CN103177169A - Method and device for obtaining parameter of metamaterial modular construction body - Google Patents
Method and device for obtaining parameter of metamaterial modular construction body Download PDFInfo
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- CN103177169A CN103177169A CN2011104321309A CN201110432130A CN103177169A CN 103177169 A CN103177169 A CN 103177169A CN 2011104321309 A CN2011104321309 A CN 2011104321309A CN 201110432130 A CN201110432130 A CN 201110432130A CN 103177169 A CN103177169 A CN 103177169A
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Abstract
The invention discloses a method and a device for obtaining a parameter of a metamaterial modular construction body. The method comprises the following steps. Multiple optimizing indexes and target fitness functions corresponding to the optimizing indexes are obtained, and a fitness value reflects the difference of an electromagnetic response parameter of a design sample in a specific condition and a specific electromagnetic response parameter of the design sample in the optimizing index in the specific condition. The design sample comprises one or a plurality of parameters of the modular construction body, a plurality of corresponding design samples provided with high fitness values and obtaining all the optimizing indexes, and optimized design samples obtained based on design sample spaces corresponding to all the optimizing indexes. By adoption of the method and the device for obtaining the parameter of the metamaterial modular construction body, rapid multi-target optimal modular construction body parameters are achieved, and metamaterial design efficiency is greatly improved.
Description
Technical field
The present invention relates to super Material Field, relate in particular to a kind of method and apparatus that obtains the parameter of super material cell structure.
Background technology
Super material is forward position cross discipline research field in the our times scope, and wide application market prospect is arranged.The optimized design of super material cell structure is the key link in artificial electromagnetic material research and design.At present the design of metamaterial structure cell cube still rested on the manual adjustment stage by rule of thumb, can't guarantee design accuracy, hindered extensive design and the commercial application of artificial electromagnetic material.Optimized design for super material cell structure is present difficult problem of needing solution badly in the world.
Prior art is by manually changing one by one the property parameters of cellular construction body to the design of super material, under the test characteristic frequency, electromagnetic wave is by the electromagnetic response after this structure, and compare with expectation electromagnetic response parameter value, until till finding the cellular construction body that approaches the most with the Expected Response value.
Can find out that from above-mentioned flow process adjustment unit structure parameter is a step very consuming time, need to adjust optimization to the cellular construction body geometric parameter of magnanimity in order to reach super design of material requirement, its workload is very huge.
Summary of the invention
Embodiment of the present invention technical matters to be solved is, a kind of method and apparatus that obtains the parameter of super material cell structure is provided.Can realize the parameter of multiobjective optimization cellular construction body fast, improve greatly the efficient of super design of material.
In order to solve the problems of the technologies described above, the embodiment of the present invention provides a kind of method that obtains the parameter of super material cell structure, and it comprises:
Obtain a plurality of optimization indexs and target fitness function corresponding to described optimization index, wherein, described optimization index comprises the super material cell structure of optimization aim specific electromagnetic response parameter under given conditions, described target fitness function is used for obtaining fitness value, described fitness value reflection design sample under described specified conditions the electromagnetic response parameter and the gap between the described specific electromagnetic response parameter in the optimization index of described specified conditions, described design sample comprises one or more parameters of cellular construction body;
Obtain respectively to optimize a plurality of design samples with larger fitness value corresponding to index, wherein, optimize an index corresponding design sample space that comprises a plurality of design samples take the parameter of cellular construction body as the parameter axle for one;
Optimize design sample space acquisition optimum design sample corresponding to index according to each.
Wherein, described basis is respectively optimized design sample space corresponding to index and is obtained the optimum design sample and can comprise:
The sample space that whether has repetition between design sample space corresponding to index is respectively optimized in judgement, have if judgment result is that the sample space that repeats, obtain parameter corresponding to the central point of sample space of described repetition as optimum results, if judgment result is that not have repeated sample, obtain apart from each parameter of optimizing the nearest some correspondence in design sample space corresponding to index as optimum results.
Described target fitness function can be:
Q
i.k＝1/(n
k(f
i)-N
i+a)
2
Wherein, f
iThe specified conditions of index i, n are optimized in expression
k(f
i) represent that design sample k is at f
iElectromagnetic response parameter under condition, N
iThe specific electromagnetic response parameter of index i is optimized in expression, and i and k are the natural number greater than 1, and a is predetermined constant, Q
i.kBe the fitness value of design sample k for optimization index i.
