CN115150284A - Communication network topology optimization method and system based on improved sparrow algorithm - Google Patents
Communication network topology optimization method and system based on improved sparrow algorithm Download PDFInfo
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Abstract
The invention discloses a communication network topology optimization method and a system based on an improved sparrow algorithm, wherein the communication network topology optimization method comprises the following steps: acquiring a parameter group corresponding to an initial communication network structure, wherein the parameter group comprises the number of network nodes, the number of links and natural connectivity of the initial communication network structure; constructing an optimization model corresponding to the communication network topological structure, and optimizing the communication network topological structure by taking the natural connectivity of the network topological structure as an index for evaluating the network reliability; and reconstructing the topological structure of the communication network by adopting an improved sparrow algorithm to obtain the topological structure of the communication network with the optimal natural connectivity, wherein the number of links and the number of nodes of the communication network are unchanged before and after reconstruction. The method can be used for improving the structure optimization of the communication network and has higher reliability when the attack occurs.
Description
Technical Field
The invention relates to the technical field of communication networks, in particular to a communication network topology optimization method and system based on an improved sparrow algorithm.
Background
The power communication network is an important infrastructure of a power system and carries core services such as power dispatching automation, power grid production, marketing, management and the like. At present, a country power grid company provides a prefecture-county integrated operation mode, and the general purpose of the prefecture-county integrated operation mode is to promote integration of prefecture-county scheduling systems and operation equipment, establish an integrated scheduling operation management platform, change the current situation of uneven operation level of each scheduling system, promote sharing of technical resources, equipment resources and data resources, and adapt to development requirements of an intelligent power grid. Under the background, traditional situations of independent web formation and construction of prefecture and county companies gradually transit to a construction mode of integrated and unified scheduling of prefecture and county. For example, the number and range of communication services change greatly, network nodes are continuously expanded, the hierarchy is gradually increased, and the topology is more complex. The existing network has many defects, which cannot meet the needs of power grid development, such as non-uniform equipment brands, idle local regional bandwidth, insufficient expandability and the like, and when network attacks occur, the problem of insufficient reliability still exists, so that structural optimization is urgently needed.
Disclosure of Invention
The invention provides a communication network topology optimization method and system based on an improved sparrow algorithm, which are used for improving the structure optimization of a communication network and have higher reliability when attacks occur.
The invention provides a communication network topology optimization method, which comprises the following steps:
acquiring a parameter group corresponding to an initial communication network structure, wherein the parameter group comprises the number of network nodes, the number of links and natural connectivity of the initial communication network structure;
constructing an optimization model corresponding to the communication network topological structure, and optimizing the communication network topological structure by taking the natural connectivity of the network topological structure as an index for evaluating the network reliability;
and reconstructing the topological structure of the communication network by adopting an improved sparrow algorithm to obtain the topological structure of the communication network with the optimal natural connectivity, wherein the number of links and the number of nodes of the communication network are unchanged before and after reconstruction.
The invention also provides a communication network topology optimization system, comprising:
the acquisition module is used for acquiring a parameter group corresponding to an initial communication network structure, wherein the parameter group comprises the number of network nodes, the number of links and natural connectivity of the initial communication network structure;
the model construction module is used for constructing an optimization model corresponding to the communication network topological structure, and optimizing the communication network topological structure by taking the natural connectivity of the network topological structure as an index for evaluating the network reliability;
and the optimization processing module is used for reconstructing the topological structure of the communication network by adopting an improved sparrow algorithm to obtain the topological structure of the communication network with the optimal natural connectivity, and the number of links and the number of nodes of the communication network are unchanged before and after reconstruction.
The topological structure of the complex network is corresponding to the adjacent matrix of the graph formed by the nodes and the links, so that the optimization problem of the topological structure is converted into the optimal solution problem of the natural connectivity of the adjacent matrix; and then, reconstructing and adjusting the topological structure by a sparrow algorithm to solve the problem of the optimal solution of the adjacency matrix, thereby providing an implementation method for improving the network reliability, and being beneficial to being applied to various network anti-attack network structures.
Drawings
In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings needed to be used in the description of the embodiments or the prior art will be briefly introduced below, and it is obvious that the drawings in the following description are some embodiments of the present invention, and for those skilled in the art, other drawings can be obtained according to these drawings without creative efforts.
Fig. 1 is a flowchart of a communication network topology optimization method according to an embodiment of the present invention;
fig. 2 is a flowchart of reconstructing a network topology by using a sparrow algorithm according to an embodiment of the present invention;
FIG. 3 is a flow chart of a method for obtaining a target-optimal sparrow position according to an embodiment of the invention;
fig. 4 is a schematic structural diagram of a communication network topology optimization system according to an embodiment of the present invention.
