CN110062390A - Based on the wireless sensor network node Optimization deployment method for improving wolf pack algorithm - Google Patents
Based on the wireless sensor network node Optimization deployment method for improving wolf pack algorithm Download PDFInfo
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Abstract
The invention discloses based on the wireless sensor network node Optimization deployment method for improving wolf pack algorithm, it applies in the node optimization deployment of wireless sensor network, the Efficient Coverage Rate for promoting wireless sensor node, uses the global search and the local search ability in later period of non-linear convergence factor balanced algorithm early period；Elitism strategy is added, accelerates convergence speed of the algorithm；Changeable weight strategy is proposed, so that the location updating of the bad individual in position is more reasonable；Meanwhile proposing that a kind of dynamic position crosses the border processing strategie, increases a possibility that searching globally optimal solution in region；It introduces dynamic variation strategy and increases wolf pack diversity, effectively expand the search range of algorithm.Advantages of the present invention: solve the problems, such as that the GWO algorithm later period is easy to fall into local optimum, IGWO algorithm improves the covering performance of wireless sensor network node, it is able to use less node and realizes more high coverage rate, reduce covering cavity, reduce the lower deployment cost of network.
Description
Technical field
The present invention relates to based on improve wolf pack algorithm wireless sensor network node Optimization deployment method, in particular to one
The method that kind improves the Efficient Coverage Rate of wireless sensor node.
Background technique
Coverage optimization is a basic problem of wireless sensor network (wireless sensor network, WSN).
In WSN, sensing node general random is shed, and causes coverage rate extremely low, or even the problems such as network is not connected to occur.Therefore, many
Researcher studies this, and obtains abundant achievement.All in all, network coverage optimization method includes two classes.First is that
Based on geometric coverage optimization, such as the covering control strategy (blind-zone based on the Tyson blind area polygon type heart
Centroid-based scheme, BCBS), it is optimized using the geometrical relationship between node.Though BCBS strategy coverage rate speed
Fastly, robustness is high, but still can not achieve 100% covering.And Deployment Algorithm is immunized in a kind of concentration based on Voronoi diagram
(centralized immune-voronoi deployment algorithm, CIVA) is weighed using Voronoi diagram attribute
Coverage area and energy consumption improve the coverage rate of WSN and extend network life.CIVA computation complexity is lower, but exists
" precocity " problem.Second is that the dynamic coverage optimization of node deployment is realized based on intelligent optimization algorithm, relative to answering for Voronoi diagram
Miscellaneous geometry derivation, intelligent optimization algorithm have many advantages, such as to be easily achieved, are adaptive strong, being answered extensively in node optimization deployment
With.The genetic algorithm (GA) of such as imitative biological evolution, imitative flock of birds are looked for food particle group optimizing (PSO) algorithm of process, and wolf pack predation is imitated
The grey wolf of process optimizes (GWO) algorithm] etc..Existing technology there are problems that falling into local optimum, convergence rate slower, exist
" precocity " situation.And the case where there are barrier and static nodes in actual deployment is not accounted for.
Summary of the invention
The present invention is deployed in actual environment in combination with wireless sensor network node for deficiency existing for existing algorithm
In the problem of encountering, consider that there are barrier and static state, dynamic pickup node and the mixing biography deposited in monitoring region herein
Under sensor network environment, based on GWO algorithm propose a kind of improved grey wolf optimization (improved GWO, IGWO) Deployment Algorithm and
It is applied, and is solved the disadvantage that GWO algorithm late convergence is slow and is easily trapped into local optimum, is improved the same of coverage rate
Dynamic adaptable of the Shi Tisheng to deployed environment.
