CN101170503B - An optimization method for multicast route ant group algorithm - Google Patents
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Abstract
The invention discloses an optimization method of an ant colony algorithm of multicast routing, which includes that: solving network multicast routing by using the present ant colony optimization algorithm, and introducing a concept of iterative feedback value in the process of running the algorithm; calculating a feedback value according to the solving status of the algorithm and the distribution of information elements in every round of iteration, and dynamically adjust the value of control parameters, and thereby the ant colony algorithm in the invention is allowed to solve the multicast path in adaptive multi-iterations and to finally find out the multicast tree of the minimum cost. The invention overcomes the drawback that the performance of the prior ant colony algorithm relies too much on the initial value of parameters, effectively avoids that the ant colony algorithm is limited in the local optimization, and improves the solution of network multicast routing.
Description
Technical Field
The invention relates to a Multicast (Multicast) routing technology of network communication, in particular to an optimization method of a Multicast routing ant colony algorithm.
Background
With the increasingly wide application of computer internet and the increasing demand of broadband network services, various internet-based services increasingly require Quality of Service (QoS) guarantees, and currently, improving the carrying capacity of the existing network becomes an important way to improve QoS. Due to the high cost of replacing or modifying hardware in large quantities, and the small number of parts that are replaced has little effect on improving the existing network carrying capacity, people are paying more and more attention to the routing strategy that can improve the network resource utilization rate and the network QoS, wherein the multicast technology is a very effective means for solving the problems at present.
The current network transmission modes include three modes, namely unicast, broadcast and multicast, wherein the multicast mode has the most development prospect. In the multicast mode, a message is transmitted in the network, and a plurality of target nodes can be ensured to receive the message from the source node through the control of the router. Compared with a unicast mode with large occupied network resources and a broadcast mode with high non-target node overhead, the multicast mode has the characteristics of small occupied resources and low invalid overhead.
The multicast routing problem proves to be a problem of Nondeterministic Polynomial (NP) difficulty, and existing methods mostly use heuristic algorithms such as genetic algorithm, KMB algorithm, ant colony algorithm and the like when solving the multicast tree. The ant colony algorithm is inspired by the foraging behavior of ants in nature, and simulates the process of searching food by an actual ant colony. In nature, ant colonies are always able to find a shortest path from a nest to a food source. This is because ants can leave a substance called Pheromone (Pheromone) on the path they travel during movement. This substance is perceived by the following ants and will gradually evaporate over time, each of which directs its own direction of movement according to the concentration of pheromones on the path and tends to move in the direction of high concentration of this substance. Thus, if more ants are walking on a path, the more pheromones are accumulated and the greater the concentration, the greater the probability that the path will be picked up by other ants at the next time. As the process proceeds, the entire ant colony eventually finds the shortest path from the ant nest to the food, since the shorter path will be visited by more ants over time. Pheromones are commonly referred to as pathway pheromones in ant colony algorithms. The ant colony algorithm just utilizes the characteristic of ant colony in nature to solve the shortest path problem. The ant colony algorithm implies parallel distribution computing capability and a probability-based solution construction method, so that the ant colony algorithm can more easily avoid network congestion and more effectively solve the multicast routing problem.
However, the ant colony algorithm is the same as many other heuristic algorithms, the performance of the ant colony algorithm is often dependent on the values of the control parameters, and because the existing ant colony algorithm rarely adjusts the values of the control parameters in the calculation process, the performance of the ant colony algorithm is not ideal if the initial values are not properly set.
Disclosure of Invention
In view of this, the main objective of the present invention is to provide an optimization method for a multicast routing ant colony algorithm, which can effectively avoid the problem that a local optimal solution may be involved when solving a multicast route, and can improve the speed of solving a network multicast route.
