CN104504424A - Multi-population symbiotic evolution based radio frequency identification network layout optimization method - Google Patents
Multi-population symbiotic evolution based radio frequency identification network layout optimization method Download PDFInfo
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Abstract
The invention discloses a multi-population symbiotic evolution based radio frequency identification network layout optimization method, and mainly solves the problems of proneness to precocity, slow convergence and poor stability due to the fact that the genetic algorithm is used for network layout in the prior art. The method includes the implementation steps: firstly, randomly grouping populations, independently calculating fitness of each sub-population objective function, designing and sequentially comparing strategies of the objective functions and deleting operators tentatively; secondly, in the process of iteration, sharing the whole population till now, and finding out the global best fitness; thirdly, updating the own position of each sub-population according to the global optimal position for each sub-population so as to find out the optimal network layout scheme. The method has the advantages of small initial population size, fast convergence and high stability, and the problem about network layout can be solved effectively.
Description
Technical field
The invention belongs to networking technology area, be specifically related to a kind of radio-frequency (RF) identification network topology optimization method, can be used for the network planning.
Background technology
In the last few years, radio frequency discrimination RFID technology, as a representative of technology of Internet of things, more and more received the concern of researcher.In rfid system, also relate to perhaps many-sided technical matters, as along with the development of RFID technique and widespread use, the layout optimization problem of RFID network just becomes a sharply challenging job.For RFID network topology optimization problem, relate to much constraint condition and target here, and be proved to be NP-hard problem.The object of RFID network topology optimization problem is exactly meet the constraint condition certain, and as tag coverage, the quantity of reader, interference, finds the optimum position of reader.
Propose a large amount of solutions in current document, be broadly divided into three classes: optimization algorithm, heuritic approach, meta-heuristic algorithm.Wherein, meta-heuristic algorithm mainly comprises genetic algorithm, simulated annealing, tabu search algorithm, particle swarm optimization algorithm etc.RFID network topology problem belongs to combinatorial optimization problem, meta-heuristic algorithm be generally considered at present performance, extensibility and be easy to realisation etc. in the best approach after balance.Wherein, genetic algorithm is the most frequently used a kind of meta-heuristic algorithm.More broadly ground is said, genetic algorithm belongs to a kind of evolution algorithm, because evolution algorithm is compared with traditional optimization, there is simple, general, strong robustness and be convenient to the advantages such as parallelization process, being widely used in the fields such as numerical optimization, Combinatorial Optimization, classifier design.But practice also shows, only using with genetic algorithm is that the evolution algorithm of representative carrys out the intelligence of mimic biology process things or far from being enough, also must more excavate and utilize biological intelligent resource in deep layer ground.In genetic algorithm, the individuality for generation of filial generation chooses from whole population according to fitness, therefore must pre-determine the fitness distribution of whole population.But there is not the overall situation at occurring in nature to select, cannot calculate the fitness distribution of the overall situation yet.In fact, natural selection itself is a kind of local phenomenon, and it is only relevant with the local environment at individual place.That is, certain one-phase, natural evolution is a local process, and it is by gradually spreading, and just makes information be that the overall situation is shared.Therefore, can not the intelligence of good mimic biology process things by genetic algorithm for solving RFID network topology problem, genetic algorithm also has the shortcomings such as easy Premature Convergence, speed of convergence are slow, poor stability in addition, thus the network topology scheme that cannot obtain.
Summary of the invention
The object of the invention is to propose a kind of radio-frequency (RF) identification network topology optimization method based on Symbiotic evolution on multiple populations, to overcome the deficiency of above-mentioned genetic algorithm, realize the better layout to RFID network.