The present invention is realized by following proposal: based on the wireless sensor network node Optimization Dept. management side for improving wolf pack algorithm
Method comprising following steps,
Step 1: starting, and t is arrangedmax, and wolf pack is initialized, t=1；
Step 2: judge whether t is less than or equal to tmax, if it is not, then exporting Xa, terminate, if so, entering step three；
Step 3: setting updates α, A and C；
Step 4: calculating the adaptive value of grey wolf and is divided into α, β, δ and ω according to adaptive value size；
Step 5: the position of ω wolf is updated
Step 6: making a variation and offside processing, then return step two.
Based on the wireless sensor network node Optimization deployment method for improving wolf pack algorithm, with the Xie Weishi of coverage rate function
It should be worth, after iteration, according to formula S=M+M2+M3+L Mn-1The connectivity for judging node, on the basis of connection, selection
The maximum grey wolf of adaptive value is as last solution comprising following steps,
Step 1: the upper and lower bound of the position of maximum the number of iterations and wolf pack individual is set, and in bound model
Enclose interior initialization wolf pack position；
Step 2: according to formulaFormulaAnd formulaInitially, it updates
α, A1, A2, A3, C1, C2 and C3；
Step 3: the adaptive value of every grey wolf is calculated；
Step 4: selecting highest three grey wolves of adaptive value is α, β, δ, and remainder is ω；
Step 5: according to formulaCarry out elitism strategy processing；
Step 6: according to formulaCalculate ω wolf between α, β and δ at a distance from；
Step 7: according to formulaFormula
With formula X (t+1)=w1*X1+w2*X2+w3*X3Location updating is carried out to ω wolf.
Step 8: according to formulaWith
Wolf pack variation adjustment is carried out, and according to formulaIt crosses the border to updated position
Processing；
Step 9: judging whether to meet iteration stopping condition, be unsatisfactory for, jump to step 2, otherwise exports grey wolf α's
Position, algorithm terminate.
Application of the improved grey wolf Optimization deployment algorithm in the node optimization deployment of wireless sensor network.
Application of the improved grey wolf Optimization deployment algorithm on the Efficient Coverage Rate for promoting wireless sensor node.
The invention has the benefit that global search and the office in later period using non-linear convergence factor balanced algorithm early period
Elitism strategy is added in portion's search capability, accelerates convergence speed of the algorithm；Changeable weight strategy is proposed, so that the bad individual in position
Location updating it is more reasonable；Meanwhile propose that a kind of dynamic position crosses the border processing strategie, increase search in region it is global most
A possibility that excellent solution.It introduces dynamic variation strategy and increases wolf pack diversity, effectively expand the search range of algorithm, and solve
The GWO algorithm later period is easy to fall into the problem of local optimum.The simulation experiment result shows that IGWO algorithm improves wireless sensor network
The covering performance of network node is able to use less node and realizes more high coverage rate, reduces covering cavity, reduces the deployment of network
Cost.
Detailed description of the invention
Fig. 1 is that the present invention is based on the signals of the process for the wireless sensor network node Optimization deployment method for improving wolf pack algorithm
Figure.
Fig. 2 is grey wolf Social Grading pyramid diagram.
Fig. 3 is GWO algorithm grey wolf location updating schematic diagram.
Fig. 4 is convergence factor comparison diagram.
Fig. 5 is test result figure of four kinds of algorithms on function F (x).
Fig. 6 is initial deployment figure.
Fig. 7 is IGWO Optimization deployment figure.
Fig. 8 is sensor node number when being 40, the coverage rate comparison diagram of GWO algorithm and IGWO algorithm.
Fig. 9 is random, coverage rate comparison diagram of the tri- kinds of algorithms of GWO and IGWO under different interstitial contents.
Figure 10 is there are rectangular obstruction and 2 nodes lose the IGWO algorithm coverage optimization deployment diagram under locomotivity.
Figure 11 is asynchronous in interstitial content to shed coverage rate, GWO algorithm coverage rate and IGWO algorithm coverage rate at random
Coverage rate comparison diagram.
Figure 12 is the Optimization deployment effect in trapezoidal obstacle environment, when 30 nodes after IGWO algorithm iteration 200 times
Figure.
