocking rate of the method of the present invention and other two existing methods in the context of a malicious node.
Detailed Description
The multi-domain optical network consists of a plurality of domains, each domain is an independent routing and recovery area, and for example, different operators divide the network into different areas for management and control;
"intra-domain path" refers to a path routed within a domain, selected to pass through that gateway or router; an "inter-domain path" refers to a path between two domains (e.g., a path between node 5 within domain 1 and node 11 within domain 2 in FIG. 3).
The following embodiments of the present invention are given, and it should be noted that the present invention is not limited to the following embodiments, and all equivalent changes based on the technical solutions of the present invention are within the protection scope of the present invention.
The invention provides a multi-domain optical network multicast route recovery method based on mixed swarm intelligence based on a distributed PCE network structure and combined with an artificial fish school and drosophila optimization method.
The method specifically comprises the following steps:
step 1, judging the position of a failed path, and if the path is an intra-domain path, executing step 2; if the path is an inter-domain path, executing step 3;
and 2, recovering the intra-domain path by a method of fusing an artificial fish model and a game theory, wherein the specific thought is as follows: performing path optimization search in the domain by using an artificial fish swarm algorithm, and determining the next advancing direction of the artificial fish at a node by adopting a non-cooperative game method in the path optimization; and finally, obtaining an intra-domain restoration path and an intra-domain optimal node.
The artificial fish swarm method is characterized in that according to the principle that the concentration of food in a water area diffuses in water, a series of behaviors are carried out when artificial fish seeks from a source node with low concentration of food to a destination node with high concentration of food; meanwhile, the non-cooperative game and the artificial fish school behavior selection are fused, and the improved game artificial fish can better reflect the multi-domain characteristics of the optical network.
Preferably, step 2 of the present invention comprises the steps of:
step 2.1, placing M artificial fishes at a source node to form an initial fish school, and setting the initial positions of the artificial fishes, the visual field range visual of the artificial fishes, the step length step of the artificial fishes and the crowding factor sigma; initializing an initial fish school;
step 2.2, calculating toxin concentration values of the current positions of the artificial fishes in the initial fish school, comparing the toxin concentration values, taking the person with the smallest toxin concentration value to enter a bulletin board, and assigning the state of the artificial fish corresponding to the minimum concentration value and the minimum concentration value to the bulletin board; in the multi-domain optical network, the toxin value is equivalent to the fault rate of different nodes. The node with the smallest toxin concentration value is taken as the optimal node.
And 2.3, evaluating each artificial fish, and performing game on behavior selection to be executed, wherein the behavior of each artificial fish comprises avoidance behavior, clustering behavior, rear-end collision behavior, swallowing behavior, jumping behavior, random behavior and the like. The specific game method comprises the following steps: calculating the utility function U of each artificial fish of the current node or the updated node in the step 2.5 by using the formula (1), and selecting the utility function UiTaking the behavior with smaller value as the next step behavior corresponding to the artificial fish;
U＝{U1,U2,…Ui,…,Un} (1)
Ui＝αD+βNf+γ·σ (2)
in the formula of UiThe utility function corresponding to the ith behavior of the artificial fish at the current node is represented, N is the number of the behaviors corresponding to the artificial fish, and D is the error rate of the current node, namely D is Nerror/Nall，NallNumber of bits, N, representing the total amount of binary data transmittederrorA number of erroneous bits in the total number of transmitted secondary data; n is a radical offThe number of the artificial fish in the current visual field range is represented by sigma, the crowding factor of the artificial fish at the current node is represented by alpha, beta and gamma, the alpha is more than or equal to 0 and less than or equal to 1, the beta is more than or equal to 0 and less than or equal to 1, the alpha + beta is 1, and the gamma is yi，yiIs the toxin concentration value of the current node.
And carrying out congestion factor evaluation on the next action, and selecting the direction with low congestion factor.
