CN111669328B - Qos routing method based on quantum maximum minimum ant colony algorithm - Google Patents

Qos routing method based on quantum maximum minimum ant colony algorithm Download PDF

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CN111669328B
CN111669328B CN202010628625.8A CN202010628625A CN111669328B CN 111669328 B CN111669328 B CN 111669328B CN 202010628625 A CN202010628625 A CN 202010628625A CN 111669328 B CN111669328 B CN 111669328B
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李亚龙
潘丹
王伟
何琳
孙静
万杰
张洁
檀斌
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ANHUI EARTHQUAKE ADMINISTRATION
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Abstract

The invention discloses a QoS routing method based on quantum maximum and minimum ant colony algorithm, which combines quantum computation and maximum and minimum ant colony algorithm, adopts attitude vector in quantum computation to carry out quantum coding on a link in network topology, updates pheromone by combining quantum rotation entanglement characteristic with maximum and minimum inspection mechanism, adds a quantum selection strategy in the 'roulette' link selection, effectively controls the evolution speed and direction under the condition of not large adaptability, is superior to the traditional ant colony algorithm in the aspects of optimizing capability and convergence performance, and can effectively and accurately solve the QoS routing problem under the condition of meeting constraint conditions.

Description

Qos routing method based on quantum maximum minimum ant colony algorithm
Technical Field
The invention relates to the technical field of network communication, in particular to a QoS routing method based on quantum maximum and minimum ant colony algorithm.
Background
With the rise of big data and cloud platform, internet network application and scale are continuously enlarged, advanced multimedia services such as images and videos are widely applied in departments such as earthquake, weather and traffic, the big data of real-time streaming service has higher requirement on quality of service (QoS) of network parameters, and how to select the route with optimal cost under the condition of meeting parameter constraint is the content of research on QoS route selection problem.
In the QoS routing problem, the constraint parameters determine the quality and precision of solution, for the routing NP problem constrained by a plurality of indexes, an intelligent bionic algorithm can be adopted for solution, and a plurality of scholars apply various intelligent bionic algorithms to the QoS routing problem solution process and obtain certain results. As a common bionic algorithm, the ant colony algorithm has numerous advantages, however, the iteration times are many in the process of solving the NP problem, the obtained solution is a local solution with high probability, and the convergence performance of the algorithm is low. Different optimization algorithms are proposed for many scholars aiming at the defects, for example, a genetic operator is added to the literature on the basis of an ant colony algorithm to solve the QoS (quality of service) selection routing problem, the literature improves ants through an pheromone optimization mechanism, so that the ant is suitable for solving the QoS unicast routing problem with bandwidth and delay constraint, and when the large-scale QoS routing problem is solved, the algorithm has more iteration times and is easy to converge prematurely.
Aiming at the defects of the existing ant colony algorithm, the invention provides a quantum maximum and minimum ant colony algorithm, which uses a state vector in quantum calculation to carry out quantum coding on a link in a network topology, updates pheromones by combining a quantum rotation entanglement characteristic with a maximum and minimum inspection mechanism, adds a quantum selection strategy in the selection of a 'roulette' link, effectively controls the evolution speed and direction and accelerates the convergence of the algorithm under the condition that the fitness is not large.
Disclosure of Invention
In view of the above existing problems, the present invention aims to provide a QoS routing method, i.e. a QoS routing method based on quantum maximum minimum ant colony algorithm.
