CN103685020B - A kind of light multicast tree minimum cost method for routing based on genetic algorithm - Google Patents
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Abstract
The invention discloses a kind of light multicast tree minimum cost method for routing based on genetic algorithm, side initialization and minimum cost multicast tree iteration two parts including network, the initialization when initialization is mainly completed in network of network, integral multiple unit capacity is represented while with a plurality of unit capacity, is easy to apply genetic algorithm optimization information transmission path and coding method.The iterative part of minimum cost light multicast tree is main to be made up of steps such as selection, intersection, variation, removal genes inferior, all some genes inferior are removed from gene pool according to the fitness function value of design during each iteration, it so can greatly reduce the size in algorithm search space, be conducive to the convergence rate of accelerating algorithm, search out the smaller light multicast tree of cost.The present invention is to provide a kind of information transfer method for routing that information transfer number of links summation is minimum, encoding operation number of times is minimum that Measurement Request rate requirement is met needed for a kind of searching.
Description
Technical field
The present invention relates to communication technical field, and in particular to a kind of light multicast tree minimum cost route based on genetic algorithm
Method.
Background technology
With the fast development of the multicast services such as video conference, Web TV, multimedia remote education, the network bandwidth disappears
The quick increase that consumption and congestion occur, traditional communication net is faced with that bandwidth resources are not enough, network throughput is low, business obstruction
The problem of rate rising etc. is serious, the resource day of network is becoming tight.Because optical-fiber network has the information transfer of high bandwidth and high-speed
Ability, it temporarily solves the problems such as bandwidth resources are not enough in traditional electrical domain network.But, recently as broadband services and many
Maintaining sustained and rapid growth for application is broadcast, optical-fiber network is again confronted with the problems such as bandwidth resources are not enough, network blocking probability persistently increases.
Network code is improved network throughput, balance network load, increases network bandwidth utilization factor, reduce network resource loss, carry
The advantages of high internet security, reduction energy expenditure, thus, the routing mode based on network code is increasingly by researcher's
Pay attention to.Network code is incorporated into optical-fiber network, optical network band width inadequate resource etc. is solved using the advantage of network code and is asked
Inscribe an effective method of can yet be regarded as.Therefore, it is proposed that a kind of building method of many place minimum cost multicast trees in single source is solved
Minimum cost light multicast tree routing problem.
Different from traditional routing mode, the routing mode based on network code not only allows for the intermediate node of network to receiving
To information stored and forwarded, moreover it is possible to enter row information coding squeeze operation.Based on network code construction light multicast tree route
Generally comprise 2 steps：(1) the information receiving velocity of network destination node is determined；(2) speed is received according to the information of destination node
Rate is that each destination node determines side disjoint paths number.In order that network destination node correctly decodes raw information, institute
Purposeful node receiving velocity is identical, and the speed is generally less than the minimum value in the max-flow equal to all purposes node.But,
Network-encoding operation is carried out in the intermediate node of communication network, it will the increase corresponding expense of communication network and cost, such as：Increase
The complexity that intermediate node is calculated；Increase the demand of buffer, the information for storing decoding input side；Bring network delay
Increase etc..Thus, the minimum cost multicast tree of the application is that number of links summation needed for searching meets light QoS routing is minimum, compiled
A kind of minimum information transferring method of code number of operations.
Present research has shown that：Minimum cost light multicast tree is a NP-complete problem, the big portion of current solution
It is that this problem is solved using heuritic approach to divide.But, the general effect in particular network of existing heuritic approach is preferable,
And effect is undesirable in other networks, and intelligent optimization algorithm, when solving the problems, such as NP-complete, effect will be excellent
In heuritic approach, genetic algorithm is a kind of algorithm more ripe in intelligent optimization algorithm.Therefore, it is proposed that one kind is based on
The light multicast tree minimum cost method for routing of genetic algorithm.
