CN108600103A - The ant group algorithm of more QoS route restrictions of oriented multilayer grade network - Google Patents
The ant group algorithm of more QoS route restrictions of oriented multilayer grade network Download PDFInfo
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- CN108600103A CN108600103A CN201810350677.6A CN201810350677A CN108600103A CN 108600103 A CN108600103 A CN 108600103A CN 201810350677 A CN201810350677 A CN 201810350677A CN 108600103 A CN108600103 A CN 108600103A
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- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
- H04L45/00—Routing or path finding of packets in data switching networks
- H04L45/12—Shortest path evaluation
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- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
- H04L45/00—Routing or path finding of packets in data switching networks
- H04L45/12—Shortest path evaluation
- H04L45/122—Shortest path evaluation by minimising distances, e.g. by selecting a route with minimum of number of hops
Abstract
The present invention provides a kind of ant group algorithm of more QoS route restrictions of oriented multilayer grade network, mainly thes improvement is that, the routing for source destination node, and in search, the method for routing for first using hop count minimum finds the routing between first nodes；For the two-level node in first nodes, ant group algorithm is called to scan in each first nodes；The shortest path connection that each first nodes inner search comes out is extended to complete routed path.Also, the present invention is modified the heuristic greedy method in ant group algorithm, and time delay, bandwidth availability ratio, Loss Rate are expressed as to the integrated value of many index, are denoted as cost.The present invention is adapted to the route search of multitiered network, it is also considered that multiple constraintss so that the calculating process of algorithm is more reasonable.
Description
Technical field
The present invention relates to a kind of routing algorithm, the ant colony of more QoS route restrictions of especially a kind of oriented multilayer grade network
Algorithm.
Background technology
QoS (Quality of Service, service quality), for solving the problems such as network delay is with blocking.
QoS routings are to carry out the routing of Path selection based on network available resource and premised on meeting and service qos requirement
Mechanism.QoS routings ask Shortest path routing to have differences with traditional, it is a kind of routing can adapt to specific business need
Mechanism predominantly reaches several targets using QoS routings：(1) it is dynamically selected destination path, is taken according to the QoS that user proposes
Business request, finds the destination path for meeting its constraints；(2) optimize Network resource allocation, and balance network load as far as possible,
So that global network resource utilization is optimal, other service requests are responded to enable the network to maximize；(3) comparison tradition
Routing mechanism can promote network entire throughput, improve network performance degenerate problem.
Therefore several requirements are proposed to QoS routing algorithms：(1) optimize function：Routing algorithm, which has, finds optimal path
Ability, what is optimal as, needs to be calculated according to measurement and weights.(2) terseness：Algorithm should be simple as possible
It is clean, reduce the expense of software or hardware to the greatest extent, this is extremely important in the limited scene of physical resource.(3) robustness：Routing
Algorithm can be in abnormal environment sometimes, such as when hardware fault, high load, node are impaired, can normal operation.(4)
Fast Convergent：When network, some anomalous event causes to route cisco unity malfunction, routing iinformation will update, and lead to best road
Diameter calculating re-starts, and causes network calculations to load, therefore routing algorithm is wanted to restrain as early as possible.