Detailed Description
In order to make the objects, technical solutions and advantages of the present invention more apparent, the technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present invention, and it is obvious that the described embodiments are some, but not all, embodiments of the present invention. All other embodiments, which can be obtained by a person skilled in the art without inventive step based on the embodiments of the present invention, are within the scope of protection of the present invention.
In order to make the technical solution of the present invention clearer, embodiments of the present invention are described in detail below with reference to the accompanying drawings.
Fig. 1 is a flowchart of a communication network topology optimization method provided in an embodiment of the present invention, and as shown in fig. 1, the optimization method of the embodiment includes:
102, constructing an optimization model corresponding to the communication network topological structure, and optimizing the communication network topological structure by taking the natural connectivity of the network topological structure as an index for evaluating the network reliability;
and 103, reconstructing the topological structure of the communication network by adopting an improved sparrow algorithm to obtain the topological structure of the communication network with the optimal natural connectivity, wherein the number of links and the number of nodes of the communication network are unchanged before and after reconstruction.
The sparrow algorithm in the embodiment realizes the solution of the optimization problem by simulating the foraging behavior of sparrows as a heuristic strategy, and when the optimization problem is solved by adopting the sparrow algorithm, the sparrows (also called population) are divided into two types, namely a finder and a follower, the different types have different social behaviors, and the fitness is different, and the concrete expression is as follows: in the sparrow population, the sparrows with the better function value of the current fitness degree are defined as discoverers, the positions of the types are closer to the food source, the foods can be obtained first, and the whole population is led to be close to the food source. When approaching to the food source, the threat of the natural enemy needs to be warned at any moment, and different action modes are adopted according to the threat degree of the surrounding environment. The remaining sparrow individuals are defined as followers, and due to the limited amount of food, only the followers which are close to the finder can obtain the food, and other followers do not obtain the food, so that the energy value is low, and the followers need to go to other areas to search for the food to supplement the energy. In the process of foraging the sparrow population, part of sparrows can be selected for warning, and when enemies approach, no matter discoverers or followers, the current food can be abandoned and flies to other positions for avoiding.
The core idea of the sparrow algorithm based on the simulation of the social division is as follows: in a solving space of a target problem, a certain number of sparrow individuals are randomly generated, the position of each individual represents a feasible solution of the target problem, and the whole population of sparrows is divided into developers and explorers according to the fitness value corresponding to each individual and the proportion of preset discoverers and followers. The developer adopts different action modes, namely different updating strategies according to the safety degree of the surrounding environment, if the environment safety degree is low, the developer conducts large-range wide search, otherwise the developer draws close together to perform collective action; the explorer adopts different behavior models, namely different updating strategies according to the distance between the explorer and the food source, the near explorer approaches the developer, and the far explorer abandons the food source searched by the developer and searches for the new food source. In the searching process, the position of the optimal food source currently searched by the population (namely the individual with the optimal current fitness) is recorded. Continuously iterating the whole sparrow in different updating modes according to the division of labor and different individuals until convergence, wherein during convergence, the recorded historical optimal food source position is the optimal solution of the target problem found by the sparrow algorithm; the core parameters of the improved sparrow algorithm comprise seeker developer proportion, alertness proportion, sparrow movement disturbance factors and the like.
The network structure (i.e. network topology) of the communication network in the embodiment of the present invention generally refers to the geometric connection shape of the network nodes and the transmission lines, which is related to the network connectivity problem. Starting from the internal topological structure attribute of the network, the communication network with higher reliability is obtained through topological optimization, and the method has important significance in practical application.
The embodiment of the invention converts the reliability problem of the complex network into the optimization problem of the model by constructing the optimization model of the topological structure to solve. In the present model, the decision variable chosen is the topology of the communication network, which is represented using the adjacency matrix. For a communication network G, the structure is a simple graph without right and direction, therefore, the adjacency matrix a of the network structure ij Value set of { a ij ＝a ji ＝1|e(v i ，v j ) E (G) } and
wherein v is i Denotes the ith node in the communication network G, and E (G) denotes the set of all edges in the communication network G.
In the embodiment of the invention, a communication network topological structure optimization model is established by taking the natural connectivity of a communication network as a target, and an objective function f of the communication network topological structure optimization model is shown as a formula 1:
wherein f is the natural connectivity of the network G, λ i Is a to ij And (3) calculating the maximum value of f to obtain the target solution to be solved according to the characteristic root of the formed adjacent matrix A (G).
