CN103648139B - Wireless sensor network node deployment method for designing based on cultural ant group algorithm - Google Patents
Wireless sensor network node deployment method for designing based on cultural ant group algorithm Download PDFInfo
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Abstract
Method for designing is disposed the present invention relates to a kind of wireless sensor network node based on cultural ant group algorithm, the algorithm is included the framework of Cultural Algorithm by ant colony optimization algorithm, optimizing is carried out using population space and the double-deck genetic structure of belief space based on ant colony optimization algorithm, pheromone update strategy, taboo list are added in group space and greedy strategy etc. is added for the sparse situation of node, the speed of searching optimization of algorithm is accelerated on the whole, improving the quality for solving.It is contemplated that discussing the number for optimizing nodes under conditions of coverage rate and connectedness is ensured.
Description
Technical field
The present invention relates to a kind of method of wireless sensor network node deployment, especially, it is related to a kind of based on cultural ant
The algorithm for design of the wireless sensor network node deployment of group's algorithm.
Background technology
Wireless sensor network gradually shows very in the environmental monitoring and event tracking that inclement condition is stood fast at nobody
Big application value.On the one hand, sensor cheap for manufacturing cost has become feasible technically and economically；On the other hand,
The wireless sensor network technology such as technology such as energy power consumption, node locating and Routing Protocol becomes more and more ripe.Therefore, portion
Extensive, the self-organizing radio sensor network that administration is made up of thousands of sensor nodes have become a reality.Node deployment
Problem can trace back to the meter for circumferentially covering the two classics that the artistic corridor of O ' Rourke propositions and Williams are proposed earliest
Calculate geometrical issues.The two geometrical issues have important reference significance for wireless sensor network node deployment issue.
Wireless sensor network node deployment issue is in certain region, by appropriate tactful placement sensor
Node is meeting certain specific demand.Generally need to meet the coverage of wireless sensor network when sensor node is disposed
And connectivity constraint.Culture presented herein-ant system algorithm considers both constraintss simultaneously, and not
Stronger stability and good performance is all shown with algorithm under the conditions of environment and different parameters.In addition, being disposed in design
Optimize interstitial content and Node distribution form when tactful, efficiently using limited sensor network resource, farthest reduce
The problem that network energy consumption should be noted that when being node deployment, it is contemplated that discussing under conditions of coverage rate and connectedness is ensured
Optimize the number of nodes.
The content of the invention
The technical problem to be solved in the present invention is：The node deployment algorithm designed using Basic Ant Group of Algorithm, due to basic
Ant group algorithm shortcoming in itself：Convergence rate is slow, search time is more long, be easy to be absorbed in stagnation and the phenomenons such as precocity occur, causes section
The deficiencies such as point Deployment Algorithm optimal time is long, be easy to be absorbed in local optimum, deployment unstable result.Therefore for these deficiencies
Place, proposes a kind of wireless sensor network node deployment algorithm for design based on cultural ant group algorithm.It is existing right having used for reference
On the basis of the achievement in research of ant group algorithm, wireless sensor node deployment issue is carried out more with cultural frame and ant group algorithm
Good solution.
Wireless sensor network node Deployment Algorithm based on cultural ant group algorithm disclosed by the invention, primarily directed to base
The deficiency of this ant group algorithm with the addition of Cultural Algorithm and Basic Ant Group of Algorithm proposed to be correspondingly improved measure, so as to improve ant
The optimizing speed and efficiency of group's algorithm.Compared with the wireless sensor network node Deployment Algorithm based on ant group algorithm, mainly by
The difference of the following aspects：
（1）The introducing of Cultural Algorithm.Comprising two independences and interactional evolution space in Cultural Algorithm：Belief space
And group space, the whole evolution mechanism of wherein Cultural Algorithm is the elite that group space will be obtained using certain evolution strategy
Individuality passes to belief space, and the elite individuality evolved in belief space can influence the evolution of group space, this feedback
The evolutionary mechanism of formula greatly speeds up evolutionary rate to a certain extent, and algorithm optimizing effect can substantially improve.This point is proper
The problems such as speed of searching optimization is slow, optimizing result is poor in existing algorithm is solved well.
（2）Greedy strategy.Impact point places more dilute during this improvement strategy is primarily directed to wireless sensor network
Thin situation.In this case, the deployment result of existing node deployment algorithm is poor, and as the increase of impact point is in
Reveal irregular change.Add this greedy strategy so that algorithm deployment result show regular variation tendency on the whole,
And there is preferably result in the case of impact point is sparse.
