CN117320027B - Controller deployment method of satellite network - Google Patents
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- H04B7/00—Radio transmission systems, i.e. using radiation field
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- H04B7/185—Space-based or airborne stations; Stations for satellite systems
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- H04W84/02—Hierarchically pre-organised networks, e.g. paging networks, cellular networks, WLAN [Wireless Local Area Network] or WLL [Wireless Local Loop]
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Abstract
The application discloses a deployment method of a controller of a satellite network, and belongs to the technical field of satellite networks. The method comprises the steps of constructing a time delay factor, a load balancing factor and a reliability factor of a control link based on a satellite network system; constructing a multi-objective optimization model based on the time delay factor, the load balancing factor and the reliability factor; based on the optimization constraint conditions, solving the multi-objective optimization model to obtain a controller deployment result of the satellite network system. The method and the device can comprehensively control various factors such as link delay, controller load balancing and control link reliability, and determine a reasonable deployment scheme of the satellite network controller.
Description
Technical Field
The application relates to the technical field of satellite networks, in particular to a controller deployment method of a satellite network.
Background
In the related art, a software-defined satellite network multi-controller deployment scheme can generally adopt multi-layer controller deployment, and the multi-layer controller deployment scheme deploys controllers on the ground and a plurality of satellite orbit layers, so that the problem that satellite orbit positions are restricted can be relieved, but the problems that control links are longer in time delay, unbalanced in load, unreliable in control links and the like still exist, so that a more reasonable satellite network controller deployment method comprehensively considering the multiple aspects is required to be sought.
Disclosure of Invention
The main purpose of the application is to provide a method for deploying controllers of a satellite network, and to provide a reasonable method for deploying controllers of a satellite network.
To achieve the above object, the present application provides a method for deploying a controller of a satellite network, which can be used for a satellite network system, the satellite network system comprising:
the first-level controller is deployed on the ground network;
a secondary controller deployed at the low earth orbit LEO satellite;
the deployment method of the controller of the satellite network comprises the following steps:
based on a satellite network system, constructing a time delay factor, a load balancing factor and a reliability factor of a control link;
constructing a multi-objective optimization model based on the time delay factor, the load balancing factor and the reliability factor;
based on the optimization constraint conditions, solving the multi-objective optimization model to obtain a controller deployment result of the satellite network system.
Optionally, solving the multi-objective optimization model to obtain a controller deployment result of the satellite network system includes:
generating an initialization honey source based on a formula to obtain an initial population;
taking the initial population as the current population;
non-dominated sorting is carried out on the current population based on a Pareto dominated mechanism, and a non-inferior front end set is obtained;
the individuals in the non-inferior front-end set are arranged in ascending order to obtain an arranged non-inferior front-end set;
selecting a honey source by using a Boltzmann strategy based on the honey source probability value;
if the preset maximum iteration times are not reached, randomly generating a new honey source, updating the current population, and returning to execute non-dominant sorting on the current population based on a Pareto dominant mechanism to obtain a non-inferior front end set until the preset maximum iteration times are reached to obtain an optimal solution set;
the first formula is:；
wherein,to initialize honey source, the drug is added with->，/>SN is the initial solution number, m=k+p is the solution space dimension, +.>Upper bound for j-th dimension variable of solution space,/->A lower bound for the j-th dimensional variable of the solution space;
and is also provided with，/>Is interval [0,1 ]]Random number of any one of the above.
Optionally, the individuals in the non-inferior front-end set are arranged in ascending order, and after the arranged non-inferior front-end set is obtained, the method further includes:
determining a dynamic crowding distance of the honey source based on a formula II;
determining a dominant intensity of the honey source based on equation three;
determining an fitness function value of the honey source based on the sum of the dynamic crowding distance and the dominant intensity;
the formula II is:；
wherein,for dynamic crowding distance->Is a random number within the range of (0.5, 1,)>The j-th objective function value for the i-th individual in the current population, < >>Representing different objective function maxima, +.>Representing the minimum of the different objective functions;
the formula III is:；
wherein,to govern strength->A hierarchical level of non-bad front-end sets, +.>。
Optionally, before selecting the honey source using the Boltzmann strategy based on the honey source probability value, the method further comprises:
determining a honey source probability value based on a formula IV;
the fourth formula is:；
wherein,is a probability value of honey source->The fitness function value of the honey source is c is the number of circulation times，/>For temperature, < >>Is the initial temperature.