For achieving the above object, technical scheme of the present invention is achieved in that
(1) model parameter is set according to radio-frequency (RF) identification network model: electromagnetic wavelength X=0.328m, the threshold power R of reader and label communication
q=-14dBm, the antenna gain G of reader
1=6.7dBi; The antenna gain G of label
2=3.7dBi;
(2) Symbiotic Evolutionary Algorithms parameter on multiple populations is set: the maximum particle number M=20 establishing symbiosis on multiple populations, optimize maximum algebraically N=1000, the algebraically gen optimized, its value is at 0 ~ N-1, and the sub-population packet count m in Symbiotic Evolutionary Algorithms on multiple populations is 3 ~ 6; Arranging the Array for structural body preserving the maximum individual information of fitness in every generation is B [m] [M], and arranging the Array for structural body preserving the maximum individual information of overall fitness is G [M];
(3) the label position coordinate in input radio frequency recognition network and number, coding initialization is carried out to the reading device position coordinate in this radio-frequency (RF) identification network and emissive power:
3a) in 50m × 50m two dimensional surface, the individuality of a random generation M reader;
3b) the position coordinates of the individuality of initialization M reader and emissive power: the position coordinates of the individuality of random generation reader, and position coordinates is a random real number in 0 ~ 50m, the emissive power of the individuality of simultaneously random generation reader, and emissive power is a random real number in 20 ~ 33dBm;
3c) establish the algebraically gen=0 of evolution;
(4) judge whether to meet the algebraically gen<N evolved, if so, perform step (5), otherwise, jump to step (10);
(5) individuality in population is equally divided into m group by its quantity, namely produces m the initial sub-population be made up of such individuality, if j is a variable which indicates organize sub-population, wherein the value of j is 0 ~ m-1, makes j=0;
(6) fitness value of each individuality in the sub-population of jth group is calculated;
(7) first sort successively according to the significance level of objective function, then the individuality the strongest to significance level sorts from big to small according to fitness value, is left in by individual information maximum for fitness value in Array for structural body B [m] [M];
(8) particle cluster algorithm is utilized to upgrade the position coordinates of the individuality in the sub-population of jth group;
(9) certainly 1 is added to j, judge whether j is less than m, if so, then return step (6); Otherwise perform step (10);
(10) compare individuality optimum in the grouping of m sub-population, calculate the optimum individual of the overall situation, its information is stored in Array for structural body G [M], and carry out tentative deletion calculating;
(11) judge whether to meet end condition: if, then the optimal location of the middle reader of export structure body array G [M], otherwise, by gen from adding 1, return step (4).
The present invention has the following advantages compared with prior art:
1. the present invention is owing to combining RFID network topology systematics and evolution algorithm, devise a kind of radio-frequency (RF) identification network topology optimization method based on Symbiotic evolution on multiple populations, compared with the model of population in traditional genetic algorithm, the network model of symbiosis on multiple populations, closer to real natural evolution mechanism, can obtain better placement scheme.
2. the present invention is owing to have employed the strategy of comparison object function successively and souning out deletion operator, and make Symbiotic Evolutionary Algorithms population scale on multiple populations little, fast convergence rate, the stability of algorithm is high.
3. the present invention is owing to have employed Symbiotic evolution method on multiple populations, and the method is not only used in and solves RFID network topology problem, also the method can be expanded to the combinatorial optimization problem solving other and limit with precedence relationship simultaneously.
Accompanying drawing explanation
Fig. 1 is realization flow figure of the present invention;
Fig. 2 is radio-frequency (RF) identification network model figure of the present invention.
Embodiment
As shown in Figure 2, radio frequency discrimination RFID network model, mainly contains label and reader composition, realize communicating by electromagnetic wave between label with read write line, reader emitted energy signal, launched by signal by the antenna of reader, label completes receiving function by its own antenna.
With reference to Fig. 1, specific embodiment of the invention step is as follows:
Step 1. parameters network parameter.
According to radio-frequency (RF) identification network model, following parameter is set:
Electromagnetic wavelength X=0.328m, the threshold power R of reader and label communication
q=-14dBm, the antenna gain G of reader
1=6.7dBi; The antenna gain G of label
2=3.7dBi.