When Figure 13 is interstitial content difference, the coverage rate comparison diagram of algorithm, GWO algorithm and IGWO algorithm is shed at random.
Figure 14 is interstitial content when being 30, IGWO Optimization deployment figure.
Figure 15 be polygon monitor area case under shed at random algorithm, GWO algorithm and IGWO algorithm interstitial content not
Coverage rate comparison diagram simultaneously.
Specific embodiment
Below with reference to Fig. 1-15, the present invention is further described, but the scope of the present invention does not limit to the content.
For clarity, not describing whole features of practical embodiments, in the following description, it is not described in detail well known function
And structure, because they can make the present invention chaotic due to unnecessary details, it will be understood that opening in any practical embodiments
In hair, it is necessary to make a large amount of implementation details to realize the specific objective of developer, such as according to related system or related business
Limitation, changes into another embodiment by one embodiment, additionally, it should think that this development may be complicated and expend
Time, but to those skilled in the art it is only routine work.
Based on the wireless sensor network node Optimization deployment method for improving wolf pack algorithm, with the Xie Weishi of coverage rate function
It should be worth, after iteration, according to formula S=M+M2+M3+L Mn-1The connectivity for judging node, on the basis of connection, selection
The maximum grey wolf of adaptive value is as last solution comprising following steps,
Step 1: the upper and lower bound of the position of maximum the number of iterations and wolf pack individual is set, and in bound model
Enclose interior initialization wolf pack position；
Step 2: according to formulaFormulaAnd formulaInitially, it updates
α、A1、A2、A3、C1、C2And C3；
Step 3: the adaptive value of every grey wolf is calculated；
Step 4: selecting highest three grey wolves of adaptive value is α, β, δ, and remainder is ω；
Step 5: according to formulaCarry out elitism strategy processing；
Step 6: according to formulaCalculate ω wolf between α, β and δ at a distance from；
Step 7: according to formulaFormula
With formula X (t+1)=w1*X1+w2*X2+w3*X3Location updating is carried out to ω wolf.
Step 8: according to formulaWith
Wolf pack variation adjustment is carried out, and according to formulaIt crosses the border to updated position
Processing；
Step 9: judging whether to meet iteration stopping condition, be unsatisfactory for, jump to step 2, otherwise exports grey wolf α's
Position, algorithm terminate.
In step each formula be related to network coverage model, network-in-dialing model, surround and seize kill, non-linear convergence factor plan
Slightly, elitism strategy, changeable weight strategy, dynamic variation, wolf pack position are crossed the border processing, and specific derivation process is as follows:
Network coverage model: assuming that the sensor node isomorphism in WSN, the perception radius and communication radius are respectively RpAnd Rc,
For the connectivity for guaranteeing wireless sensor network, the communication radius of node is set greater than or equal to the 2 of node perceived radius
Times.Assuming that the collection of one group of wireless sensor node is combined into S={ s1,s2,s3,…,sn, any one sensor node s in seti
Two-dimensional coordinate be expressed as (xi,yi), the collection of monitoring node is combined into M={ m1,m2,m3,…,mn, monitor any prison in region
Measuring point mjCoordinate be (xj,yj), then the Euclidean distance between two o'clock is
Node siTo monitoring point mjMonitoring probability be
So point-to-point m of all the sensors sectionjJoint perception probability be
S in formulaallFor all sensors node in measurement range.Assuming that the region of monitoring is the rectangle of L*W.For convenient for
It calculates, is the grid of L*W area equation by the rectangular partition, and the central point of grid is monitoring node m.By being calculated
The monitoring probability of all monitoring points, the sum of cumulative is area coverage.Coverage rate CrIt can be expressed as follows:
Network-in-dialing model: meeting the most basic self-organizing requirement of WSN is that network must be connected to.For convenient for calculating, it is assumed that
2Rp=Rc.If set N={ N1,N2,N3,…,NnBe sensor network in node, if NiWith NjThe distance between be not more than
Rp, then NiWith NjIt is adjacent, while being set as 1, it is otherwise provided as 0, and establish non-directed graph adjacency matrix M.According to the matrix of document [15]
Power algorithm, for adjacency matrix M, there are matrix S.