Optionally, after non-dominated sorting is performed on the current population based on the Pareto dominated mechanism and the non-inferior front-end set is obtained, the method further includes:
determining candidate honey sources based on a formula five;
determining the honey source quality of the candidate honey sources based on the fitness function value;
if the quality of the candidate honey source is better than that of the initialized honey source, replacing the initialized honey source with the candidate honey source;
the fifth formula is:
；
wherein,，optimal honey source corresponding to optimal individuals in the current population, < ->Is [0,1]Random number within range,/->Is [0, C]Random number in between, C is a non-negative constant, ">Is [0,1]Random number between->For the current iteration number>The maximum iteration number is preset.
Optionally, after selecting the honey source using the Boltzmann strategy based on the honey source probability value, the method further comprises:
if the fitness function value of the honey source is not optimized after the preset cycle times, generating a new honey source based on a formula I;
and updating the current population according to the new honey source, and returning to execute the step of carrying out non-dominated sorting on the current population based on the Pareto dominated mechanism to obtain a non-inferior front end set.
Optionally, constructing the delay factor of the control link based on the satellite network system includes:
determining a first time delay and a second time delay of a satellite network system; the first time delay is the sum of the propagation time delay between the first-level controller and the second-level controller and the processing time delay of the first-level controller; the second time delay is the sum of the propagation time delay between the secondary controller and the satellite switching node and the processing time delay of the secondary controller;
determining the intensity of the received flow of the primary controller and the intensity of the received flow of the secondary controller;
and constructing a time delay factor based on the first time delay, the second time delay, the received flow intensity of the primary controller and the received flow intensity of the secondary controller.
Optionally, constructing the load balancing factor based on the satellite network system includes:
determining a load variance of the primary controller based on the received traffic intensity of the primary controller;
determining a load variance of the secondary controller based on the received traffic intensity of the secondary controller;
and constructing a load balancing factor based on the load variance of the primary controller and the load variance of the secondary controller.
Optionally, constructing the reliability factor of the control link based on the satellite network system includes:
determining the outage probability of a link between any two nodes in a satellite network system; the nodes comprise switching nodes and controller nodes in the satellite network system;
determining the congestion probability of links between nodes;
based on the outage probability and the congestion probability, a reliability factor is constructed.
Optionally, the optimization constraint includes:
the number of the first-stage controllers is more than or equal to 1;
the number of the secondary controllers is more than or equal to 2;
the first time delay and the second time delay are smaller than a preset maximum time delay;
the deployment latitude of the primary controller is within a preset geographic range;
the flow intensity of the primary controller is less than or equal to 0.9 times of the maximum flow intensity which can be processed by the primary controller;
the flow intensity of the secondary controller is less than or equal to 0.9 times of the maximum flow intensity which can be processed by the secondary controller.
In the deployment method of the controller of the satellite network, the time delay factor, the load balancing factor and the reliability factor of the control link can be constructed based on the satellite network system; therefore, a multi-objective optimization model can be built based on the built time delay factor, the load balancing factor and the reliability factor; therefore, the multi-objective optimization model can be solved based on the optimization constraint conditions, and the deployment result of the controller of the satellite network system is obtained. The method can comprehensively control the factors of the delay of the link, the load balance of the controller and the reliability of the control link, and determine a reasonable deployment scheme of the satellite network controller.
Drawings
Fig. 1 is a schematic diagram of a satellite network system according to an embodiment of the present application;
fig. 2 is a flowchart of a first embodiment of a method for deploying a controller of a satellite network according to an embodiment of the present application;
fig. 3 is a schematic flow chart of a construction process of a delay factor of a control link in a first embodiment of a deployment method of a controller of a satellite network according to an embodiment of the present application;
fig. 4 is a schematic diagram of a construction flow of a load balancing factor of a controller in a first embodiment of a method for deploying the controller of a satellite network according to an embodiment of the present application;
fig. 5 is a schematic diagram of a construction flow of a reliability factor of a control link in a first embodiment of a method for deploying a controller of a satellite network according to an embodiment of the present application;
fig. 6 is a flowchart of a second embodiment of a method for deploying a controller of a satellite network according to an embodiment of the present application.
The realization, functional characteristics and advantages of the present application will be further described with reference to the embodiments, referring to the attached drawings.
Detailed Description
It should be understood that the specific embodiments described herein are for purposes of illustration only and are not intended to limit the present application.
Compared with the traditional ground network satellite network, the satellite network has the characteristics of high coverage, no limitation of terrain and the like, so that the satellite network can provide seamless connection for the ground, in addition, the satellite-ground link can provide space-based backhaul, the network capacity is improved, and the user service experience is ensured. The traditional satellite system adopts a distributed architecture of a traditional ground network, and each satellite node needs to consume a large amount of resources to complete the functions of link state collection, information synchronization, route calculation and the like. And because the network lacks a global view, an optimal routing decision cannot be obtained, so that the performance of the network is reduced.
