CN107241273B - A kind of communications ring network structure setting method based on genetic algorithm - Google Patents
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Abstract
The communications ring network structure setting method based on genetic algorithm that the invention discloses a kind of, comprising the following steps: step 1, establish model；Step 2, it is known to which two points of Origin And Destination are additionally dispersed with N number of point, two matrixes of random initializtion and the vector comprising Path selection and length information, as start node path sample database；Step 3, individual evaluation is carried out to start node path sample database, takes fitness of the 1/S as sample, and be normalized, S is the path overall length of sample；Step 4, node is carried out to cdna sample library and Path selection, path intersect and constraint, path mutation operation again, form node path sample database of new generation；Step 5, node path optimal solution is obtained.The present invention provides a kind of stabilizations, the method that quickly planning accesses transmission node cyclization problem, and can extend to more complicated multipair convergent point cyclization problem, have stronger adaptability.
Description
Technical field
The present invention relates to a kind of transport network node cyclization planing methods, especially for 1 pair in a known region or
The scene of Optical Cable information between multipair aggregation node, multiple access node positions and node, this method can quickly, surely
One or more cyclization of fixed selection route, effectively avoid same route simultaneously.
Background technique
MAN transmission network generally can be divided into three-decker: core layer, convergence layer, access layer.Core layer is by core node group
At, generally have exchange, toll switch, data center and gateway exchange etc., be responsible for core node between large capacity repeat circuit, with save/
Local long distance network interconnects, with interconnecting for other networks.Network structure is relatively stable, service reliability, safety
It is required that high.Network node quantity is few, volume of business is big, circuit allocation is frequent.Convergence layer is made of aggregation node, is responsible for certain area
It service convergence and is dredged in domain, it is desirable that there is powerful traffic scheduling ability.The presence of convergence layer avoids access point and directly enters
The problems such as core layer, caused access net span is big, trunk optical fiber consumption is serious.Access layer is in network end-point, carries out business
Access.
For many years, transmission technology is grown rapidly, after PDH (Plesiochronous Digital Hierarchy, standard
Synchronous Digital Hierarchy), SDH (Synchronous Digital Hierarchy, synchronous digital system), MSTP (Multi-
Service Transfer Platform, Multiple Spanning Tree Protocol), WDM (Wavelength Division Multiplexing,
Dense wave division multipurpose), PTN (Packet Transport Network, Packet Transport Network), OTN (optical transfer network,
The baptism of a variety of transmission technologys such as OpticalTransportNetwork), meanwhile, upper-layer service topological structure is always maintained at more
Sample, but convergence layer and access layer network topology are always based on ring network structure.The most important advantage of ring network structure is structure letter
It is single, easy to accomplish, and the implementation protection that ring network structure can be convenient, while again than transportation level protection equally can be implemented
Mesh network structure construction cost is much lower.
There are many factor for influencing the cyclization of Transmission Convergence ring and access ring, including Optical Cable is existing between node location, node
Requirement (the maximum node on such as each ring of the feasibility and cost, telecommunications maintenance department of optical cable to looped network is created between shape, node
Number same cannot route) etc..Traditional programme is based substantially on the optimization of network presence, is largely qualitative analysis, even if
There is quantitative analysis, when number of nodes is more, and Optical Cable choice is larger, can not also traverse all possibilities, be planned
Network be frequently not optimal solution.With the development of 5G, the quantity of the following 5G outdoor station will be multiplied than 4G, need a kind of energy
Enough stable, quick mathematical method planning access network cyclization problems.
Summary of the invention
Goal of the invention: the technical problem to be solved by the present invention is to be directed to the qualitative planing method of traditional network cyclization in node
It is difficult to realize optimal solution in the case where surging with number of paths, and is difficult to the case where meeting the following extensive website and path
Under, a kind of communications ring network structure setting method based on genetic algorithm is provided.
