CN111935010A - Network path optimization exploration method based on ant colony algorithm - Google Patents
Network path optimization exploration method based on ant colony algorithm Download PDFInfo
- Publication number
- CN111935010A CN111935010A CN202010813402.9A CN202010813402A CN111935010A CN 111935010 A CN111935010 A CN 111935010A CN 202010813402 A CN202010813402 A CN 202010813402A CN 111935010 A CN111935010 A CN 111935010A
- Authority
- CN
- China
- Prior art keywords
- network
- node
- matrix
- distance matrix
- ant colony
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Withdrawn
Links
Images
Classifications
-
- 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
- H04L45/02—Topology update or discovery
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
- G06N3/00—Computing arrangements based on biological models
- G06N3/004—Artificial life, i.e. computing arrangements simulating life
- G06N3/006—Artificial life, i.e. computing arrangements simulating life based on simulated virtual individual or collective life forms, e.g. social simulations or particle swarm optimisation [PSO]
-
- 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
- H04L45/12—Shortest path evaluation
- H04L45/122—Shortest path evaluation by minimising distances, e.g. by selecting a route with minimum of number of hops
-
- 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
- H04L45/12—Shortest path evaluation
- H04L45/124—Shortest path evaluation using a combination of metrics
Abstract
The invention discloses a network path optimization exploration method based on an ant colony algorithm, and relates to the technical field of network topology. Planning and calculating distance information of each communication network node in a network path system, and constructing a node distance matrix; acquiring time delay information of information transmission of communication network nodes in a network path system, and defining a time delay vector; calculating and constructing a time delay distance matrix according to the node distance matrix and the time delay vector; carrying out initialization assignment on initial parameters of the ant colony algorithm; and circularly searching in the time delay distance matrix by adopting an ant colony algorithm to obtain a shortest path matrix and a shortest path total length of the current network path system. The invention constructs the node distance matrix by using the communication network node position distance information in the network path system and the time delay information of information transmission to form the time delay distance matrix, thereby being convenient for circularly searching in the time delay distance matrix by the ant colony algorithm and further efficiently acquiring the optimal roundabout channel.
Description
Technical Field
The invention belongs to the technical field of network topology, and particularly relates to a network path optimization exploration method based on an ant colony algorithm.
Background
In the existing network topology system, in order to keep the high efficiency and security of the network topology monitoring; establishing a communication network node in a network topology system; a plurality of communication network nodes are communicated with one another, so that a more complex communication network is formed, and efficient information transmission is guaranteed. Due to the fact that communication networks are staggered and complex, when a network path where a certain communication network node is located has a problem and can not normally communicate, an optimal detour channel and the importance of the detour channel can be efficiently and quickly established in the communication networks. In the prior art, after a communication network node is temporarily blocked, an optimal detour channel cannot be efficiently explored, and the overall communication quality and efficiency of a network system are influenced.
In order to solve the problems, the invention provides a network path optimization exploration method based on an ant colony algorithm, and aims to efficiently explore an optimal roundabout channel after a communication network node is temporarily blocked.
Disclosure of Invention
The invention aims to provide a network path optimization exploration method based on an ant colony algorithm, which is applied to the construction of a wired detour channel between a communication network node and a protection intelligent center under the condition that a communication network is damaged after an emergency block of a communication network system through the ant colony algorithm, realizes the convenient construction of a shortest path channel and the construction of the channel, and solves the problem that the existing communication network node cannot efficiently explore the optimal detour channel after the temporary block.
