CN109784110A - A kind of optimization method and its optimization system of RFID network node deployment - Google Patents
A kind of optimization method and its optimization system of RFID network node deployment Download PDFInfo
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Abstract
The embodiment of the invention provides the optimization methods and its optimization system of a kind of RFID network node deployment.Wherein the optimization method is included: S100, is generated initial population using random function；S200, offspring's layout is generated with another randomly selected second layout progress subregion crossbar transistion for the first layout in population described in one of them；S300, offspring layout is mutated；S400, the performance value for comparing offspring's layout and parent's layout after mutation；Parent's layout is first layout；When S500, offspring's layout table present worth after the mutation are better than parent layout, the offspring after enabling the mutation, which is laid out, replaces parent's layout；When S600, offspring's layout table present worth after the mutation are worse than parent layout, offspring's layout is abandoned.
Description
Technical field
The present invention relates to the optimization methods and its optimization of technical field of RFID more particularly to a kind of RFID network node deployment
System.
Background technique
Since RFID network has the characteristics that random placement, environment are complicated, node is limited, so RFID network is needed to section
The layout of point optimizes deployment.The optimization of RFID node deployment is not only an important resource saving technology, additionally it is possible to be protected
Demonstrate,prove the communication quality of connection.
Currently, the method optimized for the node deployment of RFID network has very much, the methods of evolution algorithm and colony intelligence are
Caused more and more concerns.However, since evolution algorithm indicates candidate solutions using fixed-length code (FLC) scheme.
Accordingly, it is difficult to adjust the number of nodes of layout in RFID network optimization process, there are also to be developed for the prior art.
Summary of the invention
Place in view of above-mentioned deficiencies of the prior art, the purpose of the present invention is to provide a kind of RFID network node deployments
Optimization method and its optimization system, it is intended to which when solving RFID network node optimization in the prior art, the number of nodes of layout is difficult to
The problem of adjustment.
In order to achieve the above object, this invention takes following technical schemes: a kind of optimization of RFID network node deployment
Method.Wherein, the optimization method of the RFID network node deployment includes:
S100, initial population is generated using random function；
S200, first in population described in one of them is laid out, is carried out with another randomly selected second layout
Subregion crossbar transistion generates offspring's layout；
S300, offspring layout is mutated；
S400, the performance value for comparing offspring's layout and parent's layout after mutation；Parent's layout is first cloth
Office；
When S500, offspring's layout table present worth after the mutation are better than parent layout, after enabling after the mutation
Generation layout replaces parent's layout；
When S600, offspring's layout table present worth after the mutation are worse than parent layout, offspring's layout is abandoned.
The optimization method, wherein the method also includes: step S100-S600 is repeated, until the offspring
Layout meets termination criteria.
The optimization method, wherein the step S100 includes: for each layout, [1, Nmax] in the range of
Generate random number ni, wherein NmaxIt is the maximum number of node；Determine each node can layout area with the method for random sampling
Initial position.
The optimization method, wherein the subregion crossbar transistion includes: to randomly choose two from first layout
A different node；Calculate the midpoint and distance of two nodes；Using the midpoint as the center of circle, and using the distance as diameter,
Circular subregion is marked in first layout；The subregion of second layout is the subregion with first layout
Position and the identical circle of size；Exchange the subregion of first layout and second layout.
The optimization method, wherein the step S300 is specifically included: for each section in offspring layout
Point generates random number of the value range between 0 to 1；When the random number is greater than preset mutation probability, calculated according to following
Formula is mutated the node:
Nodej·Pk=Nodej·Pk+N(0,0.5)*(ubk-lbk), k=1 ..., Np (4)
Wherein, PkIndicate k-th of attribute of node j；N (0,0.5) indicates gaussian random number generator；Variable ubkAnd lbk
Respectively indicate PkUpper and lower bound；NpIndicate the attribute to be optimized sum.