The content of the invention
Deficiency of the prior art, optical-fiber network can be significantly improved it is an object of the invention to provide one kind for more than
Bandwidth resources utilization rate, reduce network-encoding operation number of times the light multicast tree minimum cost route side based on genetic algorithm
Method.Technical scheme is as follows：A kind of light multicast tree minimum cost method for routing based on genetic algorithm, it includes following
Step：
101st, network topology G is obtained（V, E）, wherein V represents network topology G set of node, and E is represented between nodes
Connection side, when connecting the capacity n >=2 on side, then by the connection while be converted into n bars side by side and capacity for 1 while, complete initial
Change, jump to step 102；
102nd, in obtaining step 101 after initialization network topology G（V, E）Source node S and destination node collection t, structure
Source node S is made to destination node collection t multicast tree, source node S is determined to destination node collection t maximum multicast rate T, and set
The receiving velocity of destination node is k, wherein 1≤k≤T；Source node S is obtained to destination node tiThe N paths of all presence, its
Middle destination node tiFor an element in purpose set of node t, destination node t is calculatediK bars side combination of paths mode
mi, gene pool is produced, and source node S is constructed to destination node collection t chromosome population using genetic algorithm, each dye body surface
Show a kind of routing mode of network, wherein each chromosome is by the U genomic constitution equal with the number of destination node, Mei Geji
Because representing source node S to correspondence destination node tiA kind of path；
103rd, in constitution step 102 chromosome fitness function
f=a1*NC(R)+a2*NCL, and a1>a2
In formula, f is fitness function value；NC (R) is the link cost for the multicast tree for meeting Measurement Request speed, NCLTo compile
Code number of links；a1、a2For weight coefficient；
It is when the fitness function f number of times updated according to genetic algorithm iteration is more than or equal to setting times N 1, then defeated
Go out path and the fitness function value f of the optimal chromosome, jump to step 106；Or when fitness function f is according to genetic algorithm
When the number of times that iteration updates is more than N2 and fitness function value f constant, then path and the fitness letter of the optimal chromosome are exported
Numerical value f, jumps to step 106, terminates；Otherwise, step 104 is jumped to；
104th, adoption rate back-and-forth method is added in initial chromosome population to the chromosome in step 103, and is passed through successively
Cross intersection step, variation step and try to achieve optimal chromosome and fitness function value；
105th, the chromosome that step 102 is set up is substituted into the optimal chromosome and fitness function value tried to achieve in step 104
In population, the gene that fitness function value is more than the optimal chromosome fitness function value is deleted；
106th, final optimal chromosome and its fitness value is exported, and according to the source node representated by the optimal chromosome
Path to destination node is route.
Further, the ratio back-and-forth method in step 104 is that wheel disc is selected or Monte Carlo back-and-forth method.
Further, the intersection step in step 104 includes：
A1,2 chromosomes are randomly selected as parent chromosome；
A2, in chromosome each gene produce one 0 to 1 between random digit, for judging 2 parents at random
Whether chromosome carries out crossover operation；
A3, when the numeral randomly generated in step A2 be less than pcWhen, 2 chromosomes carry out chiasma operation and produce 2
Whether individual child chromosome, the numeral that judgment step A2 is randomly generated is less than pcIf the random value is less than pc, then 2 parents are contaminated
Corresponding gene carries out cross exchanged in colour solid；Otherwise, corresponding gene keeps constant in 2 parent chromosomes；Wherein pcFor
Crossover probability, wherein crossover probability pcSpan is 0.6~0.98；
Whether the fitness function value of the child chromosome produced in A4, judgment step A3 is less than the adaptation of parent chromosome
Functional value is spent, if so, jumping to step A5；
A5, the larger parent chromosome of fitness function value in parent is replaced with child chromosome.
Further, the variation step in step 104 includes：
B1, one chromosome of selection, random digit between producing one 0 to 1 to each gene in chromosome；
Whether the random digit of each gene is less than mutation probability p in B2, judgment step B1m, if so, in the destination node
An integer is randomly choosed in corresponding gene pool instead of the numeral on original gene position, that is, selects another side disjoint paths group
Conjunction mode；Otherwise, the numeral in the gene position keeps constant.