Described acquisition is respectively optimized a plurality of design samples with larger fitness value corresponding to index and can be comprised:
Obtain respectively to optimize a plurality of design samples with larger fitness value corresponding to index according to particle swarm optimization algorithm, or further specifically comprise:
Obtain initialized population
Wherein, in described population, the initialization particle rapidity of each particle is v
k, particle g
kThe parameter that comprises the cellular construction body of design sample k;
Calculate the fitness value Q of each sample in described initialized population
i.k, and the fitness value that obtains particle in described initialized population is peaked particle and maximum fitness value Q
I.best
Upgrade respectively the particle rapidity of population by following formula
v
k+m＝c
0×v
k+c
1×rand×(pb
k-g
k)+c
2×rand×(gb-g
k)；
According to the following formula with the particle rapidity after upgrading
Upgrade respectively population
In each particle:
g
k+m＝g
k+v
k+m；
Repeat above-mentioned particle step of updating, upgrade end condition until satisfy, and a plurality of particles with larger fitness value corresponding to index are respectively optimized in output;
Wherein, c
0, c
1And c
2Be three constants, rand is the uniform random number between 0 and 1, and gb represents that the fitness value of particle in the population of this iteration is peaked particle, pb
kRepresent that k particle is peaked particle by the end of this iteration fitness value in the iteration renewal process.
Accordingly, the embodiment of the present invention has also proposed a kind of device that obtains the parameter of super material cell structure, and this device comprises:
Optimize the index module, be used for obtaining a plurality of optimization indexs and target fitness function corresponding to described optimization index, wherein, described optimization index comprises the super material cell structure of optimization aim specific electromagnetic response parameter under given conditions, described target fitness function is used for obtaining fitness value, described fitness value reflection design sample under described specified conditions the electromagnetic response parameter and the gap between the described specific electromagnetic response parameter in the optimization index of described specified conditions, described design sample comprises one or more parameters of cellular construction body;
Sample obtains module, is used for obtaining respectively to optimize a plurality of design samples with larger fitness value corresponding to index, wherein, optimizes an index corresponding design sample space that comprises a plurality of design samples take the parameter of cellular construction body as the parameter axle for one;
Optimize sample and obtain module, be used for optimizing design sample space acquisition optimum design sample corresponding to index according to each.
Wherein, described optimization sample obtains module and also is used for, the sample space that whether has repetition between design sample space corresponding to index is respectively optimized in judgement, have if judgment result is that the sample space that repeats, obtain parameter corresponding to the central point of sample space of described repetition as optimum results, if judgment result is that not have repeated sample, obtain apart from each parameter of optimizing the nearest some correspondence in design sample space corresponding to index as optimum results.
Described target fitness function can be:
Q
i.k＝1/(n
k(f
i)-N
i+a)
2
Wherein, f
iThe specified conditions of index i, n are optimized in expression
k(f
i) represent that design sample k is at f
iElectromagnetic response parameter under condition, N
iThe specific electromagnetic response parameter of index i is optimized in expression, and i and k are the natural number greater than 1, and a is predetermined constant, Q
i.kBe the fitness value of design sample k for optimization index i.
Described sample obtains module and also is used for obtaining respectively to optimize a plurality of design samples with larger fitness value corresponding to index according to particle swarm optimization algorithm.Further, described sample acquisition module can comprise:
Initialization unit is used for obtaining initialized population
Wherein, in described population, the initialization particle rapidity of each particle is v
k, particle g
kThe parameter that comprises the cellular construction body of design sample k;
The fitness computing unit is for the fitness value Q that calculates described initialized each sample of population
i.k, and the fitness value that obtains particle in described initialized population is peaked particle and maximum fitness value Q
I.best
The speed updating block is for upgrade respectively the particle rapidity of population by following formula
v
k+m＝c
0×v
k+c
1×rand×(pb
k-g
k)+c
2×rand×(gb-g
k)；
The particle updating block is used for according to the following formula with the particle rapidity after upgrading
Upgrade respectively population
In each particle:
g
k+m＝g
k+v
k+m；
Iteration unit is used for repeating above-mentioned particle step of updating, upgrade end condition until satisfy, and a plurality of particles with larger fitness value corresponding to index is respectively optimized in output;
Wherein, c
0, c
1And c
2Be three constants, rand is the uniform random number between 0 and 1, and gb represents that the fitness value of particle in the population of this iteration is peaked particle, pb
kRepresent that k particle is peaked particle by the end of this iteration fitness value in the iteration renewal process.