A kind of optimization system of RFID network node deployment, wherein include:
Initialization of population module, for generating initial population using random function；
Crossbar transistion module, it is randomly selected with another for being laid out for first in population described in one of them
Second layout carries out subregion crossbar transistion, generates offspring's layout；
It is mutated module, for being mutated to offspring layout；
Comparison module, for comparing the performance value of offspring's layout and parent's layout after being mutated；Parent's layout is institute
State the first layout；
Selecting module when being better than parent layout for offspring's layout table present worth after the mutation, enables described prominent
Offspring after change, which is laid out, replaces parent's layout；And
When offspring's layout table present worth after the mutation is worse than parent layout, offspring's layout is abandoned.
The optimization system, wherein the method also includes: module is terminated, is met eventually for being laid out in the offspring
Only when standard, stop the operation of the optimization system.
The optimization system, wherein the initialization of population module is specifically used for:
For each layout, [1, Nmax] in the range of generate random number ni, wherein NmaxIt is the maximum number of node；
With the method for random sampling determine each node can layout area initial position.
The optimization system, wherein the crossbar transistion module is specifically used for: it is selected at random from first layout
Select two different nodes；Calculate the midpoint and distance of two nodes；It using the midpoint as the center of circle, and is straight with the distance
Diameter marks circular subregion in first layout；The subregion of second layout is the son with first layout
Regional location and the identical circle of size；Exchange the subregion of first layout and second layout.
The optimization system, wherein the mutation module is specifically used for: for each section in offspring layout
Point generates random number of the value range between 0 to 1；When the random number is greater than preset mutation probability, calculated according to following
Formula is mutated the node:
Nodej·Pk=Nodej·Pk+N(0,0.5)*(ubk-lbk), k=1 ..., Np (4)
Wherein, PkIndicate k-th of attribute of node j；N (0,0.5) indicates gaussian random number generator；Variable ubkAnd lbk
Respectively indicate PkUpper and lower bound；NpIndicate the attribute to be optimized sum.
The utility model has the advantages that the optimization method of RFID network node deployment provided by the invention be based on Revised genetic algorithum into
Row, can be in such a way that subregion exchanges, and the adaptive number of nodes that layout is adjusted in optimization process has better
Effect of optimization.
Detailed description of the invention
Fig. 1 is the method flow diagram of the optimization method of the RFID network node deployment of the embodiment of the present invention.
Fig. 2 is the schematic diagram of the subregion crossbar transistion of the embodiment of the present invention.
Fig. 3 is the method flow diagram of the step 200 of the embodiment of the present invention.
Fig. 4 is the structural block diagram of the optimization system of the RFID network node deployment of the embodiment of the present invention.
Specific embodiment
The present invention provides the optimization method and its optimization system of a kind of RFID network node deployment.To make mesh of the invention
, technical solution and effect it is clearer, clear, the present invention is described in more detail as follows in conjunction with drawings and embodiments.
It should be appreciated that described herein, specific examples are only used to explain the present invention, is not intended to limit the present invention.
In the description of the present invention, it is to be understood that, term " center ", " longitudinal direction ", " transverse direction ", " length ", " width ",
" thickness ", "upper", "lower", "front", "rear", "left", "right", "vertical", "horizontal", "top", "bottom" "inner", "outside", " up time
The orientation or positional relationship of the instructions such as needle ", " counterclockwise ", " axial direction ", " radial direction ", " circumferential direction " be orientation based on the figure or
Positional relationship is merely for convenience of description of the present invention and simplification of the description, rather than the device or element of indication or suggestion meaning must
There must be specific orientation, be constructed and operated in a specific orientation, therefore be not considered as limiting the invention.
In addition, term " first ", " second " are used for descriptive purposes only and cannot be understood as indicating or suggesting relative importance
Or implicitly indicate the quantity of indicated technical characteristic.Define " first " as a result, the feature of " second " can be expressed or
Implicitly include one or more of the features.In the description of the present invention, the meaning of " plurality " is two or more, unless separately
There is clearly specific restriction.
In the present invention unless specifically defined or limited otherwise, term " installation ", " connected ", " connection ", " fixation " etc.
Term shall be understood in a broad sense, for example, it may be being fixedly connected, may be a detachable connection, or integral；It can be mechanical connect
It connects, is also possible to be electrically connected；It can be directly connected, can also can be in two elements indirectly connected through an intermediary
The interaction relationship of the connection in portion or two elements.It for the ordinary skill in the art, can be according to specific feelings
Condition understands the concrete meaning of above-mentioned term in the present invention.