Advantages of the present invention and have the beneficial effect that：
The present invention searches for source node to all of each multicast destination node first in the figure that network edge capacity is initialized
Path, and the combination for all side disjoint paths for meeting Measurement Request speed is found in these paths, then with side point
Chromosome, a kind of side one dye of disjoint paths combination correspondence for meeting multicast rate request are constructed from combination of paths mode
One gene of colour solid, so, a chromosome represents a light multicast tree, and genetic algorithm is selected, intersected, mutation operation
It is all based on chromosome；In order to prevent the evolution of genetic algorithm from having no purpose, thus this patent is according to minimum cost multicast tree optimization
Target is provided with corresponding fitness function to control the Evolutionary direction of light multicast tree, makes light multicast tree towards number of links summation
At least, the minimum direction of encoding operation number of times is evolved；In order to prevent that some bad genes are genetic in chromosome of future generation,
This patent reaches the purpose of the cost of the required light multicast tree of reduction by the way that these genes inferior are removed.
Brief description of the drawings
A kind of light multicast tree minimum cost method for routing flow charts based on genetic algorithm of Fig. 1；
The initialization of Fig. 2 network edges；
Fig. 3 gene pool makes；
Fig. 4 chiasmas are operated；
Fig. 5 chromosomal variations are operated.
Embodiment
Providing a non-limiting embodiment below in conjunction with the accompanying drawings, the invention will be further elaborated.
The present invention proposes a kind of light multicast tree minimum cost method for routing based on genetic algorithm.To reaching optical-fiber network input
The light Measurement Request of node, initializes the side of network according to light Measurement Request speed first, is looked in the network after being initialized on side
To the combination of all side disjoint paths for meeting multicast rate request, structural gene storehouse, and construction meets Measurement Request
Multicast tree chromosome, is selected chromosome, is intersected, being made a variation, being removed the iterative process of gene inferior to find minimum cost light
Multicast tree.Wherein, it is by calculating whether the corresponding fitness function value of gene is higher than in epicycle iteration most to remove gene inferior
The fitness function value of excellent chromosome determines whether the gene is gene inferior.
Technical scheme is described further below in conjunction with the accompanying drawings.
A kind of flow chart of the light multicast tree minimum cost method for routing based on genetic algorithm is as shown in figure 1, it is specific real
Apply step as follows：
1st step：Read network topology G（V, E）, the integral multiple of all specific dischargies of edge capacity in network, by network
Edge capacity be n (n >=2) while be converted into n bars capacity for 1 while, i.e. specific discharge side completes the initialization procedure of network edge.
2nd step：The information receiving velocity of multicast destination node is determined, chromosome population is constructed.First according to network topology
And Measurement Request, determine the receiving velocity of destination node, it is assumed that be k (1≤k≤T, T are maximum multicast rate)；Then, find
Source node, to all paths of each destination node, is the mode that each destination node determines the disjoint paths combination of k bars side, it is assumed that
Destination node i has miPlanting combination, the i.e. destination node has miPlant gene.Each chromosome is by t genome in the present invention
Into wherein t is the number of purpose node, at random from [1, mi] in choose an integer as gene i value.
3rd step：Whether evaluation algorithm meets end condition.Here end condition be 2, meet wherein any one just it is defeated
Go out optimal chromosome and fitness function value：(1) number of times of iteration, which reaches, presets times N 1, and it is basis to preset number of times
Network size size determines that iteration can be set 200 times in such as 14 meshed networks；(2) fitness value of optimal chromosome continuous N2 generations
All do not change, here the preferred values 20 of N2.Above-mentioned one of condition is met, then jumps to the 9th step.
4th step：Chromosome is selected.The effect of chromosome selection is to choose one in the chromosome population produced from the 2nd step
Part fitness function value≤N3 chromosome is added in initial chromosome population, here our adoption rates of the preferred values of N3
System of selection selective staining body, ratio back-and-forth method is also referred to as wheel disc selection or Monte Carlo back-and-forth method.The base of ratio back-and-forth method
This thought is to allow fitness function value（Here it is fj）The selected probability of less individual is larger, and fitness function value is larger
The selected probability of individual is smaller.If the selected probability of chromosome j is pj, its expression formula is：
In above formula, N is the number of chromosome, fiFor chromosome i fitness function value, all chromosome fitness functions
ValueIt is a determination value, so, molecule fjSmaller pjBigger, then probability selected chromosome j is bigger.