In order to achieve the purpose, the technical scheme adopted by the invention is as follows:
the QoS routing method based on the quantum maximum minimum ant colony algorithm is characterized by comprising the following steps of:
s1: initializing a network, setting constraints of bandwidth, delay, jitter and packet loss rate, an upper limit of iteration times and the number of ants, constructing a basic wireless communication network topology structure, and setting link connection nodes which do not meet the bandwidth to be unreachable;
the constructed wireless communication network is regarded as a directed weighted graph G (V, E), the number of nodes in G is n, n = | V |, S is a starting point, t is an ending node, wherein V represents all network nodes in G, E represents all links in G, S belongs to V, and t belongs to V- { S };
for any link E, five constraints are defined, which are:
the first constraint is a delay constraint:
Figure BDA0002567582230000021
wherein, delay generated by the information transmission of the link E is represented by Delay (E), delay generated by the relay information of the node v is represented by Delay (v), and link aggregation in a path P (s, t) from the node s to the end node is represented by E p Denotes E ∈ E p V for node aggregation in P (s, t) p Denotes that V ∈ V p The total delay required by the current routing is not more than the set delay D;
the second constraint is the bandwidth constraint:
Figure BDA0002567582230000031
wherein, bandwidth (E) represents a link set E p Bandwidth of the intermediate link e, B represents the lower limit of the bandwidth in the routing;
the third constraint is delay jitter:
Figure BDA0002567582230000032
wherein Delay _ jitter (v) represents the node Delay jitter passed by the data information, and Dj represents the Delay jitter limit of the routing;
the fourth constraint condition is packet loss rate:
Figure BDA0002567582230000033
wherein, packet _ loss (v) represents the Packet loss rate of the node where the data information passes, and Pl is the Packet loss rate limit set by the routing;
the fifth constraint is cost: the total cost is the sum of the cost from the starting point s to all nodes passed by the end point t, and the cost generated in the path P (s, t) is expressed as:
Figure BDA0002567582230000034
s2: establishing a routing table for each pair of starting points s to ending nodes t, wherein the routing table is Rou ij And (i, j ∈ V) represents that the pheromone code on each link is represented by using quantum bits, and the quantum pheromone code of each ant on each path is represented as:
Figure BDA0002567582230000041
representing the probability amplitude of the pheromone on the link between the node i and the node j as
Figure BDA0002567582230000042
Wherein:
when i ≠ j, | α ij | 2 +|β ij | 2 ＝1；
When i = j, | α ij | 2 ＝|β ij | 2 =0 (1. Ltoreq. I, j. Ltoreq. N), where α ij And beta ij The probability amplitude of pheromone on a link from a node i to a node j is obtained, and n is the number of nodes in the network;
and measuring the population quantum information to obtain the binary codes of all links, wherein the binary codes on all link paths are represented as follows:
Figure BDA0002567582230000043
s3: initializing relevant parameter values;
s4: the pheromone on the link (i, j) is τ ij The probability of an ant reaching node j through node i is expressed as
Figure BDA0002567582230000044
Figure BDA0002567582230000045
Wherein, the first and the second end of the pipe are connected with each other,
Figure BDA0002567582230000046
represents a node set selectable by ant k at node i, alpha' represents weight of pheromone accumulation degree, eta ij Represents a heuristic factor, beta' represents the weight of the heuristic factor;
when α' =0, equation (8) degenerates to greedy algorithm, heuristic factor η ij The tendency to an attractive solution is enhanced, expressed as:
η ij ＝1/C ij (9)；
s5: placing 1 ant at a starting point, initializing a taboo table, total cost, total time delay jitter and packet loss rate, adding the starting point into the taboo table, and constructing an optional node set according to the taboo table, node and link parameter information;
calculating according to the end node t and the five constraint conditions by using a greedy algorithm to obtain the maximum value tau of the pheromone volatilization amount on the link max And initializing pheromone tau = tau in the link max ；
S6: placing m ants at a starting point, initializing a taboo list, total cost, total time delay jitter and packet loss rate, adding the starting point into the taboo list, and constructing an optional node set according to the taboo list, node and link parameter information;
s7: according to the selectable node set, adopting a 'roulette' combined quantum selection strategy for determining a next node nc, if the binary code of a link between the current node and the next node nc is 1, removing nc from the selectable node set, otherwise, independently adopting the 'roulette' to determine the next node nc, and removing nc from the selectable node set;
updating an optional node set, path cost accumulation, time delay jitter accumulation and packet loss rate accumulation, if the conditions are met, modifying the current node to nc, if the ants reach an end node t or the search is trapped in a stagnation state, jumping out to find the solution, otherwise, repeating the steps;
s8: if all m ants finish solving, updating quantum information on the network link and turning to the step S9, otherwise, turning to the step S7;
s9: updating the pheromone on the link, and when the pheromone is updated, checking the pheromone;
s10: if the current iteration times are larger than the maximum iteration times, turning to the step S11 to output an optimal solution, otherwise, adding 1 to the current iteration times, and turning to the step S6;
s11: and constructing and outputting an optimal solution according to the path of the ants from the starting point to the ending point.