In the present invention unless specifically defined or limited otherwise, fisrt feature in the second feature " on " or " down " can be with
It is that the first and second features directly contact or the first and second features pass through intermediary mediate contact.Moreover, fisrt feature exists
Second feature " on ", " top " and " above " but fisrt feature be directly above or diagonally above the second feature, or be merely representative of
First feature horizontal height is higher than second feature.Fisrt feature can be under the second feature " below ", " below " and " below "
One feature is directly under or diagonally below the second feature, or is merely representative of first feature horizontal height less than second feature.
In the description of this specification, reference term " one embodiment ", " some embodiments ", " example ", " specifically show
The description of example " or " some examples " etc. means specific features, structure, material or spy described in conjunction with this embodiment or example
Point is included at least one embodiment or example of the invention.In the present specification, schematic expression of the above terms are not
It must be directed to identical embodiment or example.Moreover, particular features, structures, materials, or characteristics described can be in office
It can be combined in any suitable manner in one or more embodiment or examples.In addition, without conflicting with each other, the skill of this field
Art personnel can tie the feature of different embodiments or examples described in this specification and different embodiments or examples
It closes and combines.
Fig. 1 provides a kind of optimization method of RFID network node deployment for the embodiment of the present invention.In embodiments of the present invention,
Using Revised genetic algorithum solution node location problem, the solution space for first having to determine that improved heredity is calculated includes node layout
All solutions of problem:
POP={ X1,X2,…,Xpopsize}
Wherein, POP indicates a population；Popsize indicates Population Size, i.e., all solution numbers；XiIndicate to include one group
I-th of candidate solutions (node layout) of node:
Xi={ Nodej|(Nodej·p1,Nodej·p2) ∈ A, j=1,2 ..., ni}
Wherein, NodejIncluding all attributes to be optimized；A indicates the region that node can be placed；niIndicate i-th of candidate
The number of solution interior joint.In the present embodiment, different candidate solutions are allowed to have the node of different number, therefore
It does not pre-define, can be adjusted in the subregion crossbar transistion in search process.
As shown in Figure 1, the optimization method of the RFID network node deployment includes:
S100, initial population is generated using random function.
Specifically, can initialization population in the following way, form multiple and different layouts:
For each layout, [1, Nmax] in the range of generate random number ni, wherein NmaxIt is the maximum number of node；
With the method for random sampling determine each node can layout area initial position.
S200, first in population described in one of them is laid out, is carried out with another randomly selected second layout
Subregion crossbar transistion generates offspring's layout.
Specifically, as shown in figure 3, the step S200 may include steps of:
S210, for each layout Xi, random number r is generated in [0,1].If r is less than crossover probability PcAnd XiComprising extremely
Few two nodes, then generate offspring Y by following subregion exchange stepi.Otherwise, offspring YiWith XiIt is identical.
S220, another layout X is randomly choosed from groupjCarry out crossbar transistion.
S230, X is laid out from described firstiTwo different nodes of middle random selection；Calculate two nodes midpoint and away from
From.Then, it as shown in Fig. 2, using the midpoint as the center of circle, and using the distance as diameter, is marked in first layout
Circular subregion A, and X is laid out secondjThe middle circle for drawing same position and size is as subregion B.To the subregion A
It is swapped with the subregion B, obtains offspring and be laid out Yi。
Above-mentioned steps 210 to step 230 process can be indicated with following formula:
Wherein, randiIt is the random number generated at random in [0,1]；OperatorIndicate step 230.
In the present embodiment, as shown in Fig. 2, the number of nodes between subregion A and subregion B may be different.
Therefore, offspring's layout of generation, which can have, is laid out different number of nodes with parent, to realize the adaptive of number of nodes
It should adjust.
In addition, the position of the subregion is determined by the position for the node being randomly generated.Therefore, in this way
According to the distribution adjust automatically subregion of node, will not overlay node be difficult to the region reached and close likely in node
The region of collection.
Moreover, the size of subregion can be adjusted adaptively and (be determined by the distance between two selected nodes), big friendship
Fork region increases the diversity of population, and small intersection region is convenient for fine tuning.