SDN (Software Defined Network ) has the characteristic of separating control from forwarding, supports centralized control of the network, can allocate network resources from a global view, and makes an effective resource allocation policy, and the combination of SDN ideas and satellite networks can well solve the above challenges faced by the satellite networks. Controllers in an SDN architecture are decision-making mechanisms in the whole architecture, and an important premise for improving satellite network performance is that an efficient controller deployment strategy exists.
At present, a software-defined satellite network multi-controller deployment scheme mainly comprises single-layer controller deployment and multi-layer controller deployment, wherein the single-layer controller deployment is used for fully deploying controllers on the ground or on a single satellite track surface, the single-layer controller deployment scheme is limited by satellite track positions, the coverage range is limited, and if the controllers are fully deployed on the satellite, the problem of insufficient processing capacity exists. The multi-layer controller deployment scheme deploys controllers on the ground and multiple satellite orbit levels, and the multi-layer controller deployment manner can alleviate a series of problems existing in single-layer controller deployment.
In the related art, the multi-layer controller deployment scheme mainly deploys the controllers on any two or more of the ground station, the GEO (Geostationary Transfer Orbit, geosynchronous Orbit), the MEO (Middle Earth Orbit ) and the LEO (Low Earth Orbit) planes, but the method for deploying the satellite network controller comprehensively considering the above aspects is required because the method has higher cost for launching the MEO or the GEO, and the time delay from the MEO or the GEO satellite to the ground node is overlarge, and the problems of unbalanced load, unreliable control link and the like may exist.
In order to solve the problem, the deployment method of the controller of the satellite network is provided, and the time delay factor, the load balancing factor and the reliability factor of the control link can be constructed based on the satellite network system; therefore, a multi-objective optimization model can be built based on the built time delay factor, the load balancing factor and the reliability factor; therefore, the multi-objective optimization model can be solved based on the optimization constraint conditions, and the deployment result of the controller of the satellite network system is obtained. The method can comprehensively control the factors of the delay of the link, the load balance of the controller and the reliability of the control link, and determine a reasonable deployment scheme of the satellite network controller.
The following description and description will be made with reference to various embodiments.
Referring to fig. 1, fig. 1 is a schematic diagram of a satellite network system according to an embodiment of the present application.
As shown in fig. 1, the satellite network system includes a primary controller and a secondary controller.
The first-level controller is deployed on a ground network; the secondary controller is deployed at the LEO satellite.
It should be noted that, compared with the deployment of the controller on the MEO and GEO, the deployment of the secondary controller on the LEO satellite can effectively reduce the transmission cost, and the delay from the LEO satellite to the ground node is smaller than the delay from the MEO and GEO to the ground, so that a better communication transmission effect can be obtained.
It will be appreciated by those skilled in the art that the satellite network system schematic diagram shown in fig. 1 is not limiting of the satellite network system and may include more or fewer components than shown, or a different layout.
Based on the above satellite network system, the present application provides a first embodiment of a method for deploying a controller of a satellite network. Referring to fig. 2, fig. 2 shows a schematic flow chart of a first embodiment of a controller deployment method of the satellite network of the present application.
It should be noted that although a logical order is depicted in the flowchart, in some cases the steps depicted or described may be performed in a different order than presented herein.
In this embodiment, the method for deploying a controller for implementing a satellite network includes the following steps:
step S100, constructing a time delay factor, a load balancing factor and a reliability factor of a control link based on a satellite network system.
Specifically, the satellite network system is an important communication network architecture, and can provide communication services in the global scope, and the time delay factor of a control link in the satellite network system is the time delay required by data transmission in the control link, so that the real-time performance and response speed of communication are affected; the load balancing factor is used for controlling the data load distribution condition among all nodes on the link, and reasonable load balancing can ensure that network resources are fully utilized; the reliability factor is the reliability degree of the link transmission data, so that the time delay factor, the load balancing factor and the reliability factor need to be considered when the satellite network system controller is deployed to improve the performance and the reliability of the satellite network system.
In one embodiment, step S100 specifically includes step S101, step S102, and step S103, which are used to construct a delay factor of the control link. Referring to fig. 3, fig. 3 shows a schematic flow diagram of the construction of the delay factor of the control link.
Step S101, determining a first time delay and a second time delay of the satellite network system.
The first time delay is the sum of the propagation time delay between the first-level controller and the second-level controller and the processing time delay of the first-level controller; the second delay is the sum of the propagation delay between the secondary controller and the satellite switching node and the processing delay of the secondary controller.
Step S102, the intensity of the received flow of the primary controller and the intensity of the received flow of the secondary controller are determined.