In embodiments of the present invention, a plurality of optimization indexs and its corresponding target fitness function have been defined, and choose design sample according to the fitness value that the target fitness function obtains, and when obtaining final design sample, consideration is optimized index according to each and is obtained sample space corresponding to sample, and the final sample that obtains like this is the sample that satisfies a plurality of optimization indexs most.
Description of drawings
In order to be illustrated more clearly in the embodiment of the present invention or technical scheme of the prior art, the below will do to introduce simply to the accompanying drawing of required use in embodiment or description of the Prior Art, apparently, accompanying drawing in the following describes is only some embodiments of the present invention, for those of ordinary skills, under the prerequisite of not paying creative work, can also obtain according to these accompanying drawings other accompanying drawing.
Fig. 1 is the idiographic flow schematic diagram of method of the parameter of the super material cell structure of the acquisition in the embodiment of the present invention;
Fig. 2 is another idiographic flow schematic diagram of method of the parameter of the super material cell structure of the acquisition in the embodiment of the present invention;
Fig. 3 is one of the device concrete schematic diagram that forms of the parameter of the super material cell structure of the acquisition in the embodiment of the present invention;
Fig. 4 is the concrete schematic diagram that forms that the sample shown in Fig. 3 obtains module.
Embodiment
Below in conjunction with the accompanying drawing in the embodiment of the present invention, the technical scheme in the embodiment of the present invention is clearly and completely described, obviously, described embodiment is only the present invention's part embodiment, rather than whole embodiment.Based on the embodiment in the present invention, those of ordinary skills belong to the scope of protection of the invention not making the every other embodiment that obtains under the creative work prerequisite.
The embodiment of the present invention has proposed a kind of method of parameter of the fast finding multiobjective optimization cellular construction body of realizing by the robotization processor, has improved greatly the efficient of super design of material.Before describing specific embodiments of the invention, first describe what is meant by " multiobjective optimization cellular construction body ".
Multiobjective optimization cellular construction body refers to that the designed cellular construction body that goes out satisfies global optimum on a plurality of design objectives, as the existing a kind of cellular construction body of design that needs, satisfy simultaneously the requirement of following two kinds of refractive indexes: be 1) N1 in its refractive index of frequency f1 place; 2) be N2 in its refractive index of frequency f2 place.Wherein the expectation refractive index N1 of corresponding above-mentioned two frequencies and N2 are two fixed values.Scheme in the embodiment of the present invention can realize above-mentioned designing requirement to greatest extent.
Be illustrated in figure 1 as the idiographic flow schematic diagram of method of the parameter of the super material cell structure of acquisition in the embodiment of the present invention.This flow process comprises the steps.
101, obtain a plurality of optimization indexs and target fitness function corresponding to described optimization index, wherein, described optimization index comprises the super material cell structure of optimization aim specific electromagnetic response parameter under given conditions, described target fitness function is used for obtaining fitness value, described fitness value reflection design sample under described specified conditions the electromagnetic response parameter and the gap between the described specific electromagnetic response parameter in the optimization index of described specified conditions, described design sample comprises one or more parameters of cellular construction body.
Wherein, described target fitness function can be:
Q
i.k＝1/(n
k(f
i)-N
i+a)
2
Wherein, f
iThe specified conditions of index i, n are optimized in expression
k(f
i) represent that design sample k is at f
iElectromagnetic response parameter under condition, N
iThe specific electromagnetic response parameter of index i is optimized in expression, and i and k are the natural number greater than 1, and a is predetermined constant, Q
i.kBe the fitness value of design sample k for optimization index i.Certainly, the value of a will guarantee, electromagnetic response parameter and the gap between the described specific electromagnetic response parameter in the optimization index of described specified conditions of design sample under described specified conditions is less, and fitness value is larger.
The span of i can be set according to actual needs, and in the example of refractive index, the i value is 1 and 2 as the aforementioned, that is, a plurality of optimization indexs comprise---optimize index 1: be N1 in its refractive index of frequency f1 place; Optimize index 2: be N2 in its refractive index of frequency f 2 places.In this example, frequency f1 and f2 are two specified conditions of optimizing in index, and refractive index is that N1 and N2 are two specific electromagnetic response parameters of optimizing in index.