In order to achieve the purpose, the technical scheme of the invention is realized as follows:
a method for optimizing a multicast routing ant colony algorithm comprises the following steps:
a. initializing each network node, setting constraint conditions for all links among the network nodes, and assigning an initial value for the pheromone of each link;
b. initializing a routing table, then searching pheromones of links corresponding to the target nodes, and locally updating the pheromones;
c. iteratively calculating the evaluation function value of each possible path and the current iteration feedback value, and selecting the optimal path of the current iteration;
d. adjusting the iteration feedback value to update the control parameters of the ant colony optimization algorithm and update the global pheromone, and then entering the next round of iterative computation;
e. judging whether a termination condition is met, and if the termination condition is met, executing the step f; otherwise, returning to the step b;
f. and selecting and outputting the current iteration optimal multicast routing path.
The constraint conditions in the step a are specifically as follows:
calculating the maximum value of the sum of all link time delays from the source node to the target node as a link time delay constraint condition; and calculating the minimum value of the sum of all the link fees as the minimum cost constraint value of the multicast routing tree.
Step a, assigning an initial value to the pheromone of each link specifically comprises: tau is0＝m/Cnn(ii) a Wherein m is the number of artificial ants, CnnIs the length of the path constructed.
B, the searched target node meets the condition: <math><mrow><mi>j</mi><mo>=</mo><mi>arg</mi><msub><mi>max</mi><mrow><mi>l</mi><mo>∈</mo><msubsup><mi>N</mi><mi>i</mi><mi>k</mi></msubsup></mrow></msub><mrow><mo>{</mo><msubsup><mi>p</mi><mi>ij</mi><mi>k</mi></msubsup><mo>}</mo></mrow><mo>,</mo></mrow></math> <math><mrow><msubsup><mi>p</mi><mi>ij</mi><mi>k</mi></msubsup><mo>=</mo><mfrac><mrow><msup><mrow><mo>(</mo><msub><mi>τ</mi><mi>ij</mi></msub><mo>)</mo></mrow><mi>α</mi></msup><msup><mrow><mo>[</mo><msub><mi>η</mi><mi>ij</mi></msub><mo>]</mo></mrow><mi>β</mi></msup></mrow><mrow><msub><mi>Σ</mi><mrow><mi>l</mi><mo>∈</mo><msubsup><mi>N</mi><mi>i</mi><mi>k</mi></msubsup></mrow></msub><msup><mrow><mo>[</mo><msub><mi>τ</mi><mi>il</mi></msub><mo>]</mo></mrow><mi>α</mi></msup><msup><mrow><mo>[</mo><msub><mi>η</mi><mi>il</mi></msub><mo>]</mo></mrow><mi>β</mi></msup></mrow></mfrac><mo>;</mo></mrow></math>
wherein, <math><mrow><mi>j</mi><mo>∈</mo><msubsup><mi>N</mi><mi>i</mi><mi>k</mi></msubsup><mo>;</mo></mrow></math> alpha, beta are the sum of two control parameters pheromone and heuristic factor in path selectionThe occupied weight, and setting alpha to 1; etaijFor the heuristic on the link (i, j), take ηij＝1/cij*dij。
The step b of locally updating the pheromone specifically comprises the following steps: tau isij←(1-ρ)τijWherein rho is pheromone volatilization coefficient of one of the control parameters, and the value range is [0.05, 0.15]]。
The step c of iteratively calculating the value of the evaluation function f (k) of each path and the current iterative feedback value M [ t ] specifically includes:
c1 according to evaluation function <math><mrow><mi>F</mi><mrow><mo>(</mo><mi>k</mi><mo>)</mo></mrow><mo>=</mo><munder><mi>Σ</mi><mrow><mrow><mo>(</mo><mi>i</mi><mo>,</mo><mi>j</mi><mo>)</mo></mrow><mo>∈</mo><msub><mi>route</mi><mi>k</mi></msub></mrow></munder><mi>c</mi><mrow><mo>(</mo><mi>i</mi><mo>,</mo><mi>j</mi><mo>)</mo></mrow><mo>*</mo><munder><mi>Σ</mi><mrow><mrow><mo>(</mo><mi>i</mi><mo>,</mo><mi>j</mi><mo>)</mo></mrow><mo>∈</mo><msub><mi>route</mi><mi>k</mi></msub></mrow></munder><mi>d</mi><mrow><mo>(</mo><mi>i</mi><mo>,</mo><mi>j</mi><mo>)</mo></mrow><mo>,</mo></mrow></math> The standard deviation σ t of the evaluation function is calculated]；