Step S103, constructing a time delay factor based on the first time delay, the second time delay, the received flow intensity of the primary controller and the received flow intensity of the secondary controller.
Specifically, the system diagram of the satellite network system can be used for abstracting the satellite network model into a space undirected graph, which can be recorded as，/>The method comprises the steps of carrying out a first treatment on the surface of the Where S represents a set of switching nodes in the satellite network system,representing a total of N switching nodes in the system; />Is a set of controller nodes consisting of K primary controllers (denoted +.>) And P secondary controllers (denoted as +.>) Is composed of; />To indicate whether there is a set of paths between the various nodes,indicating whether a path exists between the secondary controller and the primary controller,it can be shown if a path exists between the switching node and the secondary controller for each element in the set E>，/>A1 indicates the presence of a pathway, ">A 0 indicates that no path exists.
In a satellite network system, the time delay of a satellite network control link mainly comprises: 1. the sum of the propagation delay between the secondary controller and the primary controller and the processing delay of the primary controller is the first delay; 2. the propagation delay between the satellite switching node and the secondary controller and the processing delay of the secondary controller are the second delay. Based on the above definition, for the mth primary controller, it can be determined that the time delay between the primary controller and the secondary controller (i.e. the first time delay) is，/>Wherein->Representing the distance between two nodes, +.>Indicating the speed of light +.>Representing the flow request rate of node j, +.>Representing the processing capacity of the mth control node; similarly, for the mth secondary controller, it may also be determined that the time delay between the secondary controller and the switching node (i.e. the second time delay) is +.>，/>. In addition, the received traffic intensity for the mth stage controllerCan be expressed as +.>The method comprises the steps of carrying out a first treatment on the surface of the Similarly, the received traffic intensity for the mth secondary controller +.>Can be expressed as +.>。
Thus, the average delay D (i.e., delay factor) of the control link can be defined based on the above-mentioned first delay, second delay, received traffic intensity of the primary controller, and the expression of received traffic intensity of the secondary controller, where the expression of D is as follows:
。
in one embodiment, step S100 specifically includes step S104, step S105 and step S106, which are used to construct a load balancing factor of the controller. Referring to fig. 4, fig. 4 shows a schematic diagram of a construction flow of the load balancing factor.
Step S104, based on the received flow intensity of the primary controller, the load variance of the primary controller is determined.
Step S105, determining the load variance of the secondary controller based on the received traffic intensity of the secondary controller.
And S106, constructing a load balancing factor based on the load variance of the primary controller and the load variance of the secondary controller.
Specifically, in satellite network systems, the controller load mainly includes a primary controller load and a secondary controller load, and the load of each controller can be measured by the intensity of the traffic received on the controller. Therefore, the load variance of the primary controller can be determined to be based on the received flow intensity of the primary controller，The method comprises the steps of carrying out a first treatment on the surface of the Similarly, based on the received flow intensity of the secondary controller, the load variance of the secondary controller can be determined to be +.>，/>。
In order to balance the load of the controller as much as possible, ensure that network resources are fully utilized and avoid the situation that certain nodes have performance bottlenecks due to overload, a load balancing factor B can be defined based on the load variances of the primary controller and the secondary controller, and the expression of the load balancing factor B is as follows:
；
in the expression of the load balancing factor described above,and->Is a weighting coefficient and satisfies +.>And is also provided with。
In one embodiment, step S100 specifically includes step S107, step S108 and step S109, for constructing a reliability factor of the control link. Referring to fig. 5, fig. 5 shows a schematic diagram of a construction flow of the reliability factor of the control link.
Step S107, determining the breaking probability of the link between any two nodes in the satellite network system.
Wherein the nodes include switching nodes and controller nodes in the satellite network system.
Step S108, determining the congestion probability of the links between the nodes.
Step S109, constructing a reliability factor based on the outage probability and the congestion probability.
Specifically, in the satellite network system, the reliability of the control link represents the timely reaching degree of the control information from the controller to the controlled node, and when the reliability of the control link is very low, the problem that the request information and the control information cannot be timely sent may be caused, and the network performance may be seriously reduced. The reliability of the control link is affected by the physical interruption of the control link and the congestion of the control link, so the reliability factor of the control link can be constructed based on both the physical interruption of the control link and the congestion of the control link.