Further, the specific operation steps of step S9 include:
s91: the volatile amount of the pheromone is expressed as:
Figure BDA0002567582230000061
wherein the pheromone is tau ij Rho represents the volatilization coefficient of the pheromone, and rho is more than 0 and less than or equal to 1;
s92: and (3) selecting the ant with the least routing cost to update the global pheromone:
Figure BDA0002567582230000062
wherein, Δ τ ij Is a link to which ants finding the optimal path pass(i, j) amount of pheromone released, Δ τ ij Is defined as:
Figure BDA0002567582230000063
wherein beta is ij Representing the quantum pheromone intensity on the i to j link,
Figure BDA0002567582230000064
gamma is quantum information beta on the link between the node i and the node j ij Weight of (1), P best (s, t) refers to a link path which is started from the starting node s and passes through to the destination node t;
s93: checking the pheromone if the updated pheromone value is greater than the maximum value tau max Making the value of the current pheromone equal to tau max If the value of the updated pheromone is less than the minimum value tau min Then the value of the current pheromone is made equal to τ min ：
Figure BDA0002567582230000065
Wherein L is best Cost of path P (s, t) constructed for optimal ants;
Figure BDA0002567582230000071
wherein, the relative coefficient increased by the pheromone is Q, and the probability that the path found by the ant in a single search is optimal is P best The probability of selecting the optimal solution is P dec The number of selectable paths is avg.
Further, the updating of the quantum information on the network link in step S8 specifically uses a quantum revolving gate to update the quantum probability amplitude of the ants on each path, and the adjustment formula of the quantum revolving gate is as follows:
Figure BDA0002567582230000072
wherein i, j =1,2,3 … n,
Figure BDA0002567582230000073
for quantum encoded information on the link between node i to node j in the t-th iteration, the quantum rotation angle of the link i to j is represented by θ.
The invention has the beneficial effects that:
first, the QoS routing method of the present invention compares the global search capability, convergence performance, and network scale impact on the algorithm with the traditional ant colony algorithm, and the solution results of the present invention are superior to the traditional ant colony algorithm in terms of performance, so it is feasible and effective for solving the NP problem of QoS routing.
Secondly, the invention combines quantum computation and a maximum and minimum ant colony algorithm, carries out quantum coding on links in network topology by adopting attitude vectors in the quantum computation, updates pheromones by combining quantum rotation entanglement characteristics and a maximum and minimum inspection mechanism, can effectively control the evolution speed and direction, is superior to the traditional ant colony algorithm in the aspects of optimizing capability and convergence performance, and can effectively and accurately solve the QoS routing problem under the condition of meeting constraint conditions.
Thirdly, the algorithm improves the link selection strategy, adds the quantum selection strategy in the traditional link selection mode of the roulette selection method, and can effectively control the evolution speed and direction under the condition of small fitness difference, thereby accelerating the convergence of the algorithm.
Drawings
FIG. 1 is a randomly generated network topology;
FIG. 2 illustrates a schematic diagram of the best path;
FIG. 3 is a diagram illustrating the convergence performance of the ant colony algorithm of the present invention;
FIG. 4 is a schematic diagram comparing the impact of network size on the present invention with conventional ant colony algorithms;
Detailed Description
In order to make those skilled in the art better understand the technical solution of the present invention, the following further describes the technical solution of the present invention with reference to the drawings and the embodiments.
The QoS routing method based on the quantum maximum minimum ant colony algorithm comprises the following steps:
s1: initializing a network, setting constraints of bandwidth, delay, jitter and packet loss rate, an upper limit of iteration times and the number of ants, constructing a basic wireless communication network topology structure, and setting link connection nodes which do not meet the bandwidth to be unreachable;
the constructed wireless communication network is regarded as a directed weighted graph G (V, E), the number of nodes in G is n, n = | V |, S is a starting point, t is an ending node, wherein V represents all network nodes in G, E represents all links in G, at most one direct link exists between two nodes, S belongs to V, and t belongs to V- { S };
supposing that a routing task exists in the network G, from a starting point s to an end node t, the key of the routing problem solving is to find a path p (s, t) meeting constraint conditions, and simultaneously, the cost of the path is optimal;
for any link E, five constraints are defined, which are:
the first constraint is a delay constraint:
Figure BDA0002567582230000091
