CN108810914A - Based on the WSN Node distribution optimization methods for improving weeds algorithm - Google Patents
Based on the WSN Node distribution optimization methods for improving weeds algorithm Download PDFInfo
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- H04W16/00—Network planning, e.g. coverage or traffic planning tools; Network deployment, e.g. resource partitioning or cells structures
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Abstract
The invention discloses a kind of based on the WSN Node distribution optimization methods for improving weeds algorithm, this method is based on improving weeds algorithm solution WSN node Optimal Distributions, it improves weeds algorithm and introduces cube mapping chaos operator to improve the local search ability of algorithm, enhance the diversity of population using Gaussian mutation operator, have the advantages that fast convergence rate, robustness are good, Data Mining ability is strong, can effectively solve WSN Node distribution optimization problems.
Description
Technical field
The present invention relates to a kind of WSN Node distributions optimization methods, and in particular to a kind of based on the WSN sections for improving weeds algorithm
Point distribution optimization method.
Background technology
Wireless sensor network (Wireless sensor networks, abbreviation WSN) is static or mobile by one group
The multi-hop wireless network that sensor node is formed in self-organizing form, the advantages such as, energy rapid deployment strong with survivability,
It has a wide range of applications in civilian used with army, is always the hot spot studied both at home and abroad in recent years.Research shows that reasonable cloth
It sets sensor node and is conducive to improve the comprehensive performance of WSN, but the phenomenon that channel disturbance and information redundancy also easily occur, cause energy
Amount waste.Therefore, how research rationally disposes sensor node, optimization network performance has become one of WSN key techniques.
For WSN Node distribution optimization problems, domestic and international many scholars are handled by intelligent algorithm.It is common
Intelligent algorithm have：Genetic algorithm (Genetic algorithm, abbreviation GA), particle cluster algorithm (Particle swarm
Optimization algorithm, abbreviation PSO), artificial bee colony algorithm (Artificial bee colony algorithm,
Abbreviation ABC) etc..However, almost all of intelligent algorithm is all easily absorbed in local optimum, occur preconvergence too early, the later stage convergence
The problem of slowing.
Invention content
It is excellent based on the WSN Node distributions for improving weeds algorithm that in order to solve the above technical problem, the present invention provides a kind of
Change method.
In order to achieve the above object, the technical solution adopted in the present invention is：
Based on the WSN Node distribution optimization methods for improving weeds algorithm, include the following steps,
1) WSN Node distribution optimization object functions are built；
2) it is based on improving weeds algorithm solution WSN node Optimal Distributions；
Detailed process is as follows：
21) initialization of population：Weeds position is randomly generated using cube mapping chaos operator；
22) growth and breeding：The fitness value for calculating each weeds calculates the seed number that each weeds generate；
23) space is spread：Seed is randomly dispersed with normal distribution in the neighborhood of its parent weeds, which part seed profit
New variation weeds are generated with Gaussian mutation operator；
24) competition is eliminated：After iteration several times, when population number is more than maximum population scale number PmaxWhen, all weeds
It is ranked up from big to small according to fitness value, P before retainingmaxA weeds；
25) stopping criterion：Step 22~24 are repeated, the individual that fitness value is best in per generation population is recorded, until repeatedly
Generation number reaches greatest iteration number, and algorithm stops, and exports iteration optimal solution, as WSN nodes Optimal Distribution.
WSN Node distribution optimization object function C,
Wherein, definition region A is separated into the grid of s × s, and n is WSN node numbers in region, and m × n is the number of grid,
CjFor mesh point pjThe probability arrived by WSN nodal tests,CijFor WSN nodes siMeasurement model,
Wherein, dijFor mesh point pjWith WSN nodes siDistance, λ1,λ2,ε1,ε2It is measurement parameter, α1=Re-R+dij,
α2=Re+R-dij, ReFor effective measurement radius of WSN nodes, R is the perception radius of WSN nodes.