In order to ensure the rationality and availability of the optimized network topology, the generated network topology must also satisfy certain constraint conditions, that is: the reconstructed network is still a connected graph, and the number of network links before and after reconstruction is consistent, so that the communication network topology structure optimization model is obtained as the following formula 2:
wherein, W is the number of network links, N is the number of network nodes, C (G) traverses each node through depth-first search, and the results of traversing all nodes can be communicated with any other node.
After the optimization model is constructed, the corresponding network topology structure can be obtained by solving the maximum value of the optimization model.
In the embodiment of the invention, a sparrow algorithm is adopted to reconstruct the topological structure of the communication network so as to obtain the topological structure of the communication network with the optimal natural connectivity. The sparrow algorithm updates the positions of the individuals through iteration to obtain a gradually optimized target solution, and then a corresponding network topology structure with high reliability is obtained.
In this embodiment, an optimization model corresponding to a communication network topology structure is constructed, and the communication network topology structure is optimized by using the natural connectivity of the network topology structure as an index for evaluating network reliability, which specifically includes:
taking the maximum value of the natural connectivity of the adjacent matrix corresponding to the communication network topological structure as an optimization target;
the reconstructed network is still used as a connected graph and the number of links before and after reconstruction does not become a constraint condition;
and constructing an optimization model corresponding to the communication network topology structure according to the optimization target and the constraint condition.
Fig. 2 is a flowchart for reconstructing a network topology by using a sparrow algorithm according to an embodiment of the present invention, as shown in fig. 2, in a specific implementation, step 103 may specifically include:
Firstly, based on an original communication network structure diagram, relevant parameters of a current network are calculated, wherein the relevant parameters comprise network link number W, initial network natural connectivity and network node number M.
Setting the population scale of sparrows as N, presetting maximum iteration times T and a dimension space D, wherein a finder accounts for a sparrow scale factor beta, a follower accounts for the sparrow scale (1-beta), a cautioner accounts for SD, and the dimension space D is (M-1) 2 /2. And calculating the number of developers in the population by using the following formula 3, wherein the rest individuals are followers.
Wherein n is leader The number of developers is shown, N is the population scale, beta discoverers account for sparrow proportion, the value of beta is usually less than 0.5 in order to ensure the convergence rate of the algorithm, and the number of followers is N-N leader 。
Initializing the location x of each individual in the population i 1 ＝(x i1 1 ，x i2 1 ，...，x iD 1 ) I =1, 2.. N, and calculates its corresponding fitness value F i 1 (i.e., the objective function value f).
Taking the current position of each individual as the historical optimal position of the individual
And taking the position of the optimal sparrow in the initial population as the optimal position of the population history
and 1033, determining the topological structure of the target communication network according to the position of the sparrow with the optimal target.
Because the position of each sparrow individual represents the topological structure of one communication network, when the optimal sparrow position of the target is obtained through continuous reconstruction optimization, the topological structure of the target communication network to be solved can be obtained.
In a specific application, the above step 1032 is described by taking the t-th iteration as an example, fig. 3 is a flowchart of a method for obtaining an optimal target position of a sparrow in the embodiment of the present invention, and as shown in fig. 3, the method for obtaining an optimal target position of a sparrow specifically includes:
in the sparrow algorithm, determining sparrow individuals with larger fitness function values in a sparrow population as discoverers based on a preset proportion, determining the remaining sparrow individuals as followers, and randomly selecting a certain proportion of sparrow individuals as cautionings; in the algorithm iteration, the position updating modes of different individuals are different, and the updating of the positions of the sparrow individuals is equivalent to once reconstruction of a network topological structure.
The sparrow finder as the non-alert person determines the first predicted position and the first fitness value of the t +1 th generation by using a finder position updating rule, wherein the finder position updating rule is as shown in a formula 4,
wherein: t denotes the current number of iterations, j = (1, 2., d),
indicating the position of the ith sparrow in the jth dimension. T represents the maximum number of iterations, α ∈ (0, 1) is a random number subject to uniform distribution, R 2 (R 2 ∈[0，1]) Represents the early warning value, ST (ST ∈ [0.5,1)]) Representing a security value. Q is obedient 0,1]Normally distributed random numbers. L is a 1 × d matrix, and each element in the matrix is 1.
When R is 2 ST indicates that there is no natural enemy nearby, and the finder implements an extensive search pattern. If R is 2 ≥STThis means that some sparrows have perceived natural enemies, and the entire population needs to go to other safe areas as soon as possible.
Determining a first predicted position and a first fitness value of a t +1 th generation by a sparrow individual serving as a follower of a non-alerter by adopting a follower position updating rule; the follower location update rule is as in equation 5.