wherein, the time Delay generated by the information transmission of the link E is represented by Delay (E), the time Delay generated by the information transmission of the node v is represented by Delay (v), and the link set in the path P (s, t) from the node s to the end node is represented by E p Denotes E ∈ E p V for node aggregation in P (s, t) p Denotes that V ∈ V p The total delay required by the current routing is not more than the set delay D;
the second constraint is the bandwidth constraint:
Figure BDA0002567582230000092
wherein Bandwidth (E) represents a link set E p Bandwidth of the intermediate link e, B represents the lower limit of the bandwidth in the routing;
the third constraint is delay jitter:
Figure BDA0002567582230000093
wherein Delay _ jitter (v) represents the node Delay jitter passed by the data information, and Dj represents the Delay jitter limit of the current routing request;
the fourth constraint condition is packet loss rate:
Figure BDA0002567582230000094
wherein, packet _ loss (v) represents the Packet loss rate of the node where the data information passes, and Pl is the Packet loss rate limit set by the routing;
the fifth constraint is cost: the total cost is the sum of the cost from the starting point s to all nodes passed by the end point t, and the cost generated in the path P (s, t) is expressed as:
Figure BDA0002567582230000101
the multi-constraint QoS routing problem is that an optimal path is searched in a given network topology, a preprocessing mechanism is adopted, the network topology path is screened according to constraint conditions, invalid paths are removed, a starting point is established, a node routing table is ended, the node routing table is used for temporarily storing solutions meeting the constraint conditions in the operation process, each ant is required to start from a starting node s, and the next node is searched according to a probability transfer formula until an ending node t is reached;
s2: establishing a routing table for each pair of starting points s to ending nodes t, wherein the routing table is Rou ij (i, j ∈ V) using qubitsExpressing pheromone codes on each link, and expressing quantum pheromone codes of each ant on each path as follows:
Figure BDA0002567582230000102
in step S1, the number of nodes in the network topology has been set to n, and quantum pheromone on the link between node i and node j can be represented as
Figure BDA0002567582230000103
Wherein:
a qubit can use a probability amplitude
Figure BDA0002567582230000104
Then, the individual probability amplitude of n qubits can be expressed as:
Figure BDA0002567582230000105
wherein alpha is i 、β i Satisfies | α i | 2 +|β i | 2 =1,i =1,2,3, …, n, the quantum individual representing an arbitrary quantum stacking state;
in classical computing, information is represented in binary form by 0 and 1, called bits, and in quantum information theory, the basic unit of storage of information is a qubit, or qubit. A simple qubit is a two-state system whose two polarization states correspond to 0's and 1's of the classical information binary memory cell state. Unlike classical bits, a qubit can be in their superposition state in addition to the 0 and 1 states.
For ease of representation and operation, dirac proposes to sign "(" non-conducting cells ")>"to denote a quantum state. The state of a qubit can be represented as a superposition of two ground states, where α and β are referred to as the probability amplitude of the qubit, | α 2 And | β | |) 2 Respectively representing the probability that the qubit is in state 0 and state 1, satisfying | alpha- 2 +|β| 2 And =1. Quantum state | phi>At |0>And |1>Once in a continuous state betweenQuantum state | phi>Measured, quantum state | phi>Will pass through | alpha- 2 Is collapsed to state |0>Is not more than | beta 2 To state |1>。
Figure BDA0002567582230000111
Representing the probability amplitude of pheromone on the link from node i to node j, when i is not equal to j, | alpha ij | 2 +|β ij | 2 =1; when i = j, | α ij | 2 ＝|β ij | 2 =0 (1. Ltoreq. I, j. Ltoreq. N), and α ij And beta ij The probability amplitude of pheromone on a link from a node i to a node j is obtained, and n is the number of nodes in the network;
for nodes i and j, when ants pass through the link from i to j, the probability amplitude beta of pheromone on the link is enabled to be larger ij The pheromone is enhanced with an increase in the value of (a); otherwise, the pheromone on the link can be volatilized;
when the optimal ant directly reaches the node j through the node i, quantum information beta is on a link between the node i and the node j ij The value of (c) increases, the tendency of the link to be selected increases; if the link is selected to have a reduced tendency, quantum information beta on the link ij The value is decreased;
and measuring the population quantum information to obtain the binary codes of each link, wherein the binary codes on each link path are represented as follows:
Figure BDA0002567582230000121
the specific measurement method comprises the following steps:
(1) Input alpha ij ，[0,1]A random number rd between;
(2) Judgment of rd and alpha ij If rd > alpha ij 2 Then b is ij =1, otherwise b ij ＝0；
(3) Output b ij A value of (d);
s3: initializing quantum maximum minimum ant colony algorithm related parametersValues, including α ', β', ρ, P best Values of Q, gamma, and the number of ants m the upper limit of iteration number NMAX, all alpha in the ant Quantum pheromone code ij ,β ij All take values as