Currently, the routing algorithm of mainstream has dijkstra algorithm, Floyd algorithms etc., but their multi-panels are advised to static path
It draws；And in Dynamic User-Optimal Route Choice algorithm, genetic algorithm is multi-objective optimization algorithm, and path is not appropriate in terms of execution efficiency
Select permeability；Simulated annealing is the disadvantage is that ability of searching optimum is poor, when solution space is prodigious, compare be difficult to find that it is optimal
Solution；There is ant group algorithm distributed computation ability, each node need to only store and oneself neighbor information, response speed
Soon, while increasing network node, can also show good performance, and the robustness of verified ant group algorithm is relatively good, energy
It is used under the conditions of network load is higher.Therefore ant group algorithm is made to solve Network route Problem, algorithm flow such as Fig. 1
It is shown；Its algorithm steps is as follows：
Step S1, initiation parameter：Each edge pheromone concentration is equal when beginning；
Each ant is placed each node by step S2, and initial value is corresponding node in the taboo list of each ant；
Step S3 takes 1 ant, and transition probability is calculated by formula (1.1)It is selected in the way of roulette next
A node updates taboo list, then calculates transition probability, one next node of reselection, then updates taboo list, until destination node
It appears in taboo list；
Step S4 calculates the pheromone amount that this ant stays in each side, which dies；
Step S5 repeats step S3 and S4, searches for and finish until m ant；M is maximum ant number；
Step S6 calculates the pheromones increment Delta τ on each sideijWith pheromone amount τij(t+1)；
Step S7 records the path of current iteration, updates current optimal path, empties taboo list；
Step S8 judges whether to reach scheduled maximum iteration, or whether stagnation behavior occurs；If so, algorithm
Terminate, exports current optimal path；Otherwise, S2 is gone to step, next iteration is carried out.
Formula is as follows with parameter declaration：
Wherein,
--- transition probability, i.e. t moment ant k climb to the probability of node j from node i；
τij(t) --- the pheromone amount on t moment side (i, j)；
Δτij--- the pheromones increment in current iteration on side (i, j)；
ηij(t) --- inverse of the t moment node i to distance between node j；
τis(t) --- the pheromone amount on t moment side (i, s)；
ηis(t) --- inverse of the t moment node i to distance between node s；s∈allowedk；
allowedk--- the set of node that selection next node allows；
The relative importance of α --- pheromones；
β --- it is the significance level coefficient of heuristic greedy method η；
τij(t+1)=(1- ρ) * τij(t)+Δτij (1.2)
ρ --- volatilization factor；
M --- maximum ant number；
Q indicates the pheromones total amount of one search process of ant release, LkIndicate the road that ant k passes by this search
Electrical path length；
Ant group algorithm above is mainly for planar network, and general be also only used in seeks single constraints (such as bandwidth)
Under shortest path, be applicable in scene it is limited.
Invention content
It is an object of the present invention to overcome the shortcomings of the prior art and provide a kind of the more of oriented multilayer grade network
The ant group algorithm of QoS route restrictions is adapted to the route search of multitiered network, it is also considered that multiple constraintss so that calculates
The calculating process of method is more reasonable.The technical solution adopted by the present invention is：
A kind of ant group algorithm of more QoS route restrictions of oriented multilayer grade network, mainly thes improvement is that,
Routing for source-destination node, in search, first use the minimum method for routing of hop count find first nodes it
Between routing；For the two-level node in first nodes, ant group algorithm is called to scan in each first nodes；By each one
Grade intra-node searches out the shortest path connection come and is extended to complete routed path.
Further, the ant group algorithm of more QoS route restrictions of the oriented multilayer grade network, specifically includes following step
Suddenly：
Step S21 initializes network；
Whether step S22 determines source node and destination node in the same level-one section according to source-destination node of setting
In point, such as exist, directly invoke ant group algorithm, calculates the routing of source-destination node；If not existing, into next step；
Step S23, the routing between first nodes are source node to the minimum routing of hop count between destination node；Selection source
First nodes on node to the minimum routing of hop count between destination node；
Step S24 determines source-destination node inside selected first nodes, as the input of ant group algorithm, calls ant colony
Algorithm, the routing between two-level node in first order calculation node；
Step S25 judges whether to meet the requirements according to two step of step S23, S24 as a result, completely route, if
Satisfaction then terminates specifically to search for；If conditions are not met, continuing to calculate back to step S23.
