CN104219154B - Method for optimizing resources under a kind of network code environment based on ant colony optimization algorithm - Google Patents
Method for optimizing resources under a kind of network code environment based on ant colony optimization algorithm Download PDFInfo
- Publication number
- CN104219154B CN104219154B CN201410486183.2A CN201410486183A CN104219154B CN 104219154 B CN104219154 B CN 104219154B CN 201410486183 A CN201410486183 A CN 201410486183A CN 104219154 B CN104219154 B CN 104219154B
- Authority
- CN
- China
- Prior art keywords
- ant
- path
- group
- pheromones
- network code
- 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.)
- Expired - Fee Related
Links
Landscapes
- Management, Administration, Business Operations System, And Electronic Commerce (AREA)
- Data Exchanges In Wide-Area Networks (AREA)
Abstract
The invention discloses a kind of method of resource optimization under network code environment based on ant colony optimization algorithm.Ant colony optimization algorithm is passed through on network topological diagram using the present invention, in the case where ensureing that transmission rate is constant, network code resource is optimized as far as possible.Distribution, the maintenance mode of multidimensional of its principal character including pheromones, for the heuristic factor that problem is proposed, and part punishment and the plain update mode of incentive message etc..Shown by research and experiment, the present invention can find optimal solution in all of test case, simultaneously, compared with other presently preferred algorithms for solving the problems, such as network code resource optimization, the present invention has significant advantage in performance and in efficiency, it was demonstrated that feasibility of the invention and availability.
Description
Technical field
The present invention relates to the method for ant colony optimization algorithm coding nodes resource optimization under network code environment, belong to many matchmakers
Body communication and network transmission technology field.
Background technology
Traditional network transmission interior joint will not do any operation to the data flow for receiving, data transfer using storage/
The mode of forwarding is carried out.Maximum flow minimum cut theorem determination can be reached however, adopting and do not ensure that in this way multicast rate
The theoretical upper bound.2000, Ahlswede et al. proposed the concept of network code first, it was demonstrated that in multicast network, utilized
Network coding technique, multicast rate can reach the upper limit of maximum flow minimum cut theorem determination.Because network code can subtract
Few data transmission times, improve network throughput and network data transmission efficiency, in recent years as research field a focus.
But after network code is introduced, node needs to carry out extra encoding operation, and (complicated mathematics is transported in finite field
Calculate), the expense of the resources such as calculating, storage can be brought.In initial research, all nodes in network are all taken as coding to save
Point is performed the encoding operation.Then there is research to point out, not all coding nodes all necessarily need to perform the encoding operation, it is only necessary to
A portion node is performed the encoding operation and ensures that peak transfer rate.So, how network transmission speed is being ensured
On the premise of, encoding operation is reduced as far as, so that the expense that network code brings is reduced, as network code research field
In an important research direction, i.e. network code resource optimization (Network Coding Resource
Minimization, NCRM) problem proposition.
At this stage, network code method for optimizing resources has following two type：
1st, the method based on greedy algorithm
C.Fragouli et al. and M.Langberg et al. propose respectively two kinds of methods based on greedy algorithm come
This problem is solved, but greedy algorithm is easily trapped into local optimum, and once improperly selection may cause to pay no attention to very much
The result thought.Generally, effect of optimization is unsatisfactory.
2nd, the method based on evolution algorithm
It is that a NP-hard problem (is also implied that above-mentioned that Kim et al. demonstrate network code resource optimization problem
Method based on greedy algorithm is difficult to solve this problem well), and two kinds are proposed based on genetic algorithm come solve problem
Method.Then, Xing et al. are respectively adopted quantum derivative algorithm, the Incremental Learning Algorithm based on population, compact heredity calculation
Method and the evolution algorithm based on path code solve the problems, such as network code resource optimization.Domestic scholar Deng Liang etc. and Shao's magnitude
Also the solution of oneself is given with genetic algorithm respectively.These methods belong to evolution algorithm, it is well known that evolution algorithm
It is that a class is based on natural evolution and the random search algorithm for selecting, is seldom used due to algorithm pattern or do not used substantially
Institute's solve problem some characteristics, so evolution algorithm has very strong robustness and adaptability, it is adaptable to various optimization necks in itself
Domain.However, also just because of this, evolution algorithm cannot effectively use local message or problem information in itself, become search
Blindly, result or deterioration of efficiency are caused.