102, obtain respectively to optimize a plurality of design samples with larger fitness value corresponding to index, wherein, optimize an index corresponding design sample space that comprises a plurality of design samples take the parameter of cellular construction body as the parameter axle for one.
Can obtain respectively to optimize a plurality of design samples with larger fitness value corresponding to index according to particle swarm optimization algorithm in this step, as, be specially:
Obtain initialized population
Wherein, in described population, the initialization particle rapidity of each particle is v
k, particle g
kThe parameter that comprises the cellular construction body of design sample k;
Calculate the fitness value Q of each sample in described initialized population
i.k, and the fitness value that obtains particle in described initialized population is peaked particle and maximum fitness value Q
I.best
Upgrade respectively the particle rapidity of population by following formula
v
k+m＝c
0×v
k+c
1×rand×(pb
k-g
k)+c
2×rand×(gb-g
k)；
According to the following formula with the particle rapidity after upgrading
Upgrade respectively population
In each particle:
g
k+m＝g
k+v
k+m；
Repeat above-mentioned particle step of updating, upgrade end condition until satisfy, and a plurality of particles with larger fitness value corresponding to index are respectively optimized in output;
Wherein, c
0, c
1And c
2Be three constants, rand is the uniform random number between 0 and 1, and gb represents that the fitness value of particle in the population of this iteration is peaked particle, pb
kRepresent that k particle is peaked particle by the end of this iteration fitness value in the iteration renewal process.
103, optimize design sample space acquisition optimum design sample corresponding to index according to each.This step specifically can comprise: the sample space that whether has repetition between design sample space corresponding to index is respectively optimized in judgement, have if judgment result is that the sample space that repeats, obtain parameter corresponding to the central point of sample space of described repetition as optimum results, if judgment result is that not have repeated sample, obtain apart from each parameter of optimizing the nearest some correspondence in design sample space corresponding to index as optimum results.
Certainly, optimizing design sample space corresponding to index can also take additive method to obtain optimal result according to each, as assemble situation according to the sample in sample space, obtain respectively to optimize the focus point in the design sample space of index, then obtain final focus point as optimum results according to each focus point; Or, obtain the weight of zones of different in the space according to sample gathering situation, then obtain optimum point etc. according to this weight.Those of ordinary skills can also obtain other various derivation process methods according to cluster isotype identification rudimentary algorithm, do not do one by one herein and describe.
In order to further illustrate embodiments of the invention, below be described with following design object.That is, in this specific embodiment, need a kind of cellular construction body of design, satisfy simultaneously the requirement of following two kinds of refractive indexes: optimize index 1: at frequency f
1Locating its refractive index is N
1Optimize index 2: at frequency f
2Locating its refractive index is N
2The expectation refractive index N of corresponding above-mentioned two frequencies wherein
1And N
2Be two fixed values.
Refractive index characterizes with variable n in this example, and the geometric parameter that defines super material cell structure is G.At frequency f place, the mapping relations between parameter value g and electromagnetic response parameter n (f) are by expression formula n (f)=y
f(g) expression.As shown in Figure 2, below describe search and obtain optimum geometric parameter values g
BestProcess.
201, obtain to optimize index 1 and its target fitness function Q
1.k, optimize index 2 and its target fitness function Q
2.k
Wherein, Q
1.k=1/ (n
k(f
1)-N
1+ 0.00001)
2
Q
2.k＝1/(n
k(f
2)-N
2+000001)
2。
Like this, the parameter value g of each cellular construction body passes through function
And Q
1.kJust can obtain the corresponding fitness value of optimizing index 1; Pass through letter
(g) and Q
2.kJust can obtain the corresponding fitness value of optimizing index 2.
202, optimize index for each, move respectively optimization algorithm based on the particle cluster in the suitable microstructure geometric parameter sample value of the geometric parameter domain search of micro unit structure.
Be specially, at first carry out following operation for optimizing index 1:
A, initialization population
Cellular construction geometric parameter space uniform sampling K time, obtain K geometric parameter sample
To each particle, initialization particle rapidity v
kFor example, can set particle rapidity v
k=0.1 * g
k
B, to each sample g
k, 1≤k≤K calculates its fitness value Q
1.kSeek maximum fitness function value, use Q
1.bestExpression.Then find corresponding to Q
1.bestG
kValue represents with gb.