Wherein the probability of a physical outage of the control link is generally determined by the hop count of the control link (the number of switching nodes through which data passes from the control node to the controlled node), so the outage probability of a link between two nodes can be defined as，The method comprises the steps of carrying out a first treatment on the surface of the The congestion probability of the control link can be generally determined by the bandwidth of the link and the traffic rate of the link, so that the congestion probability of the link between two nodes can be defined as +.>，。
In the above-mentioned definition of the formula,for the probability of a physical disruption of the link between node i and node j, +.>For the probability of failure of the link directly connecting two nodes, < + >>Is the total number of nodes passed between node i and node j, < >>For the congestion probability of the link between node i and node j,/for the link between node i and node j>For the traffic passing between node i and node j, < >>Is the minimum bandwidth of the link between node i and node j.
Thus, a failure factor I (i.e. a reliability factor) of the control link can be defined based on the outage probability expression and the congestion probability expression described above, the expression of I being as follows:
；
in the above expression of the reliability factor,and->Is a weighting coefficient and satisfies +.>And is also provided with。
Step S200, a multi-objective optimization model is constructed based on the time delay factor, the load balancing factor and the reliability factor.
And step S300, solving the multi-objective optimization model based on the optimization constraint condition to obtain a controller deployment result of the satellite network system.
Specifically, after determining the delay factor, the load balancing factor and the reliability factor based on the above steps, a multi-objective optimization model of the satellite network system can be constructed based on the three, namely, the optimization objective of the multi-objective optimization model is determined as the delay of the control link, the load of the controller and the reliability of the control link, so as to achieve the purpose of joint optimization, and the constructed multi-objective optimization model function expression is as follows:
；
the multi-objective optimization model can be further solved based on the optimization constraint conditions, and a dynamic and efficient deployment result of the LEO satellite network controller is obtained. Wherein, the optimization constraint condition of the multi-objective optimization model may include:
the number of the first-stage controllers is more than or equal to 1;
the number of the secondary controllers is more than or equal to 2;
first time delayAnd a second delay->Are all smaller than the preset maximum time delay +.>；
Primary controllerMay be defined within a preset geographic range; for example, it may be limited in the chinese range.
The flow intensity of the primary controller is less than or equal to 0.9 times of the maximum flow intensity which can be processed by the primary controller, namely，/>The maximum flow intensity which can be processed by the primary controller;
the flow intensity of the secondary controller is less than or equal to 0.9 times of the maximum flow intensity which can be processed by the secondary controller, namely，/>The maximum flow intensity that can be handled by the secondary controller.
Furthermore, the set of pathsEach element of->。
And solving the multi-objective optimization model by combining the optimization constraint conditions, wherein the obtained optimization solution set is the deployment result of the controller of the satellite network system.
It is to be understood that, in this embodiment, a delay factor, a load balancing factor, and a reliability factor of a control link may be constructed based on a satellite network system; therefore, a multi-objective optimization model can be built based on the built time delay factor, the load balancing factor and the reliability factor; therefore, the multi-objective optimization model can be solved based on the optimization constraint conditions, and the deployment result of the controller of the satellite network system is obtained. The method can comprehensively control the factors of the delay of the link, the load balance of the controller and the reliability of the control link, and determine a reasonable deployment scheme of the satellite network controller.
In addition, an artificial bee colony algorithm can be adopted for solving the multi-objective optimization model. The artificial bee colony algorithm is a population intelligent optimization algorithm and is an optimization method provided by simulating honey collecting behaviors of bees. Because the multi-target artificial bee colony algorithm can contain the characteristics of a plurality of individuals in one population, the algorithm can be ensured to obtain a plurality of Pareto optimal solutions of the problem at the same time, but because the problem of satellite system controller deployment is complex, most of the existing multi-target artificial bee colony algorithms are realized based on ideal models, have larger deviation from reality, and have the problems of low solution accuracy, poor local exploration capability, easy occurrence of premature convergence and the like. Therefore, a solution method is needed that is practical in comparison with fitting and has a strong optimizing capability.
Further, based on the above embodiment, a second embodiment of the controller deployment method of the satellite network is provided for optimizing an artificial bee colony algorithm to solve a multi-objective optimization model. Referring to fig. 6, fig. 6 shows a flow chart of a second embodiment of a controller deployment method of the satellite network of the present application.
In this embodiment, step S300 includes:
s301, generating an initialization honey source based on a formula to obtain an initial population.
The first formula is:；
wherein,for the initialization of honey source, +.>，/>SN is the initial solution number, m=k+p is the solution space dimension, +.>For the upper bound of the j-th dimensional variable of the solution space,/>a lower bound for the j-th dimensional variable of the solution space;
and is also provided with，/>Is interval [0,1 ]]Random number of any one of the above.