In general, although occurred in that various methods for network code resource optimization, but in effect of optimization and effect
Can't be entirely satisfactory in rate, particularly in network application, the consumption for time and resource is particularly valued.Ant colony is excellent
It is exactly to be used for solving the problems, such as path configuration (traveling salesman problem) to change when algorithm is proposed, the algorithm can be believed using global well
Breath and local message.And network code resource optimization problem is it can be appreciated that construction multiple meets number from starting point to specific terminal
According to the set of paths of speed, and make coding nodes problem as few as possible.Thus, the present invention is solved using ant colony optimization algorithm
The problem, it is intended to optimized simultaneously from effect and efficiency.
The content of the invention
In order to overcome the shortcoming of prior art, the present invention that network code resource optimization is solved using ant colony optimization algorithm
Problem.
1st, illustrate to be solved using ant colony optimization algorithm two basic elements of this problem, pheromones τ and a factor of heuristic η first
Construction and maintenance：
(1) pheromones are used for providing the guidance of overall importance to ant colony, so being directed to this problem, the value of pheromones is with coding
The number of node is related.Further, since the particularity of this problem, a line in network may be by the ant selection one in ant colony
It is secondary, repeatedly or do not select, if using individual traditional pheromones table, will result in the covering of pheromones, so that cannot be clear and definite
Ground is instructed by ant.The present invention is directed to this particularity, employs a kind of distribution, multidimensional pheromones maintenance mode, often
Ant one pheromones table of correspondence, only different iterationses, the ant of same position just shared same pheromones table.
(2) effect of heuristic factor is to provide local tutorial message, and the present invention proposes a kind of heuristic factor to be made for ant
With using the selected number of times in side in present case lower network topology as heuristic factor.When ant group success structure before
Make after path set, add 1 to selected number attribute of each edge in the path set, the ant construction road in ant group afterwards
This attribute will be referred to when footpath, after being decomposed using figure, each potential coding nodes only go out side, if
This potential coding nodes has the side that enters more than 1, then illustrate that the node needs coding, so ant can make with reference to this attribute
It is heuristic factor, choosing big heuristic factor as far as possible, it is ensured that present node only selection one of trying one's best enters side, that is, ensure that present node is use up
Amount does not do encoding operation.
The present invention realizes that the specific means of its goal of the invention is:
A kind of method for optimizing resources under network code environment based on ant colony optimization algorithm, construction is multiple from starting point to specific
Terminal meets the set of paths of data rate, and makes coding nodes as few as possible, with ensure transmission rate it is constant in the case of,
Optimize network code resource, including following process step as far as possible：
Step 1, input initial primary topology G (V, E), wherein comprising a source node S, d receiving node, by maximum
Stream minimal cut theorem calculates the topological maximum rate R；
Step 2, use figure decompose (bibliography 1M.Kim, V.Aggarwal, U.O Reilly, M.M é dard, and
W.Kim,"Genetic representations for evolutionary minimization of network
coding resources,":Springer, 2007, pp.21-31.) method, will be potential in input network topology structure
Coding nodes are decomposed, and make to be easy to judge transmission means of the data in potential coding nodes, so as to judge the potential coding
Whether node is coding nodes on practical significance；New topological structure makes ant colony in the topological structure as input after decomposition
On find path；
Step 3, initialization ant colony Pheromone Matrix, ant colony other specification and iterations I；
Step 4, d ant group is generated according to the number d of receiving node, is the specified receiving node of each group,
There is R ant in each ant group.Make ant group serial number k, k:=1；
Step 5, k-th ant group build path collection, make the serial number n, n of ant:=1；
Step 6, n-th ant build path, if path construction success, jumps to step 7, otherwise jump to step 8；
Step 7, the path that will be built are added in taboo list, to prevent the ant reselection path with group, n=n+
1；If n<=R, jumps to step 6, otherwise jumps to step 9；
Step 8, taboo list is emptied, the local punishment update mechanism of execution information element is held to the path that current path is concentrated
The pheromones local updating strategy of punishment of having gone effect, to prevent unaccommodated path from being selected by ant again；Pheromone update
After jump to step 5, this group of ant rebuilds path set；
Step 9, the path set successfully built using this group of ant carry out pheromones local updating reward mechanism.Using this group
Path set is updated to heuristic factor；K=k+1, if k<=d, jumps to step 5, otherwise, jumps to step 10；
Step 10, all path sets merge into a solution；The coding nodes number of the program is calculated, with the overall situation most
Excellent solution compares, and then updates globally optimal solution with current solution better than global optimum, and perform global information element renewal operation.I=I-
1, if I>0, step 4 is jumped to, otherwise, algorithm terminates, and exports globally optimal solution.
