CN105207910B - A kind of power telecom network routing optimization method based on particle group optimizing - Google Patents
A kind of power telecom network routing optimization method based on particle group optimizing Download PDFInfo
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- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
- H04L45/00—Routing or path finding of packets in data switching networks
- H04L45/02—Topology update or discovery
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- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04Q—SELECTING
- H04Q11/00—Selecting arrangements for multiplex systems
- H04Q11/0001—Selecting arrangements for multiplex systems using optical switching
- H04Q11/0005—Switch and router aspects
Abstract
The invention discloses a kind of power telecom network routing optimization method based on particle group optimizing, includes the following steps：Initiation parameter；Position, speed in initialization Discrete Particle Swarm Optimization Algorithm, and according to powerline network algorithm, each fitness value relative to multiple objective function is calculated to each particle；Carry out the classification of branch mating group and non-dominant population；To in branch mating group particle position and speed be updated；Carry out the dynamic exchange between branch mating group and non-dominant population；Test for convergence exports final optimum results.Multi-objective particle swarm algorithm optimization, the characteristics of as a result certainty adapts to easily powerline network are used, and considers and optimize overall network communication process.
Description
Technical field
The present invention relates to the automatic technologies of electric system, and in particular to it is a kind of it is based on particle group optimizing, with energy saving be
Target, powerline network routing optimization method.
Background technology
Powerline network plays important booster action to electric power transmission network, as the private wire network of professional, electricity
Power communication network is gradually formed and develops with the development need of electric system.It is mainly used to alleviate public network develop slowly and
Caused by communication capacity deficiency and fill up some special communication requirements of the implacable power department of public network, to ensure electric power
Virtual production normally and efficiently carries out, and then promotes the development of entire national economy.
In the method safeguarded for powerline network, there are two main research directions at present.One is that assessment is each
The importance of node, according to the sequence of importance, arranges personnel to carry out maintenance work when encountering emergency case.The other is
Usually just retaining certain redundant communication capability, when emergency case, router is being configured, is optimized by optimization algorithm, more
Change each node of whole network by sequence, to ensure the normal operation of network.
In the second approach, existing technical solution is initiated by fiber optic network recovery and optimization Study on Problems mostly,
Main research means are the optimization problems for going to solve multiple target using traditional optimization algorithm, can be in the case where presetting artificial weights
Carry out multiple-objection optimization.This solves while optimization problem, there are problems that two, one is that single object optimization is calculated
Method removes optimization multi-objective problem, brings the uncertainty of optional result；Second is that its optimization aim is electric without adapting to completely
The characteristics of power communication network.
Invention content
In view of this, in view of the deficiencies of the prior art, it is an object of the present invention to provide one kind based on multi-objective particle swarm it is excellent
The power telecom network routing optimization method of change uses multi-objective particle swarm algorithm optimization, and as a result certainty adapts to easily electric power
The characteristics of communication network, and consider and optimize overall network communication process.
In order to achieve the above objectives, the present invention uses following technical scheme：
A kind of power telecom network routing optimization method based on particle group optimizing, includes the following steps：
1) initiation parameter；
2) position in initialization Discrete Particle Swarm Optimization Algorithm, speed；
3) according to fitness D1 and D2, the classification of branch mating group and non-dominant population is carried out；
4) in branch mating group particle position and speed be updated；
5) dynamic exchange between branch mating group and non-dominant population is carried out；
6) it detects whether to reach maximum iteration, if reaching maximum iteration, jump procedure 4), otherwise enter step
It is rapid 7)；
7) final optimum results are exported.
The method that initiation parameter uses standard particle group optimization, setting maximum iteration, maximum speed.
Preferably, the step 1) initiation parameter, including：Determine the greatest iteration time in Discrete Particle Swarm Optimization Algorithm
The calculation formula and relevant parameter of number, population, number of dimensions, Studying factors, the value of inertia weight and all kinds of object functions；
Contain the object function of two aspects：
A. the excessively minimum communication node of all business,
Here, what D1 was represented is object function 1, and what Nd was represented is all business number, and what Xi was represented is i-th of business number
The communication section of process is counted；
B. the minimum value of the redundant communication capability of all nodes as possible big：
D2=Argmax ((min ((DC1-DT1),(DC2-DT2),…,(DCn-DTn))))
In the formula, what D2 was represented is object function 2, and DC1 indicates that be is the maximum communication power of first node, DT1 tables
What is shown is the amount of communication data of first node, and n represents all nodes.