S=M+M2+M3+L Mn-1 (5)
Wherein n is sensor node number.If there are element being zero in S, it is not connected to；Conversely, being connected between node.?
On the basis of WSN connection, minimum spanning tree is generated.
Standard grey wolf optimization algorithm grey wolf optimization algorithm (grey wolf optimizer, GWO) is Mirjalili et al.
In a kind of heuritic approach that 2014 propose.The algorithm is developed with the hierarchy of wolf pack and predation process, is had good
Good optimizing ability and convergence rate.Therefore GWO algorithm is solving the problems, such as low-carbon Job-Shop, no-manned plane three-dimensional trajectory planning,
It has a wide range of applications in the fields such as multi-machine power system stabilizer parameter optimal design.
Wolf pack is divided into four grades, is represented by α, β, δ and ω from high to low.Wherein α, β, δ indicate adaptive value in wolf pack
Positioned at the grey wolf of first three, current optimal solution, suboptimal solution and general solution are respectively represented, remaining grey wolf group ω represents potential solution.
The grade of grey wolf is divided based on adaptive value, and the more high corresponding Social Grading of adaptive value herein is higher.In Fig. 1, α layers of wolf have maximum
Adaptive value and highest Social Grading, from α layers toward ω layers corresponding to wolf grade it is lower and lower.α layers of wolf are responsible for leading ω layers
Prey is surrounded and seize, β and δ layers of wolf assist α layers of wolf to issue jointly and surround and seize order.The Social Grading of every wolf is not fixed, with iteration
It carries out, will be redistributed according to the size of adaptive value.
It surrounds and seize and kills in order to enable ω layers of wolf draw close to prey, higher α, the β and δ layer of wolf of Social Grading issues to ω layers of wolf
It surrounds and seize and kills order, so that ω layers of wolf are from all directions close to prey, update self-position.
As shown in Fig. 2, α, β and δ are nearest apart from prey in numerous grey wolf individuals, with the position simulation prey of α, β and δ
Approximate location optimizes through successive ignition, and it is further accurate to simulate.After α wolf catches and kills order to the sending of ω wolf, ω wolf updates self-position
It arrives at around prey.Calculate ω wolf between α, β and δ at a distance from, mathematical description is as follows:
Wherein DαIndicate the distance between α wolf and ω wolf, Dβ, DδAnd so on.XαIt (t) is α wolf at the t times (i.e. current)
Position in iteration, Xβ(t), Xδ(t) and so on.Xω(t) position of the ω wolf in the t times iteration, C are indicated1Indicate ω to α
Orientation variables when mobile influence the mobile orientation of ω wolf, C2、C3And so on.Random number of the rand between [0,1].
It is close with certain step-length respectively after ω is learnt at a distance from α, β and δ, and finally arrive near prey.ω
The location updating mathematical description of wolf is as follows:
X (t+1)=(X1+X2+X3)/3 (11)
X1Indicate if ω wolf only using α wolf as prey when, the mobile position of the next generation, X2And X3And so on.T is to work as
Preceding number of iterations, tmaxFor maximum number of iterations, a is convergence factor, and a increases with the number of iterations in 2 to 0 linear decreases.A1
For the mobile step-length variable of ω wolf, as | A1 | when > 1, wolf pack will be enlarged by search range, wolf pack position then can relative distribution, be conducive to
Global search；Conversely, wolf pack will reduce the ring of encirclement, position Relatively centralized is conducive to local search, A2、A3And so on.ω is under
Position X (t+1) in a generation is set as X1、X2And X3Arithmetic mean of instantaneous value.