In actual process：
In the treatment of step 3, step 6, step 8, step 9 and step 10, every ant one pheromones table of correspondence, only
The ant for having different iterationses, same position just shares same pheromones table.
In step 6 and step 9, using the selected number of times in side in present case lower network topology as heuristic factor；
After ant group Successful construct path set before, add 1 to selected number attribute of each edge in the path set；Each is dived
In coding nodes, only one goes out side, if this potential coding nodes has enters side more than 1, represents the potential coding nodes
It is actual coding node.I.e. using the attribute as heuristic factor；The as far as possible big heuristic factor of choosing.
During n-th ant build path of step 6, if ant can only be transferred to another node on one node
On, then directly redirect；If multiple nodes may be selected, then formula is used
Wherein τ (tk, n, (i, u)) and value of the representative information element on side (i, u), η (i, u) is heuristic factor at side (i, u)
On value.It will be seen that having two other parameters, receiving node t in the function of pheromones τkWith ant sequence number n.
The two parameters are distributed to the pheromones being mentioned above, multidimensional maintenance mode is related, are for location information element table.Ginseng
Number α and β is used for scaling information element and inspires the relative importance of element.Q is random number, q0It is constant, if q is more than q0The then value of ψ
Drawn according to the following probability transfer formula based on roulette：
If path construction success, jumps to step 7, step 8. is otherwise jumped to
In the local updating mode of step 8 and step 9 pheromones：
Locally punishment update mechanism is to prevent unaccommodated path from being selected by ant again to pheromones, and update mode is shown in following
Formula：
τ(tk, n, (i, j)) and=τ (tk,n,(i,j))-Δτl
The local reward update mechanism of pheromones, the strategy for successfully built path set (solution space include in it is a large amount of
Infeasible path collection) play reward；Update mode is shown in below equation：
τ(tk, n, (i, j)) and=τ (tk,n,(i,j))+Δτl
Wherein, parameter τ (tk, n, (i, j)) delegated path (i, j) pheromones, tkRepresent receiving node；ΔτlValue is
1/ | V ' |, wherein the node number after | V ' | is decomposed for figure.
Compared with prior art, beneficial effects of the present invention：Coding resource optimization is solved using ant colony optimization algorithm to ask
Topic, the effect of resource optimization is better than or is equal to the method having pointed out at present, and the efficiency for optimizing is better than current having
Method.It is right by taking less time while maximum data rate is ensured using network coding technique using the present invention
Current network topology network code resource is optimized, and reduction needs the calculate node of additional computational resources, can not only increase
Efficiency of transmission, and play a part of green energy conservation.It is significant for network application.
Brief description of the drawings
Examples of the present invention will be described by way of reference to the accompanying drawings, wherein：
Fig. 1 is the flow chart of the ant colony optimization algorithm that the present invention is used；
Fig. 2 is that figure decomposes explanation example；
Fig. 3 is ant colony construction path example 1；
Fig. 4 is ant colony construction path example 2；
Fig. 5 is for the example in unreasonable path；
Fig. 6 is the example of simulating scenes；
Fig. 7 is experiment test topological diagram parameter list；
Fig. 8 is the experimental result table of comparisons.
Specific embodiment
Below in conjunction with the accompanying drawings, implementation of the invention is described in further detail：
The present invention solves network code resource using the superior performance advantage in terms of path is set up of ant colony optimization algorithm
Optimization problem, shown in its main handling process as accompanying drawing 1.