Randomly generating weeds location formula using cube mapping chaos operator is,
Xi′=[xi′1,xi′2,…,xi′D]
Wherein, Xi′For the position of weeds i ', D is the dimension of weeds i ', D ∈ [1, D], xi′dIt is weeds i ' in the position that d is tieed up
It sets, xUAnd xLRespectively xi′dThe bound of value, yi′dD dimension values for the weeds i ' generated using chaos sequence.
The seed number formula that each weeds generate is,
Wherein, PsFor seed number, fmax,fminRespectively population maximum adaptation angle value and minimum fitness value, Smax,SminPoint
Not Wei minimum and maximum seed number, f (Xi′) be weeds i ' fitness value.
The position V of variation weedsi′For,
Vi′=Xi′+e(XB-Xi′)
Wherein, it is 0 that e, which is mean value, the Gaussian Profile that variance is 1, Xi′For the parent weeds position for the weeds that make a variation, i.e. weeds i '
Position.
As the position V of variation weedsi′When not in search range, then the position V for the weeds that make a variationi′For,
Vi′=XL+e(XU-XL)
Wherein, XUAnd XLRespectively Xi′The bound of value.
The advantageous effect that the present invention is reached：The present invention is based on improving weeds algorithm to solve WSN node Optimal Distributions, improve
Weeds algorithm introduces cube mapping chaos operator to improve the local search ability of algorithm, enhances kind using Gaussian mutation operator
The diversity of group, has the advantages that fast convergence rate, robustness are good, Data Mining ability is strong, can effectively solve WSN Node distributions
Optimization problem.
Description of the drawings
Fig. 1 is the flow chart of the present invention；
Fig. 2 is the iterativecurve of three kinds of algorithm network coverage optimal solutions；
Fig. 3 is that the network coverage compares figure；
WSN node distribution maps when Fig. 4 is initial；
Fig. 5 is the WSN node distribution maps after optimization.
Specific implementation mode
The invention will be further described below in conjunction with the accompanying drawings.Following embodiment is only used for clearly illustrating the present invention
Technical solution, and not intended to limit the protection scope of the present invention.
As shown in Figure 1, based on the WSN Node distribution optimization methods for improving weeds algorithm, include the following steps：
1) WSN Node distribution optimization object functions are built.
The grid that two dimensional surface region A is separated into s × s is defined, the area of each grid is 1, if distribution in whole region
N WSN node (i.e. wireless sensor node), each WSN nodes can be obtained by certain particular form (as GPS)
Self-position, and possess identical the perception radius R.Therefore, all WSN sets of node on the A of region are described as：
S={ s1,s2,…,sn}
Wherein, WSN nodes siCoordinate be (xi,yi), i ∈ [1, n].
Mesh point pjWith WSN nodes siDistance be,
Wherein, j ∈ [1, m × n], m × n are the number of grid, mesh point pjCoordinate be (xj,yj)。
WSN nodes siThere are mainly two types of measurement models, and one is binary measure model, another kind is probability measurement model,
Here probability measurement model is used,
Wherein, CijFor WSN nodes siMeasurement model, λ1,λ2,ε1,ε2It is measurement parameter, α1=Re-R+dij, α2=Re+
R-dij。
If it is all independent events, mesh point p that all WSN nodes, which are detected,jIt is arrived by WSN nodal tests general
Rate is：
Wherein, if CjMore than or equal to some specific threshold Ct, then it is assumed that mesh point pjIt can be by nodal test；If conversely, Cj
Less than Ct, then it is assumed that mesh point pjIt cannot be detected, select C hereint=0.75.
Pass through mesh point pjThe probability being detected weighs the coverage rate of each grid, by the mesh point that is detected
Number accounts for coverage rate C of the ratio as WSN of grid sums, specific formula is,
Therefore, the n WSN node that WSN Node distributions optimization problem can be described as on the A of region is reached pair by optimization algorithm
The covering of entire target area.It is, the problem can be exchanged into coverage rate maximization problems, i.e. WSN Node distributions optimize mesh
Scalar functions are,
Wherein, C is WSN Node distribution optimization object functions.
2) it is based on improving weeds algorithm (IIWO algorithms) solution WSN node Optimal Distributions.