The sparrow individual as the alertor adopts the alertor position updating rule to determine the first predicted position and the first fitness value of the t +1 th generation. When the population forages, part of sparrows can be selected to be responsible for warning, namely, the early warning behavior is detected. When the enemy approaches, whether the finder or the follower, the current food is abandoned and the enemy flies to another position. SD (generally 10-20%) sparrows are randomly selected from the population in each generation for 36826 row early warning. The alerter position update rule is as in equation 6.
Wherein: x is a radical of a fluorine atom best Represents the global optimal position, beta is a step adjustment coefficient, is a normally distributed random number with the mean value of 0 and the variance of 1, and k is ∈ [ -1,1]A uniform random number within the range. Here, f i Is the fitness value of the current sparrow. f. of g And f w Is sequentially the current global optimumThe worst fitness value. ε is the minimum constant to prevent the denominator from appearing 0. When f is i ＞f g When the sparrows are in the marginal zone of the population, the sparrows are very easy to attack by natural enemies; f. of i ＝f g Indicating that the sparrows in the population center perceived the danger of being struck by a natural enemy and needed to be drawn close to other sparrows. k represents the direction of the sparrow movement and is a step length adjustment coefficient.
And different individuals in the sparrow population perform position updating according to the roles and corresponding position updating rules. In order to enhance the local search capability of the algorithm, a gaussian variation search mode is further introduced in the embodiment of the present invention, that is, the following step 202 is performed to update the predicted positions of the sparrow individuals.
Updating fitness values (F) corresponding to all individuals in the population i t -＞F i t+1 ) That is, the first fitness value F of the t +1 th generation corresponding to each individual is determined based on the first predicted position of the t +1 th generation of each individual i t+1 And based on the first fitness value F of the t +1 th generation corresponding to each individual i t+1 Updating historical optimal locations for each individual
And group history optimal position
I.e. based on the first fitness value F of the t +1 th generation corresponding to each individual i t+1 Determining the t +1 generation first history optimal position of each individual
And the t +1 th generation first population historical optimal position g t+1 。
It should be noted that, the update of each individual fitness value may be to update the fitness value every time the position of one individual is updated, or to update the fitness value after all the positions are updated.
It should be further noted that the principle and specific operation of individual position updating, fitness updating, individual historical optimal position updating, and group historical optimal position updating of the sparrow algorithm are the prior art, and are not described herein again.
wherein the content of the first and second substances,
denotes the second predicted position of the ith individual in the t +1 th generation, w t Is the first adaptive weight coefficient and is,
and (3) obtaining the first historical optimal position of the ith individual in the T +1 th generation for updating, wherein Rn represents a random number obeying Gaussian distribution, and T is a preset maximum iteration number (namely an upper limit of the iteration number). According to the above formula 7, specifically, the first adaptive weight coefficient w of the tth generation is determined according to the current iteration time T and the preset maximum iteration time T t (ii) a First adaptive weight coefficient w according to t generation t And the t +1 th generation first historical optimal position of each individual
Obtaining a second predicted position of each individual at the t +1 generation
Simultaneously updating to obtain a second fitness value (F) corresponding to each individual in the t +1 th generation i t+1 -＞F i t +1 '), i.e., the number of times the individual is calculated at the t +1 st generationA second fitness value; and updating the historical optimal location of each individual
And historical optimal location of population
Namely, the second historical optimal position of the t +1 generation of each individual is determined based on the second fitness value of each individual in the t +1 generation
And the historical optimal position of the second group in the t +1 th generation
Since the local optimal premature convergence may occur in the iterative search, in order to optimize the topology of the entire network, that is, to achieve the global optimal, the embodiment of the present invention further enhances the global optimization capability by combining the search mode of Singer chaotic mapping in step 203.
In the step, whether the convergence is too early is judged by judging whether the convergence is less than a preset threshold value according to whether the variation of the fitness value of the historical optimal position of each generation of the group is less than the preset threshold value for continuous M generation, if the continuous M generation is less than the preset threshold value, the premature convergence is determined, and the obtained value is probably a local optimal value rather than a global optimal value due to the premature convergence, so that the search mode based on Singer chaotic mapping is adopted in the step to update the second predicted position of each individual in the t +1 generation so as to further obtain a larger-range search, thereby enhancing the global optimization capability of the algorithm.