Technical solution: the communications ring network structure setting method based on genetic algorithm that the invention discloses a kind of, including
Following steps:
Step 1, model is established；
Step 2, according to the model of foundation, random initializtion two or more includes the vector of Path selection and length information,
As start node path sample database；
Step 3, individual evaluation is carried out to start node path sample database, takes fitness of the 1/S as sample, and returned
One changes, and S is the path overall length of sample；
Step 4, path sample database progress node and Path selection, path are intersected according to genetic algorithm and constrained again,
Path mutation operation forms node path sample database of new generation；
Step 5, node path optimal solution is obtained.
Step 1 includes:
Establish following objective function z:
If separating k paths by starting point, the initial load-carrying of each path is 1, as deep search load-carrying becomes hk,
K=1,2,3 ... N, the weight of i-th of node are di, i=1,2,3 ... N, N be node number, node i to node j it
Between the degree of correlation be Cij, i, j=1,2,3 ... N, N are the number of node；
If nkThe node total number passed through is needed for kth paths, with set Rk{rki≤i≤nkWanted to correspond to kth paths
The node of process, wherein rkiIndicate kth paths i-th of the node to be reached, rk0Indicate the starting point of kth paths,
It is set to meet following condition:
Wherein, in formula (1), Q is node set；
Inequality (2) indicates that the weight of each path is no more than the load-carrying in path；
Inequality (3) indicates that the node summation that each path is passed through requires points no more than maximum, and wants not less than minimum
Seek points；
Formula (4) indicates that each path is not retraced one's steps, and does not also pass through identical line segment twice, and x and y are respectively rising for path
Point and terminal, kx and ky respectively indicate the start node and terminal node of kth paths, and km and kl are indicated in kth paths
Node between beginning and end, T indicate the routing passed through, TkxI.e. kth paths by starting point by routing,
TkyIndicate the routing that kth paths are returned through by terminal.
Step 2 the following steps are included:
Step 2-1, it is known to two points of Origin And Destination and other N number of nodes, by all N number of accesses that will be cyclic
Node serial number, starting point are No. 1, and terminal is No. N, the degree of correlation between calculating each node two-by-two, with one group of number CijIt indicates, if completely not
Correlation then takes such as 65525 maximal numbers, or just infinite；
Step 2-2 generates the array of a path sample, for storing the point in path and each path process, array
For the number for the node that two dimension, row one paths of storage are passed through, column store the length information in different paths, and array line number is not more than
N/MIN, columns are not more than MAX, and MIN indicates that loop minimum node number, MAX indicate loop maximum node number；
Step 2-3 limits each path by the number of node: by MIN (loop minimum node number,
Take 1) that (loop maximum node number takes and generates one group of random integers between 8), in this, as the passed through node of each path to MAX
Number；
Step 2-4, the number of nodes passed through to number of path and each path constrain, that is, need to meet the point in array
Comprising all N number of points, when requiring each node that can only utilize one time, array is adjusted to not repeating at random for whole N number of nodes
Arrangement；
Step 2-5 separately sets the information of a vector description sample, the i.e. selection in path and length information, as one
A sample gene；
Step 2-6 after obtaining a sample gene, repeats step 2-1~step 2-4 and obtains one group of sample gene space,
The gene pool of multiple sample gene compositions is obtained accordingly.It is noted that the sample gene in gene pool needs according to the actual situation
It adjusts, when problem complexity is lower, sample gene number can use less, the more samplings of need when problem complexity is higher.
Step 3 the following steps are included:
Step 3-1, consider the calculating of Gene sufficiency: target is to enable total cost minimum, and the path overall length of each sample is got over
It is long, it is remoter with target.Take function 1/S as the fitness of sample gene.
Step 3-2, evaluates the fitness of each gene, and is normalized, and so that all Gene sufficiencies is added up to 1, respectively
Gene is adjusted in the ratio of fitness.
Step 4 the following steps are included:
Step 4-1, selects gene pool, by the probability for taking random number simulation gene selected, according to random number
Big (i.e. total the to spend less) gene of fitness is divided into outstanding gene by the spatial position judgement for falling in sample fitness, remaining
Gene be not outstanding gene, choose outstanding gene；
Step 4-2, intersects gene pool, intercepts the 50% of the outstanding gene chosen, by itself and not outstanding gene into
Row is exchanged with each other, and the position of 50% node in two samples in different paths is exchanged by certain principle of probability, has carried out intersection
Again each sample is constrained after operation, it is made to meet the Model Condition in step 1；
Step 4-3, makes a variation to gene pool: for not outstanding gene, individually modifying data therein, i.e. path at random
The process sequence of distribution and node, achievees the effect that gene mutation, becomes outstanding gene；
All kinds of gene datas of the rear generation of step 4-4, recording step 4-1~step 4-3 obtain the gene of a new generation
Library.