Step 2.4, executing the behavior selected by the artificial fish, and updating the current position information of the artificial fish to obtain an updated node; the present invention preferably updates the current location information of the artificial fish based on the global information and the local information.
Step 2.5, calculating the toxin concentration value of each artificial fish of the updated node; comparing the toxin concentration value with the toxin concentration value on the bulletin board, if the toxin concentration value is less than the toxin concentration value on the bulletin board, updating the toxin concentration value and the state on the bulletin board by using the toxin concentration value and the state of the bulletin board, otherwise, keeping the state of the bulletin board unchanged;
and 2.6, repeating the steps 2.3 to 2.5 until the destination node is found, and obtaining the intra-domain restoration path and the intra-domain optimal node.
Based on the distributed PCE network structure, a source node sends a fault message to a local domain PCE, the PCE starts a recovery process, and the PCE sends a local rerouting instruction to a node destination node.
And 3, firstly, obtaining an intra-domain restoration path and an intra-domain optimal node of each domain by using the method in the step 2, wherein the intra-domain optimal node is the node with the minimum toxin concentration value. The present invention abstracts each domain into a node, i.e., the optimal node within the domain, which forms an optimal node set, as shown in FIG. 6, similar to an optimization layer. And then based on the optimal node set, obtaining a recovery path of the nodes in the optimal node set by adopting a drosophila optimization method, and finally obtaining an inter-domain recovery path. For example, the node 5, the node 7, and the node 15 in embodiment 2 are optimal nodes in three domains, and are used as nodes in an optimization layer, and then path search is performed on the nodes in the optimization layer, and finally all inter-domain restoration paths are obtained.
The process of obtaining the inter-domain restoration path among the domains by adopting the drosophila optimization method comprises the following steps:
step 3.1, knowing the source node and the destination node, which belong to two of the domains respectively, assuming that there are n domains in the multi-domain optical network. And forming an initial fruit fly population in the domain of the source node. And determining the scale K of the initial fruit fly population, recording the maximum iteration number as T, and initializing the initial position of the fruit fly population.
Step 3.2, the initial value of T is 0, and the calculation rule is as follows: t ═ T + 1; in the iterative process, the search direction of the drosophila individual in the olfactory foraging stage is random () and the search distance is RV.
And 3.3, calculating the odor concentration value of each fruit fly, wherein the specific odor concentration value can be calculated by referring to 'fruit fly optimization algorithm and application research thereof, Huhui Huo, Tai Yuan Physician university, 2015'. Then finding out the drosophila individual with the lowest odor concentration value as the optimal drosophila individual;
step 3.4, recording the lowest value of the odor concentration and the position of the optimal fruit fly individual at the moment, and carrying out visual selection on the whole fruit fly population by using a greedy selection strategy to fly to the position of the optimal fruit fly individual;
and 3.3, judging whether the iteration number is equal to T, if the iteration number is less than T, repeating the steps 3.2 to 3.3, judging whether the lowest odor concentration value at the moment is superior to the lowest odor concentration value of the previous iteration, if so, executing the step 3.4, otherwise, continuously repeating the steps 3.2 to 3.3, and circulating the process until the iteration number T is reached.
After obtaining the recovery path, there may be multiple intra-domain recovery paths or inter-domain recovery paths, and at this time, for the multiple recovery paths, the following steps are performed:
step 4, judging the number of shortest paths in the intra-domain restoration path set or the inter-domain restoration path set, and if one shortest path exists in the intra-domain restoration path set or the inter-domain restoration path set, outputting the shortest path as a restoration path; if two or more shortest paths exist, selecting a path with less nodes as a recovery path and outputting the path; if more than one path with less nodes is provided, the paths can be used as recovery paths to output any one path. FIG. 1 shows a flow chart of the method of the present invention.
Based on the distributed PCE network structure, a source node sends a fault message to a local domain PCE, the PCE starts a recovery process, and the PCE sends a local rerouting instruction to a node destination node. And the destination node sends a fault message to the PCE of the local domain, and the PCE finds the PCE of the domain where the node source node is located through a flooding mechanism and sends a rerouting instruction.