Preferably, the position and speed initialization of the step 2) discrete particle, including：
According to the fitness computational methods being arranged in powerline network algorithm, each particle is calculated relative to multiple mesh
Each fitness value D1 and D2 of scalar functions.
Preferably, the position and speed initialization of the step 2) discrete particle, including：
Initialize the positional value of discrete particle：The positional value of each particle is the state of the switch of each communication node
Value, 1 indicates to be closed, and 0 indicates to open.Assuming that when each node is opened, communication capacity can all come into operation；
Initialize the speed of discrete particle cluster：
The initialization of speed is carried out to discrete particle cluster at random, and the initial value of speed is limited in 0 or 1.
Calculate the adaptive value of each particle；
The positional value (what is switched closes or opens state) obtained after random according to all kinds of particles, is input to entire communication
In net system, corresponding topology is formed, and tested using the business number and data stream of past annual, obtain corresponding adaptation
Degree.
Preferably, the step 3) carries out the classification of branch mating group and non-dominant population, including：It is dominated based on fitness
Entire population is divided into two subgroups by concept, respectively non-dominant SUBGROUP P _ set and domination subgroup NP_set, wherein non-dominant
The number of particle is n1 in the subgroup of subgroup, and the number for dominating particle in subgroup is n2.
Preferably, the step 3) in branch mating group particle position and speed be updated, including：
1) speed is updated：
Wherein, xkIndicate the positional value of particle,Indicate velocity amplitudes of the particle k in the t times iteration, PbestIndicate each
The history optimal value of particle, gbestTo concentrate the optimal value selected non-dominant at random；
2) position is updated：
Wherein,
Preferably, the step 7) exports final optimum results, including：According to finally obtained as a result, i.e. particle
State value (on off state value), final scheme is obtained, and export final three-dimensional Pareto optimality curved surface, so that operator selects
Select corresponding switching manipulation amount.
It is an object of the present invention to solve the problems, such as the network optimization based on routing optimality of powerline network.Its major advantage
It is one to allow for actual conditions, has carried out the routing optimality selection approach the controllable powerline network of communication switch, two
It is mainly to use multi-objective particle swarm algorithm optimization, and consider and optimize overall network communication process.
Description of the drawings
Fig. 1 is the network optimization problem flow diagram based on routing optimality of powerline network of the present invention；
Fig. 2 is the specific node diagram of network optimization problem based on routing optimality of powerline network of the present invention.
Specific implementation mode
In conjunction with attached drawing 1, the network optimization problem process step based on routing optimality of powerline network of the present invention
It is as follows：
Embodiment 1：Maximum iteration, population in step 1. determination Discrete Particle Swarm Optimization Algorithm, number of dimensions,
Practise the factor, the calculation formula and relevant parameter of the value of inertia weight and all kinds of object functions.The method of the present invention --- it is based on
The target letter of two aspects is contained in the power telecom network route recovery method of multiple target discrete particle cluster intelligent optimization algorithm
Number：
[1] communication node of all business of：
Here, what Nd was represented is all business numbers, and what Xi was represented is the communication section points that i-th of business number passes through.
[2] redundant communication capability of all nodes of：
D2=Argmax ((min ((DC1-DT1),(DC2-DT2),…,(DCn-DTn))))
In the formula, DC1 indicates that be is the maximum communication power of first node, and what DT1 was indicated is the logical of first node
Letter data amount.N represents all nodes.
The position and speed of step 2. discrete particle initializes：
[1] initializes the positional value of discrete particle：
In project of the present invention, the positional value of each particle is the state value of the switch of each communication node, and 1 indicates to close
It closes, 0 indicates to open.Assuming that when each node is opened, communication capacity can all come into operation.
[2] initializes the speed of discrete particle cluster：
The initialization of speed is carried out to discrete particle cluster at random, and the initial value of speed is limited in 0 or 1.
[3] calculates the adaptive value of each particle：
The positional value (what is switched closes or opens state) obtained after random according to all kinds of particles, is input to entire communication
In net system, corresponding topology is formed, and tested using the business number and data stream of past annual, obtain corresponding adaptation
Degree.
The classification of step 3. population：
Based on the concept that fitness dominates, entire population is divided into two subgroups, respectively non-dominant SUBGROUP P _ set and branch
With subgroup NP_set.The number of particle is n1 in wherein non-dominant subgroup subgroup, and the number for dominating particle in subgroup is n2.