Improved GWO algorithm
Non-linear convergence factor strategy: GWO algorithm is proved to have preferably convergence effect than PSO algorithm, but because of its receipts
Factor linear decrease from 2 to 0 is held back, the proportion in entire search process is consistent so that global search is with local search, does not reach
To the balance of global search and local search.Therefore propose that a kind of non-linear convergence factor strategy, mathematical description are as follows:
In Fig. 3, a is in decreases in non-linear from 2 to 0, and algorithm quickly carries out global search early period, it is intended to search in global scope
More excellent solution, at this time a value variation it is relatively fast；Later period algorithm, which reduces the scope, carries out local search, and the variation of a value is smaller, is conducive to
Improve arithmetic accuracy.
Elitism strategy: in GWO algorithm, ω wolf is slowly arrived near prey with iteration, is taken a long time, and prey may just exist
Near α, β and δ, therefore consider that α, β and δ are substituted the smallest three wolves of adaptive value in ω wolf in the next generation, be described as follows:
Wherein wolf1、wolf2And wolf3Respectively represent the smallest three wolves of adaptive value.Elitism strategy is received for accelerating
It holds back speed and promotes the precision of solution.
Changeable weight strategy: in GWO algorithm, the influence that leading wolf generates ω wolf is impartial.In fact, α wolf represents currently
The optimum individual of iteration, therefore α wolf ratio β wolf and δ wolf, closer to prey, so α wolf has biggest impact to optimizing, β wolf takes second place,
δ wolf is minimum, therefore proposes a kind of changeable weight ratio strategy to guide ω wolf close to prey, utilizes the adaptive value of three leading wolves
Proportion guides.fαThe adaptive value of corresponding α wolf, fβAnd fδIt is corresponding with β and δ respectively.It is described as follows:
w1、w2、w3α, β, δ are respectively corresponded to the guidance rate of ω.
Then the next-generation position of ω wolf is expressed as follows:
X (t+1)=w1*X1+w2*X2+w3*X3 (15)
If there are negatives in three adaptive values, above-mentioned strategy is not applicable, therefore proposes another guidance rate calculative strategy,
Such as following formula (16):
One base position f is setb, the minimum value of corresponding X-axis, fα、fβ、fδTo fbDistance be respectively dα、dβAnd dδ, then
Three's sum of the distance is ds.The biggish f of adaptive valueαWith fbDistance it is maximum, the smallest f of adaptive valueδWith benchmark fbDistance it is minimum.
Then next generation's ω wolf location Update Strategy is as follows:
As shown in formula (17), guidance rate is ratio of distances constant, it is known that w1>w2>w3.The strategy has agreed with α, β, δ wolf to the shadow of ω
Ring the situation successively reduced.
Dynamic variation: GWO algorithm initial stage shows good convergence, but with iterations going on, the later period is nearly all
Grey wolf be distributed in same position, lead to the reduction of wolf pack diversity, algorithm falls into local optimum.For this purpose, proposing that a kind of wolf pack is dynamic
State Mutation Strategy.Mutation probability pv=0.5 is set, then policy depiction is as follows:
Wherein (tmax-t)/tmaxThe linear change from 1 to 0.The iteration incipient stage, dynamic variation probability is larger, is conducive to the overall situation
Search；Iteration later period mutation probability reduces, and promotes local search, avoids falling into local optimum.As shown in formula (18), meeting item
Under the premise of part, algorithm implements variation.It should be noted that if variation after adaptive value be worse than variation before, do not make a variation.Become
Different policy depiction is as follows:
On the basis of the position of α wolf, the upper limit l of grey wolf scope of activities is calculatedupper, lower limit llowerAt a distance from α wolf, point
D is not expressed as itupper、dlower.Rand is random number, and rand is greater than 0.5, then the position of ω wolf is α wolf position and rand*dupper
Summation；Conversely, ω wolf position is α wolf position and rand*dlowerDifference.