5th step：Chiasma.A genetic recombination is played a part of in chiasma operation in genetic algorithm, right
Biological constantly evolve plays vital effect, its essence is the gene of parent is interchangeable with certain rule, produces new
Individual, specific operation process is as shown in Figure 4.
6th step：Chromosomal variation.Chromosomal variation is to prevent from being absorbed in locally optimal solution in genetic algorithm iterative process, is led
Cause can not find the multicast tree of minimum cost, and chromosomal variation process is as shown in Figure 5.
7th step：Optimal chromosome and fitness function value are stored, that is, preserves the minimum cost light found in epicycle iteration many
Broadcast tree.In order to it is effective search out meet light Measurement Request speed needed for number of links summation it is minimum, encoding operation number of times is minimum
A kind of information transmission mode, the design of fitness function is just particularly critical, and according to the optimization aim of the application, we design one
Plant the cost that fitness function evaluates multicast tree：
f=a1*NC(R)+a2*NCL, and a1>a2
In formula, f is fitness function value；NC (R) is the link cost for a multicast tree for meeting Measurement Request speed R；
NCLFor coding number of links；a1、a2For the weight coefficient of setting, and a1More than a2, so that a1* NC (R) is to fitness function f
Play dominance effect.So, algorithm will look for the multicast tree of total Least-cost first, then, pass through on this basis
a2*NCLCarry out the number of coding nodes in regulating networks, finally, search out the minimum scheme of coding number of links.Such as, a1Value
10, a2Value 1.
8th step：Remove the gene inferior in gene pool., can be with according to the fitness function value of optimal chromosome in the 7th step
Gene sufficiency functional value in gene pool is more than to the gene elmination of optimal chromosome fitness function value, so, can be reduced
The size in Genetic algorithm searching space, the convergence rate of accelerating algorithm, fast searching to more excellent solution.After gene elmination inferior,
Algorithm jumps to the 3rd step；
9th step：Output final optimal chromosome and its fitness value.
In a kind of light multicast tree minimum cost method for routing based on genetic algorithm, it is necessary first to by integer edge capacity
The side initialization of network G, network edge initialization procedure is as shown in Figure 2.
In annex map 2, in network 2 (a), the capacity in upper digitized representation figure is found out in network G first
Capacity is more than or equal to 2 side, and side (3,5) are the sides that capacity is 2, and other is all the side that capacity is 1；Then by the father of side (3,5)
Substituted between node 3 and child node 5 with 2 capacity for 1 equality side, shown in such as network 2 (b).
In order to find minimum cost light multicast tree, it is necessary to build the gene pool for constituting chromosome, that is, find all side separation
The combination in path.Fig. 3 is the exemplary plot that gene pool is constructed, and Fig. 3 (a) is all side warps in a network topological diagram, network
It is all 1 to cross capacity after initialization, and source node is s, destination node t1And t2, then the gene pool construction process of network 3 (a) is as follows：
1st step：The maximum multicast rate for calculating destination node in Fig. 3 (a) is 3（Source node s to destination node t1Maximum
Flow for 3, source node s to destination node t2Max-flow be 3, so, maximum multicast rate be min (3,3)）, in order to be able to correct
Decode receive information, then destination node receiving velocity need be less than or equal to 3, here we set the receiving velocity of destination node as
2；
2nd step：Determine source node s to destination node t1All paths that may be present, shown in such as Fig. 3 (b), including 7
Path, and all path digitals are numbered；Same method, finds source node s to destination node t2All paths that may be present,
As shown in Fig. 3 (c), including 7 paths, and this 7 paths numeral is numbered；
3rd step：For purpose node t1Determine the mode of 2 side disjoint paths combinations, construction destination node t1Gene pool,
As shown in Fig. 3 (d), including 9 kinds of combinations（Represent 9 kinds of values of the destination node gene）, the digitized representation volume in combination
Number path；Same method, is purpose node t2The mode of 2 side disjoint paths combinations is found, shown in such as Fig. 3 (e), including 9 kinds
Combination（Represent 9 kinds of values of the destination node gene）；4th step：Due to the gene number correspondence destination node of chromosome
Number, so, in Fig. 3 (a) the gene number of chromosome be 2.The numeral shown in the 1st gene from Fig. 3 (d) in chromosome
In（Here it is 1 to 9）Random selection one；2nd gene of chromosome is from the numeral shown in Fig. 3 (e)（Here it is 1 to 9）With
Machine selects one.Such as chromosome（1,8）In, gene 1 represents destination node t1The routing mode of selection is（Isosorbide-5-Nitrae）Combination,
I.e.：S→A→t1With S → B → D → t1；Similarly, gene 8 represents destination node t2The routing mode of selection is（4,5）Combination,
I.e.：S→B→E→F→G→t2With S → C → t2, chromosome（1,8）With regard to the routing mode shown in corresponding Fig. 3 (f).