In order to solve the technical problems, the invention is realized by the following technical scheme:
the invention relates to a network path optimization exploration method based on an ant colony algorithm, which comprises the following processes:
a00: planning and calculating distance information of each communication network node in a network path system, and constructing a network node distance matrix;
a01: acquiring network delay information of information transmission of each communication network node in a network path system, and defining a network delay vector;
a02: planning and constructing a time delay network distance matrix according to the network node distance matrix and the network delay vector;
the method specifically comprises the following steps:
adopting the product of the network delay vector of the communication network node and the information propagation speed in the optical fiber as the network node transmission vector in the network node distance matrix;
the element n in the network node distance matrix is measuredstTransmitting vector sums multiplied by corresponding weighted nodes as element n 'of the network delay distance matrix'st；
Wherein, the weighted node transmission vector sum is the sum of the node transmission vector weighted value of the s-th node and the node transmission vector weighted value of the t-th node in the network node distance matrix;
wherein the element nstThe weighted value of the corresponding node transmission vector is equal to the weighted coefficient of the node transmission vector;
any one of the network node transmission vectors corresponds to a weighting coefficient; constructing a weighting coefficient matrix corresponding to the network node distance matrix; the weighting coefficient range is as follows: [0.2-0.6 ];
a03: carrying out initialization assignment on initial parameters of the ant colony algorithm;
a04: and circularly searching in the network delay distance matrix by adopting the ant colony algorithm to obtain a shortest path matrix and a shortest path total length of the current network path system.
Preferably, the initial parameters include a network start node, a network end node, a total number of traversal ants, a total number of loops, an pheromone matrix, pheromone residuals, a shortest path matrix, a number of shortest path nodes, and a shortest path length.
Preferably, a04 specifically comprises the following steps:
b00: the initial value of the number of traversal ants is 1, and from a network initial node, a network termination node is searched circularly in the network delay distance matrix by adopting the ant colony algorithm;
BO 1: judging whether the current network node is a network termination node or whether a current path has a downward moving node; if yes, execute B02; if not, execute B03;
b02: b03 is executed after the shortest path matrix, the shortest path node number and the shortest path length are updated;
b03: and increasing the number of the traversal ants by 1.
Preferably, step B03 is followed by the following steps:
c00: judging whether the number of the current traversal ants is larger than the total number of the traversal ants; if yes, go to C01; if not, go to C02;
c01: updating the pheromone matrix and incrementing the current number of cycles by 1;
c02: judging whether the current number of cycles is more than the total number of cycles; if yes, outputting the shortest path node number and the shortest path length; if not, go to C04;
c04: b00 is executed after the current loop number is assigned to 1.
The invention has the following beneficial effects:
1. the invention constructs the node distance matrix by using the communication network node position distance information in the network topology system and the time delay information of information transmission to form the time delay distance matrix, thereby being convenient for circularly searching in the time delay distance matrix by the ant colony algorithm and further efficiently obtaining the optimal roundabout channel.
2. The ant colony algorithm is applied to the construction of the wired detour channel between the communication network node and the protection intelligent center under the condition that the communication network is damaged after the network topology system is emergently blocked, so that the shortest path channel and the construction channel are conveniently and rapidly established, and the communication network is conveniently dredged under the condition that the network topology system is emergently blocked.
Of course, it is not necessary for any product in which the invention is practiced to achieve all of the above-described advantages at the same time.
Drawings
In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings used in the description of the embodiments will be briefly introduced below, and it is obvious that the drawings in the following description are only some embodiments of the present invention, and it is obvious for those skilled in the art that other drawings can be obtained according to the drawings without creative efforts.
FIG. 1 is a flowchart of a network path optimization exploration method based on an ant colony algorithm according to the present invention;
fig. 2 is a flowchart of the ant colony algorithm-based network path optimization exploration method of the present invention, which uses the ant colony algorithm to search in a time delay distance matrix in a cyclic manner.
Detailed Description
The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
Referring to fig. 1, the present invention is a network path optimization exploration method based on ant colony algorithm, including the following steps:
a00: planning and calculating distance information of each communication network node in a network path system, and constructing a network node distance matrix;
a01: acquiring network delay information of information transmission of each communication network node in a network path system, and defining a network delay vector;
a02: planning and constructing a time delay network distance matrix according to the network node distance matrix and the network delay vector;
the network node distance matrix and the network delay distance matrix are constructed by utilizing the communication network node position distance information in the network topology system and the network delay information transmitted by the information, so that the cyclic search in the network delay distance matrix through the ant colony algorithm is facilitated, and the optimal roundabout channel is efficiently obtained; because the communication network channels are numerous and the time delay is small, a foundation is provided for the establishment of the wired detour channel.