After obtaining the second fitness value corresponding to each individual in the t +1 th generation in the step 202, the method in the embodiment of the present invention further includes: and determining the second historical optimal position of the t +1 th generation and the second group historical optimal position of the t +1 th generation of each individual based on the second fitness value of each individual in the t +1 th generation. Therefore, the second predicted position of each individual in the t +1 th generation is updated by adopting a search mode based on Singer chaotic mapping, and a third predicted position of each individual in the t +1 th generation is obtained, which specifically comprises the following steps:
determining a second adaptive weight coefficient beta of the T generation according to the current iteration time T, the preset maximum iteration time T, the preset maximum adaptive expansion coefficient and the preset minimum adaptive expansion coefficient t (ii) a And generating a reference position corresponding to each individual based on the second predicted position of the t +1 th generation of each individual and the historical optimal position of the t +1 th generation second population
The specific calculation uses the following equations 8 and 9.
Wherein T is a preset maximum iteration number, beta max And beta min The preset maximum adaptive expansion coefficient and the preset minimum adaptive expansion coefficient may be set according to actual requirements, for example, set to 0.3 and 0.01, respectively. Delta is a 1 xd matrix of random numbers obeying a uniform distribution between 0-1,
and the historical optimal position of the t +1 th generation second population obtained in the previous step.
Determining the search variation range of each individual based on the reference position corresponding to each individual and the second adaptive weight coefficient of the t generation;
wherein ub is d And lb d Respectively representing the upper limit and the lower limit of the decision variable in the D-th dimension of the search space, D =1,2, \8230, D, namely UB = [ UB = 1 ，ub 2 ，…，ub D ]，LB＝[lb 1 ，lb 2 ，…，lb D ]；β t Namely the second self-adaptive weight coefficient;
is that
Values in the d-th dimension of the search space, i.e.
Scaling the reference position corresponding to each individual to 0-1 based on the search variation range of each individual, and obtaining the scaled position
That is, it is calculated using the following equation 11.
Post-zoom position based on individual
Generating reference values of all individuals by using Singer chaotic mapping
The specific calculation is as in equation 12.
Where μ is a preset parameter, and may be set according to actual requirements, for example, may be set to a value in the range of [0,4 ].
Determining a third predicted position of each individual in the t +1 th generation based on the search variation range of each individual and the reference value of each individual
I.e. converting the reference value of the individual into the actual value, and calculating as formula 13.
Step 203 is an improved method adopted when the algorithm is converged prematurely, if the algorithm is not converged prematurely, the t +1 th generation second historical optimal position of each individual is used
As the historical optimal position of the t +1 th generation, the historical optimal position of the t +1 th generation second population
And (4) entering the t +1 th iteration as the historical optimal position of the t +1 th generation group.
And 204, if the current iteration times t reach the preset maximum iteration times, determining the historical optimal positions of all individuals of the population in the t generation as the final solving result of the algorithm.
Specifically, if the current iteration time t reaches a preset maximum iteration time, a tth generation third fitness value corresponding to a third predicted position of each individual in a tth generation is obtained; updating the historical optimal position and the population historical optimal position of each individual based on the third predicted position of each individual in the tth generation and the third fitness value of each individual in the tth generation; and taking the historical optimal position of the population as the optimal position of the target to be solved.
If the current iteration time T does not reach the preset maximum iteration time T, after the third predicted position of each individual in the T +1 th generation and the third adaptability value corresponding to each individual in the T +1 th generation are obtained in the T-th iteration, the historical optimal position and the group historical optimal position of each individual are updated based on the third adaptability value corresponding to each individual in the T +1 th generation, the T + 1-th generation third history optimal position and the T + 1-th generation third group historical optimal position of each individual are obtained and recorded, the T + 1-th generation third history optimal position of each individual is used as the T + 1-th generation historical optimal position of the individual, the T + 1-th generation third group historical optimal position is used as the T + 1-th generation group historical optimal position, the T + 1-th iteration is carried out based on the T + 1-th generation historical optimal position and the T + 1-th generation group historical optimal position of each individual, the iteration cycle process is executed, and the analogy is repeated until the current iteration time reaches the preset maximum iteration time T.
In the embodiment of the invention, the topological structure of the complex network is corresponding to the adjacent matrix of the graph formed by the nodes and the links, so that the optimization problem of the topological structure is converted into the optimal solution problem of the natural connectivity of the adjacent matrix; and then, reconstructing and adjusting the topological structure by a sparrow algorithm to solve the problem of the optimal solution of the adjacency matrix, thereby providing an implementation method for improving the network reliability, and being beneficial to being applied to various network anti-attack network structures.