c2, standard deviation sigma t]Continuous algebra N [ t ] of current iteration optimal path in t-th iteration]Substituting into an iterative feedback value calculation formula <math><mrow><mi>M</mi><mo>[</mo><mi>t</mi><mo>]</mo><mo>=</mo><mi>A</mi><mo>·</mo><mfrac><mrow><mi>N</mi><mo>[</mo><mi>t</mi><mo>]</mo></mrow><mrow><mi>σ</mi><mo>[</mo><mi>t</mi><mo>]</mo></mrow></mfrac></mrow></math> In the method, the t-th iteration feedback value M [ t ] is obtained]。
D, adjusting the iterative feedback value to update the control parameter of the ant colony optimization algorithm and update the global pheromone, wherein the steps comprise:
d1, modifying the value of the weight beta of the heuristic factor, and if the feedback value M [ t ] of the current iteration is larger than the feedback value M (t-1) of the previous iteration, calculating the value of the weight beta by using the condition that beta (t +1) is beta (t)/0.95; otherwise, calculating the value of the weight β by using β (t +1) ═ 0.95 × β (t), wherein the value range of β is [0, 5 ];
d2, calculating and modifying the value of the pheromone volatilization coefficient rho; if the current iteration feedback value M [ t ] is larger than the previous iteration feedback value M (t-1), calculating rho (t +1) which is rho (t)/0.95; otherwise, calculating rho (t +1) to be 0.95 × rho (t), wherein the value range of rho is [0.5, 1.5 ];
d3, after the value of the control parameter is adjusted, updating the global pheromone, wherein the updating rule of the global pheromone is as follows: <math><mrow> <msub> <mi>τ</mi> <mi>ij</mi> </msub> <mo>←</mo><mrow><mo>(</mo><mn>1</mn><mo>-</mo><mi>ρ</mi><mo>)</mo></mrow><msub><mi>τ</mi><mi>ij</mi></msub><mo>+</mo><mi>ρΔ</mi><msubsup><mi>τ</mi><mi>ij</mi><mi>bs</mi></msubsup><mo>,</mo></mrow></math> wherein,(i，j)∈Tbs；Δτij bsand f (best) is an evaluation function value of the current iteration optimal path.
The termination conditions in step e are: the set maximum iteration number and/or the precision of the optimal solution for solving the multicast routing.
The optimization method of the multicast routing ant colony algorithm provided by the invention has the following advantages:
1) the invention introduces the concept of iterative feedback value in the ant colony algorithm, and calculates the feedback value according to the multicast routing solving state and the distribution of pheromones in each iteration, thereby dynamically adjusting the value of the control parameter, and effectively solving the defect that the obtained multicast routing path is often trapped in local optimum caused by the ant colony algorithm depending on the initial value of the parameter too much.
2) The optimization method of the multicast routing ant colony algorithm dynamically modifies the control parameters according to the solving state and the distribution of the pheromone in the operation process of the algorithm, improves the speed of solving the multicast routing problem by the ant colony, and can better meet the requirement of network dynamic change.
Drawings
Fig. 1 is a flowchart of a processing procedure of the multicast routing ant colony optimization algorithm of the present invention.
Detailed Description
The method of the present invention will be described in further detail below with reference to the accompanying drawings and embodiments of the invention.
In this embodiment, the computer communication network is represented by an undirected graph G (V, E), where V (V1, V2, V3.., vn) is the set of network nodes; e is a set of links, each link E (i, j) belongs to E, and has two parameters: link cost c (i, j), link latency d (i, j). In the undirected graph, i and j represent network nodes, and the lines between i and j are called links and edges in the undirected graph.
In addition, a source node is denoted by s, a set of destination nodes is denoted by D (D1, D2.., dn), and a delay constraint of all destination nodes is defined as Δ.