Below in conjunction with the accompanying drawings 2 and step in step 2 pair figure decomposition method illustrate.Each possesses multiple-input and multiple-output
Node m be potential coding nodes, such Node Decomposition is two set of intermediate nodes, input set of intermediate nodes and defeated by we
Go out set of intermediate nodes, one input intermediate node of each input side correspondence of origin node, each output side correspondence one is defeated
Go out intermediate node, input intermediate node is connected two-by-two respectively with output intermediate node again then.As shown in Fig. 2 Fig. 2 (a) is
Former topological structure, wherein node4 and node7 are potential coding nodes, and Fig. 2 (b) illustrates the topological structure after decomposing, node4
It is broken down into input set of intermediate nodes：Node4_i_1 and node4_i_2, and output set of intermediate nodes：Node4_o_1 and
node4_o_2.Node7 is also similarly.So, the topological structure after being decomposed clearly exhibition information can flow through coding nodes
When how to transmit.In addition, by after decomposition, any output intermediate node only has an output side, if so node
There is a plurality of input side, indicate that the node needs to perform the encoding operation, whether same easy decision node is coding nodes
Process.
Below in conjunction with the accompanying drawings 3 and step in step 3- steps 10 process of ant colony build path is explained.Fig. 3 (a)
The topological diagram after being decomposed is illustrated, artwork is shown in Fig. 2 (a).The figure has a source node node1, two receiving node node8 and
Node9, while the data rate R=2 of the figure.So, according to the quantity of receiving node, it is small that our ant colony generates two ants
Group.Further according to data rate, then there are two ants in each ant group.Ant group 1 is responsible for finding from node1 to node8
Two independent paths, ant group 2 is responsible for two independent pathways from node1 to node9.As shown in Fig. 3 (b)-(c), ant
Two ants of ant group 1 construct path p respectively1(1,8)=1->2->8 and p2(1,8)=1->3->4_i_2->4_o_1->5-
>7_i_1->7_o_1->8.Meanwhile, two ants of ant group 2 construct path p respectively1(1,9)=1->2->4_i_1->4_
o_1->5->7_i_1->7_o_2->9 and p2(1,9)=1->3->9.Then the path set of ant group 1 is Paths (1,8)={ p1
(1,8),p2(1,8) }, the path set of ant group 2 is Paths (1,9)={ p1(1,9),p2(1,9)}.Finally, one it is complete
Solution build complete, i.e. Solution1(GD)={ Paths (1,8), Paths (1,9) }, such as shown in Fig. 3 (d).The solution
Certainly the coding nodes number of scheme is 1, and coding nodes are node4_o_1.Accompanying drawing 4 with Fig. 3 similarly, but illustrates final solution party
Case is the building process of optimal (0 coding nodes).
The necessity that 5 special interpretation procedures 8 update with local information element in step 9 below in conjunction with the accompanying drawings, due to originally asking
There is substantial amounts of illegal path in topic, especially for an ant group for, the previous irrational path of ant
Ant cannot set up the path independently of path before after selecting may result in, shown in such as Fig. 5 (a), if ant group 1
Ant 1 constructs path p1(1,8)=1->2->4_i_1->4_o_1->5->7_i_1->7_o_1->8, ant 2 is anyway
Cannot all construct from node1 to node8 independently of p1Shown in the path set of (1,8), such as Fig. 5 (b) and (c).So again
, it is necessary to the penalty mechanism of use information element local updating avoids similar p before construction1(1,8) path is selected again.Together
When, as described above, relative to feasible path set, there are a greater amount of infeasible path collection in solution space, so using letter
Cease the reward mechanism of plain local updating to reward feasible path collection, can more effectively guide ant to set up out feasible
Path set.
Embodiment
In order to verify the present invention based on ant colony optimization algorithm solves the problems, such as the validity of network code resource optimization method with
Feasibility, has carried out emulation experiment.
1st, the present invention tests 14 groups of topological diagrams altogether, wherein 4 groups of topological diagrams and 10 groups of random topology figures of set form.Gu
The topological diagram (Fix1-4) of the formula that fixes is n-copy topological diagrams, and accompanying drawing 6 (b) gives the pattern of 3-copy, wherein accompanying drawing 6 (a)
It is the source figure of n-copy.The directed networkses topology that 10 groups of random topology figures (Rnd1-10) are made up of the 20-60 node not waited
Figure.The parameter of each topological diagram is given by Fig. 7.
2nd, the present invention is compared with classical way, it is necessary to the method tested and compare is as follows：
● genetic algorithm (GA):Bibliography 1
● the inventive method.
