CN103186686B - The method for designing of artificial electromagnetic material unit structure and device - Google Patents
The method for designing of artificial electromagnetic material unit structure and device Download PDFInfo
- Publication number
- CN103186686B CN103186686B CN201110454496.6A CN201110454496A CN103186686B CN 103186686 B CN103186686 B CN 103186686B CN 201110454496 A CN201110454496 A CN 201110454496A CN 103186686 B CN103186686 B CN 103186686B
- Authority
- CN
- China
- Prior art keywords
- attribute parameter
- parameter value
- unit structure
- module
- value
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Active
Links
- 239000000463 material Substances 0.000 title claims abstract description 36
- 238000000034 method Methods 0.000 title claims abstract description 36
- 238000004422 calculation algorithm Methods 0.000 claims abstract description 13
- 239000002245 particle Substances 0.000 claims description 31
- 238000012546 transfer Methods 0.000 claims description 25
- 238000004364 calculation method Methods 0.000 claims description 14
- 238000013461 design Methods 0.000 claims description 9
- 238000013507 mapping Methods 0.000 claims description 6
- 230000006978 adaptation Effects 0.000 abstract 2
- 230000001413 cellular effect Effects 0.000 abstract 2
- 238000010276 construction Methods 0.000 abstract 2
- 230000006870 function Effects 0.000 description 34
- 238000010586 diagram Methods 0.000 description 5
- 230000004044 response Effects 0.000 description 4
- 239000000758 substrate Substances 0.000 description 3
- 238000005070 sampling Methods 0.000 description 2
- 230000009286 beneficial effect Effects 0.000 description 1
- 238000004590 computer program Methods 0.000 description 1
- 239000004020 conductor Substances 0.000 description 1
- 230000003287 optical effect Effects 0.000 description 1
- 238000005457 optimization Methods 0.000 description 1
- 229920002994 synthetic fiber Polymers 0.000 description 1
- 238000012360 testing method Methods 0.000 description 1
Landscapes
- Management, Administration, Business Operations System, And Electronic Commerce (AREA)
Abstract
The embodiment of the invention discloses a kind of method for designing and device of artificial electromagnetic material unit structure, wherein said method comprises: by clan's algorithm and the target fitness function preset, in cellular construction property parameters territory, search optimum attributes parameter value, described in the optimum attributes parameter value that finds out described target fitness function can be made to export maximum adaptation angle value; Using the optimum cell structure of cellular construction corresponding for the optimum attributes parameter found out as artificial electromagnetic material.Adopt the present invention, its mode relative to manual adaptation unit structure attribute parameter is quicker and convenient.
Description
Technical Field
The invention relates to the field of artificial electromagnetic materials, in particular to a method and a device for designing a unit structure of an artificial electromagnetic material.
Background
The artificial electromagnetic material is also called as metamaterial, is an artificial synthetic material capable of responding to electromagnetic waves, and consists of a substrate and artificial microstructures attached to the substrate, wherein the artificial microstructures are generally structures which are arranged by conductive materials and have certain geometric patterns, so that the artificial microstructures can respond to the electromagnetic waves, and the metamaterial integrally shows electromagnetic properties different from those of the substrate.
In the prior art, the metamaterial is designed by manually changing attribute parameters of a unit structure one by one, testing an electromagnetic response parameter value (generally, a refractive index) of an electromagnetic wave passing through the structure under a specific frequency, and comparing the electromagnetic response parameter value with an expected electromagnetic response parameter value until a unit structure closest to the expected response value is found.
The inventor finds that in the process of implementing the invention, the manual adjustment of the attribute parameters of the unit structure in the prior art is a very time-consuming work, and the workload is very huge because the adjustment and optimization of the geometric parameters of massive unit structures are required to meet the metamaterial design requirements.
Disclosure of Invention
The technical problem to be solved by the embodiments of the present invention is to provide a method and an apparatus for designing a unit structure of an artificial electromagnetic material, which are faster and more convenient than a method for manually adjusting attribute parameters of a unit structure to design a unit structure.
Specifically, the design method of the artificial electromagnetic material unit structure provided by the embodiment of the present invention includes:
searching an optimal attribute parameter value in a unit structure attribute parameter domain through a tribal algorithm and a preset target fitness function, wherein the searched optimal attribute parameter value can enable the target fitness function to output a maximum fitness value;
and taking the unit structure corresponding to the found optimal attribute parameter as the optimal unit structure of the artificial electromagnetic material.