The problem of solving the multicast routing belongs to the problem of a multicast routing algorithm with limited time delay, and specifically comprises the following steps: and constructing a multicast tree T with the minimum cost, the root of which is the source node s and covers all the destination nodes, and simultaneously requiring that the time delay of each destination node in the tree meets the time delay constraint delta.
Wherein, the delay d (i, j) of the destination node refers to the sum of the delay of each edge on the path p (s, di) from the source node to the destination node, and the delay d (t) of the multicast tree refers to the maximum value of the delay of the destination node:
wherein T is a multicast tree; i. j is a network node; d (i, j) is the link delay; delta is a set delay constraint threshold.
And the minimum cost constraint C (T) of the multicast routing tree has a value of:
wherein T is a multicast tree; i. j is a network node; c (i, j) is the link cost.
Fig. 1 is a flowchart of a processing procedure of the multicast routing ant colony optimization algorithm of the present invention, and as shown in fig. 1, the processing procedure of the method includes:
step 101: initializing the network nodes, and giving out the constraint conditions of all links among the network nodes and the initial values of pheromones on all the links.
Here, the constraint condition is a time delay and a cost value, and the time delay is d (t) and is calculated by formula (1); the expense value is C (T) and is calculated by the formula (2). The factors that determine the delay and cost values of links between network nodes are mainly: the distance of the links between the nodes, the connection mode of the links between the nodes, the data transmission rate between the nodes and the like.
The initialization network node also sets an initial pheromone value tau for all links between adjacent nodes in the network0＝m/CnnWherein m represents the number of ants, CnnIs the length of the path constructed by the nearest-neighbor heuristic.
Step 102: a routing table is initialized.
The routing table refers to a table of network addresses that exist in the memory of a node device, such as a router. Assuming that the routing table is route, when initializing the routing table, m artificial ants are set to start from the source node, and the distance from the source node is 0, that is, s is 0.
The artificial ant is a small segment of program with a specific addressing function, and a plurality of such programs are usually set to simultaneously perform parallel addressing operation according to actual needs.
Step 103: and enabling the artificial ants to search the pheromones of all the target nodes and locally update the searched pheromones.
When the artificial ant searches for the target node, if the artificial ant k searches for the s-th node, it is denoted as routek(s). When the artificial ant k at the node i transfers to the next node j, the node j should satisfy:
Wherein, <math><mrow><mi>arg</mi><msub><mi>max</mi><mrow><mi>l</mi><mo>∈</mo><msubsup><mi>N</mi><mi>i</mi><mi>k</mi></msubsup></mrow></msub><mo>{</mo><msubsup><mi>p</mi><mi>ij</mi><mi>k</mi></msubsup><mo>}</mo></mrow></math> represented in a set of nodes Ni kIn, p of node jij kThe value is maximum; s is the set of all existing nodes, routekRepresenting the route set of the node passed by the kth artificial ant; tau isijIs the pheromone value on edge (i, j); etaijFor the heuristic on edge (i, j), take ηij＝1/cij*dij，cij、dijRespectively, the delay and cost values for edge (i, j); comparing them with a delay constraint Δ and a cost constraint c (t), and if the constraints are satisfied, making a transition; otherwise, the other is selectedAnd when all possible paths do not meet the constraint conditions, the artificial ants exit the searching process.
α and β are two control parameters, which respectively represent the weight occupied by the pheromone and the heuristic factor in the path selection. Assuming that α is 1 and β varies within the range of [0, 5], when each edge is selected, a pheromone local update rule is invoked to update the pheromone value of the selected edge.
The local pheromone updating rule is as follows:
τij←(1-ρ)τij
where ρ is a control parameter called pheromone volatility coefficient, and ρ varies in the range of [0.05, 0.15 ]. In the process of searching for the target node by the artificial ants, the node transfer behavior is continued until all the artificial ants find all the target nodes.
Here, the method of determining whether the artificial ants find all the target nodes is: in the multicast routing problem, a message is transmitted in the network, and a set D of target nodes with multicast requests is generated before the message starts to be transmitted (D1, D2. When the message is transmitted, target node information is added into the information carried by the artificial ants, and if the transferred node is in the set, the node is taken as the target node to transmit the message to the node; if the transferred node does not belong to the set, the artificial ant continues to find the next node until all target nodes are finally found.