Specifically, in the artificial bee colony algorithm, the individuals of the population refer to candidate solutions in the search space, the honey sources are specific positions of the solutions in the solution space, and a corresponding relationship exists between the individuals of the population and the honey sources. In the initialization stage of the artificial swarm algorithm, feasible solutions generated by population initialization of the basic swarm algorithm are generally randomly generated in a search space, so that the quality of an optimized solution may not be ensured, and in order to make the solution of the multi-objective optimized model higher in precision and improve the convergence speed, chaotic initialization can be added in the process of solving the optimized value in the honeyed source initialization stage.
The chaotic initialization is to use a chaotic sequence to replace a random number sequence of a traditional bee colony algorithm as an initial value, so that the global searching capability and the convergence rate of the algorithm can be improved. In the initialization stage, the maximum iteration number can be setAlgebraic +.f. of honey source continuously kept unchanged>The number of initialized cycles is recorded as +.>. Can use the general Singer chaotic mapping expression, namely +.>Instead of the random number sequence of the traditional swarm algorithm, based on equation one: />An initial honey source is generated. In particular in the interval [0,1 ]]Is selected randomly by a number +.>As an initial value of the Singer chaotic map, and obtaining a value corresponding to +.>Is->Then iterating continuously according to the Singer chaotic expression, and obtaining the initial honey source by using a formula I>。
S302, taking the initial population as the current population.
S303, non-dominated sorting is conducted on the current population based on the Pareto dominated mechanism, and a non-inferior front end set is obtained.
S304, the individuals in the non-inferior front-end set are arranged in an ascending order, and the arranged non-inferior front-end set is obtained.
Specifically, the multi-objective problem generally involves multiple maximization or minimization targets in the optimization process, the solution of the multi-objective problem is generally a set of solutions after weighing multiple optimization targets, no standardized order relation exists, and the suitability value of the unique problem solution cannot be used for evaluating the advantages and disadvantages of the problem solution, so that a Pareto dominant mechanism can be used for the current populationThe individuals in (Iter is the current iteration number) are subjected to non-dominant ranking, solutions which are not subjected to other individuals under a plurality of optimization targets are determined, and the non-dominant solutions can form a non-inferior front end set. To better select dominant individuals in the population, the solution of the multi-objective optimization model is made to more approximate to the ideal Pareto front, may be further based on the formula: />Determining non-bad front end setsIs +.>Value and according to the calculated +.>Value pair non-bad front-end set +.>The individuals in (a) are arranged in ascending order to obtain a non-inferior front end set after arrangement, wherein ∈10>Is a random number within the range of (0, 1,)>A hierarchical hierarchy of non-bad front-end sets in non-dominant ordering.
It should be noted that, after the individuals in the non-inferior front-end set are arranged in ascending order, the dynamic crowding distance of the honey source can be determined based on the formula II; determining a dominant intensity of the honey source based on equation three; determining an fitness function value of the honey source based on the sum of the dynamic crowding distance and the dominant intensity;
the formula II is:；
wherein,for dynamic crowding distance->Is a random number within the range of (0.5, 1,)>The j-th objective function value for the i-th individual in the current population, < >>Representing different objective function maxima, +.>Representing the minimum of the different objective functions;
the formula III is:；
wherein,for the dominant intensity, ++>A hierarchical level of non-bad front-end sets, +.>。
From this, the fitness function value can be determined based on the dynamic congestion distance and the dominant strength，。
The second formula is a calculation method of the dynamic congestion distance. The traditional crowding distance and dominant intensity calculation method can have the defect of distribution of solution sets, so that the solution distribution is poor, meanwhile, the traditional crowding distance calculation method is based on a straight line distance mode, the crowding degree of the Pareto curved surface fluctuation position cannot be well reflected, enough neighborhood information cannot be provided, the diversity of bee population can be improved by using an adaptive function obtained based on improved dynamic crowding distance and dominant intensity, enough neighborhood information is provided, the global adaptability of feasible solutions can be improved, and the solution sets are uniformly distributed on the Pareto front surface.
In addition, in the hiring stage of the artificial bee colony algorithm, in order to improve the convergence rate of the bee colony algorithm, enhance the local searching capability of the algorithm and ensure better global searching capability, the improved variation rule of the differential evolution algorithm can be integrated into the traditional artificial bee colony algorithm for generating candidate honey sources.
Candidate honey sources may be determined based on equation five; determining the honey source quality of the candidate honey sources based on the fitness function value; if the quality of the candidate honey source is better than that of the initialized honey source, replacing the initialized honey source with the candidate honey source;
the fifth formula is:
；
wherein,，optimal honey source corresponding to optimal individuals in the current population, < ->Is [0,1]Random number within range,/->Is [0, C]Random number in between, C is a non-negative constant, ">Is [0,1]Random number between->For the current iteration number>The preset maximum iteration times are set. If candidate solution->Is superior to->Then +.>Substitute->Otherwise, the original honey source is reserved>. New honey sources (i.e., candidate honey sources) may be generated based on the above-described method after the non-bad front-end sets are sorted in ascending order.