Detailed process is as follows：
21) initialization of population：Weeds position is randomly generated using cube mapping chaos operator.
In weeds algorithm (IWO algorithms), the initial position for randomly generating weeds is possible to that position distribution can be caused uneven
It is even, it is contemplated that chaos operator have the characteristics that randomness with regularity, and can in a certain range repeat traversal institute it is stateful,
In chaotic model, cube mapping maps the sequence generated evenly than common Logistic, therefore uses cube mapping here
Chaos operator improves the initialization of weeds position.
It is as follows to initialize detailed process：
A) for M individual in D dimension spaces, a D dimensional vectors Y is randomly generated；
B) M-1 iteration is carried out by dimension to Y using chaos sequence, this generates remaining M-1 individuals；
Y (k+1)=4y (k)3- 3y (k) (k=0,1 ...)
Wherein, k is iterations, chaos sequence when y (k) is kth time iteration, when y (k+1) is+1 iteration of kth
Chaos sequence；
C) Chaos Variable of generation is mapped in the search space of solution according to the following formula：
Wherein, [1, D] D ∈, xi′dIt is weeds i ' in the position that d is tieed up, xUAnd xLRespectively xi′dThe bound of value, yi′dFor
Utilize the d dimension values for the weeds i ' that chaos sequence generates.
Initializing obtained weeds location formula is,
Xi′=[xi′1,xi′2,…,xi′D]
Wherein, Xi′For the position of weeds i ', D is the dimension of weeds i '.
22) growth and breeding：The fitness value for calculating each weeds calculates the seed number that each weeds generate；Population is more excellent
The seed number that good weeds generate is also more,
The seed number formula that each weeds generate is,
Wherein, PsFor seed number, fmax,fminRespectively population maximum adaptation angle value and minimum fitness value, Smax,SminPoint
Not Wei minimum and maximum seed number, f (Xi′) be weeds i ' fitness value.
23) space is spread：Seed is with normal stateDistribution is randomly dispersed in the neighborhood of its parent weeds, wherein
Some seeds generate new variation weeds using Gaussian mutation operator.
During algorithm iteration, the rule of standard deviation variation can be described as：
Wherein, w is the Nonlinear Adjustment factor, σIAnd σFPrimary standard difference and the ultimate criterion for respectively sowing seed are poor,
σiterFor the standard deviation in the i-th ter generations, iter is iterations, itermaxFor maximum iteration.
To avoid IWO algorithms from being absorbed in local optimum, Some seeds are randomly choosed, adopts and is generated newly using Gaussian mutation operator
Make a variation weeds, the position V for the weeds that make a variationi′For,
Vi′=Xi′+e(XB-Xi′)
Wherein, it is 0 that e, which is mean value, the Gaussian Profile that variance is 1, Xi′For the parent weeds position for the weeds that make a variation, i.e. weeds i '
Position, XBFor the highest weeds position of fitness value in current population.
For variation weeds there are the random disturbances item of a Gaussian distributed between parent and current optimal weeds, this can
The position of variation weeds can be caused to exceed the search range of algorithm.So as the position V of variation weedsi′Not in search range
When (i.e. IWO algorithm search range) is interior, then the position V for the weeds that make a variationi′For,
Vi′=XL+e(XU-XL)
Wherein, XUAnd XLRespectively Xi′The bound of value.
24) competition is eliminated：After iteration several times, when population number is more than maximum population scale number PmaxWhen, all weeds
It is ranked up from big to small according to fitness value, P before retainingmaxA weeds.
25) stopping criterion：Step 22~24 are repeated, the individual that fitness value is best in per generation population is recorded, until repeatedly
Generation number reaches greatest iteration number, and algorithm stops, and exports iteration optimal solution, as WSN nodes Optimal Distribution.
In order to test the performance of above-mentioned improvement weeds algorithm (IIWO algorithms), using document (Lei L, Shiru Q.Path
Planning for Unmanned Air Vehicles Using an Improved Artificial Bee Colony
Algorithm[C]//Control Conference(CCC),2012 31st Chinese.IEEE,2012:2486-2491.)