Step 5 includes: circulating repetition step 2~step 4, makes gene pool continuous renewal and heredity, records every generation
Optimal solution information, when solution no longer updates (after such as operation 1 minute) whithin a period of time, judgement optimal solution tends towards stability, at this time
Export the optimal solution.
The utility model has the advantages that the present invention solves the problems, such as complex network cyclization, have the characteristics that stablize, it is quick, and have compared with
Strong adaptability and practicability.
Detailed description of the invention
The present invention is done with reference to the accompanying drawings and detailed description and is further illustrated, of the invention is above-mentioned
And/or otherwise advantage will become apparent.
Fig. 1 is modeling procedure figure.
Fig. 2 is the flow chart the present invention is based on genetic algorithm.
Fig. 3 is that more convergent points use schematic diagram of the invention.
Fig. 4 is that double convergent points use schematic diagram of the invention
Specific embodiment
The present invention will be further described with reference to the accompanying drawings and embodiments.
1, founding mathematical models
Objective function:
So that meeting following condition:
In above-mentioned expression formula:
Formula (1) indicates that all discrete points should all traverse, but reusable；
Inequality (2) indicates that the weight of each path is no more than the load-carrying in path；
Inequality (3) indicates that the discrete point summation that each path is passed through requires points no more than maximum, and not less than minimum
It is required that points；
Formula (4) indicates that each path is not retraced one's steps, and does not also pass through identical line segment twice, that is, avoids into cable ring.
As shown in Figure 1, being modeling procedure figure.By taking 2 convergent points as an example, in loop maximum node number MAX=8, with all the way
Diameter must not topological structure be optimal, networking spends minimum mathematical model by establishing under 2 inferior qualifications.Fig. 2 is the present invention
Flow chart based on genetic algorithm.Illustrate genetic algorithm by sample gene pool using duplication, intersect, variation etc. modes into
Row screening, superposition obtain the process of optimal solution.
2, according to the model of foundation, it is known to two points of Origin And Destination are additionally dispersed with N number of point, each point two-by-two it
Between may connection (in reality i.e. it is necessary to contact), may not also be connected to.It is reached eventually it is required that beginning to pass through certain points by starting point
Point, the point passed through have quantity to limit (minimum is set as MIN, and maximum is set as MAX), number of path without limitation, but it is final and must time
Go through each point.
Firstly, doing some basic handlings: all 1-N number of points being numbered, starting point is No. 1, and terminal is No. N.Calculate each point two
The degree of correlation between two is indicated with one group of number, if completely uncorrelated, takes relatively large number, or just infinite.
Secondly, to generate one group of sample space, steps are as follows:
(1) array for generating a sample, for storing the point in path and each path process, array is two dimension, row
The number of the point of paths process is stored, column store the information in different paths.Array line number is not more than N/MIN, and columns is little
In MAX.
(2) number of each path passing point being limited, the present invention generates one group of random integers between MIN to MAX,
In this, as the number of each path institute passing point.When allowing some path to pass through more points, can be adjusted according to probability:
Rand ()=10%.
(3) points passed through to number of path and each path constrain, that is, needing to meet the point in array includes institute
There is N number of point, when requiring each point that can only utilize one time, what array can also be adjusted to whole N number of points does not repeat random alignment.
(4) situations such as separately setting the selection and length of the information, i.e. path of a vector description sample.
(5) it can get portion sample more than, repeating (1)-(4) can be obtained one group of sample space, avoid respectively wherein to check
The similitude of sample is excessive, in the hope of obtaining more comprehensive " gene pool ".It is noted that the sample size in sample space needs
It adjusts according to the actual situation, when problem complexity is lower, sample number can use less (taking 3 in this example), when problem complexity
More samplings are needed when higher.