Figure BDA0002567582230000125
S4: cost C between node i and node j ij The pheromone on link (i, j) is τ ij The probability of an ant reaching node j through node i is expressed as
Figure BDA0002567582230000122
Figure BDA0002567582230000123
Wherein the content of the first and second substances,
Figure BDA0002567582230000124
represents the node set that ant k can select at node i, α' represents the weight of pheromone accumulation degree, η ij Represents the heuristic factor, beta' represents the weight of the heuristic factor;
when α '=0, the probability transition formula (8) is degenerated into a greedy algorithm, when β' =0, the attractiveness of the next network node is ignored, the efficiency of the exploration will decrease, and the heuristic factor η ij The tendency towards attractive solutions is enhanced, expressed as:
η ij ＝1/C ij (9)；
when ants directly reach the node j through the node i, pheromones on the link can be accumulated, and quantum information beta is obtained ij Will increase, otherwise, the quantum information beta ij Decrease;
s5: placing 1 ant at a starting point, initializing a taboo table, total cost, total time delay jitter and packet loss rate, adding the starting point into the taboo table, and constructing an optional node set according to the taboo table, node and link parameter information;
wherein the tabu table is used for recording the current walking of antsThe list of nodes that have passed, where the tabu list is initialized to record the path taken by the 1 st ant using the greedy algorithm, so as to initialize τ max A value of (d);
calculating and constructing solution P (s, t) by using a greedy algorithm according to the end node t and the constraint condition, and solving the maximum value tau of the pheromone volatilization amount on the link by adopting a formula (13) max And initializing pheromone tau = tau in the link max The current iteration number count =1;
s6: placing m ants at a starting point, initializing a taboo list, total cost, total time delay jitter and packet loss rate, adding the starting point into the taboo list, and constructing an optional node set according to the taboo list, node and link parameter information;
s7: according to the selectable node set, establishing probability distribution according to a probability transfer formula (8), determining a next node nc by adopting a 'roulette selection method' in combination with a quantum selection strategy, if the binary code of a link between the current node and the next node nc is 1, removing nc from the selectable node set, otherwise, independently determining the next node nc by adopting the 'roulette selection method', and removing nc from the selectable node set;
updating an optional node set, path cost accumulation, time delay jitter accumulation and packet loss rate accumulation, if the conditions are met, modifying the current node to nc, if the ants reach an end node t or the search is trapped in a stagnation state, jumping out to find the solution, otherwise, repeating the steps;
in the ant colony algorithm, in order to ensure the randomness of ant selection paths, paths with high probability are selected with high probability when the paths are selected, but paths with low probability are also possible to be selected instead of directly selecting paths with high probability, so that all ants do not make the same selection at the place, and the algorithm loses the randomness.
In order to avoid the algorithm losing randomness, a roulette method is used for selection when selecting the path. Considering the probability of each path as a sector of the wheel, rotating the wheel, on which sector the pointer stops, the path corresponding to the probability is selected, by using a random number rand between [0,1] to simulate the sector to which the pointer points when it stops.
Assuming that paths A, B, C, D, E correspond to probabilities of 0.1, 0.2, 0.1, 0.5, and 0.1, respectively, 0-yarn range < =0.1 corresponds to a path a sector, 0.1 yarn range < =0.3 corresponds to a path B sector, 0.3 yarn range < =0.4 corresponds to a path C sector, 0.4 yarn range < =0.9 corresponds to a path D sector, and 0.9 yarn range < =1 corresponds to a path E sector.
The quantum selection strategy refers to that binary coding information of each link obtained after measuring population quantum information is introduced into node selection, and a next node is selected jointly by combining a roulette selection method;
s8: if all m ants complete the solution, updating the quantum information on the network link by adopting a formula (15) and turning to the step S9, otherwise, turning to the step S7;
s9: updating pheromones on the link by adopting a formula (10) -a formula (12), and after the pheromones are updated, checking the pheromones on the link by adopting a formula (13) and a formula (14);
s10: if the current iteration number count is greater than the maximum iteration number NMAX, the step S11 is switched to output the optimal solution, otherwise, the count = count +1, that is, the current iteration number is increased by 1, and the step S6 is switched to;
s11: and constructing and outputting an optimal solution according to the path of the ants from the starting point to the ending point.