Further, the node transition rule in the ant group algorithm is：
Wherein,
λ1、λ2、λ3For weighting coefficient,For bandwidth availability ratio,For loss rate,For time delay；
Ant group algorithm is：
Step S1, initiation parameter：Each edge pheromone concentration is equal when beginning；
Each ant is placed each node by step S2, and initial value is corresponding node in the taboo list of each ant；
Step S3 takes 1 ant, and transition probability is calculated by formula (2.5)It is selected in the way of roulette next
A node updates taboo list, then calculates transition probability, one next node of reselection, then updates taboo list, until destination node
It appears in taboo list；
Step S4 calculates the pheromone amount that this ant stays in each side, which dies；
Step S5 repeats step S3 and S4, searches for and finish until m ant；M is maximum ant number；
Step S6 calculates the pheromones increment Delta τ on each sideijWith pheromone amount τij(t+1)；
Step S7 records the path of current iteration, updates current optimal path, empties taboo list；
Step S8 judges whether to reach scheduled maximum iteration, or whether stagnation behavior occurs；If so, algorithm
Terminate, exports current optimal path；Otherwise, S2 is gone to step, next iteration is carried out；
--- transition probability, i.e. t moment ant k climb to the probability of node j from node i；
τij(t) --- the pheromone amount on t moment side (i, j)；
Δτij--- the pheromones increment in current iteration on side (i, j)；
ηij(t) --- inverse of the t moment node i to distance between node j；
τis(t) --- the pheromone amount on t moment side (i, s)；s∈allowedk；
allowedk--- the set of node that selection next node allows；
The relative importance of α --- pheromones；
β --- it is the significance level coefficient of heuristic greedy method η；
τij(t+1)=(1- ρ) * τij(t)+Δτij (1.2)
ρ --- volatilization factor；
M --- maximum ant number；
Q indicates the pheromones total amount of one search process of ant release, LkIndicate the road that ant k passes by this search
Electrical path length.
The advantage of the invention is that：
1) present invention seeks the ant group algorithm of shortest path compared to tradition, is adapted to the route search of multitiered network.
2) multiple constraintss are integrated into a weighted comprehensive value by the present invention, it is contemplated that multiple constraintss.
3) present invention is in terms of the scope of application, it is proposed that the thinking of autgmentability.
Description of the drawings
Fig. 1 is existing ant group algorithm flow chart.
Fig. 2 is the multi-layer network diagram of the present invention.
Fig. 3 is the improved ant group algorithm flow chart of the present invention.
Specific implementation mode
With reference to specific drawings and examples, the invention will be further described.
The ant group algorithm of more QoS route restrictions of oriented multilayer grade network proposed by the present invention, can be adapted for such as Fig. 2 institutes
The double layer network shown；
The double layer network includes multiple first nodes, such as first nodes x, y, a, b, c；Routing between each first nodes
Method is using the minimum method for routing of hop count；
Each first nodes can be subdivided into x0, x1, x2, x3, x4, x5 comprising several two-level nodes, such as x again；
The routing between first nodes, such as x- are first looked in search for the routing x1-c4 of source-destination node>a->
C and x->b->C, it is assumed that the routing between selected first nodes is x->a->C is scanned for；Then it is called in each first nodes
Ant group algorithm scans for；X1- at this time>x3、a2->a2、c3->C4 becomes source-purpose routing of ant group algorithm, by each level-one
Intra-node searches out the shortest path connection come and is extended to complete routed path.
The ant group algorithm of more QoS route restrictions of oriented multilayer grade network, specifically includes following steps：
Step S21 initializes network；
Whether step S22 determines source node and destination node in the same level-one section according to source-destination node of setting
In point, such as exist, directly invoke ant group algorithm, calculates the routing of source-destination node；If not existing, into next step；
Step S23, the routing between first nodes are source node to the minimum routing of hop count between destination node；Selection source
First nodes on node to the minimum routing of hop count between destination node；
Step S24 determines source-destination node inside selected first nodes, as the input of ant group algorithm, calls ant colony
Algorithm, the routing between two-level node in first order calculation node；
Step S25 judges whether to meet the requirements according to two step of step S23, S24 as a result, completely route, if
Satisfaction then terminates specifically to search for；If conditions are not met, continuing to calculate back to step S23.