Specific embodiments of intra-domain recovery and inter-domain recovery of the present invention are given below, and it should be noted that the present invention is not limited to the following specific embodiments, and all equivalent changes based on the technical solutions of the present application fall within the protection scope of the present invention.
Example 1
In this embodiment, a multi-domain optical network as shown in fig. 2 is given, assuming that the multi-domain optical network has three different domains, and a temporary multicast request R is given as { 2; 5,7,11,12,13,16,17,19}, under the condition that multicast needs to be recovered quickly, designing a relevant scheme to ensure that an optimal recovery path can be found within a certain time after a fault occurs, and ensuring the smoothness of the service. Fig. 3 is a multicast work tree: routes 2-5-11-17-16, 2-5-11-17-19, 2-5-11-7-13, 2-5-11-7-12. The present embodiment assumes a link failure of node 2 to node 5 (i.e., intra-domain failure).
The method according to step 2 of the present invention searches in the domain, the searching direction is shown in fig. 4, the node 5 is used as the destination node, M artificial fishes are placed at the source node 2 for path optimization, except for the upstream node 1, there are 3 directions in which the searching can be performed, and the dotted arrows in the figure represent links 2-3, 2-4, 2-6. And performing non-cooperative game on the three directions to determine the direction of the next advancing of the artificial fish, and then continuing the game to select a path from the three nodes. Fig. 5 selects the path for the final search, i.e., path 2-6-5.
Example 2
The present embodiment also gives a multi-domain optical network as shown in fig. 2, and also gives a temporary multicast request R ═ { 2; 5,7,11,12,13,16,17,19}, and fig. 3 is a multicast work tree, i.e., paths 2-5-11-17-16, 2-5-11-17-19, 2-5-11-7-13, 2-5-11-7-12. It is assumed that the nodes 5 to 11 fail (inter-domain failure).
In order to construct a reliable inter-domain recovery link and reduce the influence of next attack, the method according to step 3 of the invention searches in the domain: firstly, intra-domain artificial fish optimization is performed in three domains respectively, optimal path information in each domain (namely a foraging-like layer) is found and uploaded to an optimization layer, for example, in fig. 6, the optimal path information is obtained after each domain is abstracted into a node, and three points 5,7 and 15 with the lowest failure rate (toxin concentration) represent node information of the domain in the optimization layer. After the source node and the destination node are confirmed, the drosophila optimization method is called, the domain where the destination node 11 is located is found first, then the shortest optimization path between the nodes 5 and 7 is found quickly, and fig. 7 is the final recovery path, so that the recovery service and the optimized transmission are achieved.
Simulation experiment
Experimental setup:
the invention selects a QPSO-CS method of documents 'QoS multicast routing model based on quantum particle swarm optimization CS algorithm, charcot, proceedings of Liuzhou professional technology college, 2019,19(05):113 and 116' and a document 'QoS multicast routing optimization based on improved discrete firefly algorithm, two multicast routing methods of BRDFA method of Zhongzhiping, Hangzhou electronic technology university, 2019' as comparison objects through an NS-2 simulation platform and on the basis of an optical network simulation system SRSP-NA developed and designed by a subject group, relevant modules of the three methods are compiled, a single-domain network topology structure generated by a script generator NSG2 in figure 8, and a network topology structure of a distributed multi-domain optical network in figure 9.
Experimental results and analysis:
firstly, the method (MIAMR) based multi-domain optical network performance change before and after the fault is verified to be effective. And then, in order to compare the influence of different methods on the performance in multicast recovery and the survivability of the optical network after recovery, a QPSO-CS method and a BRDFA method are selected for comparative analysis. The two methods are both integrated with different group intelligent methods to improve the method, and are more effective methods for solving the problem of multicast routing at present. The QPSO-CS method fully combines the advantages of the QPSO method and the CS method, and the convergence of the quantum particle swarm method at the later stage is improved; the improved firefly method adopted in the BRDFA method has fewer parameters and is simple to operate, and errors can be reduced in the optimization process.
