CN109784110B - Optimization method and optimization system for RFID network node deployment - Google Patents
Optimization method and optimization system for RFID network node deployment Download PDFInfo
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Abstract
The embodiment of the invention provides an optimization method and an optimization system for RFID network node deployment. The optimization method comprises the following steps: s100, generating an initial population by using a random function; s200, performing sub-region cross transformation on the first layout in one of the populations and another randomly selected second layout to generate a descendant layout; s300, mutating the offspring layout; s400, comparing the expression values of the mutated offspring layout and the parent layout; the parent layout is the first layout; s500, when the mutant offspring layout has a better representation value than the parent layout, replacing the parent layout with the mutant offspring layout; s600, when the performance value of the mutated offspring layout is lower than that of the parent layout, discarding the offspring layout.
Description
Technical Field
The invention relates to the technical field of RFID (radio frequency identification devices), in particular to an optimization method and an optimization system for RFID network node deployment.
Background
Because the RFID network has the characteristics of random deployment, complex environment and limited nodes, the RFID network needs to perform optimized deployment on the layout of the nodes. The RFID node deployment optimization is not only an important resource-saving technology, but also can ensure the communication quality.
At present, a plurality of methods for node deployment optimization of the RFID network exist, and methods such as evolutionary algorithm and group intelligence have attracted more and more attention. However, since the evolutionary algorithm uses a fixed length coding scheme to represent the candidate solutions.
Therefore, it is difficult to adjust the number of nodes in the layout during the RFID network optimization process, and the prior art has yet to be developed.
Disclosure of Invention
In view of the defects of the prior art, the invention aims to provide an optimization method and an optimization system for RFID network node deployment, and aims to solve the problem that the number of distributed nodes is difficult to adjust when RFID network nodes are optimized in the prior art.
In order to achieve the purpose, the invention adopts the following technical scheme: an optimization method for RFID network node deployment. The optimization method for the RFID network node deployment comprises the following steps:
s100, generating an initial population by using a random function;
s200, performing sub-region cross transformation on the first layout in one of the populations and another randomly selected second layout to generate a descendant layout;
s300, mutating the offspring layout;
s400, comparing the expression values of the mutated offspring layout and the parent layout; the parent layout is the first layout;
s500, when the mutant offspring layout has a better representation value than the parent layout, replacing the parent layout with the mutant offspring layout;
s600, when the performance value of the mutated offspring layout is lower than that of the parent layout, discarding the offspring layout.
The optimization method, wherein the method further comprises: repeating steps S100-S600 until the descendant layout meets a termination criterion.
The optimization method, wherein the step S100 includes: for each layout, in [1, Nmax]Within a range of (1) generating a random number niIn which N ismaxIs the maximum number of nodes; and determining the initial position of each node in the arrangeable area by using a random sampling method.
The optimization method, wherein the sub-region cross-transformation comprises: randomly selecting two different nodes from the first layout; calculating the midpoint and the distance of the two nodes; drawing a circular sub-area in the first layout by taking the midpoint as a circle center and the distance as a diameter; the sub-area of the second layout is a circle with the same position and size as the sub-area of the first layout; swapping sub-regions of the first layout and the second layout.
The optimization method, wherein the step S300 specifically includes: generating a random number with a value range of 0 to 1 for each node in the descendant layout; when the random number is larger than the preset mutation probability, mutating the node according to the following formula:
Nodej·Pk＝Nodej·Pk+N(0,0.5)*(ubk-lbk),k＝1,…,Np (4)
wherein, PkA k-th attribute representing node j; n (0,0.5) represents a Gaussian random number generator; variable ubkAnd lbkRespectively represent PkThe upper and lower limits of (d); n is a radical ofpRepresenting the total number of attributes to be optimized.