4 standard test functions proposed are tested, and are carried out with CPSO algorithms (Chaotic PSO, abbreviation CPSO), IWO algorithms
Compare.The theoretical global minimum of 4 functions is 0, and simulated environment is 7 operating systems of Windows, MATLAB R2016a volumes
Translate software.For the optimization performance of fair more each algorithm, the population scale that each algorithm is arranged is 40, the dimension of each test function
It is 30, iterations are 500 times.Then, 30 independent tests are carried out to each test function by each algorithm respectively, table 1 provides
The statistical result of test, including average value (Mean) and standard deviation (SD).
1 canonical function test result of table
As can be seen from the table, CGSO algorithms are on Sphere, Ackley and Rastrigin function, either average value
Or standard deviation is superior to IWO algorithms and CPSO algorithms, and has the apparent order of magnitude to be promoted.IIWO algorithms are in Sphere letters
On number 40 and 2 orders of magnitude are improved than IWO algorithm, CPSO algorithms respectively.Although IIWO algorithms are on Griewank functions
Performance outline is less than CPSO algorithms, but the average value of the two and standard deviation are still on the same order of magnitude.This show with other two
Kind algorithm is compared, and IIWO algorithms can fully develop the information of searched object, has higher solution quality.
In order to examine IIWO algorithms in the validity of processing WSN Node distribution optimization problems, the effective of WSN is arranged in this emulation
Monitoring range is the square area of 100m × 100m, and the perception radius of each sensor node is 7m, in monitoring range with
Machine is dispersed with 100 WSN nodes.The layout of these nodes is carried out respectively by IWO algorithms, CPSO algorithms and IIWO algorithms excellent
Change, all 500 generation of iteration, repetitive operations 30 times, records and therein preferably solve.In addition, the population number of three kinds of algorithms is disposed as
20, the setting of other parameters is as shown in table 2.
The parameter setting of 2 three kinds of algorithms of table
After algorithm end of run, the optimal coverage rate and average coverage rate of each algorithm are counted, the results are shown in Table 3.From
As can be seen that the probability of success through chaos operator and the improved IWO algorithm process WSN distribution optimizations of Gaussian mutation operator in table
It greatly improves.The WSN average coverage rates obtained through IIWO algorithms have been higher by 2.8% He respectively than other two kinds of algorithms
13.3%.
3 three kinds of arithmetic result comparisons of table
The iterativecurve of three kinds of algorithm process network node covering optimal solutions is as shown in Figure 2.It can be seen from the figure that IWO
Algorithm just starts to restrain to 479 times and 197 iteration respectively with CPSO algorithms, and IIWO algorithms just start to 95 iteration
Convergence.In addition, the optimal network coverage rate that this paper algorithms obtain is 99.39%, and the difference that CPSO algorithms are obtained with IWO algorithms
For 99.08% and 97.75%.This shows that IIWO algorithm the convergence speed is fast, and ability of searching optimum is strong, can break away from local optimum
Constraint.
In addition, in order to verify influence of the population scale to algorithm performance, setting Population Size is respectively 10,20 and 30, respectively
In 300 generation of iteration, records next algorithm and finally obtains the network coverage, as shown in table 4.As can be seen from the table, as population is advised
The low optimization accuracy of the expansion of mould, IIWO algorithms is higher than other algorithms.This shows that carried algorithm can obtain herein and more searches
Rope information and the diversity for keeping population have stronger robustness.
The optimum results of each algorithm compare under 4 different population scale of table
In order to further verify the performance of IIWO algorithms, the network coverage emulation based on different interstitial contents is devised.
The network coverage of three kinds of algorithms is as shown in Figure 3 with the change curve of node density.It can be seen from the figure that realize 95% with
On coverage rate, CPSO algorithms and IWO algorithms respectively need to arrange 150 and 200 nodes, and IIWO algorithms only need arrangement 125
A node.For this explanation compared with other two kinds of algorithms, IIWO algorithms are strong to the mining ability of global information.