In ant group algorithm, traditional inverse for asking the heuristic greedy method η of shortest path to be generally shortest path, but if face
Face following multiple constraintss：
(1) it is route on each path l of w at one, bandwidth is available to be limited to：
B(l)≥Bw (2.1)
B (l) is the actual bandwidth on the l of path, BwIndicate the bandwidth of qos requirement；
(2) it is route on w at one, end-to-end time delay is limited to：
On each node and each path time delay and≤Dw (2.2)
DwIndicate qos requirement time delay；
(3) it is route on w at one, end-to-end loss rate is limited to：
LR (n) is the loss rate of node n, V1For the node set on routing w；LwIndicate qos requirement loss rate；
It then needs to modify heuristic greedy method, then needs to modify heuristic greedy method, by time delay, bandwidth usage
Rate, Loss Rate are expressed as the integrated value of many index, are denoted as cost：
λ1、λ2、λ3For weighting coefficient,For bandwidth availability ratio,For loss rate,For time delay.
Then node transition rule, that is, formula (1.1) of ant group algorithm is revised as：
Wherein,
It should be noted last that the above specific implementation mode is merely illustrative of the technical solution of the present invention and unrestricted,
Although being described the invention in detail with reference to example, it will be understood by those of ordinary skill in the art that, it can be to the present invention
Technical solution be modified or replaced equivalently, without departing from the spirit of the technical scheme of the invention and range, should all cover
In the scope of the claims of the present invention.
Claims (3)
1. a kind of ant group algorithm of more QoS route restrictions of oriented multilayer grade network, which is characterized in that
Routing for source-destination node, in search, between the method for routing searching first nodes for first using hop count minimum
Routing；For the two-level node in first nodes, ant group algorithm is called to scan in each first nodes；By each level-one section
The shortest path connection that point inner search comes out is extended to complete routed path.
2. the ant group algorithm of more QoS route restrictions of oriented multilayer grade network as described in claim 1, which is characterized in that tool
Body includes the following steps：
Step S21 initializes network；
Step S22 determines source node and destination node whether in the same first nodes according to source-destination node of setting,
Such as exist, directly invoke ant group algorithm, calculates the routing of source-destination node；If not existing, into next step；
Step S23, the routing between first nodes are source node to the minimum routing of hop count between destination node；Select source node
First nodes in the routing minimum to hop count between destination node；
Step S24 determines source-destination node inside selected first nodes, as the input of ant group algorithm, ant colony is called to calculate
Method, the routing between two-level node in first order calculation node；
Step S25 judges whether to meet the requirements according to two step of step S23, S24 as a result, completely route, if met
Then terminate specifically to search for；If conditions are not met, continuing to calculate back to step S23.
3. the ant group algorithm of more QoS route restrictions of oriented multilayer grade network as claimed in claim 2, which is characterized in that
Node transition rule in the ant group algorithm is：
Wherein,
λ1、λ2、λ3For weighting coefficient,For bandwidth availability ratio,For loss rate,For time delay；
Ant group algorithm is：
Step S1, initiation parameter：Each edge pheromone concentration is equal when beginning；
Each ant is placed each node by step S2, and initial value is corresponding node in the taboo list of each ant；
Step S3 takes 1 ant, and transition probability is calculated by formula (2.5)Next section is selected in the way of roulette
Point updates taboo list, then calculates transition probability, one next node of reselection, then updates taboo list, until destination node occurs
In taboo list；
Step S4 calculates the pheromone amount that this ant stays in each side, which dies；
Step S5 repeats step S3 and S4, searches for and finish until m ant；M is maximum ant number；
Step S6 calculates the pheromones increment Delta τ on each sideijWith pheromone amount τij(t+1)；
Step S7 records the path of current iteration, updates current optimal path, empties taboo list；
Step S8 judges whether to reach scheduled maximum iteration, or whether stagnation behavior occurs；If so, algorithm terminates,
Export current optimal path；Otherwise, S2 is gone to step, next iteration is carried out；
--- transition probability, i.e. t moment ant k climb to the probability of node j from node i；
τij(t) --- the pheromone amount on t moment side (i, j)；
Δτij--- the pheromones increment in current iteration on side (i, j)；
ηij(t) --- inverse of the t moment node i to distance between node j；
τis(t) --- the pheromone amount on t moment side (i, s)；s∈allowedk；
allowedk--- the set of node that selection next node allows；
The relative importance of α --- pheromones；
β --- it is the significance level coefficient of heuristic greedy method η；
τij(t+1)=(1- ρ) * τij(t)+Δτij (1.2)
ρ --- volatilization factor；
M --- maximum ant number；
Q indicates the pheromones total amount of one search process of ant release, LkIndicate the path length that ant k passes by this search
Degree.