The method specifically comprises the following steps:
adopting the product of the network delay vector of the communication network node and the information propagation speed in the optical fiber as the network node transmission vector in the network node distance matrix; wherein, defining the distance matrix of network nodes as T, nstExpressed as the elements of the sth row and the tth column in the distance matrix of the node T; and set to unconnected nodesInfinity; meanwhile, constructing a delay vector J, wherein J is S and is a column vector, and the mth element J in the delay vector JmRepresenting the information transmission delay of the m nodes;
the element n in the network node distance matrix is measuredstTransmitting vector sums multiplied by corresponding weighted nodes as element n 'of the network delay distance matrix'st；
Wherein, the weighted node transmission vector sum is the sum of the node transmission vector weighted value of the s-th node and the node transmission vector weighted value of the t-th node in the network node distance matrix;
wherein the element nstThe weighted value of the corresponding node transmission vector is equal to the weighted coefficient of the node transmission vector;
any one of the network node transmission vectors corresponds to a weighting coefficient; constructing a weighting coefficient matrix corresponding to the network node distance matrix; the weighting coefficient range is as follows: [0.2-0.6 ];
any node transmission vector corresponds to a weighting coefficient; constructing a weighting coefficient matrix corresponding to the node distance matrix; specifically, the weighting coefficient matrix Q is an sxs matrix, and the corresponding node distance matrix is T;
a03: carrying out initialization assignment on initial parameters of the ant colony algorithm; specifically, the initial parameters include a network start node, a network end node, the total number of traversal ants, the total number of cycles, an pheromone matrix, pheromone residues, a shortest path matrix, the number of shortest path nodes, and the length of a shortest path; in actual use, the network starting node is any network communication node, and the network terminating node is any protection intelligent center node; generally, the total number of traversal ants is 0.5 of the total number of network communication nodes (rounding up the obtained result); the cycle total number is set according to the complexity of a communication network, and the range is generally between dozens and hundreds; the correspondence between the pheromone matrix and the node distance matrix is an S multiplied by S matrix, the pheromone matrix is a unit matrix, and the pheromone residue is a percentage number which is generally between 85 and 95 percent;
a04: and circularly searching in the time delay distance matrix by adopting an ant colony algorithm to obtain a shortest path matrix and the total length of the shortest path of the current network topology system.
Please refer to fig. 2, which specifically includes the following steps:
b00: the initial value of the number of the traversal ants is 1, and from the initial node of the network, the ant colony algorithm is adopted to search the terminal node of the network circularly in the time delay distance matrix;
BO 1: judging whether the current node is a network termination node or whether a downward moving node exists in the current path; if yes, execute B02; if not, execute B03;
b02: b03 is executed after the shortest path matrix, the shortest path node number and the shortest path length are updated;
b03: the number of ants will be traversed and increased by 1.
In actual use, the cycle number k is assigned as 1, and the initial ant number is set as 1; starting with a network starting node, searching in a node distance matrix T matrix by using an ant colony algorithm, and recording a search path; if the communication network node is traversed, the searching is not traversed; if the current ant number of the current cycle times is successfully traversed to the network termination node; then, the shortest path matrix is updated and the traversal path of the ant is recorded, the number of shortest path nodes is updated, the number of nodes traversed by the ant is recorded, and the total length of the shortest path is updated. By the technical scheme, the ant colony algorithm is applied to the construction of the wired detour channel between the communication network node and the protection intelligent center under the condition that the communication network is damaged after the network topology system is emergently blocked, so that the shortest path channel and the constructed channel are conveniently and rapidly established, and the communication network is conveniently dredged under the condition that the network topology system is emergently blocked.