By above-mentioned 4 step, we can construct a chromosome to meet the business of multicast rate request in network.To
Chosen good chromosome carries out crossover operation and realizes genetic recombination, produces new child chromosome, plays optimization chromosome
Effect, promotes multicast tree cost to reduce.Chiasma operating process is as shown in Figure 4.The flow of chiasma concrete operations
As shown in Fig. 4 (a), process is：
1st step：2 chromosomes are randomly selected as parent chromosome；
2nd step：Random digit between producing one 0 to 1 to each gene in chromosome, for judging 2 at random
Whether parent chromosome carries out crossover operation；
3rd step：When the numeral randomly generated in step (2) is less than pcWhen, 2 chromosomes carry out chiasma operation production
2 child chromosomes are given birth to, child chromosome generation rule is：Whether the numeral that judgment step (2) is randomly generated is less than pcIf, should
Random value is less than pc, then corresponding gene in 2 parent chromosomes is subjected to cross exchanged, such as number from top to bottom in Fig. 4 (b)
The numeral that 3rd gene is produced is less than pc, thus the 3rd corresponding number of gene that will from top to bottom be counted in 2 parent chromosomes
Word is exchanged, and similarly the 7th gene of number has also carried out digital exchange from top to bottom；If the random value is not less than pc, 2 parent dyes
Corresponding gene keeps constant in colour solid.pcFor crossover probability.Crossover probability pcValue is appropriate, if crossover probability pcValue is too
Greatly, the excellent individual in chromosome population will rapidly disappear；If crossover probability pcValue is too small, can cause search speed too again
Slowly.Therefore, general crossover probability pcSpan is 0.6~0.98, and we take p in this processc=0.8。
4th step：Whether the fitness function value of the child chromosome produced in judgment step (3) is less than parent chromosome
Fitness function value, if so, jumping to the 5th step；Otherwise, algorithm terminates.
5th step：The larger parent chromosome of fitness function value in parent is replaced with child chromosome.
In order to prevent chromosome to be absorbed in local optimum in iteration, thus minimum cost light multicast tree can not be found, it is necessary to
Mutation operation is carried out to chromosome, increases the diversity of chromosome, increases alternative light multicast tree.Chromosomal variation operation stream
Journey is as shown in figure 5, concretely comprise the following steps：
1st step：A chromosome is selected, random digit between producing one 0 to 1 to each gene in chromosome,
For judging whether the gene enters row variation.
2nd step：Whether the random digit of each gene is less than p in judgment step (1)m(pmFor mutation probability), if so,
An integer is randomly choosed in the corresponding gene pool of the destination node instead of the numeral on original gene position, that is, selects another side
Disjoint paths combination；Otherwise, the numeral in the gene position keeps constant..Research shows：Work as pmEffect compares when=0.1
It is good, pmFor mutation probability；Thus, also take p herem=0.1。
The above embodiment is interpreted as being merely to illustrate the present invention rather than limited the scope of the invention.
After the content for the record for having read the present invention, technical staff can make various changes or modifications to the present invention, these equivalent changes
Change and modification equally falls into what the light multicast tree minimum cost method for routing claim of the invention based on genetic algorithm was limited
Scope.