Fig. 4 is a schematic structural diagram of a communication network topology optimization system according to an embodiment of the present invention, and as shown in fig. 4, the communication network topology optimization system according to the embodiment includes: an obtaining module 10, configured to obtain a parameter set corresponding to an initial communication network structure, where the parameter set includes a number of network nodes, a number of links, and a natural connectivity of the initial communication network structure; the model construction module 20 is configured to construct an optimization model corresponding to the communication network topology structure, and optimize the communication network topology structure by using the natural connectivity of the network topology structure as an index for evaluating network reliability; and the optimization processing module 30 is configured to reconstruct the topology structure of the communication network by using an improved sparrow algorithm, obtain the topology structure of the communication network with the optimal natural connectivity, and keep the number of links and the number of nodes of the communication network unchanged before and after reconstruction.
In this embodiment, the obtaining module 10 is configured to determine initial parameters of a network, the model building module is configured to build an optimization model for evaluating reliability of the network, and the optimization processing module 30 is configured to optimize the built optimization model by using a sparrow algorithm to obtain a required result.
The embodiment can implement the technical solution of the method embodiment and achieve the technical effect of the method embodiment, and is not described herein again.
Finally, it should be noted that: the above examples are only intended to illustrate the technical solution of the present invention, and not to limit it; although the present invention has been described in detail with reference to the foregoing embodiments, it will be understood by those of ordinary skill in the art that: the technical solutions described in the foregoing embodiments may still be modified, or some technical features may be equivalently replaced; and the modifications or the substitutions do not make the essence of the corresponding technical solutions depart from the scope of the technical solutions of the embodiments of the present invention.
Claims (9)
1. A method for communication network topology optimization, comprising:
acquiring a parameter set corresponding to an initial communication network structure, wherein the parameter set comprises the number of network nodes, the number of links and natural connectivity of the initial communication network structure;
constructing an optimization model corresponding to the communication network topological structure, and optimizing the communication network topological structure by taking the natural connectivity of the network topological structure as an index for evaluating the network reliability;
and reconstructing the topological structure of the communication network by adopting an improved sparrow algorithm to obtain the topological structure of the communication network with the optimal natural connectivity, wherein the number of links and the number of nodes of the communication network are unchanged before and after reconstruction.
2. The method according to claim 1, wherein reconstructing the topology of the communication network by using an improved sparrow algorithm to obtain the topology of the communication network with the optimal natural connectivity specifically comprises:
determining initial positions of sparrow individuals in a sparrow algorithm based on the topological structure of the initial communication network, degree distribution of an adjacent matrix corresponding to the topological structure and the sparrow algorithm, and determining an initial fitness value corresponding to each sparrow individual, wherein the position of each sparrow individual represents the topological structure of one communication network;
performing iterative updating according to the initial positions of the sparrows, the initial fitness value corresponding to each individual and a preset iterative rule to obtain the optimal sparrow position of the target;
and determining the topological structure of the target communication network according to the optimal sparrow position of the target.
3. The method according to claim 2, wherein iterative updating is performed according to the initial positions of the sparrow individuals, the initial fitness value corresponding to each individual and a preset iteration rule to obtain the optimal sparrow position of the target, and specifically includes:
aiming at the T iteration, determining a first prediction position of each individual in the T +1 th generation and determining a first fitness value of each individual in the T +1 th generation based on the optimal position of the sparrow population in the T generation, the current position of the T generation of each individual, the historical optimal position of each individual and a preset position updating rule corresponding to each individual, wherein T =1, 2.. T; t is a preset maximum iteration number; during 1 st iteration, the 1 st generation historical optimal position of each sparrow is the initial position of each sparrow;
updating the first predicted position of each individual in the t +1 th generation by adopting a preset Gaussian variation search mode to obtain a second predicted position of each individual in the t +1 th generation, and updating the first fitness value corresponding to each individual in the t +1 th generation to obtain a second fitness value corresponding to each individual in the t +1 th generation;
judging whether premature convergence occurs or not based on a second fitness value corresponding to each individual in the t +1 th generation;
if the convergence is too early, updating the second predicted position of each individual in the t +1 th generation by adopting a search mode based on Singer chaotic mapping to obtain a third predicted position of each individual in the t +1 th generation;
and if the current iteration time t reaches the preset maximum iteration time, the optimal historical positions of all individuals of the population in the t generation are the final solving result of the algorithm.