Step 4. in branch mating group particle position and speed be updated：
[1] is updated speed.
Wherein, xkIndicate the positional value of particle,Indicate that particle k exists
Velocity amplitude when the t times iteration, PbestIndicate the history optimal value of each particle.gbestIt is selected at random in non-dominant concentration
Optimal value.Position is updated
Wherein,
Dynamic exchange between step 5. mating group and non-dominant population：
Each particle in the subgroups NP_set is compared one by one with each particle in the subgroups P_set, and carries out phase
The swap operation answered, so that all particles in the final subgroups NP_set are to dominate particle, i.e. adaptive value is dominant in P_set
The adaptive value of particle in subgroup.Also, it is last to delete the particle repeated in the subgroups P_set and the subgroups NP_set.
The test for convergence of step 6. method：
Entire Discrete Particle Swarm Optimization Algorithm is examined whether to reach maximum iterations, in the present embodiment, iterations
It is 500 times, if reaching iterations has reached set maximum times, iterative step is jumped out, into next step 7)；
If being also not up to maximum iteration, step 4) is returned to.
Step 7. exports final optimum results：
According to finally obtained as a result, the i.e. state value (on off state value) of particle, obtains final scheme, and export most
Whole three-dimensional Pareto optimality curved surface, so that operator selects corresponding switching manipulation amount.
Embodiment 2：A kind of power telecom network routing optimization method based on particle group optimizing, includes the following steps：
1) initiation parameter；The method optimized using standard particle group, setting maximum iteration, maximum speed, this reality
It applies in example, maximum iteration is set as 1000 times, and maximum speed is reciprocal 2 times of entire number of nodes, for Fig. 2,
There are 7 nodes in Fig. 2, then maximum speed is 2/7, and the number of nodes of normal general task is as unit of hundred；
2) position in Discrete Particle Swarm Optimization Algorithm, speed are initialized, in the present embodiment, position be search space with
Machine value, speed are the inverse of entire number of nodes after normalization, have 7 nodes for Fig. 2, in Fig. 2, then speed is 1/
7, and the number of nodes of normal general task is as unit of hundred.It is calculated according to the fitness being arranged in this powerline network algorithm
Method calculates each fitness value D1 and D2 relative to multiple objective function to each particle；
3) according to fitness D1 and D2, the classification of branch mating group and non-dominant population is carried out；
4) in branch mating group particle position and speed be updated；
5) dynamic exchange between branch mating group and non-dominant population is carried out；
6) it detects whether to reach maximum iteration, if reaching maximum iteration, jump procedure 4), otherwise enter step
It is rapid 7)；
7) final optimum results are exported.
Preferably, the step 1) initiation parameter, including：Determine the greatest iteration time in Discrete Particle Swarm Optimization Algorithm
The calculation formula and relevant parameter of number, population, number of dimensions, Studying factors, the value of inertia weight and all kinds of object functions；
Contain the object function of two aspects：
A. all business cross communication node few as possible, for Fig. 2, if there are one business from No. 7 cities to No. 1
City, then this business is exactly 7-5-1 by the path of minimum communication node, rather than other paths.
Here, what Nd was represented is all business numbers, and what Xi was represented is the communication section points that i-th of business number passes through；
B. the minimum value of the redundant communication capability of all nodes as possible big, for Fig. 2, if there is 1000 business
It is transferred to any one in 1~No. 7 city from any one in 1~No. 7 city, it require that the redundancy communication energy of 7 nodes
The absolute value of that minimum value of power as possible big.
D2=Argmax ((min ((DC1-DT1),(DC2-DT2),…,(DCn-DTn))))
In the formula, DC1 indicates that be is the maximum communication power of first node, and what DT1 was indicated is the logical of first node
Letter data amount, n represent all nodes.
Preferably, the position and speed initialization of the step 2) discrete particle, including：
Initialize the positional value of discrete particle：The positional value of each particle is the state of the switch of each communication node
Value, 1 indicates to be closed, and 0 indicates to open.Assuming that when each node is opened, communication capacity can all come into operation.
Initialize the speed of discrete particle cluster：
The initialization of speed is carried out to discrete particle cluster at random, and the initial value of speed is limited in 0 or 1.