Further, when the ants all reach the set end point, the pheromones on each link need to be updated, the pheromones on the links are reduced by a certain numerical value due to volatilization, and then the corresponding pheromones are increased according to the link through which the optimal ants pass, wherein the specific operation steps of the step S9 include:
s91: the volatile amount of the pheromone is expressed as:
Figure BDA0002567582230000151
wherein the pheromone is tau ij Rho represents the volatilization coefficient of pheromone, and rho is more than 0 and less than or equal to 1, and the parameter can effectively control pheromone on a linkThe accumulated amount of (c);
s92: and selecting the ant with the least routing cost to update the global pheromone, wherein the formula is as follows:
Figure BDA0002567582230000152
wherein, Δ τ ij Is the amount of pheromone, Δ τ, released by the ant seeking the optimal path to the link (i, j) it passes through ij Is defined as:
Figure BDA0002567582230000153
wherein beta is ij Representing the quantum pheromone intensity on the i to j link,
Figure BDA0002567582230000154
gamma is quantum information beta on the link between the node i and the node j ij Weight of (B), P best (s, t) refers to the link path from the departure node s to the destination node t;
s93: when the pheromone is updated, the pheromone is checked, and if the value of the updated pheromone is larger than the maximum value tau max Making the value of the current pheromone equal to tau max ；
If the value of the updated pheromone is less than the minimum value tau min Then the value of the current pheromone is made equal to τ min ：
Figure BDA0002567582230000161
Wherein L is best Cost of path P (s, t) constructed for optimal ants;
Figure BDA0002567582230000162
wherein the pheromone is increasedThe added relative coefficient is Q, the probability that the path found by the ant in a single search is optimal is P best The probability of selecting the optimal solution is P dec ，P best The number of alternative paths is avg, preferably P best Selecting the value of the traditional maximum and minimum ant colony algorithm, and when the algorithm is converged, the probability P of the optimal solution is selected best Is a number greater than 0, P dec By passing
Figure BDA0002567582230000163
And (6) calculating.
Further, the updating of the quantum information on the network link in step S8 specifically uses a quantum revolving gate to update the quantum probability amplitude of the ant on each path:
when there are m ants, the n x n matrix R is a solved path from the starting point to the ending point in the network of n nodes, R [ i, j]=1, indicating that there is an edge from node i to node j in path R, and that there must be R [ i, j when i = j]=0. The optimal solution obtained from the former generation of ants is represented by R, and the optimal solution obtained from the former generation of ants is represented by R best It is shown that quantum information in the network link is updated using the quantum revolving gate, and the adjustment formula of the quantum revolving gate is:
Figure BDA0002567582230000164
wherein i, j =1,2,3 … n,
Figure BDA0002567582230000165
for quantum coding information on a link between a node i and a node j in the t iteration, quantum rotation angles of the link i to the node j are represented by theta, and values of the quantum rotation angles are obtained by looking up a table 1:
TABLE 1 rotation Angle strategy
Figure BDA0002567582230000171
Where f (x) is the objective function, herein the routing solution completed for antsThe cost required; s (. Alpha.) is ij ,β ij ) Indicating the direction of the rotation angle offset, is used to control the progress and direction of convergence of the algorithm.
Example (b):
first, a network topology is randomly generated with reference to the Salama model, and only the node part parameters are displayed in order to make the topology data clear. Setting the QoS delay constraint as D =95, the bandwidth in the link B =70, the delay jitter Dj =1000, the packet loss ratio Pl =1000 (10 e-5), and the remaining parameters as α '=1, β' =2, ρ =0.02, γ =2,P, respectively best =0.05, q =5, m =50, maximum evolution algebra NMAX =100, and then experiments were performed from three aspects of the algorithm's global search capacity, convergence performance, and network size impact on the algorithm, respectively:
firstly, verifying from global search capability, selecting 50 network nodes, randomly generating a network topology structure, initializing constraint conditions and algorithm parameters, performing 50 separate experiments on each routing request by using the algorithm of the invention, solving (1,50) the generated optimal path in the randomly generated network topology structure shown in the attached drawing 1, recording the optimal solution found in 50 experiments, the required cost and the corresponding time delay and bandwidth, drawing the final results obtained by 50 independent experiments, and combining the results shown in the attached drawing 2 with the simulation experiment result table 1, so that different starting points can be obtained, the QoS routing method based on the quantum maximum minimum ant colony algorithm provided by the invention is used for calculation, and the optimization solution can be effectively performed in a global range aiming at the QoS routing problem under the constraint condition;