Next, the sample space to acquisition carries out individual evaluation, steps are as follows:
(1) consider the calculating of Gene sufficiency.Target is to enable total cost minimum, and the path overall length of each sample is longer, with
Target is remoter.Take function 1/S as the fitness of sample gene.
(2) each gene is evaluated, obtains respective fitness, and be normalized, even if all Gene sufficiencies are added
It is 1, each gene is scaled.
Then carry out the key operation in genetic algorithm, i.e., to " gene pool: selected, intersected, mutation operation:
(1) it selects.By the probability for taking random number simulation gene selected, the sky of sample fitness is fallen according to random number
Between position judge that big " outstanding " gene of fitness is easy selected, and the gene of " not outstanding " will not be abandoned completely.
(2) intersect.The a part for intercepting the gene chosen, is then exchanged with each other.Two are exchanged by certain principle of probability
The position of a part point in sample in different paths.Outstanding gene may be born in intersection.
(3) it needs to constrain each sample again after having carried out crossover operation, it is made to meet conditions of problems.
(4) it makes a variation.For the gene of some " not outstanding " in contrast, data therein, i.e. path are individually modified at random
That distributes and put passes through sequence.This change can be larger, to achieve the effect that " gene mutation ", makes it possible to become
The gene of " outstanding ".
(5) data for recording (1)-(4), obtain " gene pool " of a new generation.
Finally, making " gene pool " continuous renewal and heredity by the circulating repetition above process, recording the optimal of every generation
Solve information.The condition of iteration ends is following two:
(1) the number of iterations is artificially set, and iteration ends when such as settable reaching 1000 times, last solution is considered as optimal
Solution.The visual actual conditions of the setting of the number of iterations determine.
(2) optimal solution for observing every generation, when solution no longer updates within a very long time, i.e., it is believed that tending towards stability,
Exportable optimal solution at this time.
Fig. 3 is that more convergent points use schematic diagram of the invention.Wherein ABCD is four convergent points, and number z1~z77 is section
Point.Under the scene of more convergent points, the result of optimal topological path is obtained using the method for the present invention.
Fig. 4 is double convergent points using schematic diagram of the invention, and wherein AB is two convergent points, and number 1~10 is node.?
Under the scene of double convergent points, the result of optimal topological path is obtained using the method for the present invention.
The communications ring network structure setting method based on genetic algorithm that the present invention provides a kind of, implements the technology
There are many method and approach of scheme, the above is only a preferred embodiment of the present invention, it is noted that for the art
Those of ordinary skill for, various improvements and modifications may be made without departing from the principle of the present invention, these change
It also should be regarded as protection scope of the present invention into retouching.The available prior art of each component part being not known in the present embodiment adds
To realize.
Claims (5)
1. a kind of communications ring network structure setting method based on genetic algorithm, which comprises the following steps:
Step 1, model is established；
Step 2, according to the model of foundation, random initializtion two or more includes the vector of Path selection and length information, as
Start node path sample database；
Step 3, individual evaluation is carried out to start node path sample database, takes fitness of the 1/S as sample, and carry out normalizing
Change, S is the path overall length of sample；
Step 4, node is carried out to path sample database according to genetic algorithm and Path selection, path intersect and constraint, path again
Mutation operation forms node path sample database of new generation；
Step 5, node path optimal solution is obtained；
Step 1 includes:
Establish following objective function z:
If separating k paths by starting point, the initial load-carrying of each path is 1, as deep search load-carrying becomes hk, k=1,
2,3 ... N, the weight of i-th of node are di, i=1,2,3 ... N, N are the number of node, node i to the phase between node j
Guan Du is Cij, i, j=1,2,3 ... N, N are the number of node；
If nkThe node total number passed through is needed for kth paths, with set Rk{rki≤i≤nkTo pass through to correspond to kth paths
Node, wherein rkiIndicate kth paths i-th of the node to be reached, rk0Indicate the starting point of kth paths,
It is set to meet following condition:
Wherein, in formula (1), Q is node set；
Inequality (2) indicates that the weight of each path is no more than the load-carrying in path；
Inequality (3) indicates that the node summation that each path is passed through requires points no more than maximum, and requires a little not less than minimum
Number；
Formula (4) indicates that each path is not retraced one's steps, and does not also pass through identical line segment twice, x and y be respectively path starting point with
Terminal, kx and ky respectively indicate the start node and terminal node of kth paths, and km and kl indicate to be located in kth paths
Node between beginning and end, T indicate the routing passed through, TkyI.e. kth paths by starting point by routing, TkyTable
Show the routing that kth paths are returned through by terminal.