Calculate the adaptive value of each particle：
The positional value (what is switched closes or opens state) obtained after random according to all kinds of particles, is input to entire communication
In net system, corresponding topology is formed, and tested using the business number and data stream of past annual, obtain corresponding adaptation
Degree.
Preferably, the step 3) carries out the classification of branch mating group and non-dominant population, including：It is dominated based on fitness
Entire population is divided into two subgroups by concept, respectively non-dominant SUBGROUP P _ set and domination subgroup NP_set, wherein non-dominant
The number of particle is n1 in the subgroup of subgroup, and the number for dominating particle in subgroup is n2.
Preferably, the step 3) in branch mating group particle position and speed be updated, including：
1) speed is updated：
Wherein, xkIndicate the positional value of particle,Indicate velocity amplitudes of the particle k in the t times iteration, PbestIndicate each
The history optimal value of particle, gbestTo concentrate the optimal value selected non-dominant at random；
2) position is updated
[1] wherein,
Preferably, the step 7) exports final optimum results, including：According to finally obtained as a result, i.e. particle
State value (on off state value), final scheme is obtained, and export final three-dimensional Pareto optimality curved surface, so that operator selects
Select corresponding switching manipulation amount.
Embodiment 3：A kind of power telecom network routing optimization method based on particle group optimizing, includes the following steps：
1) initiation parameter；2) position in initialization Discrete Particle Swarm Optimization Algorithm, speed, according to this power telecom network
The fitness computational methods being arranged in network algorithm, to each particle calculate relative to multiple objective function each fitness value D1 and
D2；
3) classification of branch mating group and non-dominant population is carried out；
4) in branch mating group particle position and speed be updated；
5) dynamic exchange between branch mating group and non-dominant population is carried out；
6) it detects whether to reach maximum iteration, if reaching maximum iteration, jump procedure 4), otherwise enter step
It is rapid 7)；
7) final optimum results are exported.
Preferably, the step 1) initiation parameter, including：Determine the greatest iteration time in Discrete Particle Swarm Optimization Algorithm
The calculation formula and relevant parameter of number, population, number of dimensions, Studying factors, the value of inertia weight and all kinds of object functions；
Contain the object function of two aspects：
A. all business cross communication node few as possible, for Fig. 2, if there are one business from No. 7 cities to No. 1
City, then this business is exactly 7-5-1 by the path of minimum communication node, rather than other paths.
Here, what Nd was represented is all business numbers, and what Xi was represented is the communication section points that i-th of business number passes through.
B. the minimum value of the redundant communication capability of all nodes as possible big, for Fig. 2, if there is 1000 business
It is transferred to any one in 1~No. 7 city from any one in 1~No. 7 city, it require that the redundancy communication energy of 7 nodes
The absolute value of that minimum value of power as possible big.
D2=Argmax ((min ((DC1-DT1),(DC2-DT2),…,(DCn-DTn))))
In the formula, DC1 indicates that be is the maximum communication power of first node, and what DT1 was indicated is the logical of first node
Letter data amount, n represent all nodes.
Preferably, the position and speed initialization of the step 2) discrete particle, including：
Initialize the positional value of discrete particle：The positional value of each particle is the state of the switch of each communication node
Value, 1 indicates to be closed, and 0 indicates to open.Assuming that when each node is opened, communication capacity can all come into operation.
Initialize the speed of discrete particle cluster：
The initialization of speed is carried out to discrete particle cluster at random, and the initial value of speed is limited in 0 or 1.
Calculate the adaptive value of each particle：
The positional value (what is switched closes or opens state) obtained after random according to all kinds of particles, is input to entire communication
In net system, corresponding topology is formed, and tested using the business number and data stream of past annual, obtain corresponding adaptation
Degree.
Preferably, the step 3) carries out the classification of branch mating group and non-dominant population, including：It is dominated based on fitness
Entire population is divided into two subgroups by concept, respectively non-dominant SUBGROUP P _ set and domination subgroup NP_set, wherein non-dominant
The number of particle is n1 in the subgroup of subgroup, and the number for dominating particle in subgroup is n2.
Preferably, the step 3) in branch mating group particle position and speed be updated, including：
1) speed is updated：
Wherein, xkIndicate the positional value of particle,Indicate velocity amplitudes of the particle k in the t times iteration, PbestIndicate each
The history optimal value of particle, gbestTo concentrate the optimal value selected non-dominant at random.