Step 104: and iteratively calculating the evaluation function value of each path and the current iteration feedback value, and selecting the optimal path of the current iteration.
The evaluation function is <math><mrow><mi>F</mi><mrow><mo>(</mo><mi>k</mi><mo>)</mo></mrow><mo>=</mo><munder><mi>Σ</mi><mrow><mrow><mo>(</mo><mi>i</mi><mo>,</mo><mi>j</mi><mo>)</mo></mrow><mo>∈</mo><msub><mi>route</mi><mi>k</mi></msub></mrow></munder><mi>c</mi><mrow><mo>(</mo><mi>i</mi><mo>,</mo><mi>j</mi><mo>)</mo></mrow><mo>*</mo><munder><mi>Σ</mi><mrow><mrow><mo>(</mo><mi>i</mi><mo>,</mo><mi>j</mi><mo>)</mo></mrow><mo>∈</mo><msub><mi>route</mi><mi>k</mi></msub></mrow></munder><mi>d</mi><mrow><mo>(</mo><mi>i</mi><mo>,</mo><mi>j</mi><mo>)</mo></mrow><mo>,</mo></mrow></math> The selection of the optimal path of the iteration refers to finding a multicast path with the minimum evaluation function value.
Here, the iterative feedback value of this time needs to be calculated to prepare for adjusting the control parameters in the next step. The calculation formula of the iterative feedback value is as follows:
wherein M [ t ] is the iteration feedback value of the t-th iteration; n [ t ] is the continuous algebra of the current iteration optimal path in the t-th iteration; σ t is the standard deviation of all path evaluation function values in the t-th iteration; a is a correction value. The larger M [ t ] is, the more the control parameters are adjusted to enable the algorithm to explore a solution space far away from the current iterative optimal solution, otherwise, the probability that the algorithm constructs a new solution near the iterative optimal solution is increased.
Step 105: and adjusting the iteration feedback value to update the control parameters of the ant colony optimization algorithm and updating the global pheromone.
The ant colony optimization algorithm involves three control parameters which are respectively: state transition control parameters α, β and pheromone volatility coefficient ρ.
Generally, the ratio of pheromones to the weight of the heuristic factor in the path selection is first adjusted by modifying the value of β. The dynamic adjustment formula of beta is as follows:
wherein M [ t ] is an iterative feedback value of the t iteration, and M (t-1) is an iterative feedback value of t-1 iteration; if the feedback value of the current iteration is larger than that of the previous iteration, the value of beta (t +1) is beta (t)/0.95; otherwise, β (t +1) ═ 0.95 × β (t). As M [ t ] increases, the pheromone distribution becomes more concentrated, and increasing the value of β increases the weight of the heuristic. The value range of beta is [0, 5], and the value is automatically adjusted in the value range of beta, so that the quality of the obtained path is more stable. If the beta value is a fixed value, although a better solution can be obtained in a certain solution, it cannot be guaranteed that a solution with satisfactory quality can be obtained for ten times and hundred times. But the adoption of the mode of dynamically adjusting the value of beta can ensure that the solution with better quality can be solved.
In addition, the local update rule of pheromone and/or the global update rule of pheromone can be used simultaneously, the two rules use the same pheromone volatilization coefficient rho, and the formula is as follows:
wherein M [ t ] is an iterative feedback value of the t iteration, and M (t-1) is an iterative feedback value of t-1 iteration; if the feedback value of the current iteration is larger than that of the previous iteration, rho (t +1) is rho (t)/0.95; otherwise, ρ (t +1) is 0.95 × ρ (t). Increasing the volatilization coefficient of pheromones when M [ t ] is increased, so that the possibility of constructing a new routing path is increased; when M [ t ] is decreased, the value of ρ is decreased so that ants have a greater probability of searching for a more excellent path near the known iterative optimal path. If the value of rho is a certain fixed value, if the value is too high in the iteration process, the algorithm can be converged quickly and falls into the situation of local optimum, and if the value of rho is too low, pheromones on a high-quality path can not be gathered. According to experiments, the effect is better when the value range of rho is [0.5, 1.5 ].