S305, selecting a honey source by using a Boltzmann strategy based on the honey source probability value.
And S306, if the preset maximum iteration number is not reached, randomly generating a new honey source, updating the current population, and returning to execute non-dominant ranking on the current population based on a Pareto dominant mechanism to obtain a non-inferior front end set until the preset maximum iteration number is reached to obtain an optimal solution set.
Specifically, in the stage of observing bees of the artificial bee colony algorithm, each observed bee can select a proper honey source position for development by using a Boltzmann strategy according to the probability value of the position of the food source (i.e. the honey source), so as to quickly find out the local optimal solution of the problem. Determining a honey source probability value based on a formula four;
the fourth formula is:；
wherein,for the probability value of the honey source, +.>The fitness function value of honey source, c is the number of circulation times,/->For temperature, < >>Is the initial temperature.
If the new honey sources (i.e., candidate honey sources) generated after the non-inferior front-end set is arranged in ascending order can dominate the old honey sources (i.e., the new honey sources are better than the old honey sources), the new honey sources can be greedy selected to replace the old honey sources according to the formula four, and the non-dominant ordering can be performed on all the current honey sources again to determine the current preferred solution. Further, the set of calculations can be calculated using the above formula twoCutting the crowded distance of each individual to obtain a current optimal solution part, performing greedy selection on honey sources again by using the formula four, and performing non-dominant sorting on all the honey sources again after greedy selection to further determine the optimal solution of the current model. If the iteration number of the current target swarm algorithm does not reach the preset maximum iteration number +.>Then a new honey source can be randomly generated, the current population corresponding to the honey source is updated, and the step S303 is executed until the iteration number of the target bee colony algorithm reaches the preset maximum iteration number of the initial stage, namely +.>The non-dominant front solution set, i.e., the optimal solution set of the multi-objective optimization model deployed by the satellite network controller, may be output at this time.
In addition, according to the probability value of the honey source, if the fitness function value of the honey source passes through the preset cycle number (i.e.)Secondary), a new honey source can be generated based on the formula I; and updating the current population according to the new honey source, and returning to execute step S303. I.e. the fitness of a honey sourceThe function value is passed throughIf the circulation is not improved, the honey source is abandoned by the employment bees, and the corresponding employment bees are converted into investigation bees to continue to find potential solution space. And a new honey source randomly generated based on the Singer chaotic mapping sequence (i.e. a random honey source regenerated based on the formula one) is added +.>And (3) performing de-duplication processing and then using the obtained solution again.
It is easy to understand that based on the optimized artificial bee colony algorithm, the searching capability of the algorithm can be enhanced by solving the multi-objective optimization model, so that the optimal front edge of the Pareto solution set which is uniformly distributed is obtained, and the deployment result of the controller of the satellite network system obtained by solving can ensure the effective deployment of the controller in a large-scale, multi-scene and high-dynamic network environment, thereby realizing the efficient distribution of satellite network resources.
The foregoing description is only of the preferred embodiments of the present application, and is not intended to limit the scope of the claims, and all equivalent structures or equivalent processes using the descriptions and drawings of the present application, or direct or indirect application in other related technical fields are included in the scope of the claims of the present application.
Claims (9)
1. A method of deploying a controller for a satellite network, the method comprising:
the primary controller is deployed on a ground network;
a secondary controller deployed at a low earth orbit LEO satellite;
the method comprises the following steps:
constructing a time delay factor, a load balancing factor and a reliability factor of a control link based on the satellite network system;
constructing a multi-objective optimization model based on the time delay factor, the load balancing factor and the reliability factor;
based on optimization constraint conditions, solving the multi-objective optimization model to obtain a controller deployment result of the satellite network system;
the solving the multi-objective optimization model to obtain a controller deployment result of the satellite network system comprises the following steps:
generating an initialization honey source based on a formula to obtain an initial population;
taking the initial population as a current population;
non-dominated sorting is carried out on the current population based on a Pareto dominated mechanism, and a non-inferior front end set is obtained;
the individuals in the non-inferior front-end set are arranged in an ascending order to obtain an arranged non-inferior front-end set;
selecting a honey source by using a Boltzmann strategy based on the honey source probability value;
if the preset maximum iteration number is not reached, randomly generating a new honey source, updating the current population, and returning to execute non-dominant sorting on the current population based on a Pareto dominant mechanism to obtain a non-inferior front end set until the preset maximum iteration number is reached to obtain an optimal solution set;
the first formula is:；
wherein,for the initialization of honey source, +.>，/>SN is the number of initial solutions, m=k+p is the number of solution space dimensions, K is the number of primary controllers, P is the number of secondary controllers,/s>Upper bound for j-th dimension variable of solution space,/->A lower bound for the j-th dimensional variable of the solution space;
and is also provided with，/>Is interval [0,1 ]]Random number of any one of the above.