4. The method according to claim 3, wherein for the t iteration, based on the optimal position of the sparrow population in the t generation, the current position of the t generation of each individual, the historical optimal position of each individual and a preset position updating rule corresponding to each individual, a first predicted position of each individual in the t +1 generation is determined, and a first fitness value corresponding to each individual in the t +1 generation is determined, and the method specifically comprises:
for the t-th iteration:
in the sparrow population, determining sparrow individuals with larger fitness function values as discoverers based on a preset proportion, determining the rest sparrow individuals as followers, and randomly selecting a certain proportion of sparrow individuals as cautionary persons;
determining a first predicted position and a first fitness value of a t +1 th generation by a discoverer sparrow individual serving as a non-alerter by adopting a discoverer position updating rule;
determining a first predicted position and a first fitness value of a t +1 th generation by a sparrow individual serving as a follower of a non-alerter by adopting a follower position updating rule;
the sparrow individual as the alertor adopts the alertor position updating rule to determine the first predicted position and the first fitness value of the t +1 th generation.
5. The method of claim 3, wherein after determining the first predicted position of each individual at the t +1 th generation and determining the first fitness value of each individual at the t +1 th generation based on the t-th generation historical optimal position of each individual of sparrows and the preset position updating rule corresponding to each individual for the t-th iteration, the method further comprises:
determining a first historical optimal position of the t +1 th generation and a first historical optimal position of the t +1 th generation of the first population of each individual based on the first fitness value of the t +1 th generation of each individual;
correspondingly, the updating the first predicted position of each individual in the t +1 th generation by adopting a preset gaussian variation search mode to obtain the second predicted position of each individual in the t +1 th generation comprises:
determining a first adaptive weight coefficient of a tth generation according to the current iteration time T and a preset maximum iteration time T;
and obtaining a second predicted position of each individual in the t +1 th generation according to the first self-adaptive weight coefficient of the t th generation and the first historical optimal position of the t +1 th generation of each individual.
6. The method of claim 3, wherein after updating the first predicted position of each individual in the t +1 th generation by using a preset gaussian variation search method to obtain the second predicted position of each individual in the t +1 th generation, and updating the first fitness value corresponding to each individual in the t +1 th generation to obtain the second fitness value corresponding to each individual in the t +1 th generation, the method further comprises:
determining a t +1 generation second historical optimal position and a t +1 generation second group historical optimal position of each individual based on the second fitness value of each individual in the t +1 generation;
correspondingly, the updating the second predicted position of each individual in the t +1 th generation by using a search mode based on Singer chaotic mapping to obtain a third predicted position of each individual in the t +1 th generation includes:
determining a second adaptive weight coefficient of the T generation according to the current iteration time T, a preset maximum iteration time T, a preset maximum adaptive expansion coefficient and a preset minimum adaptive expansion coefficient; generating a reference position corresponding to each individual based on the second predicted position of the t +1 th generation of each individual and the historical optimal position of the t +1 th generation of the second population;
determining the search variation range of each individual based on the reference position corresponding to each individual and the second adaptive weight coefficient of the t generation;
scaling the reference position corresponding to each individual to 0-1 based on the search variation range of each individual to obtain a scaled position;
generating a reference value of each individual by adopting Singer chaotic mapping based on the scaled position of each individual;
and determining a third predicted position of each individual in the t +1 th generation based on the search variation range of each individual and the reference value of each individual.
7. The method according to claim 3, wherein if the current iteration number t reaches a preset maximum iteration number, the population history optimal positions of all individuals in the population in the t-th generation are the final solution result of the algorithm, and specifically comprises:
if the current iteration time t reaches a preset maximum iteration time, acquiring a tth generation third fitness value corresponding to a third predicted position of each individual in the tth generation;
updating the historical optimal position and the population historical optimal position of each individual based on the third predicted position of each individual in the tth generation and the third fitness value of each individual in the tth generation;
and taking the historical optimal position of the population as the optimal position of the target to be solved.
8. The method according to any one of claims 1 to 7, wherein an optimization model corresponding to the communication network topology structure is constructed, and the communication network topology structure is optimized by taking the natural connectivity of the network topology structure as an index for evaluating the reliability of the network, and specifically comprises the following steps:
taking the maximum value of the natural connectivity of the adjacent matrix corresponding to the communication network topological structure as an optimization target;
the reconstructed network is still used as a connected graph and the number of links before and after reconstruction does not become a constraint condition;
and constructing an optimization model corresponding to the communication network topology structure according to the optimization target and the constraint condition.
9. A communication network topology optimization system, comprising:
an obtaining module, configured to obtain a parameter set corresponding to an initial communication network structure, where the parameter set includes a number of network nodes, a number of links, and a natural connectivity of the initial communication network structure;
the model construction module is used for constructing an optimization model corresponding to the communication network topological structure, and optimizing the communication network topological structure by taking the natural connectivity of the network topological structure as an index for evaluating the network reliability;
and the optimization processing module is used for reconstructing the topological structure of the communication network by adopting an improved sparrow algorithm to obtain the topological structure of the communication network with the optimal natural connectivity, and the number of links and the number of nodes of the communication network are unchanged before and after reconstruction.