2. the method according to claim 1, wherein step 2 the following steps are included:
Step 2-1, it is known to two points of Origin And Destination and other N number of nodes, by all N number of access nodes that will be cyclic
Number, starting point are No. 1, and terminal is No. N, the degree of correlation between calculating each node two-by-two, with one group of number CijIt indicates, if not phase completely
It closes, then takes maximal number, or just infinite；
Step 2-2 generates the array of a path sample, for storing the point in path and each path process, array two
Dimension, the number for the node that row one paths of storage pass through, column store the length information in different paths, and array line number is not more than N/
MIN, columns are not more than MAX, and MIN indicates that loop minimum node number, MAX indicate loop maximum node number；
Step 2-3 limits each path by the number of node: random by generating one group between MIN to MAX
Integer, in this, as the number of the passed through node of each path；
Step 2-4, the number of nodes passed through to number of path and each path constrain, that is, needing to meet the point in array includes
All N number of points, when require each node can only be using one time when, do not repeat random alignment for what array was adjusted to whole N number of nodes；
Step 2-5 separately sets the information of a vector description sample, the i.e. selection in path and length information, as a sample
This gene；
Step 2-6 after obtaining a sample gene, repeats step 2-1~step 2-4 and obtains one group of sample gene space, accordingly
Obtain the gene pool of multiple sample gene compositions.
3. according to the method described in claim 2, it is characterized in that, step 3 the following steps are included:
Step 3-1, consider the calculating of Gene sufficiency: target is to enable total cost minimum, and the path overall length of each sample is longer, with
Target is remoter, takes function 1/S as the fitness of sample gene；
Step 3-2, evaluates the fitness of each gene, and is normalized, and so that all Gene sufficiencies is added up to 1, each gene
It is adjusted in the ratio of fitness.
4. according to the method described in claim 3, it is characterized in that, step 4 the following steps are included:
Step 4-1, selects gene pool, by the probability for taking random number simulation gene selected, is fallen according to random number
The spatial position of sample fitness judges, the big gene of fitness is divided into outstanding gene, remaining gene is not outstanding base
Cause chooses outstanding gene；
Step 4-2, intersects gene pool, intercepts the 50% of the outstanding gene chosen, and by it and not outstanding gene carries out phase
It is interchangeable, the position of 50% node in two samples in different paths is exchanged by certain principle of probability, has carried out crossover operation
It constrains each sample again afterwards, it is made to meet the Model Condition in step 1；
Step 4-3, makes a variation to gene pool: for not outstanding gene, individually modifying data therein, i.e. path allocation at random
And the process sequence of node, achieve the effect that gene mutation, becomes outstanding gene；
All kinds of gene datas of the rear generation of step 4-4, recording step 4-1~step 4-3 obtain the gene pool of a new generation.
5. according to the method described in claim 4, making base it is characterized in that, step 5 includes: circulating repetition step 2~step 4
Yin Ku is constantly updated and heredity, records the optimal solution information of every generation, and when solution no longer updates whithin a period of time, judgement is optimal
Solution tends towards stability, and exports the optimal solution at this time.
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| CN108551403B (en) * | 2018-04-16 | 2021-02-09 | 胡昱 | Transmission network planning method and system |
| CN109525910B (en) * | 2019-01-04 | 2021-06-08 | 国网四川省电力公司经济技术研究院 | Power system protection OTN network double-path planning method for minimum ring |
| CN113473266B (en) * | 2020-03-31 | 2023-04-11 | 中国移动通信集团浙江有限公司 | Method and device for optimizing bandwidth utilization rate of PTN transmission loop and computing equipment |
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