3rd, the performance indications that the present invention compares are as follows：
● average value and variance (Mean and standard deviation, SD) experiment run 50 suboptimum results
Average value and variance.The index embodies the overall performance of algorithm.
● the success rate of optimal solution is found out in success rate (Successful ratio, SR) experiment operation for 50 times, embodies calculation
The overall search capability of method.
● average calculation times (Average computational time, ACT) experiment 50 average costs of operation
Time, embody the time complexity and availability of algorithm.
4th, parameter setting, setting, α=0.8, β=4, ρ=0.1 and q in ant colony optimization algorithm0=0.6.Other algorithms
Assignment is carried out by the parameter be given in bibliography.
5th, by taking topological diagram Fix-1 as an example, ant colony optimization algorithm optimization network code resource problem implements step
It is rapid as follows：
A. input initial primary topology G (V, E), Fix-1.Wherein include a source node, 4 receiving nodes, by most
Big stream minimal cut theorem calculates the topological maximum rate R=2.The method decomposed using figure, by input network topology structure
Potential coding nodes are decomposed.New topological structure is used as input after decomposition.
B. ant colony Pheromone Matrix is initialized, generation d*R=4*2=8 opens pheromones table, and pheromones is first on every paths
Initial value τ0=1/ | V ' |=1/49, wherein the node number after | V ' | is decomposed for figure.Ant colony other specification α=0.8, β=4, ρ=
0.1 and q0=0.6 and iterations I=100.
C. 4 ant groups are generated, is that each group specifies a receiving node, each ant group there are 2 ants.Order
Ant group serial number k, k:=1.
D. k-th ant group build path collection, makes the serial number n, n of ant:=1.
E. n-th ant build path, if ant can only be transferred on another node on one node, directly
Connect and redirect；If multiple nodes may be selected, then selected using formula.
If path construction success, jumps to f, g. is otherwise jumped to
F. the path that will be built is added in taboo list, n=n+1.If n<=2, e is jumped to, otherwise jump to h.
G. taboo list is emptied, the pheromones local updating plan of punishment effect has been performed to the path that current path is concentrated
Slightly, update mode is shown in below equation：
τ(tk, n, (i, j)) and=τ (tk,n,(i,j))-Δτl
Δτl=1/ | V ' |=1/49.D is jumped to after Pheromone update, this group of ant rebuilds path set.
H. the path set for successfully being built using this group of ant has carried out the pheromones local updating of reward effect：
τ(tk, n, (i, j)) and=τ (tk,n,(i,j))+Δτl
After Pheromone update, heuristic factor is updated using this group of path set.K=k+1, if k<=4, jump to
D, otherwise, jumps to i.
I. all ant groups path set is all successfully constructed, and all path sets merge into a solution.Calculate the party
The coding nodes number of case, compares with globally optimal solution, then updates globally optimal solution with current solution better than global optimum, and perform
Global information element updates operation, and more new formula is as follows：
τ(tk, n, (i, j)) and=(1- ρ) τ (tk,n,(i,j))+ρΔτg
Wherein Δ τg=1/ coding nodes number.I=I-1, if I>0, c is jumped to, otherwise, algorithm terminates, and output is complete
Office's optimal solution.
6th, the comparative result of each index is shown in Fig. 8, is marked optimal value with black matrix in each table.From average value, variance and into
Power is much better than classic algorithm GA weighing the present invention, and optimal solution can be found out under all of use-case.It is aided with average meter again
The comparing of evaluation time, it is found that calculating speed of the present invention is also much better than classic algorithm, it was demonstrated that using ant colony optimization algorithm
Characteristic is better than using evolution algorithm to solve this problem.Feasibility of the invention and availability are embodied, particularly in network
In encoding this network application.