Cross the border processing wolf pack position: after successive ignition, the position of ω wolf is possible to cross the border GWO algorithm, i.e., wolf pack is super
Regulated boundary out at this time must correct the position of ω wolf.For this to X1、X2And X3Position be adjusted, for convenient for chatting
It states, X X1、X2And X3One of.Position adjustable strategies have following three kinds:
1. if ω wolf coordinate is updated to l grey wolf position is under wolf pack scope of activities lower limitlower；If grey wolf position
Beyond the wolf pack scope of activities upper limit, then ω wolf position coordinates are updated to lupper, mathematical description is as follows:
2. boundary is crossed in grey wolf body position, then the coordinate of a position replacement ω wolf is generated at random in boundary.Description
It is as follows:
X=llower+(lupper-llower)*rand (21)
3. grey wolf of crossing the border is replaced using high-grade grey wolf position, if X1It is offside, then it is replaced with α wolf, X2And X3Then use respectively
β and δ replacement.
Value in the first Scheme Choice bound is replaced, and more and more grey wolves will be in up and down in iterative process
The boundary of limit causes wolf pack diversity to weaken；Though second scheme expands the diversity of wolf pack population, fail to force ω wolf
Nearly leading wolf, is not inconsistent hop algorithm original design intention；The third scheme then directly results in the multifarious decline of wolf pack, easily falls into algorithm
Local optimum.For this purpose, proposing a kind of more effective location Update Strategy.With X1For α wolf, X2、X3And so on.Mathematics is retouched
It states as follows:
X1Less than lower limit llowerWhen, rlowerIndicate α, X1Between distance and α, llowerBetween ratio of distances constant, then X1Position
It is updated to llowerWith α * rlowerThe sum of.
X1Greater than upper limit lupperWhen, rupperIndicate lupper, distance and X between α1, ratio of distances constant between α, then X1Position
It is updated to α and rupper*(X1The sum of-α).
When the position of grey wolf is crossed the border, dynamic proportion adjustment is carried out to its position according to above formula.The strategy more meets
The ω wolf principle close to three leading wolves
Simulating, verifying: to verify IGWO algorithm performance, two kinds of experiments have been done altogether, experiment one calculates IGWO using test function
The constringency performance of method is tested, experiment two the wireless sensor node Optimization deployment the problem of on arithmetic accuracy is surveyed
Examination.Experiment be under Intel core i5 10 environment of double-core CPU, dominant frequency 2.4GHz, memory 8GB, operating system Windows into
Row, experiment simulation software use matlab 2014b.
Experiment one: F (x): maxf (x)=xsin (x) cos (2x) -2 xsin (3x) multimodal is chosen in Algorithm Convergence comparison
Function carries out function optimizing to algorithm and convergence is tested, and the search range F (x) is set as [0,30], and uses GA, PSO, GWO
Three kinds of intelligent optimization algorithms compare.All comparison algorithms 30 search agents of unified setting, the setting of algorithm iteration number
It for 200 times and runs 20 times, finally averages to 20 experimental results.To reduce error, mean value is expressed as two extreme values of removal
Average value except (maximum and minimum).
Fig. 5 describes test result of four kinds of algorithms on function F (x).As seen from the figure, when searching out optimal solution,
IGWO algorithm iteration 21 times, tri- kinds of optimization algorithms of GA, PSO, GWO equal used time 60 times or so, IGWO convergence speed of the algorithm has
Clear superiority.Because using non-linear convergence factor in IGWO, make to carry out a large amount of global search algorithm early period, effectively guidance ash
Wolf is close to the direction of global optimum, accelerates optimizing rate.The strategy makes IGWO algorithm have good optimizing ability and receipts
Hold back characteristic.
Experiment two: the comparison of coverage rate: wireless sensor network node coverage optimization effect is tested.It considers
The complexity of sensor network nodes deployed environment devises 4 groups of comparative experimentss, is respectively as follows: that clear situation, there are rectangles
Barrier situation, there are trapezoidal obstacle principle shape, overlay area be polygon situation.