An optimization system for RFID network node deployment, comprising:
the population initialization module is used for generating an initial population by using a random function;
the cross transformation module is used for carrying out sub-region cross transformation on the first layout in one population and the second layout randomly selected to generate a descendant layout;
a mutation module to mutate the progeny layout;
the comparison module is used for comparing the performance values of the mutated offspring layout and the parent layout; the parent layout is the first layout;
a selection module for causing the mutated offspring layout to replace the parent layout when the mutated offspring layout has a better representation than the parent layout; and is
Discarding the offspring layout when the mutated offspring layout performs worse than the parental layout.
The optimization system, wherein the method further comprises: a termination module to stop operation of the optimization system when the descendant layouts meet termination criteria.
The optimization system, wherein the population initialization module is specifically configured to:
for each layout, in [1, Nmax]Within a range of (1) generating a random number niIn which N ismaxIs the maximum number of nodes; and determining the initial position of each node in the arrangeable area by using a random sampling method.
The optimization system, wherein the cross-transformation module is specifically configured to: randomly selecting two different nodes from the first layout; calculating the midpoint and the distance of the two nodes; drawing a circular sub-area in the first layout by taking the midpoint as a circle center and the distance as a diameter; the sub-area of the second layout is a circle with the same position and size as the sub-area of the first layout; swapping sub-regions of the first layout and the second layout.
The optimization system, wherein the mutation module is specifically configured to: generating a random number with a value range of 0 to 1 for each node in the descendant layout; when the random number is larger than the preset mutation probability, mutating the node according to the following formula:
Nodej·Pk＝Nodej·Pk+N(0,0.5)*(ubk-lbk),k＝1,…,Np (4)
wherein, PkA k-th attribute representing node j; n (0,0.5) represents a Gaussian random number generator; variable ubkAnd lbkRespectively represent PkThe upper and lower limits of (d); n is a radical ofpRepresenting the total number of attributes to be optimized.
Has the advantages that: the optimization method for RFID network node deployment provided by the invention is carried out based on an improved genetic algorithm, and the number of nodes in layout can be adaptively adjusted in the optimization process in a sub-region exchange mode, so that the optimization effect is better.
Drawings
Fig. 1 is a flowchart of a method for optimizing RFID network node deployment according to an embodiment of the present invention.
Fig. 2 is a schematic diagram of sub-region cross-transformation according to an embodiment of the present invention.
FIG. 3 is a flowchart of a method of step 200 according to an embodiment of the present invention.
Fig. 4 is a block diagram of an optimization system for RFID network node deployment according to an embodiment of the present invention.
Detailed Description
The invention provides an optimization method and an optimization system for RFID network node deployment. In order to make the objects, technical solutions and effects of the present invention clearer and clearer, the present invention is further described in detail below with reference to the accompanying drawings and examples. It should be understood that the specific embodiments described herein are merely illustrative of the invention and are not intended to limit the invention.
In the description of the present invention, it is to be understood that the terms "central," "longitudinal," "lateral," "length," "width," "thickness," "upper," "lower," "front," "rear," "left," "right," "vertical," "horizontal," "top," "bottom," "inner," "outer," "clockwise," "counterclockwise," "axial," "radial," "circumferential," and the like are used in the orientations and positional relationships indicated in the drawings for convenience in describing the invention and to simplify the description, and are not intended to indicate or imply that the referenced devices or elements must have a particular orientation, be constructed and operated in a particular orientation, and are therefore not to be considered limiting of the invention.
Furthermore, the terms "first", "second" and "first" are used for descriptive purposes only and are not to be construed as indicating or implying relative importance or implicitly indicating the number of technical features indicated. Thus, a feature defined as "first" or "second" may explicitly or implicitly include one or more of that feature. In the description of the present invention, "a plurality" means two or more unless specifically defined otherwise.
In the present invention, unless otherwise expressly stated or limited, the terms "mounted," "connected," "secured," and the like are to be construed broadly and can, for example, be fixedly connected, detachably connected, or integrally formed; can be mechanically or electrically connected; either directly or indirectly through intervening media, either internally or in any other relationship. The specific meanings of the above terms in the present invention can be understood by those skilled in the art according to specific situations.