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| CN111152214A (en) * | 2019-12-13 | 2020-05-15 | 珠海格力电器股份有限公司 | Four-degree-of-freedom palletizing robot, control system and palletizing path planning method |
| CN111666212A (en) * | 2020-05-28 | 2020-09-15 | 平安医疗健康管理股份有限公司 | Method, apparatus, device and medium for generating test case by ant colony algorithm |
| CN111722925A (en) * | 2020-06-05 | 2020-09-29 | 中国科学院自动化研究所 | Cooperative scheduling method, system and device for dynamic edge calculation |
| CN112637061A (en) * | 2020-12-16 | 2021-04-09 | 中盈优创资讯科技有限公司 | Dynamic multi-factor path calculation method based on heuristic algorithm |
| CN112822747A (en) * | 2021-03-02 | 2021-05-18 | 吉林大学 | Routing strategy based on genetic algorithm and ant colony algorithm in wireless sensor network |
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| CN114666805A (en) * | 2022-04-28 | 2022-06-24 | 中国人民解放军国防科技大学 | Optical network planning method and system suitable for multi-granularity service |
| CN116192720A (en) * | 2021-11-26 | 2023-05-30 | 中国联合网络通信集团有限公司 | Link optimization method and device, electronic equipment and storage medium |
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| CN111152214B (en) * | 2019-12-13 | 2021-08-03 | 珠海格力电器股份有限公司 | Four-degree-of-freedom palletizing robot, control system and palletizing path planning method |
| CN111152214A (en) * | 2019-12-13 | 2020-05-15 | 珠海格力电器股份有限公司 | Four-degree-of-freedom palletizing robot, control system and palletizing path planning method |
| CN111666212A (en) * | 2020-05-28 | 2020-09-15 | 平安医疗健康管理股份有限公司 | Method, apparatus, device and medium for generating test case by ant colony algorithm |
| CN111722925A (en) * | 2020-06-05 | 2020-09-29 | 中国科学院自动化研究所 | Cooperative scheduling method, system and device for dynamic edge calculation |
| CN111722925B (en) * | 2020-06-05 | 2024-03-15 | 中国科学院自动化研究所 | Collaborative scheduling method, system and device for dynamic edge calculation |
| CN112637061B (en) * | 2020-12-16 | 2022-10-04 | 中盈优创资讯科技有限公司 | Dynamic multi-factor path calculation method based on heuristic algorithm |
| CN112637061A (en) * | 2020-12-16 | 2021-04-09 | 中盈优创资讯科技有限公司 | Dynamic multi-factor path calculation method based on heuristic algorithm |
| CN113014484A (en) * | 2021-02-09 | 2021-06-22 | 浙江工商大学 | Network route planning method and system based on BP neural network ant colony algorithm |
| CN112822747A (en) * | 2021-03-02 | 2021-05-18 | 吉林大学 | Routing strategy based on genetic algorithm and ant colony algorithm in wireless sensor network |
| CN116192720A (en) * | 2021-11-26 | 2023-05-30 | 中国联合网络通信集团有限公司 | Link optimization method and device, electronic equipment and storage medium |
| CN114666805A (en) * | 2022-04-28 | 2022-06-24 | 中国人民解放军国防科技大学 | Optical network planning method and system suitable for multi-granularity service |
| CN114666805B (en) * | 2022-04-28 | 2023-11-10 | 中国人民解放军国防科技大学 | Optical network planning method and system suitable for multi-granularity service |
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