To exclude error caused by randomness, each experiment scene has carried out 20 experiments, has taken final average result
It is compared.Monitoring region Line Integral is not 2500m2、2300m2、2050m2And 2050m2.All the sensors node it is removable and
Isomorphism, setting node perceived radius are 5m, communication radius 10m.When node motion function breaks down, node perceived radius
It is set as 7.5m, communication radius 15m.Design parameter setting is as shown in the table.
1 parameter setting of table
Fig. 6-9 describes clear and without the node deployment effect and algorithm coverage rate under static sensor node situation
Comparison.Fig. 6 is coverage effect of the node in the case where sowing at random, it can be seen that Node distribution is uneven, and part of nodes even occurs
It is overlapped situation, and cannot be connected between node.Using IGWO algorithm to node deployment position optimization after, as shown in fig. 7, node location
Relatively uniform, on the basis of guaranteeing Connectivity, coverage rate is highly improved.Fig. 8 describes sensor node number
When being 40, the coverage rate of GWO algorithm and IGWO algorithm is compared, it can be seen that IGWO algorithm enters when iteration is to 150 times or so
Converged state, coverage rate reaches about 97.5% at this time, and GWO algorithm iteration is to 200 times and not converged.Fig. 9 describe with
The coverage rate comparison of tri- kinds of machine, GWO and IGWO algorithms under different interstitial contents.It is recognised that in same node point quantity
Under the premise of, GWO and IGWO algorithm coverage rate is obviously higher by random algorithm, this is because intelligent optimization algorithm be it is didactic into
Row search carries out function evaluation to the position of each search, obtains a current best position, then straight from the location finding
To target.In the comparison of IGWO and GWO, coverage rate close to 100% can be realized when interstitial content is greater than 55, but
When number of nodes is less than 55, IGWO algorithm coverage rate is higher, due to making algorithm which employs changeable weight and dynamic variation strategy
The precision for searching solution is higher.
For the actual deployment environment of more coincidence senser node, barrier, shape difference are provided in simulated environment
For rectangle (200m2), trapezoidal (450m2) and triangle (450m2).Movement of the sensor node since cost is relatively low, after disposing
Ability may be lost, therefore mobile node translates into static node, but its perception radius will also increase with it.Figure 10 is described
There are rectangular obstruction and 2 nodes lose the IGWO algorithm coverage optimization deployment diagram under locomotivity, when sensor node number
When mesh reaches 35, coverage rate has reached 97.83%.Figure 11 is described sheds coverage rate, GWO algorithm coverage rate and IGWO at random
Algorithm coverage rate is in the asynchronous coverage rate comparison of interstitial content.As can be seen that IGWO and GWO are calculated when interstitial content is 55
The optimization coverage rate of method has all reached 100%, and shedding coverage rate at random at this time is only 82%.And interstitial content be 30 when, IGWO
The optimization coverage rate of algorithm reaches 95.43%, improves 31.05% than shedding at random, improves 2.47% than GWO algorithm.Value
It is noted that experiment show cover circumference and barrier it is tangent strategy than it is nontangential strategy coverage rate it is slightly higher.
Optimization deployment effect such as Figure 12 in trapezoidal obstacle environment, when 30 nodes after IGWO algorithm iteration 200 times
Shown, coverage rate has reached 96.50% at this time.When Figure 13 is interstitial content difference, algorithm, GWO algorithm and IGWO are shed at random
The coverage rate of algorithm compares.At this point, the coverage rate performance of IGWO is still better than GWO algorithm, and in the requirement of identical coverage rate
Under, the interstitial content of required deployment is less.Finally, being tested to the node deployment situation in polygon monitoring region.Work as section
When point number is 30, IGWO algorithm coverage rate has reached 95.63%, as shown in figure 14.Figure 15 then describes polygon monitoring section
Algorithm, GWO algorithm and IGWO algorithm are shed under the situation of domain at random in the asynchronous coverage rate performance of interstitial content.It can see
Out, 50 nodes are only needed when IGWO algorithm reaches 100% coverage rate, and GWO algorithm needs 55 nodes.Secondly, from difference
Under barrier situation from the point of view of the performance of GWO algorithm and IGWO algorithm, IGWO algorithm is to the node optimization under complex barrier substance environment
Deployment has stronger adaptability, this is because dynamic variation strategy makes the IGWO algorithm later period jump out local optimum, to the overall situation
Optimal direction search.Therefore 100% coverage rate can be realized in the case where less node.