Preferably, the searching for the optimal attribute parameter value in the unit structure attribute parameter domain through the clan algorithm and the preset target fitness function includes:
randomly extracting a plurality of unit structure attribute parameter values from a unit structure attribute parameter space structure to form a clan, wherein each extracted attribute parameter value is a particle in the clan, and the iteration number is recorded as 0;
calculating a fitness value of each attribute parameter value in the clan through a preset target fitness function;
when the fitness value corresponding to each attribute parameter value in the tribe is calculated, performing quality classification on the attribute parameter values in the tribe according to the calculated fitness values, and executing particle transfer operation by applying a particle transfer strategy according to the quality classification;
recording the attribute parameter value corresponding to the currently calculated maximum fitness value;
judging whether an iteration termination condition is met, and if so, taking the recorded attribute parameter value as the searched optimal attribute parameter value; if not, adding 1 to the counted iteration times, returning to the step of executing each attribute parameter value in the clan and calculating the fitness value through the target fitness function.
Preferably, after recording the attribute parameter value corresponding to the currently calculated maximum fitness value, the method further includes:
judging whether the current counted iteration times reach half of the preset total iteration times or not, and if so, adjusting the tribe structure through a tribe structure adjustment rule; if not, continuously judging whether the iteration termination condition is met.
Preferably, ,
the iteration termination condition is as follows: the iteration times reach the preset total iteration times;
or,
the iteration termination condition is as follows: after the predetermined number of iterations, the attribute parameter value recorded in the step of recording the attribute parameter value corresponding to the currently calculated maximum fitness value is substantially unchanged.
Preferably, the method further comprises:
and presetting a target fitness function.
Preferably, ,
the target fitness function is noted asWherein，for the value of the attribute parameter of the unit structure,is the refractive index corresponding to the parameter value of the unit structure property,is composed ofAndthe mapping relationship between the two groups of the data,is the desired refractive index.
Correspondingly, the device for designing the artificial electromagnetic material unit structure provided by the embodiment of the invention comprises:
the setting module is used for setting a target fitness function;
the searching module is used for searching an optimal attribute parameter value in the unit structure attribute parameter domain through a tribal algorithm and a target fitness function preset by the setting module, and the searched optimal attribute parameter value can enable the target fitness function to output a maximum fitness value;
and the determining module is used for taking the unit structure corresponding to the found optimal attribute parameter as the optimal unit structure of the artificial electromagnetic material.
Preferably, the searching module includes:
the initialization module is used for randomly extracting a plurality of unit structure attribute parameter values from a unit structure attribute parameter space structure to form a clan, wherein each extracted attribute parameter value is a particle in the clan, and the iteration number is recorded as 0;
the calculation module is used for calculating a fitness value of each attribute parameter value in the clan through a preset target fitness function;
the transfer module is used for carrying out quality classification on the attribute parameter values in the tribe according to the calculated sizes of the multiple fitness values when the fitness values corresponding to the attribute parameter values in the tribe are calculated, and executing particle transfer operation by applying a particle transfer strategy according to the quality classification;
the storage module is used for recording the attribute parameter value corresponding to the maximum fitness value calculated by the calculation module each time, recording the preset total iteration times, counting the iteration times, wherein the counted initial iteration times is 0, and recording the iteration termination condition;
the first judging module is used for judging whether an iteration termination condition is met or not according to the storage content of the storage module, and if so, taking the recorded attribute parameter value as the searched optimal attribute parameter value; if not, the storage module is informed to add 1 to the iteration number, and then the calculation module is returned to continue calculating.
Preferably, the searching module further comprises:
the tribal adjustment module is used for adjusting the tribal structure according to the tribal structure adjustment rule;
the second judgment module is used for judging whether the current iteration times reach half of the preset total iteration times or not, and if so, informing the tribe adjustment module of adjusting the tribe structure through a tribe structure adjustment rule; if the judgment result is no, the first judgment module is informed to execute corresponding judgment.
Preferably, ,
the iteration termination condition is as follows: the iteration times reach the preset total iteration times;
or,
the iteration termination condition is as follows: after the predetermined number of iterations, the attribute parameter value recorded in the step of recording the attribute parameter value corresponding to the currently calculated maximum fitness value is substantially unchanged.