C, to each particle g
k, upgrade particle rapidity in order to lower equation:
v
k+m＝c
0×v
k+c
1×rand×(pb
k-g
k)+c
2×rand×(gb-g
k)
Wherein, c
0, c
1And c
2Three constants, as setting c
0=0.5, c
1=2, c
2=2; Rand is the uniform random number between 0 and 1.pb
kRepresent in iterative search procedures the local best points of encountering in k particle search course.M characterizes current iterations.
D, then upgrade particle position in order to lower equation:
g
k+m＝g
k+v
k+m。
Whether C, detection search end condition satisfy, if satisfy, and one group of structural parameters sample sequence G1 of M the fitness value that output correspondence in search procedure is maximum, the number of samples in this sequence is also M, stops search procedure; Otherwise, return to step B and continue iterative search.Above-mentioned end condition can be set as, and the search iteration number of times reaches a certain fixed value, and for example 1000; Or the fitness value of sample reaches certain predetermined threshold value, etc.
For optimizing index 2, the modification fitness function is Q
2.kCarry out above-mentioned A-D step, obtain one group of structure sample parameter sequence G2 of M fitness value of corresponding maximum.
203, check whether G1 and G2 exist cross-domain, and obtain accordingly Optimal Parameters.Whether have the identical discrete values of part as between G1 and G2, or no existence is positioned at the discrete values of same area.If G1 and G2 exist cross-domain find out the point of crossing of G1 and G2 (as, the discrete values that part is identical) or the intersection region (as, if there is the discrete values that is positioned at same area, refer to this same area), the central point of exporting above-mentioned point of crossing or being positioned at the intersection region is as final result output; If G1 and G2 do not exist, export as net result at structure attribute parameter field detection range G1 and the nearest point of G2.
Provide the Multipurpose Optimal Method for artificial electromagnetic material (i.e. super material) microstructure design in the embodiment of the present invention, be used for the micro unit structure that design can be satisfied a plurality of optimization indexs simultaneously.In specific embodiment, adopt the global optimization searching method based on the particle group optimizing strategy, guaranteed the design accuracy of artificial electromagnetic material structural unit.These methods all can realize by the computing machine concurrent program, help the rapid automatized design of artificial electromagnetic material structural unit.
accordingly, the embodiment of the present invention has also proposed a kind of device that obtains the parameter of super material cell structure, as shown in Figure 3, this device 3 comprises: optimize index module 30, be used for obtaining a plurality of optimization indexs and target fitness function corresponding to described optimization index, wherein, described optimization index comprises the super material cell structure of optimization aim specific electromagnetic response parameter under given conditions, described target fitness function is used for obtaining fitness value, described fitness value reflection design sample under described specified conditions the electromagnetic response parameter and the gap between the described specific electromagnetic response parameter in the optimization index of described specified conditions, described design sample comprises one or more parameters of cellular construction body, sample obtains module 32, is used for obtaining respectively to optimize a plurality of design samples with larger fitness value corresponding to index, wherein, optimizes an index corresponding design sample space that comprises a plurality of design samples take the parameter of cellular construction body as the parameter axle for one, optimize sample and obtain module 34, be used for optimizing design sample space acquisition optimum design sample corresponding to index according to each.
Wherein, described optimization sample obtains module and also is used for, the sample space that whether has repetition between design sample space corresponding to index is respectively optimized in judgement, have if judgment result is that the sample space that repeats, obtain parameter corresponding to the central point of sample space of described repetition as optimum results, if judgment result is that not have repeated sample, obtain apart from each parameter of optimizing the nearest some correspondence in design sample space corresponding to index as optimum results.
Described target fitness function can be:
Q
i.k＝1/(n
k(f
i)-N
i+a)
2
Wherein, f
iThe specified conditions of index i, n are optimized in expression
k(f
i) represent that design sample k is at f
iElectromagnetic response parameter under condition, N
iThe specific electromagnetic response parameter of index i is optimized in expression, and i and k are the natural number greater than 1, and a is predetermined constant, Q
i.kBe the fitness value of design sample k for optimization index i.