Claims (4)
1. a kind of light multicast tree minimum cost method for routing based on genetic algorithm, it is characterised in that comprise the following steps：
101st, network topology G (V, E) is obtained, wherein V represents network topology G set of node, and E represents the company between nodes
Edge fit, when connecting the capacity n >=2 on side, then by the connection while be converted into n bars side by side and capacity for 1 while, complete initialization,
Jump to step 102；
102nd, in obtaining step 101 after initialization network topology G (V, E) source node S and destination node collection t, construct source
Node S determines source node S to destination node collection t maximum multicast rate T, and set purpose to destination node collection t multicast tree
The receiving velocity of node is k, wherein 1≤k≤T；Source node S is obtained to destination node tiThe N paths of all presence, wherein mesh
Node tiFor an element in purpose set of node t, destination node t is calculatediK bars side combination of paths mode, produce
Gene pool, and source node S is constructed to destination node collection t chromosome population using genetic algorithm, each chromosome represents network
A kind of routing mode, wherein each chromosome is by the U genomic constitution equal with the number of destination node, each gene representation
Source node S to correspondence destination node tiA kind of path；
103rd, in constitution step 102 chromosome fitness function
F=a1*NC(R)+a2*NCL, and a1>a2
In formula, f is fitness function value；NC (R) is the link cost for the multicast tree for meeting Measurement Request speed, NCLFor coding strand
Way mesh；a1、a2For weight coefficient；
When the fitness function f number of times updated according to genetic algorithm iteration is more than or equal to setting times N 1, correspondence is optimal
Chromosome, then export path and the fitness function value f of the optimal chromosome, jump to step 106；Or as fitness function f
When the number of times updated according to genetic algorithm iteration is more than N2 and fitness function value f constant, then the road of the optimal chromosome is exported
Footpath and fitness function value f, jump to step 106, terminate；Otherwise, step 104 is jumped to；
104th, adoption rate back-and-forth method is added in initial chromosome population to the chromosome in step 103, and sequentially passes through friendship
Fork step, variation step try to achieve optimal chromosome and fitness function value；
105th, the chromosome population that step 102 is set up is substituted into the optimal chromosome and fitness function value tried to achieve in step 104
In, delete the gene that fitness function value is more than the optimal chromosome fitness function value；
106th, final optimal chromosome and its fitness value is exported, and according to the source node representated by the optimal chromosome to mesh
The path of node route.
2. a kind of light multicast tree minimum cost method for routing based on genetic algorithm according to claim 1, its feature exists
In：Ratio back-and-forth method in step 104 is selected or Monte Carlo back-and-forth method for wheel disc.
3. a kind of light multicast tree minimum cost method for routing based on genetic algorithm according to claim 1, its feature exists
In the intersection step in step 104 includes：
A1,2 chromosomes are randomly selected as parent chromosome；
A2, one 0 to 1 is produced to each gene in chromosome between random digit, judge that 2 parents are dyed for random
Whether body carries out crossover operation；
A3, when the numeral randomly generated in step A2 be less than pcWhen, 2 chromosomes carry out chiasma and produce 2 filial generation dyeing
Whether body, the numeral that judgment step A2 is randomly generated is less than pcIf the random value is less than pc, then by correspondence in 2 parent chromosomes
Gene be interchangeable；Otherwise, corresponding gene keeps constant in 2 parent chromosomes；Wherein pcFor crossover probability, wherein handing over
Pitch Probability pcSpan is 0.6~0.98；
Whether the fitness function value of the child chromosome produced in A4, judgment step A3 is less than the fitness letter of parent chromosome
Numerical value, if so, jumping to step A5；
A5, the larger parent chromosome of fitness function value in parent is replaced with child chromosome.
4. a kind of light multicast tree minimum cost method for routing based on genetic algorithm according to claim 1, its feature exists
In the variation step in step 104 includes：
B1, one chromosome of selection, random digit between producing one 0 to 1 to each gene in chromosome；
Whether the random digit of each gene is less than mutation probability p in B2, judgment step B1m, if so, in destination node correspondence
Gene pool in one integer of random selection replace numeral on original gene position, that is, select another side disjoint paths combination side
Formula；Otherwise, the numeral in the gene position keeps constant.
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