2. The method for deploying a controller for a satellite network according to claim 1, wherein the step of arranging the individuals in the non-bad front-end set in ascending order, after obtaining the arranged non-bad front-end set, further comprises:
determining a dynamic crowding distance of the honey source based on a formula II;
determining a dominant intensity of the honey source based on equation three;
determining an fitness function value of the honey source based on the sum of the dynamic crowding distance and the dominant intensity;
the formula II is as follows:；
wherein,for the dynamic congestion distance,/>Is a random number within the range of (0.5, 1,)>For the j-th objective function value of the i-th individual in said current population,/th objective function value of the i-th individual in said current population>Representing different objective function maxima, +.>Representing the minimum of the different objective functions;
the formula III is:；
wherein,for the dominant intensity, ++>For the hierarchical level of the non-bad front-end set,/->。
3. The method of claim 2, wherein prior to selecting a honey source using a Boltzmann strategy based on the honey source probability values, the method further comprises:
determining the probability value of the honey source based on a formula IV;
the formula IV is:；
wherein,for the probability value of the honey source, +.>The fitness function value of honey source, c is the number of circulation times,/->Is warmDegree (f)>Is the initial temperature.
4. The method of claim 2, wherein the non-dominated sorting of the current population based on Pareto dominated mechanisms, after obtaining a non-bad front end set, further comprises:
determining candidate honey sources based on a formula five;
determining the honey source quality of the candidate honey sources based on the fitness function value;
if the quality of the candidate honey source is better than that of the initialized honey source, replacing the initialized honey source with the candidate honey source;
the fifth formula is:
；
wherein,，for the optimal honey source corresponding to the optimal individual in the current population,/a->Is [0,1]Random number within range,/->Is [0, C]Random number in between, C is a non-negative constant, ">Is [0,1]Random number between->For the current number of iterations,and presetting the maximum iteration times.
5. The method of claim 1, wherein after selecting a honey source using a Boltzmann strategy based on the honey source probability values, the method further comprises:
if the fitness function value of the honey source is not optimized after the preset cycle times, generating a new honey source based on the formula I;
and updating the current population according to the new honey source, and returning to the step of executing the non-dominated sorting on the current population based on the Pareto dominated mechanism to obtain a non-inferior front end set.
6. The method for deploying a controller for a satellite network according to claim 1, wherein the constructing a delay factor for a control link based on the satellite network system comprises:
determining a first time delay and a second time delay of the satellite network system; wherein the first delay is the sum of the propagation delay between the primary controller and the secondary controller and the processing delay of the primary controller; the second time delay is the sum of the propagation time delay between the secondary controller and the satellite switching node and the processing time delay of the secondary controller;
determining the received flow intensity of the primary controller and the received flow intensity of the secondary controller;
and constructing the time delay factor based on the first time delay, the second time delay, the received traffic intensity of the primary controller and the received traffic intensity of the secondary controller.
7. The method for deploying a controller for a satellite network according to claim 6, wherein the constructing a load balancing factor based on the satellite network system comprises:
determining a load variance of the primary controller based on a received traffic intensity of the primary controller;
determining a load variance of the secondary controller based on a received traffic intensity of the secondary controller;
and constructing the load balancing factor based on the load variance of the primary controller and the load variance of the secondary controller.
8. The method for deploying a controller for a satellite network according to claim 7, wherein the constructing a reliability factor for a control link based on the satellite network system comprises:
determining the outage probability of a link between any two nodes in the satellite network system; wherein the nodes comprise switching nodes and controller nodes in the satellite network system;
determining the congestion probability of links between the nodes;
and constructing the reliability factor based on the outage probability and the congestion probability.
9. The method of controller deployment for a satellite network of claim 8, wherein the optimization constraints comprise:
the number of the first-stage controllers is more than or equal to 1;
the number of the secondary controllers is more than or equal to 2;
the first time delay and the second time delay are smaller than a preset maximum time delay;
the deployment latitude of the primary controller is within a preset geographic range;
the flow intensity of the primary controller is smaller than or equal to 0.9 times of the maximum flow intensity which can be processed by the primary controller;
the flow intensity of the secondary controller is smaller than or equal to 0.9 times of the maximum flow intensity which can be processed by the secondary controller.
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