table 1 simulation test results
Figure BDA0002567582230000181
Secondly, verifying from convergence performance, selecting a random production network topology of 100 network nodes, initializing constraint conditions and algorithm parameters, performing routing solution according to steps S1-S11, comparing a solution result with a traditional ant colony algorithm, and verifying a routing request (3,99), wherein the iterative solution conditions of the two algorithms are shown in figure 3 under the condition, so that the optimal path cost and convergence time obtained by the QMAS (QMAS) based on the quantum maximum and minimum ant colony in the invention are superior to those of the traditional ant colony Algorithm (ACS), the traditional ant colony algorithm has an initial convergence block and a slow later convergence speed, the obtained solution is an effective solution in a local range, and the method effectively controls the optimizing direction through quantum rotation, has a fast early convergence speed, and has a good effect of solving the multi-constraint QoS routing problem in a global range;
thirdly, verifying the influence of the network scale on the algorithm, initializing constraint conditions and algorithm parameters, setting the number of initial network nodes as 15, sequentially increasing 10, setting the upper limit of the number of nodes as 100, carrying out routing solution according to the steps S1-S11 under different network nodes, and comparing the QMAS (QMAS) method based on the quantum maximum and minimum ant colony algorithm with the traditional ant colony algorithm, wherein the comparison result is shown in figure 4, and as can be seen from figure 4, when the network node scale is small (the number of nodes is below 25), the solving efficiency of the traditional ant colony algorithm and the QMAS method based on the quantum maximum and minimum ant colony algorithm are the same, but with the increase of the network nodes, the solution cost obtained by the method in the invention is always superior to that of the traditional ant colony algorithm.
The foregoing shows and describes the general principles, principal features, and advantages of the invention. It will be understood by those skilled in the art that the present invention is not limited to the embodiments described above, which are described in the specification and illustrated only to illustrate the principle of the present invention, but that various changes and modifications may be made therein without departing from the spirit and scope of the present invention, which fall within the scope of the invention as claimed. The scope of the invention is defined by the appended claims and equivalents thereof.

Claims (1)

1. The QoS routing method based on the quantum maximum minimum ant colony algorithm is characterized by comprising the following steps of:
s1: initializing a network, setting constraints of bandwidth, delay, jitter and packet loss rate, an upper limit of iteration times and the number of ants, constructing a basic wireless communication network topology structure, and setting link connection nodes which do not meet the bandwidth to be unreachable;
the constructed wireless communication network is regarded as a directed weighted graph G (V, E), the number of nodes in G is n, n = | V |, S is a starting point, t is an ending node, wherein V represents all network nodes in G, E represents all links in G, S belongs to V, and t belongs to V- { S };
for any link E ∈ E, five constraints are defined, which are:
the first constraint is a delay constraint:
Figure FDA0003765497210000011
wherein, delay generated by the information transmission of the link E is represented by Delay (E), delay generated by the relay information of the node v is represented by Delay (v), and link aggregation in a path P (s, t) from the node s to the end node is represented by E p Denotes E ∈ E p V for node aggregation in P (s, t) p Denotes that V ∈ V p The total delay required by the current routing is not more than the set delay D;
the second constraint is the bandwidth constraint:
Figure FDA0003765497210000012
wherein, bandwidth (E) represents a link set E p Bandwidth of the intermediate link e, B represents the lower limit of the bandwidth in the routing;
the third constraint is delay jitter:
Figure FDA0003765497210000021
wherein Delay _ jitter (v) represents the node Delay jitter passed by the data information, and Dj represents the Delay jitter limit of the routing;
the fourth constraint condition is packet loss rate:
Figure FDA0003765497210000025
wherein, packet _ loss (v) represents the Packet loss rate of the node where the data information passes, and Pl is the Packet loss rate limit set by the routing;
the fifth constraint is cost: the total cost is the sum of the cost from the starting point s to all nodes passed by the end point t, and the cost generated in the path P (s, t) is expressed as:
Figure FDA0003765497210000022
s2: establishing a routing table for each pair of starting point s to ending point t, wherein the routing table is Rou ij And (i, j ∈ V) represents that the pheromone code on each link is represented by using quantum bits, and the quantum pheromone code of each ant on each path is represented as:
Figure FDA0003765497210000023
representing the probability amplitude of the pheromone on the link between node i and node j as
Figure FDA0003765497210000024