The embodiment of the invention has the following beneficial effects:
according to the embodiment of the invention, the optimal attribute parameter value is searched in the unit structure attribute parameter domain through the tribal algorithm and the preset target fitness function, so that the automatic acquisition of the optimal attribute parameter value of the unit structure attribute parameter is realized, and compared with the conventional mode of searching the optimal attribute parameter through manually adjusting the unit structure attribute parameter, the method is quicker and more convenient, so that the designed unit structure is more optimal.
Drawings
In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings used in the description of the embodiments or the prior art will be briefly described below, it is obvious that the drawings in the following description are only some embodiments of the present invention, and for those skilled in the art, other drawings can be obtained according to the drawings without creative efforts.
FIG. 1 is a schematic flow chart illustrating an embodiment of a method for designing a cell structure of an artificial electromagnetic material according to the present invention;
FIG. 2 is a schematic flow chart diagram illustrating a method for designing an artificial electromagnetic material unit structure according to another embodiment of the present invention;
FIG. 3 is a schematic structural composition diagram of an embodiment of a device for designing a unit structure of an artificial electromagnetic material;
FIG. 4 is a schematic structural composition diagram of another embodiment of a design device for an artificial electromagnetic material unit structure.
Detailed Description
The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
The embodiment of the invention discloses a design method of an artificial electromagnetic material unit structure, which comprises the following steps: searching an optimal attribute parameter value in a unit structure attribute parameter domain through a tribal algorithm and a preset target fitness function, wherein the searched optimal attribute parameter value can enable the target fitness function to output a maximum fitness value; and taking the unit structure corresponding to the found optimal attribute parameter as the optimal unit structure of the artificial electromagnetic material. According to the embodiment of the invention, the optimal attribute parameter value is searched in the unit structure attribute parameter domain through the tribal algorithm and the preset target fitness function, so that the automatic acquisition of the optimal attribute parameter value of the unit structure attribute parameter is realized, and compared with the conventional mode of searching the optimal attribute parameter through manually adjusting the unit structure attribute parameter, the method is quicker and more convenient, so that the designed unit structure is more optimal.
Fig. 1 is a schematic flow chart of an embodiment of a method for designing a unit structure of an artificial electromagnetic material according to the present invention. As shown in fig. 1, the method of the present embodiment includes:
step S110, randomly extracting a plurality of unit structure attribute parameter values from the unit structure attribute parameter space structure to form a clan, where the number of iterations is recorded as 0. In a specific implementation, step S110 may be understood as an initialization step, where each extracted attribute parameter value is a particle in the clan, and each particle is generated by uniformly sampling from a spatial structure of unit structure attribute parameters. As a specific example 1, it can be assumed that M unit structure attribute parameter values are randomly extracted, wherein each attribute parameter value is recorded as，Then M unit structure attribute parameter values constitute a clan, whereFor the particles in the clan, M is the number of particles included in the clan, t represents the number of iterations, and t =0 corresponds to the initialization step.
Step S111 calculates a fitness value for each attribute parameter value in the clan by using a preset target fitness function. In a specific implementation, the target fitness function may be preset and may be recorded as
Wherein，for the value of the attribute parameter of the unit structure,is the refractive index corresponding to the parameter value of the unit structure property,is composed ofAndthe mapping relationship between the two groups of the data,is the desired refractive index. If the above example 1 is combined, in step S111, the value of each attribute parameter in the clan is subjected toBy passingAndcalculate eachCorresponding fitness valueAnd t = 0. Wherein,the implementation of (2) can be obtained by simulating a curve through cubic spline interpolation, which is not described herein.
And step S112, when the fitness value corresponding to each attribute parameter value in the clan is calculated, performing quality classification on the attribute parameter values in the clan according to the calculated fitness values, and executing particle transfer operation by applying a particle transfer strategy according to the quality classification. In a specific implementation, the attribute parameter values in the clan may be classified into two or more levels at step S112, and the three levels may be recorded as "excellent", "good", and "poor" when classified into three levels, and may be recorded as "good" and "poor" when classified into two levels. The classification rule can be realized by setting corresponding grade threshold values, and when the calculated fitness value is greater than or equal to the corresponding grade threshold value, the attribute parameter values can be classified into the corresponding grade. For example, if the calculated overall fitness value is in the range of 0 to 10, 6.7 may be regarded as a ranking threshold value for dividing "excellent" and 3.3 may be regarded as a ranking threshold value for "good", so that the calculated fitness value is classified as "excellent" when it is equal to or greater than 6.7, as "good" when it is equal to or greater than 3.3 and less than 6.7, and as "poor" when it is less than 3.3 and small. The application of the particle transfer strategy to perform the particle transfer operation is a known technique, and will not be described herein.