Step B03 is followed by the following steps:
c00: judging whether the number of the current traversal ants is larger than the total number of the traversal ants; if yes, go to C01; if not, go to C02;
c01: updating the pheromone matrix and automatically increasing the current cycle number by 1;
c02: judging whether the current number of cycles is more than the total number of cycles; if yes, outputting the shortest path node number and the shortest path length; if not, go to C04;
c04: b00 is executed after the current loop number is assigned as one;
in practical use, if the number of the current traversal ants is increased by 1 and then is larger than the total number of the traversal ants, the pheromone concentration is calculated, the pheromone matrix is updated, and nodes contained in the shortest path are sequentially searched in the shortest path matrix. Then setting the current cycle number self-increment 1, and if the current cycle number is greater than the total number of cycles, outputting the shortest path matrix and the total length of the shortest path; otherwise, the current cycle number is assigned to be 1 again, and the end node of the path to be searched is searched in the time delay distance matrix in a cycle mode again. The ant colony algorithm is applied to the construction of the wired detour channel between the communication network node and the protection intelligent center under the condition that the communication network is damaged after the communication network system is emergently blocked, so that the shortest path channel and the constructed channel are conveniently and rapidly established, and the communication network is conveniently dredged under the condition that the communication network system is emergently blocked.
It should be noted that, in the above system embodiment, each included unit is only divided according to functional logic, but is not limited to the above division as long as the corresponding function can be implemented; in addition, specific names of the functional units are only for convenience of distinguishing from each other, and are not used for limiting the protection scope of the present invention.
In addition, it is understood by those skilled in the art that all or part of the steps in the method for implementing the embodiments described above may be implemented by a program instructing associated hardware, and the corresponding program may be stored in a computer-readable storage medium.
The preferred embodiments of the invention disclosed above are intended to be illustrative only. The preferred embodiments are not intended to be exhaustive or to limit the invention to the precise embodiments disclosed. Obviously, many modifications and variations are possible in light of the above teaching. The embodiments were chosen and described in order to best explain the principles of the invention and the practical application, to thereby enable others skilled in the art to best utilize the invention. The invention is limited only by the claims and their full scope and equivalents.
Claims (4)
1. The network path optimization exploration method based on the ant colony algorithm is characterized by comprising the following processes:
a00: planning and calculating distance information of each communication network node in a network path system, and constructing a network node distance matrix;
a01: acquiring network delay information of information transmission of each communication network node in a network path system, and defining a network delay vector;
a02: planning and constructing a time delay network distance matrix according to the network node distance matrix and the network delay vector;
the method specifically comprises the following steps:
adopting the product of the network delay vector of the communication network node and the information propagation speed in the optical fiber as the network node transmission vector in the network node distance matrix;
the element n in the network node distance matrix is measuredstTransmitting vector sums multiplied by corresponding weighted nodes as element n 'of the network delay distance matrix'st；
Wherein, the weighted node transmission vector sum is the sum of the node transmission vector weighted value of the s-th node and the node transmission vector weighted value of the t-th node in the network node distance matrix;
wherein the element nstThe weighted value of the corresponding node transmission vector is equal to the weighted coefficient of the node transmission vector;
any one of the network node transmission vectors corresponds to a weighting coefficient; constructing a weighting coefficient matrix corresponding to the network node distance matrix; the weighting coefficient range is as follows: [0.2-0.6 ];
a03: carrying out initialization assignment on initial parameters of the ant colony algorithm;
a04: and circularly searching in the network delay distance matrix by adopting the ant colony algorithm to obtain a shortest path matrix and a shortest path total length of the current network path system.
2. The ant colony algorithm-based network path optimization exploration method according to claim 1, wherein the initial parameters comprise a network start node, a network end node, a total number of traversal ants, a total number of cycles, an pheromone matrix, an pheromone residual, a shortest path matrix, a number of shortest path nodes, and a shortest path length.
3. The ant colony algorithm-based network path optimization exploration method according to claim 2, wherein a04 specifically comprises the following steps:
b00: the initial value of the number of traversal ants is 1, and from a network initial node, a network termination node is searched circularly in the network delay distance matrix by adopting the ant colony algorithm;
BO 1: judging whether the current network node is a network termination node or whether a current path has a downward moving node; if yes, execute B02; if not, execute B03;
b02: b03 is executed after the shortest path matrix, the shortest path node number and the shortest path length are updated;
b03: and increasing the number of the traversal ants by 1.