Wherein:
when i ≠ j, | α ij | 2 +|β ij | 2 ＝1；
When i = j, | α ij | 2 ＝|β ij | 2 =0 (1. Ltoreq. I, j. Ltoreq. N), where α ij And beta ij The probability amplitude of pheromone on a link from a node i to a node j is obtained, and n is the number of nodes in the network;
and measuring the population quantum information to obtain the binary codes of each link, wherein the binary codes on each link path are represented as follows:
Figure FDA0003765497210000031
s3: initializing relevant parameter values;
s4: the pheromone on the link (i, j) is τ ij The probability of an ant reaching node j through node i is expressed as
Figure FDA0003765497210000032
Figure FDA0003765497210000033
Wherein, the first and the second end of the pipe are connected with each other,
Figure FDA0003765497210000034
represents a node set selectable by ant k at node i, alpha' represents weight of pheromone accumulation degree, eta ij Represents a heuristic factor, beta' represents the weight of the heuristic factor;
when α' =0, equation (8) degenerates to greedy algorithm, heuristic factor η ij The tendency to an attractive solution is enhanced, expressed as:
η ij ＝1/C ij (9)；
s5: placing 1 ant at a starting point, initializing a taboo table, total cost, total time delay jitter and packet loss rate, adding the starting point into the taboo table, and constructing an optional node set according to the taboo table, node and link parameter information;
calculating according to the end node t and the five constraint conditions by using a greedy algorithm to obtain the maximum value tau of the pheromone volatilization amount on the link max And initializing pheromone tau = tau in the link max ；
S6: placing m ants at a starting point, initializing a taboo table, total cost, total time delay jitter and packet loss rate, adding the starting point into the taboo table, and constructing an optional node set according to the taboo table, nodes and link parameter information;
s7: according to the selectable node set, adopting a 'roulette' combined quantum selection strategy for determining a next node nc, if the binary code of a link between the current node and the next node nc is 1, removing nc from the selectable node set, otherwise, independently adopting the 'roulette' to determine the next node nc, and removing nc from the selectable node set;
updating an optional node set, path cost accumulation, time delay jitter accumulation and packet loss rate accumulation, if the conditions are met, modifying the current node to nc, if the ants reach an end node t or the search is trapped in a stagnation state, jumping out to find the solution, otherwise, repeating the steps;
s8: if all m ants finish solving, updating quantum information on the network link and turning to the step S9, otherwise, turning to the step S7;
s9: updating pheromones on the link, and when the pheromones are updated, checking the pheromones;
s10: if the current iteration times are larger than the maximum iteration times, the step S11 is switched to output the optimal solution, otherwise, the current iteration times are added with 1, and the step S6 is switched to;
s11: constructing an optimal solution according to the path from the starting point to the ending point of the ant and outputting the optimal solution;
updating the quantum information on the network link in step S8, specifically using a quantum revolving gate to update the quantum probability amplitude of the ants on each path, and the adjustment formula of the quantum revolving gate is:
Figure FDA0003765497210000041
wherein i, j =1,2,3 … n,
Figure FDA0003765497210000042
for quantum coding information on the link between the node i and the node j in the t-th iteration, the quantum rotation angle of the link i to the node j is represented by theta；
The specific operation steps of step S9 include:
s91: the volatile amount of the pheromone is expressed as:
Figure FDA0003765497210000051
wherein the pheromone is tau ij Rho represents the volatilization coefficient of the pheromone, and rho is more than 0 and less than or equal to 1;
s92: and (3) selecting the ant with the least routing cost to update the global pheromone:
Figure FDA0003765497210000052
wherein, Δ τ ij Is the amount of pheromone released by the ant seeking the optimal path to the link (i, j) it passes through, Δ τ ij Is defined as:
Figure FDA0003765497210000053
wherein beta is ij Representing the quantum pheromone intensity on the i to j link,
Figure FDA0003765497210000054
gamma is quantum information beta on the link between the node i and the node j ij Weight of (B), P best (s, t) refers to a link path which is passed by the optimal ant from the starting node s to the destination node t;
s93: checking the pheromone if the updated pheromone value is greater than said maximum value tau max Making the value of the current pheromone equal to tau max If the value of the updated pheromone is less than the minimum value tau min Then the value of the current pheromone is made equal to τ min ：
Figure FDA0003765497210000055
Wherein L is best Cost of path P (s, t) constructed for optimal ants;
Figure FDA0003765497210000056
wherein, the relative coefficient increased by the pheromone is Q, and the probability that the path found by the ant in a single search is optimal is P best The probability of selecting the optimal solution is P dec The number of selectable paths is avg.
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