In step S113, the attribute parameter value corresponding to the currently calculated maximum fitness value is recorded. If the above example 1 is combined, the maximum fitness value recorded in step S113 isAnd the value of t is increased from 0 to the maximum value L.
Step S114, judging whether an iteration termination condition is met, and if so, executing step S115; if not, adding 1 to the counted iteration number, and returning to execute the step S111. In a specific implementation, the iteration termination condition is as follows: the iteration times reach the preset total iteration times; or, the iteration termination condition is: after the predetermined number of iterations, the attribute parameter value recorded in the step of recording the attribute parameter value corresponding to the currently calculated maximum fitness value is substantially unchanged. With reference to the above example 1, the total number of iterations may be set to L in advance, and when the iteration termination condition is: when the iteration number reaches the preset total iteration number, judging whether the value of t is equal to L in step S114, if so, executing step S115; if not, adding 1 to the value of the counted iteration times t, and returning to execute the step S111. And when the iteration termination condition is: after the predetermined number of iterations, if the attribute parameter value recorded in the step of recording the attribute parameter value corresponding to the currently calculated maximum fitness value is substantially unchanged, the number of consecutive iterations may be preset to 5, and then it is determined in step S114 whether the attribute parameter value recorded in the step of recording the attribute parameter value corresponding to the currently calculated maximum fitness value is substantially unchanged for 5 consecutive times, if so, step S115 is executed; if not, adding 1 to the value of the counted iteration times t, and returning to execute the step S111.
And step S115, taking the recorded attribute parameter value as the found optimal attribute parameter value.
And S116, taking the unit structure corresponding to the found optimal attribute parameter as the optimal unit structure of the artificial electromagnetic material.
FIG. 2 is a schematic flow chart of another embodiment of the method for designing the unit structure of the artificial electromagnetic material of the present invention. In this embodiment, before extracting a plurality of unit structure attribute parameter values from the unit structure attribute parameter space structure, a target fitness function is preset, and in the process of searching for an optimal attribute parameter value, the tribal structure is adjusted according to a tribal structure adjustment rule, so as to further optimize the structure of the tribal. Specifically, as shown in fig. 2, the method of this embodiment includes:
and S210, presetting a target fitness function.
In a specific implementation, the target fitness function may be written as
Wherein，for the value of the attribute parameter of the unit structure,is the refractive index corresponding to the parameter value of the unit structure property,is composed ofAndthe mapping relationship between the two groups of the data,is the desired refractive index. Wherein,the implementation of (2) can be obtained by simulating a curve through cubic spline interpolation, which is not described herein.
Step S211, randomly extracting a plurality of unit structure attribute parameter values from the unit structure attribute parameter space structure to form a clan, where the number of iterations is recorded as 0. In a specific implementation, step S211 is the same as step S210 in fig. 1, and is not described herein again.
In step S212, a fitness value is calculated for each attribute parameter value in the clan by a preset target fitness function. In a specific implementation, step S212 is the same as step S111 in fig. 1, and is not described herein again.
Step S213, when calculating the fitness value corresponding to each attribute parameter value in the clan, according to the calculated fitness values, performing quality classification on the attribute parameter values in the clan, and according to the quality classification, applying a particle transfer strategy to execute particle transfer operation. In a specific implementation, step S213 is the same as step S112 in fig. 1, and is not described herein again.
Step S214, recording the attribute parameter value corresponding to the currently calculated maximum fitness value. In a specific implementation, step S214 is the same as step S113 in fig. 1, and is not described herein again.
Step S215, judging whether the current counted iteration frequency reaches half of the preset total iteration frequency, if so, executing step S216; if not, step S217 is performed.
In step S216, the clan structure is adjusted according to the clan structure adjustment rule, and then step S217 is performed. In a specific implementation, the adjustment of the structure of the land is the prior art, and is not described herein again.
Step S217, judging whether an iteration termination condition is met, and if so, executing step S218; if not, the step S212 is executed after adding 1 to the counted iteration count. In a specific implementation, step S217 is the same as step S114 in fig. 1, and is not described herein again.
Step S218, the recorded attribute parameter value is used as the found optimal attribute parameter value. In the specific implementation, step S2187 is the same as step S115 in fig. 1, and is not described herein again.