Described sample obtains module and also is used for obtaining respectively to optimize a plurality of design samples with larger fitness value corresponding to index according to particle swarm optimization algorithm.Further, as shown in Figure 4, described sample obtains module 32 and can comprise:
v
k+m＝c
0×v
k+c
1×rand×(pb
k-g
k)+c
2×rand×(gb-g
k)；
g
k+m＝g
k+v
k+m；
Wherein, c
0, c
1And c
2Be three constants, rand is the uniform random number between 0 and 1, and gb represents that the fitness value of particle in the population of this iteration is peaked particle, pb
kRepresent that k particle is peaked particle by the end of this iteration fitness value in the iteration renewal process.
Need to prove that consistent in this device embodiment in the definition of each term and details and preceding method embodiment do not done one by one and given unnecessary details herein.
In embodiments of the present invention, a plurality of optimization indexs and its corresponding target fitness function have been defined, and choose design sample according to the fitness value that the target fitness function obtains, and when obtaining final design sample, consideration is optimized index according to each and is obtained sample space corresponding to sample, and the final sample that obtains like this is the sample that satisfies a plurality of optimization indexs most.
One of ordinary skill in the art will appreciate that all or part of flow process that realizes in above-described embodiment method, to come the relevant hardware of instruction to complete by computer program, described program can be stored in a computer read/write memory medium, this program can comprise the flow process as the embodiment of above-mentioned each side method when carrying out.Wherein, described storage medium can be magnetic disc, CD, read-only store-memory body (Read-Only Memory, ROM) or random store-memory body (Random Access Memory, RAM) etc.
Above disclosed is only a kind of preferred embodiment of the present invention, certainly can not limit with this interest field of the present invention, and the equivalent variations of therefore doing according to claim of the present invention still belongs to the scope that the present invention is contained.
Claims (10)
1. a method that obtains the parameter of super material cell structure, is characterized in that, described method comprises:
Obtain a plurality of optimization indexs and target fitness function corresponding to described optimization index, wherein, described optimization index comprises the super material cell structure of optimization aim specific electromagnetic response parameter under given conditions, described target fitness function is used for obtaining fitness value, described fitness value reflection design sample under described specified conditions the electromagnetic response parameter and the gap between the described specific electromagnetic response parameter in the optimization index of described specified conditions, described design sample comprises one or more parameters of cellular construction body;
Obtain respectively to optimize a plurality of design samples with larger fitness value corresponding to index, wherein, optimize an index corresponding design sample space that comprises a plurality of design samples take the parameter of cellular construction body as the parameter axle for one;
Optimize design sample space acquisition optimum design sample corresponding to index according to each.
2. the method for claim 1, is characterized in that, described basis is respectively optimized design sample space acquisition optimum design sample corresponding to index and comprised:
The sample space that whether has repetition between design sample space corresponding to index is respectively optimized in judgement, have if judgment result is that the sample space that repeats, obtain parameter corresponding to the central point of sample space of described repetition as optimum results, if judgment result is that not have repeated sample, obtain apart from each parameter of optimizing the nearest some correspondence in design sample space corresponding to index as optimum results.
3. method as claimed in claim 1 or 2, is characterized in that, described target fitness function is:
Q
i.k＝1/(n
k(f
i)-N
i+a)
2
Wherein, f
iThe specified conditions of index i, n are optimized in expression
k(fi) expression design sample k is at f
iElectromagnetic response parameter under condition, N
iThe specific electromagnetic response parameter of index i is optimized in expression, and i and k are the natural number greater than 1, and a is predetermined constant, Q
i.kBe the fitness value of design sample k for optimization index i.
4. method as claimed in claim 3, is characterized in that, described acquisition is respectively optimized a plurality of design samples with larger fitness value corresponding to index and comprised:
Obtain respectively to optimize a plurality of design samples with larger fitness value corresponding to index according to particle swarm optimization algorithm.