The rambling node of original state is can be seen that from Fig. 4 and Fig. 5 to postpone through IIWO algorithm optimization cloth, is distributed ratio
More uniform, overlapping coverage rate is relatively small.Therefore, proposed in this paper based on the WSN Node distributions optimization side for improving weeds algorithm
Method can rationally solve network coverage optimization problem, and can effectively improve the network coverage.
The above is only a preferred embodiment of the present invention, it is noted that for the ordinary skill people of the art
For member, without departing from the technical principles of the invention, several improvement and deformations can also be made, these improvement and deformations
Also it should be regarded as protection scope of the present invention.
Claims (6)
1. based on the WSN Node distribution optimization methods for improving weeds algorithm, it is characterised in that：Include the following steps,
1) WSN Node distribution optimization object functions are built；
2) it is based on improving weeds algorithm solution WSN node Optimal Distributions；
Detailed process is as follows：
21) initialization of population：Weeds position is randomly generated using cube mapping chaos operator；
22) growth and breeding：The fitness value for calculating each weeds calculates the seed number that each weeds generate；
23) space is spread：Seed is randomly dispersed with normal distribution in the neighborhood of its parent weeds, and which part seed utilizes height
This mutation operator generates new variation weeds；
24) competition is eliminated：After iteration several times, when population number is more than maximum population scale number PmaxWhen, all weeds according to
Fitness value is ranked up from big to small, P before retainingmaxA weeds；
25) stopping criterion：Step 22~24 are repeated, the individual that fitness value is best in per generation population is recorded, until iteration time
Number reaches greatest iteration number, and algorithm stops, and exports iteration optimal solution, as WSN nodes Optimal Distribution.
2. according to claim 1 based on the WSN Node distribution optimization methods for improving weeds algorithm, it is characterised in that：WSN
Node distribution optimization object function C,
Wherein, definition region A is separated into the grid of s × s, and n is WSN node numbers in region, and m × n is the number of grid, CjFor
Mesh point pjThe probability arrived by WSN nodal tests,CijFor WSN nodes siMeasurement model,
Wherein, dijFor mesh point pjWith WSN nodes siDistance, λ1,λ2,ε1,ε2It is measurement parameter, α1=Re-R+dij, α2=
Re+R-dij, ReFor effective measurement radius of WSN nodes, R is the perception radius of WSN nodes.
3. according to claim 1 based on the WSN Node distribution optimization methods for improving weeds algorithm, it is characterised in that：Profit
Randomly generating weeds location formula with cube mapping chaos operator is,
Xi′=[xi′1,xi′2,…,xi′D]
Wherein, Xi′For the position of weeds i ', D is the dimension of weeds i ', D ∈ [1, D], xi′dIt is weeds i ' in the position that d is tieed up, xU
And xLRespectively xi′dThe bound of value, yi′dD dimension values for the weeds i ' generated using chaos sequence.
4. according to claim 1 based on the WSN Node distribution optimization methods for improving weeds algorithm, it is characterised in that：Often
The seed number formula that a weeds generate is,
Wherein, PsFor seed number, fmax,fminRespectively population maximum adaptation angle value and minimum fitness value, Smax,SminRespectively
Minimum and maximum seed number, f (Xi′) be weeds i ' fitness value.
5. according to claim 1 based on the WSN Node distribution optimization methods for improving weeds algorithm, it is characterised in that：Become
The position V of different weedsi′For,
Vi′=Xi′+e(XB-Xi′)
Wherein, it is 0 that e, which is mean value, the Gaussian Profile that variance is 1, Xi′For the parent weeds position for the weeds that make a variation, the i.e. position of weeds i '
It sets.
6. according to claim 5 based on the WSN Node distribution optimization methods for improving weeds algorithm, it is characterised in that：When
The position V of variation weedsi′When not in search range, then the position V for the weeds that make a variationi′For Vi′=XL+e(XU-XL)
Wherein, XUAnd XLRespectively Xi′The bound of value.
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Application publication date: 20181113 |