Step S219, the unit structure corresponding to the found optimal attribute parameter is used as the optimal unit structure of the artificial electromagnetic material.
Correspondingly, the embodiment of the invention discloses a device for designing the unit structure of the artificial electromagnetic material, which comprises a setting module, a calculating module and a calculating module, wherein the setting module is used for setting a target fitness function; the searching module is used for searching an optimal attribute parameter value in the unit structure attribute parameter domain through a tribal algorithm and a target fitness function preset by the setting module, and the searched optimal attribute parameter value can enable the target fitness function to output a maximum fitness value; and the determining module is used for taking the unit structure corresponding to the found optimal attribute parameter as the optimal unit structure of the artificial electromagnetic material. According to the embodiment of the invention, the optimal attribute parameter value is searched in the unit structure attribute parameter domain through the tribal algorithm and the preset target fitness function, so that the automatic acquisition of the optimal attribute parameter value of the unit structure attribute parameter is realized, and the method is quicker and more convenient compared with the existing method for searching the optimal attribute parameter through manually adjusting the unit structure attribute parameter.
FIG. 3 is a schematic structural diagram of an apparatus for designing a unit structure of an artificial electromagnetic material according to an embodiment. Which can be used to execute the method shown in fig. 1, as shown in fig. 3, the apparatus provided in the embodiment of the present invention includes: a setting module 31, a lookup module 32, and a determination module 33, wherein:
the setting module 31 is configured to set a target fitness function. In a specific implementation, the target fitness function set by the setting module 31 can be recorded asWherein，for the value of the attribute parameter of the unit structure,is the refractive index corresponding to the parameter value of the unit structure property,is composed ofAndthe mapping relationship between the two groups of the data,is the desired refractive index. Wherein,the implementation of (2) can be obtained by simulating a curve through cubic spline interpolation, which is not described herein.
The searching module 32 is configured to search an optimal attribute parameter value in the unit structure attribute parameter domain through a tribal algorithm and a target fitness function preset by the setting module, where the searched optimal attribute parameter value enables the target fitness function to output a maximum fitness value. Still referring to fig. 1, the lookup module 32 may further include: an initialization module 321, a calculation module 322, a transfer module 323, a storage module 324, and a first determination module 325, wherein:
the initialization module 321 is configured to randomly extract a plurality of unit structure attribute parameter values from a unit structure attribute parameter space structure to form a clan, where each extracted attribute parameter value is a particle in the clan, and the number of iterations is recorded as 0. In a specific implementation, each attribute parameter value extracted by the initialization module 321 is a particle in the clan, and each particle is generated by uniformly sampling from a unit structure attribute parameter space structure. In connection with the foregoing example 1, the initialization module 321 may assume that M unit structure attribute parameter values are randomly extracted, where each attribute parameter value is recorded as，Then M unit structure attribute parameter values constitute a clan, whereFor the particles in the clan, M is the number of particles included in the clan, t represents the number of iterations, and t =0 corresponds to the initialization step.
The calculating module 322 is configured to calculate a fitness value for each attribute parameter value in the clan through a preset target fitness function. If example 1 above is combined, calculation module 322 may determine the value of each attribute parameter in the clanBy passingAndcalculate eachCorresponding fitness valueAnd t = 0. Wherein,the implementation of (2) can be obtained by simulating a curve through cubic spline interpolation, which is not described herein.
The transfer module 323 is configured to, when calculating a fitness value corresponding to each attribute parameter value in the clan, perform quality classification on the attribute parameter values in the clan according to the calculated fitness values, and perform a particle transfer operation by applying a particle transfer strategy according to the quality classification. In particular implementations, the transfer module 323 can classify attribute parameter values in a clan into two or more levels, which can be recorded as "excellent", "good", and "poor" when classified into three levels, and can be recorded as "good" and "poor" when classified into two levels. The classification rule can be realized by setting corresponding grade threshold values, and when the calculated fitness value is greater than or equal to the corresponding grade threshold value, the attribute parameter values can be classified into the corresponding grade. For example, if the calculated overall fitness value is in the range of 0 to 10, 6.7 may be regarded as a ranking threshold value for dividing "excellent" and 3.3 may be regarded as a ranking threshold value for "good", so that the calculated fitness value is classified as "excellent" when it is equal to or greater than 6.7, as "good" when it is equal to or greater than 3.3 and less than 6.7, and as "poor" when it is less than 3.3 and small. The application of the particle transfer strategy to perform the particle transfer operation is a known technique, and will not be described herein.