5. method as claimed in claim 4, is characterized in that, describedly obtains respectively to optimize a plurality of design samples with larger fitness value corresponding to index according to particle swarm optimization algorithm and comprise:
Obtain initialized population
Wherein, in described population, the initialization particle rapidity of each particle is v
k, particle g
kThe parameter that comprises the cellular construction body of design sample k;
Calculate the fitness value Q of each sample in described initialized population
i.k, and the fitness value that obtains particle in described initialized population is peaked particle and maximum fitness value Q
I.best
Upgrade respectively the particle rapidity of population by following formula
v
k+m＝c
0×v
k+c
1×rand×(pb
k-g
k)+c
2×rand×(gb-g
k)；
According to the following formula with the particle rapidity after upgrading
Upgrade respectively population
In each particle:
g
k+m＝g
k+v
k+m；
Repeat above-mentioned particle step of updating, upgrade end condition until satisfy, and a plurality of particles with larger fitness value corresponding to index are respectively optimized in output;
Wherein, c
0, c
1And c
2Be three constants, rand is the uniform random number between 0 and 1, and gb represents that the fitness value of particle in the population of this iteration is peaked particle, pb
kRepresent that k particle is peaked particle by the end of this iteration fitness value in the iteration renewal process.
6. a device that obtains the parameter of super material cell structure, is characterized in that, described device comprises:
Optimize the index module, be used for obtaining a plurality of optimization indexs and target fitness function corresponding to described optimization index, wherein, described optimization index comprises the super material cell structure of optimization aim specific electromagnetic response parameter under given conditions, described target fitness function is used for obtaining fitness value, described fitness value reflection design sample under described specified conditions the electromagnetic response parameter and the gap between the described specific electromagnetic response parameter in the optimization index of described specified conditions, described design sample comprises one or more parameters of cellular construction body;
Sample obtains module, is used for obtaining respectively to optimize a plurality of design samples with larger fitness value corresponding to index, wherein, optimizes an index corresponding design sample space that comprises a plurality of design samples take the parameter of cellular construction body as the parameter axle for one;
Optimize sample and obtain module, be used for optimizing design sample space acquisition optimum design sample corresponding to index according to each.
7. device as claimed in claim 6, it is characterized in that, described optimization sample obtains module and also is used for, the sample space that whether has repetition between design sample space corresponding to index is respectively optimized in judgement, have if judgment result is that the sample space that repeats, obtain parameter corresponding to the central point of sample space of described repetition as optimum results, if judgment result is that not have repeated sample, obtain apart from each parameter of optimizing the nearest some correspondence in design sample space corresponding to index as optimum results.
8. device as described in claim 6 or 7, is characterized in that, described target fitness function is:
Q
i.k＝1/(n
k(f
i)-N
i+a)
2
Wherein, f
iThe specified conditions of index i, n are optimized in expression
k(fi) expression design sample k is at f
iElectromagnetic response parameter under condition, N
iThe specific electromagnetic response parameter of index i is optimized in expression, and i and k are the natural number greater than 1, and a is predetermined constant, Q
i.kBe the fitness value of design sample k for optimization index i.
9. device as claimed in claim 8, is characterized in that, described sample obtains module and also is used for obtaining respectively to optimize a plurality of design samples with larger fitness value corresponding to index according to particle swarm optimization algorithm.
10. device as claimed in claim 9, is characterized in that, described sample obtains module and comprises: initialization unit is used for obtaining initialized population
Wherein, in described population, the initialization particle rapidity of each particle is v
k, particle g
kThe parameter that comprises the cellular construction body of design sample k;
The fitness computing unit is for the fitness value Q that calculates described initialized each sample of population
i.k, and the fitness value that obtains particle in described initialized population is peaked particle and maximum fitness value Q
I.best
The speed updating block is for upgrade respectively the particle rapidity of population by following formula
v
k+m＝c
0×v
k+c
1×rand×(pb
k-g
k)+c
2×rand×(gb-g
k)；
The particle updating block is used for according to the following formula with the particle rapidity after upgrading
Upgrade respectively population
In each particle:
g
k+m＝g
k+v
k+m；
Iteration unit is used for repeating above-mentioned particle step of updating, upgrade end condition until satisfy, and a plurality of particles with larger fitness value corresponding to index is respectively optimized in output;
Wherein, c
0, c
1And c
2Be three constants, rand is the uniform random number between 0 and 1, and gb represents that the fitness value of particle in the population of this iteration is peaked particle, pb
kRepresent that k particle is peaked particle by the end of this iteration fitness value in the iteration renewal process.
Priority Applications (1)
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| CN107423529A (en) * | 2017-08-30 | 2017-12-01 | 同济大学 | Metamaterial Precise spraying method |
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