Step 2. arranges Symbiotic Evolutionary Algorithms parameter on multiple populations.
Usually use symbiosis algorithm on multiple populations to carry out objective optimization in function optimization field, because the parameter related in symbiosis algorithm on multiple populations is a lot, this example arranges following parameter as required:
If the maximum particle number M=20 of symbiosis on multiple populations, optimizes maximum algebraically N=1000, the algebraically gen of optimization is 0 ~ N-1, and the sub-population packet count m in Symbiotic Evolutionary Algorithms on multiple populations is 3 ~ 6;
Arranging the Array for structural body preserving the maximum individual information of fitness in every generation is B [m] [M], and arranging the Array for structural body preserving the maximum individual information of overall fitness is G [M].
Label position coordinate in step 3. input radio frequency recognition network and number, carry out coding initialization to the reading device position coordinate in this radio-frequency (RF) identification network and emissive power.
3a) in 50m × 50m two dimensional surface, the individuality of a random generation M reader;
3b) the position coordinates of the individuality of initialization M reader and emissive power: the position coordinates of the individuality of random generation reader, and position coordinates is a random real number in 0 ~ 50m, the emissive power of the individuality of simultaneously random generation reader, and emissive power is a random real number in 20 ~ 33dBm;
3c) establish the algebraically gen=0 of evolution.
Step 4. judges whether to meet the algebraically gen<N evolved, and if so, performs step 5, otherwise, jump to step 11.
Individuality in step 5. pair population divides into groups.
Individuality in population is equally divided into m group by its quantity, in order to express easily, definition m
kbe the control variable of the sub-population of kth group, wherein the value of k is 0 ~ m-1, and k is initialized as 0.
Step 6. calculates the fitness value of each individuality in the sub-population of kth group.
The fitness value of each individuality comprises tag coverage COV, the interference ITF of reader, reader quantity N
r, being calculated as follows of these parameters:
6a) the tag coverage COV of radio-frequency (RF) identification network is defined as:
COV＝∑
t∈TSC
v(t)/N
t×100％
Wherein, C
vt () represents the coverage rate of each label, N
tthe number of the label be distributed in perform region,
be label t receive from reader r
1power, R
qthe minimum power communicated between read write line with label,
be label t receive from reader r
2power, RS is the set of reader, r
1and r
2be the element that in reader set two are different, TS represents the set of label;
6b) calculate the interference ITF of reader:
ITF＝∑
t∈TSγ(t)
Wherein γ (t) represents the interference of each label, γ (t)=∑ P
r,t-max{P
r,t, r ∈ RS ∩ P
r,t>=R
q, R
qthe minimum power communicated between read write line with label, P
r,tbe the power from reader r that label t receives, RS is the set of reader, and r is an element in reader set, and t is an element in tag set;
6c) by reader quantity N in radio-frequency (RF) identification network topology problem
rbe expressed as: N
r=N
max-N
red, wherein N
maxrepresent the sum being distributed in reader in perform region, N
redrepresent the number finding unnecessary reader.
Step 7. sorts successively according to the significance level of objective function.
7a) fitness value of the current individual calculated in step 6 individuality that preadaptation angle value is maximum is with it compared judgement:
If the tag coverage in current individual fitness value is less than the maximum tag coverage before individuality, then terminate the significance level sequence of current goal function;
If the tag coverage in current individual fitness value is larger than the maximum tag coverage before individuality, then upgrade the individuality that current fitness value is maximum;
If two values are the same, then the interference value of reader minimum before individuality for the interference ratio of current reader is compared;
If the minimum interference value of the reader before the interference ratio individuality of current reader is large, then terminate the significance level sequence of current goal function;
If the number of the current reader interference value more minimum than the reader before individuality is little, then upgrade the individuality that current fitness value is maximum;
If the minimum interference value of the reader of the interference of current reader before individuality is equal, then by the number of current reader than with individuality before the minimum individual numerical value of reader compare; Before being not more than individuality for the number of current reader, the situation of reader minimum number, then upgrade the individuality that current fitness value is maximum; Otherwise, terminate the significance level sequence of current goal function.