S300, offspring layout is mutated.Specifically, can be laid out with offspring of the preset probability to generation into
Row Gauss mutation.
In some embodiments, the step of progress Gauss mutation is specific as follows:
For each node in offspring layout, random number of the value range between 0 to 1 is generated；It is described with
When machine number is greater than preset mutation probability, the node is mutated according to following formula:
Nodej·Pk=Nodej·Pk+N(0,0.5)*(ubk-lbk), k=1 ..., Np
Wherein, PkIndicate k-th of attribute of node j；N (0,0.5) indicates gaussian random number generator；Variable ubkAnd lbk
Respectively indicate PkUpper and lower bound；NpIndicate the attribute to be optimized sum.
S400, the performance value for comparing offspring's layout and parent's layout after mutation；Parent's layout is first cloth
Office.
In the present embodiment, since each offspring is laid out YiIt is all by the first layout XiWith the second layout XjIt generates, and offspring
It is laid out YiBasic structure be from XiIt inherits, so XiReferred to as YiParent layout.
It after region crossbar transistion and mutation, is needing more preferably to be laid out relatively and to selection, to YiWith XiIt carries out competing
It strives and compares to enter next iteration.
If offspring's layout is more preferable than its parent layout, it just will use offspring's layout next time and be iterated；Otherwise, make
Continue iteration with parent's layout and abandons offspring's layout.The mathematical notation of the process is as follows:
When S500, offspring's layout table present worth after the mutation are better than parent layout, after enabling after the mutation
Generation layout replaces parent's layout.
When S600, offspring's layout table present worth after the mutation are worse than parent layout, offspring's layout is abandoned.
In some embodiments, it is also necessary to offspring's layout after each iteration optimization be judged, it is determined whether meet
Preset termination criteria.If so, output optimum results.If it is not, then return step S200, continues Optimized Iterative.
The embodiment of the present invention still further provides a kind of optimization system of RFID network node deployment.As shown in figure 4, should
Optimization system may include: initialization of population module 410, crossbar transistion module 420, be mutated module 430, comparison module 440 with
And selecting module 450.
Wherein, the initialization of population module 410 is used to generate initial population using random function.Crossbar transistion module
420 for carrying out subregion with another randomly selected second layout for the first layout in population described in one of them
Crossbar transistion generates offspring's layout.Mutation module 430 is used to be mutated offspring layout.Comparison module 440 be used for than
Compared with the performance value of offspring's layout and parent's layout after mutation；Parent's layout is first layout and second cloth
Office.When selecting module 450 is better than parent layout for offspring's layout table present worth after the mutation, after enabling the mutation
Offspring be laid out and replace parent layout and when offspring's layout table present worth after the mutation is worse than parent layout,
Abandon offspring's layout.
In some embodiments, as shown in figure 4, the optimization system further includes terminating module 460.The termination module 460
For stopping the operation of the optimization system when offspring layout meets termination criteria.
Specifically, the initialization of population module 410 is specifically used for: for each layout, [1, Nmax] in the range of
Generate random number ni, wherein NmaxIt is the maximum number of node；Determine each node can layout area with the method for random sampling
Initial position.
The crossbar transistion module 420 is specifically used for: randomly choosing two different nodes from first layout；Meter
Calculate the midpoint and distance of two nodes；Using the midpoint as the center of circle, and using the distance as diameter, in first layout
Mark circular subregion；The subregion of second layout is identical with the sub-window position of first layout and size
It is round；Exchange the subregion of first layout and second layout.
The mutation module 430 is specifically used for: for each node in offspring layout, generating value range 0
Random number between to 1；When the random number is greater than preset mutation probability, dash forward according to following formula to the node
Become:
Nodej·Pk=Nodej·Pk+N(0,0.5)*(ubk-lbk), k=1 ..., Np
Wherein, PkIndicate k-th of attribute of node j；N (0,0.5) indicates gaussian random number generator；Variable ubkAnd lbk
Respectively indicate PkUpper and lower bound；NpIndicate the attribute to be optimized sum.
It, can according to the technique and scheme of the present invention and this hair it is understood that for those of ordinary skills
Bright design is subject to equivalent substitution or change, and all these changes or replacement all should belong to the guarantor of appended claims of the invention
Protect range.