And finally, after the value of the control parameter is adjusted, updating the global pheromone. The global pheromone may be updated as follows:
wherein: delta tauij bsB/f (best); b is a constant; f (best) is the evaluation function value of the current iteration optimal path。
Step 106: judging whether the ant colony optimization algorithm meets a termination condition, if so, executing a step 107; otherwise, return to step 102.
The termination condition is set according to specific requirements, and can be set as the maximum iteration number or the accuracy of the optimal solution.
Step 107: and selecting and outputting the current iteration optimal multicast routing path.
And comparing the evaluation function values F (k) of all the paths, and selecting the path which enables the F (k) to obtain the minimum value as the optimal path obtained in the current iteration.
The above description is only a preferred embodiment of the present invention, and is not intended to limit the scope of the present invention.
Claims (7)
1. A method for optimizing a multicast routing ant colony algorithm is characterized by comprising the following steps:
a. initializing each network node, setting constraint conditions for all links among the network nodes, and assigning an initial value for the pheromone of each link;
b. initializing a routing table, then searching pheromones of links corresponding to the target nodes, and locally updating the pheromones;
c. iteratively calculating the evaluation function value of each path and the current iteration feedback value, and selecting the optimal path of the current iterationDiameter; the iterative feedback value is calculated by an evaluation function
The standard deviation σ t of the evaluation function is calculated](ii) a The standard deviation sigma [ t ]]Continuous algebra N [ t ] of current iteration optimal path in t-th iteration]Substituting into an iterative feedback value calculation formulaIn the method, the t-th iteration feedback value M [ t ] is obtained](ii) a Wherein c (i, j) is link cost, d (i, j) is link delay d (i, j), i and j represent network nodes in the undirected graph, and a line between i and j is called a link; a is a correction value;
d. adjusting the iteration feedback value to update the control parameters of the ant colony optimization algorithm and update the global pheromone, and then entering the next round of iterative computation;
e. judging whether a termination condition is met, and if the termination condition is met, executing the step f; otherwise, returning to the step b;
f. and selecting and outputting the current iteration optimal multicast routing path.
2. The optimization method according to claim 1, wherein the constraint conditions of step a are specifically:
calculating the maximum value of the sum of all link time delays from the source node to the target node as a link time delay constraint condition; and calculating the minimum value of the sum of all the link fees as the minimum cost constraint value of the multicast routing tree.
3. The optimization method according to claim 1, wherein the step a of initializing pheromones of each link specifically comprises: tau is0＝m/Cnn(ii) a Wherein m is the number of artificial ants, CnnIs the length of the path constructed by the nearest neighbor heuristic.
4. The method of claim 1The optimization method of (c), wherein the target node in step b satisfies the condition:
wherein,
α, β are weights occupied by two control parameter pheromones and heuristic factors in path selection, and α is set to be 1; etaijFor the heuristic on the link (i, j), take ηij＝1/cij*dij；cij、dijRespectively, the delay and cost values for edge (i, j).
5. The optimization method according to claim 1, wherein the local updating of the pheromone in step b specifically comprises: tau isij←(1-ρ)τijWherein rho is pheromone volatilization coefficient of one of the control parameters, and the value range is [0.05, 0.15]]。
6. The optimization method according to claim 1, wherein the adjusting the iterative feedback value in step d updates the control parameters of the ant colony optimization algorithm and updates the global pheromone, including:
d1, modifying the value of the weight beta of the heuristic factor, and if the feedback value M [ t ] of the current iteration is larger than the feedback value M (t-1) of the previous iteration, calculating the value of the weight beta by using the condition that beta (t +1) is beta (t)/0.95; otherwise, calculating the value of the weight β by using β (t +1) ═ 0.95 × β (t), wherein the value range of β is [0, 5 ];
d2, calculating and modifying the value of the pheromone volatilization coefficient rho; if the current iteration feedback value M [ t ] is larger than the previous iteration feedback value M (t-1), calculating rho (t +1) which is rho (t)/0.95; otherwise, calculating rho (t +1) to be 0.95 × rho (t), wherein the value range of rho is [0.5, 1.5 ];
d3, after the value of the control parameter is adjusted, updating the global pheromone, wherein the updating rule of the global pheromone is as follows:wherein,Δτij bsb/f (best), f (best) is an evaluation function value of the current iteration optimal path; b is a constant, TbsThe tree T is multicast for a minimum cost covering all destination nodes.