In the present invention, unless otherwise expressly stated or limited, the first feature "on" or "under" the second feature may be directly contacting the first and second features or indirectly contacting the first and second features through an intermediate. Also, a first feature "on," "over," and "above" a second feature may be directly or diagonally above the second feature, or may simply indicate that the first feature is at a higher level than the second feature. A first feature being "under," "below," and "beneath" a second feature may be directly under or obliquely under the first feature, or may simply mean that the first feature is at a lesser elevation than the second feature.
In the description herein, references to the description of the term "one embodiment," "some embodiments," "an example," "a specific example," or "some examples," etc., mean that a particular feature, structure, material, or characteristic described in connection with the embodiment or example is included in at least one embodiment or example of the invention. In this specification, the schematic representations of the terms used above are not necessarily intended to refer to the same embodiment or example. Furthermore, the particular features, structures, materials, or characteristics described may be combined in any suitable manner in any one or more embodiments or examples. Furthermore, various embodiments or examples and features of different embodiments or examples described in this specification can be combined and combined by one skilled in the art without contradiction.
Fig. 1 provides an optimization method for RFID network node deployment according to an embodiment of the present invention. In the embodiment of the invention, the node layout problem is solved by using an improved genetic algorithm, and firstly, a solution space of the improved genetic algorithm is determined to comprise all solutions of the node layout problem:
POP＝{X1,X2,…,Xpopsize}
wherein POP represents a population; popsize indicates population size, i.e., all solutions; xiRepresent the ith candidate solution (node layout) comprising a set of nodes:
Xi＝{Nodej|(Nodej·p1,Nodej·p2)∈A,j＝1,2,…,ni}
wherein, NodejIncluding all attributes to be optimized; a represents an area where a node can be placed; n isiIndicating the number of nodes in the ith candidate solution. In this embodiment, different candidate solutions are allowed to have different numbers of nodes, and therefore are not predefined, which can be adjusted when sub-regions are cross-transformed during the search.
As shown in fig. 1, the method for optimizing the deployment of the RFID network node includes:
and S100, generating an initial population by using a random function.
Specifically, the population may be initialized to form a plurality of different layouts as follows:
for each layout, in [1, Nmax]Within a range of (1) generating a random number niIn which N ismaxIs the maximum number of nodes; and determining the initial position of each node in the arrangeable area by using a random sampling method.
S200, performing sub-region cross transformation on the first layout in one of the populations and the second layout randomly selected to generate the offspring layout.
Specifically, as shown in fig. 3, the step S200 may include the following steps:
s210, for each layout XiIn [0,1 ]]A random number r is generated. If r is less than the crossover probability PcAnd XiContaining at least two nodes, generating offspring Y by the following sub-region exchange stepsi. Otherwise, the descendant YiAnd XiThe same is true.
S220, randomly selecting another layout X from the groupjAnd performing cross transformation.
S230, from the first layout XiRandomly selecting two different nodes; the midpoint and distance of the two nodes are calculated. Then, as shown in fig. 2, with the midpoint as the center of the circle and the distance as the diameter, a circular sub-area a is scribed in the first layout, and a second layout X is performedjDrawing a circle of the same position and size as the sub-region B. Exchanging the sub-region A and the sub-region B to obtain a descendant layout Yi。
The above process from step 210 to step 230 can be represented by the following formula:
wherein, randiIs [0,1 ]]A randomly generated random number; operator
Step 230 is shown.
In this embodiment, as shown in fig. 2, the number of nodes between the sub-area a and the sub-area B may not be the same. Thus, the generated descendant layout may have a different number of nodes than the parent layout, thereby enabling adaptive adjustment of the number of nodes.
In addition, the position of the sub-region is determined by the position of the randomly generated node. In this way, therefore, the sub-regions can be automatically adjusted according to the distribution of the nodes, and the regions which are difficult to reach by the nodes are not covered and the regions which are dense in the nodes are likely to be located.
Furthermore, the size of the sub-region can be adaptively adjusted (determined by the distance between two selected nodes), large cross-over regions increase the diversity of the population, while small cross-over regions facilitate fine-tuning.