2) position is updated：
[2] wherein,
Preferably, the step 7) exports final optimum results, including：According to finally obtained as a result, i.e. particle
State value (on off state value), final scheme is obtained, and export final three-dimensional Pareto optimality curved surface, so that operator selects
Select corresponding switching manipulation amount.
Finally illustrate, the above examples are only used to illustrate the technical scheme of the present invention and are not limiting, this field is common
Other modifications or equivalent replacement that technical staff makes technical scheme of the present invention, without departing from technical solution of the present invention
Spirit and scope, be intended to be within the scope of the claims of the invention.
Claims (4)
1. a kind of power telecom network routing optimization method based on particle group optimizing, includes the following steps：
1) initiation parameter；
2) position in initialization Discrete Particle Swarm Optimization Algorithm, speed；
3) classification of branch mating group and non-dominant population is carried out；
4) in branch mating group particle position and speed be updated；
5) dynamic exchange between branch mating group and non-dominant population is carried out；
6) it detects whether to reach maximum iteration, if reaching maximum iteration, jump procedure 4), otherwise enter step
7)；
7) final optimum results are exported；
The method that the initiation parameter uses standard particle group optimization, setting maximum iteration, maximum speed；
Step 1) the initiation parameter, including：Determine maximum iteration in Discrete Particle Swarm Optimization Algorithm, population,
The calculation formula and relevant parameter of number of dimensions, Studying factors, the value of inertia weight and all kinds of object functions；
Contain the object function of two aspects：
A. all business cross communication node few as possible：
Here, D1What is represented is object function 1, and what Nd was represented is all business number, and what Xi was represented is that i-th of business number passes through
Communication section points；
B. the redundant communication capability of all nodes：
D2=Argmax ((min ((DC1-DT1),(DC2-DT2),…,(DCn-DTn))))
In the formula, D1What is represented is object function 1, D2What is represented is object function 2, DCnWhat is indicated is the maximum of n-th of node
Communicate power, DTnThat indicate is the amount of communication data of n-th of node, DC1What expression was is the maximum communication power of first node,
DT1What is indicated is the amount of communication data of first node, and n represents all nodes；
The step 4) in branch mating group particle position and speed be updated, including：
1) speed is updated：
Wherein, xkIndicate the positional value of particle,Indicate velocity amplitudes of the particle k in the t times iteration, PbestIndicate each particle
History optimal value, gbestTo concentrate the optimal value selected non-dominant at random；
2) position is updated：
Wherein,
2. the power telecom network routing optimization method based on particle group optimizing as described in claim 1, it is characterised in that：
The position and speed of the step 2) discrete particle initializes, including：
Initialize the positional value of discrete particle：The positional value of each particle is the state value of the switch of each communication node, 1 table
Show closure, 0 indicates to open；
Initialize the speed of discrete particle cluster：
The initialization of speed is carried out to discrete particle cluster at random, and the initial value of speed is limited in 0 or 1；
Calculate the adaptive value of each particle；
It according to the positional value that all kinds of particles obtain after random, is input in entire network system, forms corresponding topology, and adopt
It is tested with the business number of past annual and data stream, obtains corresponding fitness.
3. the power telecom network routing optimization method based on particle group optimizing as described in claim 1, it is characterised in that：It is described
Step 3) carries out the classification of branch mating group and non-dominant population, including：Entire population is divided into two subgroups, it is respectively non-dominant
SUBGROUP P _ set and domination subgroup NP_set dominates particle in subgroup wherein the number of particle is n1 in non-dominant subgroup subgroup
Number is n2.
4. the power telecom network routing optimization method based on particle group optimizing as described in claim 1, it is characterised in that：It is described
Step 7) exports final optimum results, including：According to finally obtained as a result, the i.e. state value of particle, obtains final side
Case, and final three-dimensional Pareto optimality curved surface is exported, so that operator selects corresponding switching manipulation amount.
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| CN107677646B (en) * | 2017-10-13 | 2020-06-05 | 中国水利水电科学研究院 | Water quality parameter monitoring method and device for improving DBPSO |
| CN111210361B (en) * | 2019-12-30 | 2021-07-13 | 国网江苏省电力有限公司信息通信分公司 | Power communication network routing planning method based on reliability prediction and particle swarm optimization |
| CN113542115B (en) * | 2020-04-22 | 2022-10-04 | 国家电网有限公司 | SDN power communication network-based data path determination method, device and system |
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