The storage module 324 is configured to record the attribute parameter value corresponding to the maximum fitness value calculated by the calculation module each time, record a preset total iteration count, count an iteration count, where an initial iteration count counted by the iteration count is 0, and record an iteration termination condition.
The first judging module 325 is configured to judge whether an iteration termination condition is satisfied according to the storage content of the storage module, and if yes, take the recorded attribute parameter value as the found optimal attribute parameter value; if not, the storage module is informed to add 1 to the iteration number, and then the calculation module is returned to continue calculating. In a specific implementation, the iteration termination condition is as follows: the iteration times reach the preset total iteration times; or, the iteration termination condition is: after the predetermined number of iterations, the attribute parameter value recorded in the step of recording the attribute parameter value corresponding to the currently calculated maximum fitness value is substantially unchanged. With reference to the above example 1, the total number of iterations may be set to L in advance, and when the iteration termination condition is: when the iteration number reaches the preset total iteration number, the first judging module 325 may judge whether the value of t is equal to L, and if yes, the recorded attribute parameter value is used as the found optimal attribute parameter value; if not, the storage module is informed to add 1 to the iteration number, and then the calculation module is returned to continue calculating. And when the iteration termination condition is: after the preset iteration times, when the recorded attribute parameter value in the step of recording the attribute parameter value corresponding to the currently calculated maximum fitness value is basically unchanged, the continuous iteration times can be preset to be 5 times, the first judgment module judges whether the recorded attribute parameter value in the step of recording the attribute parameter value corresponding to the currently calculated maximum fitness value is continuously 5 times and is basically unchanged, and if the judgment is yes, the recorded attribute parameter value is used as the searched optimal attribute parameter value; if not, the storage module is informed to add 1 to the iteration number, and then the calculation module is returned to continue calculating.
The determining module 33 is configured to use the unit structure corresponding to the found optimal attribute parameter as the optimal unit structure of the artificial electromagnetic material.
FIG. 4 is a schematic structural composition diagram of another embodiment of a design device for an artificial electromagnetic material unit structure. Which can be used to execute the method shown in fig. 2, as shown in fig. 4, the apparatus provided in the embodiment of the present invention includes: a setting module 41, a lookup module 42, and a determination module 33, wherein: the setting module 41 and the determining module 43 have the same functions as the setting module 31 and the determining module 33 in fig. 3, and are not described herein again. The searching module 42 in this embodiment includes: the functions of the initialization module 421, the calculation module 422, the transfer module 423, the storage module 424, the second determination module 425, the clan adjustment module 426, and the first determination module 427 are respectively the same as the functions of the initialization module 321, the calculation module 322, the transfer module 323, the storage module 324, and the first determination module 325 shown in fig. 3, and thus are not described herein again. The functions of the second determining module 425 and the clan adjusting module 426 of the embodiment are described below, wherein:
the second determining module 425 is configured to determine whether the current iteration number reaches half of a preset total iteration number, and if yes, notify the clan adjusting module 426 to adjust the clan structure according to a clan structure adjusting rule; if the judgment is no, the first judgment module 427 is notified to execute the corresponding judgment.
The clan adjusting module 426 is configured to adjust a clan structure according to a clan structure adjusting rule.
It will be understood by those skilled in the art that all or part of the processes of the methods of the embodiments described above can be implemented by a computer program, which can be stored in a computer-readable storage medium, and when executed, can include the processes of the embodiments of the methods described above. The storage medium may be a magnetic disk, an optical disk, a Read-only memory (ROM), a Random Access Memory (RAM), or the like.
The above disclosure is only for the purpose of illustrating the preferred embodiments of the present invention, and it is therefore to be understood that the invention is not limited by the scope of the appended claims.