7b) by the sequential update of individuality maximum for fitness value be current goal function significance level sequence.
The step 8. pair individuality that significance level is the strongest sorts from big to small according to fitness value, is left in by individual information maximum for fitness value in Array for structural body B [m] [M].
Step 9. utilizes particle cluster algorithm to upgrade the position coordinates of the individuality in the sub-population of kth group.
9a) by individual updating location information maximum for fitness value in every sub-population be the optimum individual pBest of every sub-population
i;
9b) individuality that fitness value in the sub-population of difference is maximum is sorted according to the size of fitness value, individuality maximum for fitness value is updated to the optimum individual gBest of the overall situation;
9c) calculate the individual velocity V in every sub-population
i:
V
i＝(ω×V
i+c
1×rand
1)×(pBest
i-X
i)+c
2×rand
2×(gBest-X
i)
Wherein, i is the index of each particle, i=1,2 ..., M, ω are inertia weight inertia coefficients, and value is 0.4 ~ 0.9, c
1, c
2two accelerator coefficients, c
1=c
2=2.0, rand
1and rand
2two random numbers between [0,1], X
iit is contemporary positional information individual in every sub-population;
9d) utilize individual velocity V
iupgrade the individual positional information X in every sub-population
i': X
i'=X
i+ V
i;
Step 10. couple k, from adding 1, judges whether k is less than m, if so, then returns step 6; Otherwise perform step 11;
Step 11. compares individuality optimum in the grouping of m sub-population, calculates the optimum individual of the overall situation, its information is stored in Array for structural body G [M], and carries out tentative deletion calculating, carries out deletion according to the following steps and calculates:
11a) calculate the coverage rate of the label of now global optimum's individuality; If coverage rate is now greater than 90%, then carry out 11b); Otherwise continue to keep;
11b) number of first tentative minimizing read write line, if coverage rate still reaches requirement after reducing, then deletes; Otherwise, continue to keep.
Step 12. judges whether to meet end condition: if, then the optimal location of the middle reader of export structure body array G [M], otherwise, by gen from adding 1, return step 4.
Effect of the present invention can be verified by following emulation experiment:
1. test running environment and condition setting
The environment that experiment runs: processor is Intel (R) Core (TM) 2Duo CPU E6550@2.33GHz 2.33GHz, inside save as 1.99GB, hard disk is 500G, operating system is Microsoft windows XP Professional 2002, and programmed environment is MATLAB 7.13.
Arrange the size M=20 of particle cluster algorithm population, maximum iteration algebraically is set to 1000, and inertia coefficient ω is set to 0.4 ~ 0.9, accelerator coefficient c
1=c
2=2.0; The transmission power adjustment scope 20 ~ 33dBm of reader; Electromagnetic wavelength X=0.328m; The threshold power R of reader and label communication
q=-14dBm; The gain G of the antenna of reader
1=6.7dBi; The antenna gain G of label
2=3.7dBi.
2. experiment content and interpretation of result
Emulation experiment 1 with the present invention to when being 30 labels heterogeneous, 50 labels and 100 labels in the distribution of two dimensional surface, carry out test emulation respectively, result is as shown in table 1, wherein C30 indicates 30 labels, C50 indicates 50 labels, and C100 indicates 100 labels, in order to get rid of initialized randomness, 20 times are all calculated to often kind of example, obtains the mean value of three objective functions and the report the test of optimal value.
The result of table 1 label non-uniform Distribution
As can be seen from Table 1, the average value ranges of the coverage rate of all example label is greater than 95%, and the coverage rate optimal value of all example label all reaches 100%; Interference mean value and optimal value all reach 0.000; To the number of example C30 reader substantially at 4, to the number of example C50 reader substantially at 5, to the number of example C100 reader substantially at 5.