In conclusion no matter having clear, or barrier when IGWO algorithm is applied to wireless sensor node Optimization deployment
How hinder object shape, IGWO algorithm all has good result in the coverage optimization problem of wireless sensor network node.It compares
In GWO, IGWO algorithm has benefited from the design of non-linear convergence factor and the introducing of elitism strategy and Mutation Strategy, enables algorithm
It is enough to realize optimizing in a shorter time, and the precision solved is higher.
Although having done more detailed elaboration to technical solution of the present invention and having enumerated, it should be understood that for ability
For field technique personnel, modifications to the embodiments described above may be made or uses equivalent alternative solution, this is to those skilled in the art
It is it is clear that these modifications or improvements without departing from theon the basis of the spirit of the present invention, belong to the present invention for member
Claimed range.
Claims (5)
1. based on the wireless sensor network node Optimization deployment method for improving wolf pack algorithm, it is characterised in that: it includes following
Step,
Step 1: starting, and t is arrangedmax, and wolf pack is initialized, t=1；
Step 2: judge whether t is less than or equal to tmax, if it is not, then exporting Xa, terminate, if so, entering step three；
Step 3: setting updates α, A and C；
Step 4: calculating the adaptive value of grey wolf and is divided into α, β, δ and ω according to adaptive value size；
Step 5: the position of ω wolf is updated
Step 6: making a variation and offside processing, then return step two.
2. the wireless sensor network node Optimization deployment method according to claim 1 based on improvement wolf pack algorithm,
It is characterized in that: using the solution of coverage rate function as adaptive value, after iteration, according to formula S=M+M2+M3+L Mn-1Judge node
Connectivity select the maximum grey wolf of adaptive value as last solution on the basis of connection.
3. the wireless sensor network node Optimization deployment method according to claim 2 based on improvement wolf pack algorithm,
Be characterized in that: it includes the following steps,
Step 1: the upper and lower bound of the position of maximum the number of iterations and wolf pack individual is set, and in upper and lower limits
Initialize wolf pack position；
Step 2: according to formulaFormulaAnd formulaInitially, update α,
A1, A2, A3, C1, C2 and C3；
Step 3: the adaptive value of every grey wolf is calculated；
Step 4: selecting highest three grey wolves of adaptive value is α, β, δ, and remainder is ω；
Step 5: according to formulaCarry out elitism strategy processing；
Step 6: according to formulaCalculate ω wolf between α, β and δ at a distance from；
Step 7: according to formulaFormula
With formula X (t+1)=w1*X1+w2*X2+w3*X3Location updating is carried out to ω wolf.
Step 8: according to formulaWithIt carries out
Wolf pack variation adjustment, and according to formulaPlace of crossing the border is carried out to updated position
Reason；
Step 9: judging whether to meet iteration stopping condition, be unsatisfactory for, jump to step 2, otherwise exports the position of grey wolf α,
Algorithm terminates.
4. a kind of application based on claim 1 or 3, it is characterised in that: improved grey wolf Optimization deployment algorithm is in wireless sensing
Application in the node optimization deployment of device network.
5. a kind of application based on claim 1 or 3, it is characterised in that: improved grey wolf Optimization deployment algorithm is being promoted wirelessly
Application on the Efficient Coverage Rate of sensor node.
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