And S300, mutating the offspring layout. Specifically, the generated offspring layout may be subjected to gaussian mutation with a preset probability.
In some embodiments, the step of performing a gaussian mutation is specifically as follows:
generating a random number with a value range of 0 to 1 for each node in the descendant layout; when the random number is larger than the preset mutation probability, mutating the node according to the following formula:
Nodej·Pk＝Nodej·Pk+N(0,0.5)*(ubk-lbk),k＝1,…,Np
wherein, PkA k-th attribute representing node j; n (0,0.5) represents a Gaussian random number generator; variable ubkAnd lbkRespectively represent PkThe upper and lower limits of (d); n is a radical ofpRepresenting the total number of attributes to be optimized.
S400, comparing the expression values of the mutated offspring layout and the parent layout; the parent layout is the first layout.
In the present embodiment, since each descendant layout YiAre all made byA layout XiAnd a second layout XjGenerate, and then later generate layout YiIs from XiInherited, so XiIs called YiThe parent layout of (1).
After cross-region transformation and mutation, a comparison is needed and a more optimal layout is selected for YiAnd XiA competition comparison is performed to enter the next iteration.
If the descendant layout is better than its parent layout, then the next iteration will be performed using the descendant layout; otherwise, iteration continues using the parent layout and discarding descendant layouts. The mathematical representation of the process is as follows:
s500, when the performance value of the mutated offspring layout is better than that of the parent layout, the mutated offspring layout is used for replacing the parent layout.
S600, when the performance value of the mutated offspring layout is lower than that of the parent layout, discarding the offspring layout.
In some embodiments, it is further necessary to determine whether the optimized descendant layout satisfies a predetermined termination criterion after each iteration. And if so, outputting an optimization result. If not, returning to the step S200 and continuing the optimization iteration.
The embodiment of the invention further provides an optimization system for the deployment of the RFID network nodes. As shown in fig. 4, the optimization system may include: a population initialization module 410, a cross-transformation module 420, a mutation module 430, a comparison module 440, and a selection module 450.
The population initialization module 410 is configured to generate an initial population using a random function. The cross-transformation module 420 is configured to perform sub-region cross-transformation on a first layout in one of the populations and another randomly selected second layout to generate a descendant layout. Mutation module 430 is configured to mutate the progeny layout. The comparison module 440 is configured to compare the mutated offspring layout with the performance value of the parent layout; the parent layout is the first layout and the second layout. The selection module 450 is configured to replace the parent layout with the mutated offspring layout when the mutated offspring layout has a better performance value than the parent layout and discard the offspring layout when the mutated offspring layout has a worse performance value than the parent layout.
In some embodiments, as shown in FIG. 4, the optimization system further includes a termination module 460. The termination module 460 is configured to stop operation of the optimization system when the descendant layouts meet termination criteria.
Specifically, the population initialization module 410 is specifically configured to: for each layout, in [1, Nmax]Within a range of (1) generating a random number niIn which N ismaxIs the maximum number of nodes; and determining the initial position of each node in the arrangeable area by using a random sampling method.
The cross-transform module 420 is specifically configured to: randomly selecting two different nodes from the first layout; calculating the midpoint and the distance of the two nodes; drawing a circular sub-area in the first layout by taking the midpoint as a circle center and the distance as a diameter; the sub-area of the second layout is a circle with the same position and size as the sub-area of the first layout; swapping sub-regions of the first layout and the second layout.
The mutation module 430 is specifically configured to: generating a random number with a value range of 0 to 1 for each node in the descendant layout; when the random number is larger than the preset mutation probability, mutating the node according to the following formula:
Nodej·Pk＝Nodej·Pk+N(0,0.5)*(ubk-lbk),k＝1,…,Np
wherein, PkA k-th attribute representing node j; n (0,0.5) represents a Gaussian random number generator; variable ubkAnd lbkRespectively represent PkThe upper and lower limits of (d); n is a radical ofpRepresenting the total number of attributes to be optimized.