Emulation experiment 2, with the present invention to when being uniform 30 labels, 50 labels and 100 labels in the distribution of two dimensional surface, carry out test emulation respectively, result is as shown in table 2, it is in order to get rid of initialized randomness, 20 times are all calculated to often kind of example, obtains the mean value of three objective functions and the report the test of optimal value.
The equally distributed result of table 2 label
As can be seen from Table 2, the average value ranges of the coverage rate of all example label is greater than 90%, and the coverage rate optimal value of all example label all reaches 100%; Interference mean value and optimal value all reach 0.000; To the number of example C30 reader substantially at 6, to the number of example C50 reader substantially at 8, to the number of example C100 reader substantially at 9.
Emulation experiment 3 the present invention utilizes the distribution of the particle swarm optimization algorithm of genetic algorithm and symbiosis on multiple populations to label to be uniform 30 labels, carry out test emulation, result is as shown in table 3, and wherein GA represents it is genetic algorithm, and symbiosis PSO represents the particle swarm optimization algorithm of symbiosis on multiple populations.
Table 3 label number is 30 to be uniformly distributed, algorithms of different test result
As can be seen from Table 3, after utilizing the particle group optimizing method of symbiosis on multiple populations, relatively traditional genetic algorithm, the performance of objective function is improved all to a certain extent, and the validity of the particle group optimizing method of symbiosis on multiple populations is described.
Claims (4)
1., based on a radio-frequency (RF) identification network topology optimization method for Symbiotic evolution on multiple populations, comprise the steps:
(1) model parameter is set according to radio-frequency (RF) identification network model: electromagnetic wavelength X=0.328m, the threshold power R of reader and label communication
q=-14dBm, the antenna gain G of reader
1=6.7dBi; The antenna gain G of label
2=3.7dBi;
(2) Symbiotic Evolutionary Algorithms parameter on multiple populations is set: the maximum particle number M=20 establishing symbiosis on multiple populations, optimize maximum algebraically N=1000, the algebraically gen optimized, its value is at 0 ~ N-1, and the sub-population packet count m in Symbiotic Evolutionary Algorithms on multiple populations is 3 ~ 6; Arranging the Array for structural body preserving the maximum individual information of fitness in every generation is B [m] [M], and arranging the Array for structural body preserving the maximum individual information of overall fitness is G [M];
(3) the label position coordinate in input radio frequency recognition network and number, coding initialization is carried out to the reading device position coordinate in this radio-frequency (RF) identification network and emissive power:
3a) in 50m × 50m two dimensional surface, the individuality of a random generation M reader;
3b) the position coordinates of the individuality of initialization M reader and emissive power: the position coordinates of the individuality of random generation reader, and position coordinates is a random real number in 0 ~ 50m, the emissive power of the individuality of simultaneously random generation reader, and emissive power is a random real number in 20 ~ 33dBm;
3c) establish the algebraically gen=0 of evolution;
(4) judge whether to meet the algebraically gen<N evolved, if so, perform step (5), otherwise, jump to step (10);
(5) individuality in population is equally divided into m group by its quantity, namely produces m the initial sub-population be made up of such individuality, if j is a variable which indicates organize sub-population, wherein the value of j is 0 ~ m-1, makes j=0;
(6) fitness value of each individuality in the sub-population of jth group is calculated;
(7) first sort successively according to the significance level of objective function, then the individuality the strongest to significance level sorts from big to small according to fitness value, is left in by individual information maximum for fitness value in Array for structural body B [m] [M];
(8) particle cluster algorithm is utilized to upgrade the position coordinates of the individuality in the sub-population of jth group;
(9) certainly 1 is added to j, judge whether j is less than m, if so, then return step (6); Otherwise perform step (10);
(10) compare individuality optimum in the grouping of m sub-population, calculate the optimum individual of the overall situation, its information is stored in Array for structural body G [M], and carry out tentative deletion calculating;
(11) judge whether to meet end condition: if, then the optimal location of the middle reader of export structure body array G [M], otherwise, by gen from adding 1, return step (4).