Claims (4)
1. method for optimizing resources under a kind of network code environment based on ant colony optimization algorithm, constructs multiple from starting point to specific end
Point meets the set of paths of data rate, and makes coding nodes as few as possible, most with the case where ensureing that transmission rate is constant
Amount optimization network code resource, including following process step：
Step 1, input initial primary topology G (V, E), wherein comprising a source node S, d receiving node, by max-flow most
Small theorem of cutting calculates the topological maximum rate R；
Step 2, the method decomposed using figure, potential coding nodes in input network topology structure are decomposed, and make to be easy to
Transmission means of the data in potential coding nodes is judged, so as to judge whether the potential coding nodes are volume on practical significance
Code node；New topological structure makes ant colony find path on the topological structure as input after decomposition；
Step 3, initialization ant colony Pheromone Matrix, ant colony other specification and iterations I；
Step 4, d ant group is generated according to the number d of receiving node, be the specified receiving node of each group, each
There is R ant in ant group：Make ant group serial number k, k:=1；
Step 5, k-th ant group build path collection, make the serial number n, n of ant:=1；
Step 6, n-th ant build path, if path construction success, jumps to step 7, otherwise jump to step 8；
Step 7, the path that will be built are added in taboo list, to prevent the ant reselection path with group, n=n+1；Such as
Fruit n<=R, jumps to step 6, otherwise jumps to step 9；
Step 8, taboo list is emptied, the local punishment update mechanism of execution information element is performed to the path that current path is concentrated
The pheromones local updating strategy of punishment effect, to prevent unaccommodated path from being selected by ant again；Jumped after Pheromone update
Step 5 is gone to, this group of ant rebuilds path set；
Step 9, the path set successfully built using this group of ant carry out pheromones local updating reward mechanism；Use this group of path
Set pair heuristic factor is updated；K=k+1, if k<=d, jumps to step 5, otherwise, jumps to step 10；
Step 10, all path sets merge into a solution；Calculate the coding nodes number of the program, same to globally optimal solution
Compare, then update globally optimal solution with current solution better than global optimum, and perform global information element renewal operation；I=I-1, such as
Fruit I>0, step 4 is jumped to, otherwise, algorithm terminates, and exports globally optimal solution.
2. method for optimizing resources, its feature under the network code environment based on ant colony optimization algorithm according to claim 1
It is, in the treatment of step 3, step 6, step 8, step 9 and step 10, every ant one pheromones table of correspondence, only
Different iterationses, the ant of same position just shares same pheromones table.
3. method for optimizing resources, its feature under the network code environment based on ant colony optimization algorithm according to claim 1
It is, in step 6 and step 9, using the selected number of times in side in present case lower network topology as heuristic factor；When it
After preceding ant group Successful construct path set, add 1 to selected number attribute of each edge in the path set；Each potential volume
Code node only goes out side, if this potential coding nodes has enters side more than 1, represents the potential coding nodes as in fact
Border coding nodes；I.e. using the attribute as heuristic factor；The as far as possible big heuristic factor of choosing.
4. under the network code environment based on ant colony optimization algorithm according to claim 1 resource optimization method, it is special
Levy and be, in the local updating mode of step 8 and step 9 pheromones：
Locally punishment update mechanism is to prevent unaccommodated path from being selected by ant again to pheromones, and update mode is shown in following public affairs
Formula：
τ(tk, n, (i, j)) and=τ (tk,n,(i,j))-Δτl
Pheromones locally reward update mechanism, and the strategy plays reward for the path set for successfully being built；Update mode is shown in
Below equation：
τ(tk, n, (i, j)) and=τ (tk,n,(i,j))+Δτl
Wherein, parameter τ (tk, n, (i, j)) delegated path (i, j) pheromones, tkRepresent receiving node；ΔτlValue is 1/ | V '
|, wherein the node number after | V ' | is decomposed for figure.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201410486183.2A CN104219154B (en) | 2014-09-22 | 2014-09-22 | Method for optimizing resources under a kind of network code environment based on ant colony optimization algorithm |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201410486183.2A CN104219154B (en) | 2014-09-22 | 2014-09-22 | Method for optimizing resources under a kind of network code environment based on ant colony optimization algorithm |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN104219154A CN104219154A (en) | 2014-12-17 |