It should be understood that the technical solutions and concepts of the present invention may be equally replaced or changed by those skilled in the art, and all such changes or substitutions should fall within the protection scope of the appended claims.
Claims (6)
1. An optimization method for RFID network node deployment is characterized by comprising the following steps:
s100, generating an initial population by using a random function;
s200, performing sub-region cross transformation on the first layout in one of the populations and another randomly selected second layout to generate a descendant layout;
s300, mutating the offspring layout;
s400, comparing the expression values of the mutated offspring layout and the parent layout; the parent layout is the first layout;
s500, when the mutant offspring layout has a better representation value than the parent layout, replacing the parent layout with the mutant offspring layout;
s600, when the mutated offspring layout has a performance value lower than that of the parent layout, discarding the offspring layout;
the step S100 includes:
for each layout, in [1, Nmax]Within a range of (1) generating a random number niIn which N ismaxIs the maximum number of nodes;
determining the initial position of each node in the arrangeable area by a random sampling method;
the sub-region cross-transforming comprises:
randomly selecting two different nodes from the first layout;
calculating the midpoint and the distance of the two nodes;
drawing a circular sub-area in the first layout by taking the midpoint as a circle center and the distance as a diameter; the sub-area of the second layout is a circle with the same position and size as the sub-area of the first layout;
swapping sub-regions of the first layout and the second layout.
2. The optimization method according to claim 1, further comprising:
repeating steps S100-S600 until the descendant layout meets a termination criterion.
3. The optimization method according to claim 1, wherein the step S300 specifically includes:
generating a random number with a value range of 0 to 1 for each node in the descendant layout;
when the random number is larger than the preset mutation probability, mutating the node according to the following formula:
Nodej·Pk＝Nodej·Pk+N(0，0.5)*(ubk-lbk)，k＝1，…，Np (4)
wherein, PkA k-th attribute representing node j; n (0,0.5) represents a Gaussian random number generator; variable ubkAnd lbkRespectively represent PkThe upper and lower limits of (d); n is a radical ofpRepresenting the total number of attributes to be optimized.
4. An optimization system for RFID network node deployment, comprising:
the population initialization module is used for generating an initial population by using a random function;
the cross transformation module is used for carrying out sub-region cross transformation on the first layout in one population and the second layout randomly selected to generate a descendant layout;
a mutation module to mutate the progeny layout;
the comparison module is used for comparing the performance values of the mutated offspring layout and the parent layout; the parent layout is the first layout;
a selection module for causing the mutated offspring layout to replace the parent layout when the mutated offspring layout has a better representation than the parent layout; and is
Discarding the offspring layout when the mutated offspring layout performs worse than the parental layout;
the population initialization module is specifically configured to:
for each layout, in [1, Nmax]Within a range of (1) generating a random number niIn which N ismaxIs the maximum number of nodes; determining the initial position of each node in the arrangeable area by a random sampling method;
the cross-transform module is specifically configured to:
randomly selecting two different nodes from the first layout; calculating the midpoint and the distance of the two nodes; drawing a circular sub-area in the first layout by taking the midpoint as a circle center and the distance as a diameter; the sub-area of the second layout is a circle with the same position and size as the sub-area of the first layout; swapping sub-regions of the first layout and the second layout.
5. The optimization system of claim 4, wherein the method further comprises: a termination module to stop operation of the optimization system when the descendant layouts meet termination criteria.
6. The optimization system of claim 4, wherein the mutation module is specifically configured to: generating a random number with a value range of 0 to 1 for each node in the descendant layout;
when the random number is larger than the preset mutation probability, mutating the node according to the following formula:
Nodej·Pk＝Nodej·Pk+N(0，0.5)*(ubk-lbk)，k＝1，…，Np (4)
wherein, PkA k-th attribute representing node j; n (0,0.5) represents a Gaussian random number generator; variable ubkAnd lbkRespectively represent PkThe upper and lower limits of (d); n is a radical ofpRepresenting the total number of attributes to be optimized.
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