Claims (7)
1. A design method of an artificial electromagnetic material unit structure is characterized by comprising the following steps:
searching an optimal attribute parameter value in a unit structure attribute parameter domain through a tribal algorithm and a preset target fitness function, wherein the searched optimal attribute parameter value can enable the target fitness function to output a maximum fitness value, and the target fitness function is recorded asWherein n ═ y: (g) G is a unit structure attribute parameter value, N is a refractive index corresponding to the unit structure attribute parameter value, y (g) is a mapping relation between N and g, and N0Is a desired refractive index;
taking the unit structure corresponding to the found optimal attribute parameter as the optimal unit structure of the artificial electromagnetic material;
the step of searching the optimal attribute parameter value in the unit structure attribute parameter domain through the clan algorithm and the preset target fitness function comprises the following steps:
randomly extracting a plurality of unit structure attribute parameter values from a unit structure attribute parameter space structure to form a clan, wherein each extracted attribute parameter value is a particle in the clan, and the iteration number is recorded as 0;
calculating a fitness value of each attribute parameter value in the clan through a preset target fitness function;
when the fitness value corresponding to each attribute parameter value in the tribe is calculated, performing quality classification on the attribute parameter values in the tribe according to the calculated fitness values, and executing particle transfer operation by applying a particle transfer strategy according to the quality classification;
recording the attribute parameter value corresponding to the currently calculated maximum fitness value;
judging whether an iteration termination condition is met, and if so, taking the recorded attribute parameter value as the searched optimal attribute parameter value; if not, adding 1 to the counted iteration times, returning to the step of executing each attribute parameter value in the clan and calculating the fitness value through the target fitness function.
2. The method for designing the unit structure of the artificial electromagnetic material according to claim 1, wherein after recording the attribute parameter value corresponding to the currently calculated maximum fitness value, the method further comprises:
judging whether the current counted iteration times reach half of the preset total iteration times or not, and if so, adjusting the tribe structure through a tribe structure adjustment rule; if not, continuously judging whether the iteration termination condition is met.
3. The method of designing an artificial electromagnetic material unit structure according to claim 1 or 2,
the iteration termination condition is as follows: the iteration times reach the preset total iteration times;
or,
the iteration termination condition is as follows: after the predetermined number of iterations, the attribute parameter value recorded in the step of recording the attribute parameter value corresponding to the currently calculated maximum fitness value is substantially unchanged.
4. The method of designing an artificial electromagnetic material cell structure of claim 1, further comprising:
and presetting a target fitness function.
5. A design device of an artificial electromagnetic material unit structure is characterized by comprising:
a setting module for setting a target fitness function, wherein the target fitness function is recorded asWherein, N ═ y (g), g is the parameter value of the unit structure attribute, N is the refractive index corresponding to the parameter value of the unit structure attribute, y (g) is the mapping relation between N and g, N is0Is a desired refractive index;
the searching module is used for searching an optimal attribute parameter value in the unit structure attribute parameter domain through a tribal algorithm and a target fitness function preset by the setting module, and the searched optimal attribute parameter value can enable the target fitness function to output a maximum fitness value;
the determining module is used for taking the unit structure corresponding to the found optimal attribute parameter as the optimal unit structure of the artificial electromagnetic material;
the searching module comprises:
the initialization module is used for randomly extracting a plurality of unit structure attribute parameter values from a unit structure attribute parameter space structure to form a clan, wherein each extracted attribute parameter value is a particle in the clan, and the iteration number is recorded as 0;
the calculation module is used for calculating a fitness value of each attribute parameter value in the clan through a preset target fitness function;
the transfer module is used for carrying out quality classification on the attribute parameter values in the tribe according to the calculated sizes of the multiple fitness values when the fitness values corresponding to the attribute parameter values in the tribe are calculated, and executing particle transfer operation by applying a particle transfer strategy according to the quality classification;
the storage module is used for recording the attribute parameter value corresponding to the maximum fitness value calculated by the calculation module each time, recording the preset total iteration times, counting the iteration times, wherein the counted initial iteration times is 0, and recording the iteration termination condition;
the first judging module is used for judging whether an iteration termination condition is met or not according to the storage content of the storage module, and if so, taking the recorded attribute parameter value as the searched optimal attribute parameter value; if not, the storage module is informed to add 1 to the iteration number, and then the calculation module is returned to continue calculating.
6. The apparatus for designing an artificial electromagnetic material unit structure according to claim 5, wherein the search module further comprises:
the tribal adjustment module is used for adjusting the tribal structure according to the tribal structure adjustment rule;
the second judgment module is used for judging whether the current iteration times reach half of the preset total iteration times or not, and if so, informing the tribe adjustment module of adjusting the tribe structure through a tribe structure adjustment rule; if the judgment result is no, the first judgment module is informed to execute corresponding judgment.