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Cited By (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN117811848A (en) * | 2024-03-01 | 2024-04-02 | 北京长亭科技有限公司 | Dynamic updating method and device for network topology |
Citations (10)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20130185234A1 (en) * | 2012-01-16 | 2013-07-18 | Amrinder Arora | System and Method for Using Genetic Algorithm for Optimization of Targeting Systems, Based on Aggregated Scoring Models |
| WO2016038139A1 (en) * | 2014-09-12 | 2016-03-17 | Voellmy Andreas R | Managing network forwarding configurations using algorithmic policies |
| CN108667650A (en) * | 2018-04-10 | 2018-10-16 | 北京航空航天大学 | Consider the topology optimization design method of operation flow feature |
| CN112329934A (en) * | 2020-11-17 | 2021-02-05 | 江苏科技大学 | RBF neural network optimization algorithm based on improved sparrow search algorithm |
| CN112714397A (en) * | 2020-12-25 | 2021-04-27 | 杭州电子科技大学 | WSN node positioning method based on ranging optimization and improved sparrow search algorithm |
| CN113269252A (en) * | 2021-05-26 | 2021-08-17 | 安徽理工大学 | Power electronic circuit fault diagnosis method for optimizing ELM based on improved sparrow search algorithm |
| CN113505998A (en) * | 2021-07-15 | 2021-10-15 | 河海大学 | Solar radiation estimation model based on sparrow search algorithm-deep belief network |
| CN113947009A (en) * | 2021-08-31 | 2022-01-18 | 西北大学 | Sparse LSTM landslide dynamic prediction method based on Cauchy disturbance sparrow optimization |
| CN114545766A (en) * | 2022-02-28 | 2022-05-27 | 上海电机学院 | BP neural network PID control method optimized by sparrow search algorithm |
| CN114565259A (en) * | 2022-02-24 | 2022-05-31 | 武汉大学 | Power distribution network line loss calculation method for optimizing BP neural network by improving sparrow search |
-
2022
- 2022-06-13 CN CN202210661477.9A patent/CN115150284B/en active Active
Patent Citations (10)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20130185234A1 (en) * | 2012-01-16 | 2013-07-18 | Amrinder Arora | System and Method for Using Genetic Algorithm for Optimization of Targeting Systems, Based on Aggregated Scoring Models |
| WO2016038139A1 (en) * | 2014-09-12 | 2016-03-17 | Voellmy Andreas R | Managing network forwarding configurations using algorithmic policies |
| CN108667650A (en) * | 2018-04-10 | 2018-10-16 | 北京航空航天大学 | Consider the topology optimization design method of operation flow feature |
| CN112329934A (en) * | 2020-11-17 | 2021-02-05 | 江苏科技大学 | RBF neural network optimization algorithm based on improved sparrow search algorithm |
| CN112714397A (en) * | 2020-12-25 | 2021-04-27 | 杭州电子科技大学 | WSN node positioning method based on ranging optimization and improved sparrow search algorithm |
| CN113269252A (en) * | 2021-05-26 | 2021-08-17 | 安徽理工大学 | Power electronic circuit fault diagnosis method for optimizing ELM based on improved sparrow search algorithm |
| CN113505998A (en) * | 2021-07-15 | 2021-10-15 | 河海大学 | Solar radiation estimation model based on sparrow search algorithm-deep belief network |
| CN113947009A (en) * | 2021-08-31 | 2022-01-18 | 西北大学 | Sparse LSTM landslide dynamic prediction method based on Cauchy disturbance sparrow optimization |
| CN114565259A (en) * | 2022-02-24 | 2022-05-31 | 武汉大学 | Power distribution network line loss calculation method for optimizing BP neural network by improving sparrow search |
| CN114545766A (en) * | 2022-02-28 | 2022-05-27 | 上海电机学院 | BP neural network PID control method optimized by sparrow search algorithm |
Non-Patent Citations (3)
| Title |
|---|
| WEIFENG WANG;FANG LIU;WEI WANG;MOWEN CHENG: "The chaotic time series prediction method based on sparrow search algorithm optimization" * |
| 吴建生;刘丽萍;金龙;: "粒子群-神经网络集成学习算法气象预报建模研究" * |
| 王家宝;缪祥华: "利用麻雀搜索算法优化深度置信网络的入侵检测研究" * |
Cited By (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN117811848A (en) * | 2024-03-01 | 2024-04-02 | 北京长亭科技有限公司 | Dynamic updating method and device for network topology |
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