Claims (10)
1. a kind of optimization method of RFID network node deployment characterized by comprising
S100, initial population is generated using random function；
S200, first in population described in one of them is laid out, carries out sub-district with another randomly selected second layout
Domain crossbar transistion generates offspring's layout；
S300, offspring layout is mutated；
S400, the performance value for comparing offspring's layout and parent's layout after mutation；Parent's layout is first layout；
When S500, offspring's layout table present worth after the mutation are better than parent layout, offspring's cloth after enabling the mutation
Office replaces parent's layout；
When S600, offspring's layout table present worth after the mutation are worse than parent layout, offspring's layout is abandoned.
2. optimization method according to claim 1, which is characterized in that the method also includes:
Step S100-S600 is repeated, until offspring layout meets termination criteria.
3. optimization method according to claim 1, which is characterized in that the step S100 includes:
For each layout, [1, Nmax] in the range of generate random number ni, wherein NmaxIt is the maximum number of node；
With the method for random sampling determine each node can layout area initial position.
4. optimization method according to claim 3, which is characterized in that the subregion crossbar transistion includes:
Two different nodes are randomly choosed from first layout；
Calculate the midpoint and distance of two nodes；
Using the midpoint as the center of circle, and using the distance as diameter, circular subregion is marked in first layout；Institute
The subregion for stating the second layout is circle identical with the sub-window position of first layout and size；
Exchange the subregion of first layout and second layout.
5. optimization method according to claim 3, which is characterized in that the step S300 is specifically included:
For each node in offspring layout, random number of the value range between 0 to 1 is generated；
When the random number is greater than preset mutation probability, the node is mutated according to following formula:
Nodej·Pk=Nodej·Pk+N(0,0.5)*(ubk-lbk), k=1 ..., Np (4)
Wherein, PkIndicate k-th of attribute of node j；N (0,0.5) indicates gaussian random number generator；Variable ubkAnd lbkRespectively
Indicate PkUpper and lower bound；NpIndicate the attribute to be optimized sum.
6. a kind of optimization system of RFID network node deployment characterized by comprising
Initialization of population module, for generating initial population using random function；
Crossbar transistion module, for in population described in one of them first layout, with another randomly selected second
Layout carries out subregion crossbar transistion, generates offspring's layout；
It is mutated module, for being mutated to offspring layout；
Comparison module, for comparing the performance value of offspring's layout and parent's layout after being mutated；Parent layout is described the
One layout；
Selecting module, when being better than parent layout for offspring's layout table present worth after the mutation, after enabling the mutation
Offspring be laid out and replace parent layout；And
When offspring's layout table present worth after the mutation is worse than parent layout, offspring's layout is abandoned.
7. optimization system according to claim 6, which is characterized in that the method also includes: module is terminated, in institute
Offspring's layout is stated when meeting termination criteria, stops the operation of the optimization system.
8. optimization system according to claim 6, which is characterized in that the initialization of population module is specifically used for:
For each layout, [1, Nmax] in the range of generate random number ni, wherein NmaxIt is the maximum number of node；With with
Machine sampling method determine each node can layout area initial position.
9. optimization system according to claim 8, which is characterized in that the crossbar transistion module is specifically used for:
Two different nodes are randomly choosed from first layout；Calculate the midpoint and distance of two nodes；In described
Point is the center of circle, and using the distance as diameter, marks circular subregion in first layout；Second layout
Subregion is circle identical with the sub-window position of first layout and size；Exchange first layout and described second
The subregion of layout.
10. optimization system according to claim 8, which is characterized in that the mutation module is specifically used for: after described
Each node in generation layout generates random number of the value range between 0 to 1；
When the random number is greater than preset mutation probability, the node is mutated according to following formula:
Nodej·Pk=Nodej·Pk+N(0,0.5)*(ubk-lbk), k=1 ..., Np (4)
Wherein, PkIndicate k-th of attribute of node j；N (0,0.5) indicates gaussian random number generator；Variable ubkAnd lbkRespectively
Indicate PkUpper and lower bound；NpIndicate the attribute to be optimized sum.
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