7. The designing apparatus of the artificial electromagnetic material unit structure as recited in claim 5 or 6,
the iteration termination condition is as follows: the iteration times reach the preset total iteration times;
or,
the iteration termination condition is as follows: after the predetermined number of iterations, the attribute parameter value recorded in the step of recording the attribute parameter value corresponding to the currently calculated maximum fitness value is substantially unchanged.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201110454496.6A CN103186686B (en) | 2011-12-30 | 2011-12-30 | The method for designing of artificial electromagnetic material unit structure and device |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201110454496.6A CN103186686B (en) | 2011-12-30 | 2011-12-30 | The method for designing of artificial electromagnetic material unit structure and device |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN103186686A CN103186686A (en) | 2013-07-03 |
| CN103186686B true CN103186686B (en) | 2016-04-13 |
Family
ID=48677853
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN201110454496.6A Active CN103186686B (en) | 2011-12-30 | 2011-12-30 | The method for designing of artificial electromagnetic material unit structure and device |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN103186686B (en) |
Families Citing this family (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN114239439B (en) * | 2021-12-24 | 2022-11-08 | 浙江金乙昌科技股份有限公司 | Automatic filter design method based on tribal algorithm |
-
2011
- 2011-12-30 CN CN201110454496.6A patent/CN103186686B/en active Active
Non-Patent Citations (4)
| Title |
|---|
| "粒子群算法中随机数参数的设置与试验分析";刘志雄等;《控制理论与应用》;20101130;第27卷(第11期);第1489-1496页 * |
| 一种遗传算法适应度函数的改进方法;张思才等;《计算机应用与软件》;20060228;第23卷(第2期);第108-110页 * |
| 粒子群算法在最优化问题中的研究;梁军;《中国优秀硕士学位论文全文数据库（电子期刊）信息科技辑》;20090915;第2008年卷(第09期);I138-13页 * |
| 遗传算法中适应度函数的研究;刘英;《兰州工业高等专科学校学报》;20060930;第13卷(第3期);第1-4页 * |
Also Published As
| Publication number | Publication date |
|---|---|
| CN103186686A (en) | 2013-07-03 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| Yan et al. | Kapur’s entropy for underwater multilevel thresholding image segmentation based on whale optimization algorithm | |
| CN103888541B (en) | Method and system for discovering cells fused with topology potential and spectral clustering | |
| CN108320293A (en) | A kind of combination improves the quick point cloud boundary extractive technique of particle cluster algorithm | |
| CN113537316B (en) | Vehicle detection method based on 4D millimeter wave radar point cloud | |
| CN111062425B (en) | Unbalanced data set processing method based on C-K-SMOTE algorithm | |
| CN104850712B (en) | Surface sampled data topology Region Queries method in kind | |
| CN104778477A (en) | Optimized ABC (artificial bee colony) clustering method | |
| CN109151727B (en) | WLAN fingerprint positioning database construction method based on improved DBN | |
| CN108766464B (en) | Digital audio tampering automatic detection method based on power grid frequency fluctuation super vector | |
| CN109657147A (en) | Microblogging abnormal user detection method based on firefly and weighting extreme learning machine | |
| CN113269791A (en) | Point cloud segmentation method based on edge judgment and region growth | |
| CN106610977B (en) | Data clustering method and device | |
| CN102881015A (en) | Method for extracting boundary characteristics of unorganized point cloud of product model | |
| CN115906303A (en) | Planar microwave filter design method and device based on machine learning | |
| CN103186686B (en) | The method for designing of artificial electromagnetic material unit structure and device | |
| CN114898118A (en) | Automatic statistical method and system for power transmission line house removal amount based on multi-source point cloud | |
| CN103473437B (en) | Cloud service platform selection method based on fuzzy ideal target analysis | |
| CN104749954B (en) | Intelligent ash bin control system device based on IMC (Internal Model Control) strategy and control method | |
| CN104700458B (en) | Surface sampled data in kind boundary spot identification method | |
| Yuan et al. | An optimized initialization center K-means clustering algorithm based on density | |
| CN110309424A (en) | A kind of socialization recommended method based on Rough clustering | |
| Cui et al. | Weighted particle swarm clustering algorithm for self-organizing maps | |
| CN114792138A (en) | Machine learning model interpretation method, device and storage medium | |
| CN105808718B (en) | Text feature selection method based on unbalanced data set | |
| Yong et al. | A real-time detection algorithm based on nanodet for pavement cracks by incorporating attention mechanism |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| C06 | Publication | ||
| PB01 | Publication | ||
| C10 | Entry into substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| C14 | Grant of patent or utility model | ||
| GR01 | Patent grant |