7. The optimization method according to claim 1, wherein the termination conditions in step e are: the set maximum iteration number and/or the precision of the optimal solution for solving the multicast routing.
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| Publication number | Priority date | Publication date | Assignee | Title |
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| CN101483598B (en) * | 2009-02-20 | 2011-09-07 | 西部矿业股份有限公司 | Minimized bandwidth consumption multicast routing method based on label optimization |
| CN101827418B (en) * | 2010-05-07 | 2012-05-23 | 北京科技大学 | Energy optimization-based cognitive radio multicast routing method |
| CN101867991B (en) * | 2010-05-10 | 2012-12-05 | 北京科技大学 | Cognitive radio multicast routing method based on delay constraint |
| CN101925100B (en) * | 2010-09-03 | 2012-12-05 | 东华大学 | Fault-tolerant routing recovery method of heterogeneous wireless sensor network |
| CN101986628B (en) * | 2010-12-15 | 2012-11-07 | 山东大学 | Method for realizing multisource multicast traffic balance based on ant colony algorithm |
| CN102595548B (en) * | 2012-03-26 | 2014-11-05 | 中南大学 | Minimum cost multicast route method in cognitive wireless Mesh network |
| CN102970231B (en) * | 2012-11-20 | 2018-05-01 | 中兴通讯股份有限公司 | Multicast data flow forwards implementation method and route-bridge（RB） |
| CN103327082B (en) * | 2013-06-07 | 2016-06-29 | 同济大学 | A kind of colony evacuation optimal change method |
| CN103338491B (en) * | 2013-06-09 | 2015-08-19 | 南京邮电大学 | A kind of mobile beacon routing resource based on ant colony algorithm |
| CN104901892A (en) * | 2015-06-03 | 2015-09-09 | 安徽理工大学 | QoS multicasting route optimizer based on ant colony algorithm and realization method of QoS multicasting route optimizer |
| CN104994021B (en) * | 2015-07-21 | 2018-06-15 | 三星电子（中国）研发中心 | Determine the method and device of optimal path |
| CN105763451A (en) * | 2016-04-28 | 2016-07-13 | 南阳理工学院 | Ant colony algorithm-based QoS fault-tolerant route selection method in Internet of Vehicles |
| CN106789631B (en) * | 2017-01-20 | 2020-04-03 | 深圳市风云实业有限公司 | Multicast routing reconstruction method and routing equipment |
| CN109120523B (en) * | 2017-06-23 | 2021-12-14 | 阿里巴巴集团控股有限公司 | Multi-node path selection method and device, and cloud platform resource scheduling method and device |
| CN108337167B (en) * | 2018-02-11 | 2020-07-31 | 福州大学 | Video multi-channel parallel transmission and distribution method and system based on ant colony algorithm |
| CN108512772B (en) * | 2018-03-09 | 2021-07-16 | 重庆邮电大学 | Data center flow scheduling method based on service quality |
| CN108596389B (en) * | 2018-04-24 | 2021-08-31 | 宁波三星医疗电气股份有限公司 | Intelligent meter reading method for power concentrator based on ant colony algorithm |
| CN110139319B (en) * | 2019-05-25 | 2022-04-01 | 西南电子技术研究所（中国电子科技集团公司第十研究所） | Routing method for minimizing transmission delay of high dynamic delay network |
| CN116720638B (en) * | 2023-04-13 | 2024-03-26 | 广东工业大学 | Method and system for adjusting inverse shortest path weight based on improved evolutionary algorithm |
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