(1) Method convergence comparison
In the case where the number of domains is 7, the network load is 80Erl, and the proportion of malicious nodes is 5%, convergence comparison is obtained as in the three methods of fig. 10. Experimental results show that the QPSO-CS method has a high convergence rate at the beginning, but is quickly slowed down later, which indicates that the QPSO-CS method is easy to fall into local optimum; the BRDFA method introduces a firefly disturbance mechanism, is not easy to fall into local optimum, but has slower overall convergence speed. In comparison, under different iteration times, the MIAMR method is better than the QPSO-CS method and the BRDFA method in convergence.
(2) Different method recovery time comparison
In the case that the number of domains is 7, the network load is 80Erl, and the proportion of malicious nodes is 5%, as shown in fig. 11, the three improved route recovery methods have great advantages in recovery time, and particularly, the difference is not obvious in comparison when the number of iterations is small; and as the iteration number increases and reaches more than 30, compared with the QPSO-CS method and the BRDFA method, the MIAMR method needs less recovery time, which indicates that the method is quicker in the route recovery process.
(3) Blocking rate in a malicious node environment
Under the condition that the number of domains is 7, the network load is 80Erl, and the proportion of malicious nodes changes between 5% and 40%, the blocking rate conditions under different proportions of malicious nodes are observed, and as a result, as shown in fig. 12, with the increase of the proportion of malicious nodes, the blocking rates of the QPSO-CS method and the BRDFA method sharply increase, which already exceeds the tolerance of the network, and causes congestion of the network, thereby affecting the transmission of information; in the case of a large number of malicious nodes, the blocking rate of the MIAMR method rises slowly, and the network can still maintain good performance, which indicates that the MIAMR method can better adapt to the situation of a high malicious node ratio.
It can be seen that the method optimizes the selection of inter-domain and intra-domain paths through the swarm intelligence optimization method, and can rapidly generate more reliable recovery paths. Experimental results and analysis show that the method has good convergence rate, shorter recovery time compared with a QPSO-CS method and a BRDFA method, and reduced blocking rate under malicious nodes.

Claims (4)

1. A multi-domain optical network multicast routing recovery method based on hybrid swarm intelligence is characterized in that when a path between a source node and a destination node of a multi-domain optical network multicast routing fails, the recovery path is obtained by adopting the following steps:
step 1, judging the position of a failed path, and if the path is an intra-domain path, executing step 2; if the path is an inter-domain path, executing step 3;
step 2, calculating the toxin concentration value of the artificial fish of the current node by using an artificial fish swarm algorithm, determining the next selected behavior of the artificial fish of the current node by adopting a non-cooperative game method, and executing the selected behavior of the artificial fish to obtain a new node; the current node is a source node or a new node;
repeating the process of the step 2 until the destination node is reached; comparing the toxin concentration values of all the nodes, taking the node with the minimum toxin concentration value as an optimal node, and forming an intra-domain restoration path by the path formed by connecting all the nodes;
and 3, obtaining an intra-domain recovery path and an intra-domain optimal node of each domain by using the method in the step 2, forming an optimal node set by all the intra-domain optimal nodes, and then performing path search on the nodes in the optimal node set to finally obtain an inter-domain recovery path.