| CN104219154B true CN104219154B (en) | 2017-06-13 |
Family
ID=52100302
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN201410486183.2A Expired - Fee Related CN104219154B (en) | 2014-09-22 | 2014-09-22 | Method for optimizing resources under a kind of network code environment based on ant colony optimization algorithm |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN104219154B (en) |
Families Citing this family (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN107329815B (en) * | 2017-06-26 | 2020-08-04 | 浙江工业大学 | BP-Tabu search-based cloud task load balancing scheduling method |
| CN109981526A (en) * | 2017-12-27 | 2019-07-05 | 中移（杭州）信息技术有限公司 | A kind of method, apparatus, medium and the equipment of determining Attack Source |
| CN108628682B (en) * | 2018-04-17 | 2021-09-24 | 西南交通大学 | Spark platform cost optimization method based on data persistence |
| CN109699091B (en) * | 2019-01-28 | 2021-09-14 | 南京邮电大学 | Wireless sensor network system |
Citations (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN101043444A (en) * | 2007-04-26 | 2007-09-26 | 浙江大学 | Distributed quality of service multicast routing process based on ant group optimization |
-
2014
- 2014-09-22 CN CN201410486183.2A patent/CN104219154B/en not_active Expired - Fee Related
Patent Citations (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN101043444A (en) * | 2007-04-26 | 2007-09-26 | 浙江大学 | Distributed quality of service multicast routing process based on ant group optimization |
Non-Patent Citations (2)
| Title |
|---|
| "Research of Network Coding Resources Optimization Based on Ant Colony Optimization";Jinkai Li,etc;《Computer Application and System Modeling(ICCASM)》;20101024;全文 * |
| "网络组播中的网络编码构造与优化研究";岳国庆;《中国优秀硕士学位论文全文数据库 信息科技辑》;20120415(第4期);全文 * |
Also Published As
| Publication number | Publication date |
|---|---|
| CN104219154A (en) | 2014-12-17 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| Papadopoulos et al. | Network geometry inference using common neighbors | |
| Wang et al. | A modified ant colony optimization algorithm for network coding resource minimization | |
| CN104219154B (en) | Method for optimizing resources under a kind of network code environment based on ant colony optimization algorithm | |
| CN108112049B (en) | A kind of wireless sensor network efficiency optimization cluster-dividing method based on whale group algorithm | |
| Patel et al. | A hybrid ACO/PSO based algorithm for QoS multicast routing problem | |
| CN104618982A (en) | Method for finding optimal path for Adhoc network based on improved genetic-ant colony algorithm | |
| Sun et al. | Multiple constraints QoS multicast routing optimization algorithm in MANET based on GA | |
| CN101616074B (en) | Multicast routing optimization method based on quantum evolution | |
| CN109617812A (en) | A kind of RPL method for routing, device and network topology based on greed insertion | |
| CN101447936B (en) | Multicast routing method based on particle swarm algorithm | |
| Rajadurai et al. | Optimization of multicast ad hoc on-demand routing protocol based on genetic algorithm with backup paths in MANET | |
| Wakamiya et al. | Toward overlay network symbiosis | |
| Asgarnezhad et al. | Connected dominating set problem and its application to wireless sensor networks | |
| CN103619047B (en) | A kind of chance method for routing in many concurrently stream wireless mesh networks | |
| Mahseur et al. | Using bio-inspired approaches to improve the quality of service in a multicast routing | |
| Cheng et al. | QoS multicast tree construction in IP/DWDM optical internet by bio-inspired algorithms | |
| Ding et al. | Improved ant colony algorithm with multi-strategies for QoS routing problems | |
| CN109948001A (en) | A kind of sublinear Annual distribution formula calculates the minimum community discovery method of girth | |
| Huang et al. | A novel hybrid QoS multicast routing based on clonal selection and artificial fish swarm algorithm | |
| Song et al. | A game theoretical approach to gateway selections in multi-domain wireless networks | |
| Vijayalakshmi et al. | A novel hybrid immune-based GA for dynamic routing to multiple destinations for overlay networks | |
| US9838946B2 (en) | Heuristic spanning method and system for link state routing | |
| Nivetha et al. | ACO-GA hybrid meta-heuristic (AGHM) optimization for multi-constrained quality of service routing in MANETs | |
| Mao et al. | Research on Multi-hop Transmission in Quantum Wireless Communication Networks Based on Improved Ant Colony Algorithm | |
| Deeka et al. | Dynamically Packet Routing for QoS Assurances on Internet Networks |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| C06 | Publication | ||
| PB01 | Publication | ||
| C10 | Entry into substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| GR01 | Patent grant | ||
| GR01 | Patent grant | ||
| CF01 | Termination of patent right due to non-payment of annual fee |
Granted publication date: 20170613 Termination date: 20190922 |
|
| CF01 | Termination of patent right due to non-payment of annual fee |