2., as claimed in claim 1 based on the radio-frequency (RF) identification network topology optimization method of Symbiotic evolution on multiple populations, it is characterized in that: the fitness value of the computing function described in step (6), to carry out according to following steps:
6a) the tag coverage COV of radio-frequency (RF) identification network is defined as:
COV＝∑
t∈TSC
v(t)/N
t×100％
Wherein, C
vt () represents the coverage rate of each label, N
tthe number of the label be distributed in perform region,
be label t receive from reader r
1power, R
qthe minimum power communicated between read write line with label,
be label t receive from reader r
2power, RS is the set of reader, r
1and r
2be the element that in reader set two are different, TS represents the set of label;
6b) calculate the interference ITF of reader:
ITF＝∑
t∈TSγ(t)
Wherein γ (t) represents the interference of each label, γ (t)=∑ P
r,t-max{P
r,t, r ∈ RS ∩ P
r,t>=R
q, R
qthe minimum power communicated between read write line with label, P
r,tbe the power from reader r that label t receives, RS is the set of reader, and r is an element in reader set, and t is an element in tag set;
6c) by reader quantity N in radio-frequency (RF) identification network topology problem
rbe expressed as: N
r=N
max-N
red, wherein N
maxrepresent the sum being distributed in reader in perform region, N
redrepresent that algorithm finds the number of unnecessary reader.
3. as claimed in claim 1 based on the radio-frequency (RF) identification network topology optimization method of Symbiotic evolution on multiple populations, it is characterized in that: described step sorts successively according to the significance level of objective function in (7), try the fitness value of the current individual calculated in step (6) individuality that preadaptation angle value is maximum with it to compare judgement:
If the tag coverage in current individual fitness value is less than the maximum tag coverage before individuality, then terminate the significance level sequence of current goal function;
If the tag coverage in current individual fitness value is larger than the maximum tag coverage before individuality, then upgrade the individuality that current fitness value is maximum;
If two values are the same, then the interference value of reader minimum before individuality for the interference ratio of current reader is compared;
If the minimum interference value of the reader before the interference ratio individuality of current reader is large, then terminate the significance level sequence of current goal function;
If the number of the current reader interference value more minimum than the reader before individuality is little, then upgrade the individuality that current fitness value is maximum;
If the minimum interference value of the reader of the interference of current reader before individuality is equal, then by the number of current reader than with individuality before the minimum individual numerical value of reader compare; Before being not more than individuality for the number of current reader, the situation of reader minimum number, then upgrade the individuality that current fitness value is maximum; Otherwise terminate the significance level sequence of current goal function.
4., as claimed in claim 1 based on the radio-frequency (RF) identification network topology optimization method of Symbiotic evolution on multiple populations, it is characterized in that: the position coordinates in the sub-population of renewal described in step (8), upgrades according to following step:
8a) by individual updating location information maximum for fitness value in every sub-population be the optimum individual pBest of every sub-population
i;
8b) individuality that fitness value in the sub-population of difference is maximum is sorted according to the size of fitness value, individuality maximum for fitness value is updated to the optimum individual gBest of the overall situation;
8c) calculate the individual velocity V in every sub-population
i:
V
i＝(ω×V
i+c
1×rand
1)×(pBest
i-X
i)+c
2×rand
2×(gBest-X
i)
Wherein, i is the index of each particle, i=1,2 ..., M, ω are inertia weight inertia coefficients, and value is 0.4 ~ 0.9, c
1, c
2two accelerator coefficients, c
1=c
2=2.0, rand
1and rand
2two random numbers between [0,1], X
iit is contemporary positional information individual in every sub-population;
8d) utilize individual velocity V
iupgrade the individual positional information X ' in every sub-population
i: X '
i=X
i+ V
i.
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