4. The ant colony algorithm-based network path optimization exploration method according to claim 3, wherein step B03 is followed by the following steps:
c00: judging whether the number of the current traversal ants is larger than the total number of the traversal ants; if yes, go to C01; if not, go to C02;
c01: updating the pheromone matrix and incrementing the current number of cycles by 1;
c02: judging whether the current number of cycles is more than the total number of cycles; if yes, outputting the shortest path node number and the shortest path length; if not, go to C04;
c04: b00 is executed after the current loop number is assigned to 1.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202010813402.9A CN111935010A (en) | 2020-08-13 | 2020-08-13 | Network path optimization exploration method based on ant colony algorithm |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202010813402.9A CN111935010A (en) | 2020-08-13 | 2020-08-13 | Network path optimization exploration method based on ant colony algorithm |
Publications (1)
| Publication Number | Publication Date |
|---|---|
| CN111935010A true CN111935010A (en) | 2020-11-13 |
Family
ID=73311721
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202010813402.9A Withdrawn CN111935010A (en) | 2020-08-13 | 2020-08-13 | Network path optimization exploration method based on ant colony algorithm |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN111935010A (en) |
Cited By (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN115242702A (en) * | 2022-09-22 | 2022-10-25 | 广州优刻谷科技有限公司 | Internet of things node optimal path planning method and system |
-
2020
- 2020-08-13 CN CN202010813402.9A patent/CN111935010A/en not_active Withdrawn
Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN115242702A (en) * | 2022-09-22 | 2022-10-25 | 广州优刻谷科技有限公司 | Internet of things node optimal path planning method and system |
| CN115242702B (en) * | 2022-09-22 | 2022-12-13 | 广州优刻谷科技有限公司 | Internet of things node optimal path planning method and system |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| Yang et al. | BrainIoT: Brain-like productive services provisioning with federated learning in industrial IoT | |
| WO2023116422A1 (en) | Network resource processing method, storage medium, and electronic device | |
| CN111242171A (en) | Model training, diagnosis and prediction method and device for network fault and electronic equipment | |
| CN111628932A (en) | Electric power path optimization exploration method based on ant colony algorithm | |
| CN110768899B (en) | Shortest path determination method and device, storage medium and electronic device | |
| Yang et al. | Deep reinforcement learning based wireless network optimization: A comparative study | |
| CN114666204B (en) | Fault root cause positioning method and system based on causal reinforcement learning | |
| CN113254669B (en) | Knowledge graph-based power distribution network CIM model information completion method and system | |
| CN113988464A (en) | Network link attribute relation prediction method and equipment based on graph neural network | |
| CN111935010A (en) | Network path optimization exploration method based on ant colony algorithm | |
| CN105069698B (en) | Path searching method therefor, device and electric system based on ant group algorithm | |
| CN107483079B (en) | Double-population genetic ant colony routing method for low-voltage power line carrier communication | |
| CN113869521A (en) | Method, device, computing equipment and storage medium for constructing prediction model | |
| US9065743B2 (en) | Determining connectivity in a failed network | |
| CN107241273B (en) | A kind of communications ring network structure setting method based on genetic algorithm | |
| CN110290001B (en) | Single-chain structure optimization method, device and equipment for power communication network | |
| CN108270608B (en) | Link prediction model establishment and link prediction method | |
| CN109450587A (en) | Spectral integration processing method, device and storage medium | |
| CN114372516A (en) | XGboost-based federal learning training and prediction method and device | |
| CN113660687A (en) | Network difference cell processing method, device, equipment and storage medium | |
| CN115297048B (en) | Routing path generation method and device based on optical fiber network | |
| CN111083577B (en) | Power communication topological structure optimization method | |
| CN108667659B (en) | Method and system for searching loop node in network topological graph | |
| CN114390489A (en) | Service deployment method for end-to-end network slice | |
| Norros et al. | Network models with a'soft hierarchy': A random graph construction with loglog scalability |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| WW01 | Invention patent application withdrawn after publication | ||
| WW01 | Invention patent application withdrawn after publication |
Application publication date: 20201113 |