2. The method for multicast route recovery for a multi-domain optical network based on mixed group intelligence as claimed in claim 1, wherein said step 2 comprises the following steps:
step 2.1, placing artificial fish at a source node to form an initial fish school, and initializing the initial fish school;
step 2.2, calculating the toxin concentration value of each artificial fish current node of the initial fish school; comparing the concentration values of the toxins, and assigning the states of the artificial fish with the minimum concentration value and the minimum concentration value to a bulletin board;
step 2.3, calculating the utility function U of each artificial fish of the current node or the updated node in the step 2.5 by using the formula (1), and selecting the utility function UiTaking the behavior with smaller value as the next step behavior corresponding to the artificial fish;
U＝{U1,U2,…Ui,…,Un} (1)
Ui＝αD+βNf+γ·σ (2)
in the formula of UiRepresenting the effect corresponding to the ith behavior of the artificial fish at the current nodeUsing a function, wherein N is the number of corresponding behaviors of the artificial fish, D is the error rate of the current node, and NfThe number of the artificial fish in the current visual field range is shown, sigma is a crowding factor of the artificial fish, alpha, beta and gamma are control variables, alpha is more than or equal to 0 and less than or equal to 1, beta is more than or equal to 0 and less than or equal to 1, alpha + beta is 1, and gamma is the toxin concentration value of the current node;
step 2.4, executing the behavior selected by the artificial fish, and updating the current position information of the artificial fish to obtain an updated node;
step 2.5, calculating the toxin concentration value of each artificial fish of the updated node; comparing the toxin concentration value with the toxin concentration value on the bulletin board, if the toxin concentration value is less than the toxin concentration value on the bulletin board, updating the toxin concentration value and the state on the bulletin board by using the toxin concentration value and the state of the bulletin board, otherwise, keeping the state of the bulletin board unchanged;
step 2.6, repeating the step 2.3 to the step 2.5 until the destination node is reached; and obtaining the node with the minimum toxin concentration value, taking the node with the minimum toxin concentration value as an optimal node, and forming an intra-domain restoration path by connecting the nodes.
3. The hybrid swarm intelligence based multi-domain optical network multicast routing recovery method of claim 1, wherein in step 3, the Drosophila optimization method is used to perform path search on the nodes in the optimization layer.
4. The hybrid group intelligence based multi-domain optical network multicast route restoration method according to claim 1, further comprising the step of 4: judging the number of shortest paths in the restoration paths obtained in the step 2 or the step 3, and if one shortest path exists, outputting the shortest path as the restoration path; if two or more shortest paths exist, selecting a path with less nodes as a recovery path and outputting the path; if the number of the paths with fewer nodes is more than one, the paths can be used as recovery paths and any one path is output.
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Cited By (1)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN114142993A (en) * 2021-08-31 2022-03-04 北京邮电大学 Fault recovery method, device, electronic equipment and medium for key distribution network

Citations (3)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN105357120A (en) * 2015-10-30 2016-02-24 中国人民武装警察部队工程大学 Multi-domain optical network attack perception multicast routing method based on game artificial fish swarm
CN109348518A (en) * 2018-11-12 2019-02-15 厦门大学 The method for finding routing with artificial fish-swarm algorithm in underwater sound cooperative communication network
CN111030933A (en) * 2019-11-22 2020-04-17 中国人民武装警察部队工程大学 Multi-domain optical network secure multicast routing method based on distributed PCE

Patent Citations (3)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN105357120A (en) * 2015-10-30 2016-02-24 中国人民武装警察部队工程大学 Multi-domain optical network attack perception multicast routing method based on game artificial fish swarm
CN109348518A (en) * 2018-11-12 2019-02-15 厦门大学 The method for finding routing with artificial fish-swarm algorithm in underwater sound cooperative communication network
CN111030933A (en) * 2019-11-22 2020-04-17 中国人民武装警察部队工程大学 Multi-domain optical network secure multicast routing method based on distributed PCE

Non-Patent Citations (1)

* Cited by examiner, † Cited by third party
Title
赵文: "智能光网络保护及恢复功能的研究与仿真", 《中国优秀硕士学位论文全文数据库》 *

Cited By (1)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN114142993A (en) * 2021-08-31 2022-03-04 北京邮电大学 Fault recovery method, device, electronic equipment and medium for key distribution network

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