（3）Convergence decision plan.The problems such as this tactful emphasis solves the unstability of existing algorithm, on the one hand realizes calculation
Method Fast Convergent ensures that the stability of each optimizing of algorithm is identical, on the other hand causes that algorithm avoids judging by accident and obtaining faster most
Excellent solution.
To achieve the above object, the invention discloses following technical scheme：
A kind of wireless sensor network node deployment method for designing based on cultural ant group algorithm, it is characterised in that including
Following steps：
（1）Population space is designed；
（2）Belief space is safeguarded；
（3）Convergence decision algorithm.
Wherein described the step of（1）Comprise the following steps：
（2.A）, the calculating of heuristic information, in the individual transition probability formula of ant, except the information in ant system
Plain concentrationOutward, the heuristic information of priori effect is also represented；
（2.B）, taboo list and the work space information renewal of ant are safeguarded, ant individuality safeguards one during search
Individual taboo list, is used to avoid repeatedly be transferred to same position；Taboo list is initially empty, after ant makes transfer selection, to finger
Position while putting sensor addition sensor network, it is necessary to update taboo list, the position that will increase sensor node newly adds
Enter taboo list, then update covering point set with effective point set to be selected；
（2.C）, the local release pheromone that ant individuality passes through at it in ant group algorithm, but pheromones can be over time
Evaporated with the change of environment, then the Pheromone update formula on the j of working space midpoint is：Wherein,ρTable
Show the evaporation coefficient of pheromones, andRepresent the increment of pheromones of the ant by being left at that point after certain mesh point；AndPlay a part of a guiding, it is defined as follows：
, wherein,QIt is a constant, it represents that ant stays tracking quantity, variablesenserUsedFor representing antkCurrent iteration uses the quantity of sensor.
Described step（2）Comprise the following steps：
（3.A）, the initialization of belief space, after generation population generates, Cultural Algorithm can be performed to it according to rule and connect
By function or influence function：When execution receives function, the excellent individual in population can enter the belief space of Cultural Algorithm；Perform
During influence function, some excellent individuals in belief space can replace the poor individuality in current population.
（3.B）, receiver function receives function and is mainly used to compare the elitist ants individuality for obtaining and arrive in transmission group space
Belief space.The present invention takes the strategy for regularly updating, i.e., in the evolutionary process of group space, it is stipulated that whenever ant group optimization is calculated
The multiple of method operation AcceptStep for when, the individual of global optimum is replaced worst in belief space in choosing population space
Body.Here AcceptStep values are 2.
（3.C）, influence function, influence function refers to upper strata belief space after the iteration for completing elite disaggregation updates, instead
Evolutionary direction of the lower floor's group space of feeding to instruct its follow-up, the opportunity to influenceing population uses dynamic strategy, by setting
One variable InfluenceStep of dynamic change, in belief space population often run the multiple of InfluenceStep for when,
The preferable certain amount of adaptive value in belief space is individual, and the more bad same number of adaptive value is individual in replacement group space,
InfluenceStep is represented by following formula：
, wherein, N1、N2For normal
Number,iter maxRepresent the maximum iteration of algorithm；CurrentStep is current group space evolutionary generation.Here N1, N2 take
Value is respectively 2 and 5.
Described step（3）Comprise the following steps：
（4.A）, calculate the variances sigma of each ant ideal adaptation angle value in belief space, i.e., it is every in calculating belief space
The variance of sensor node quantity needed for individual corresponding wireless sensor network, if the σ is less than a standard value, then enter
Row is counted, and performs step 2；
（4.B）If receiving still have σ in function implementation procedure below<Then counter i cumulative 1, performs step 3；It is no
Then terminate counting process counter O reset, return to step 1；
（4.C）If counter i is more than a constant value I for setting, algorithmic statement is represented, appropriate I value can be with
Effectively evade erroneous judgement in algorithm early stage to be disposed using a kind of new wireless sensor network node based on cultural ant group algorithm
Algorithm for design, is preferably solved to wireless sensor node deployment issue.
The more detailed method of the present invention is as follows：
（1）The individual transition rule of ant
Ant in ant system is individual when solution is built step by step, and the transition rule of next step can be entered by certain probability
Row selection.Selection of the ant to transition rule is expressed as below.
Positioned at the ant k of node i, according to following rule selection node j：
, wherein r~U (0,1),∈ [0,1], by user's system
Determine design parameter value；J∈T () is according to the randomly selected node of lower probability：
, wherein,T () is optional node set.In above formula,Generation
Table moves to the posteriority effect of node j from node i, in node deployment problem, in expression current candidate point set on candidate point
Pheromone concentration；Representative moves to the priori effect of node j from node i, is calculated by heuristic information, node
The number of monitoring point near a certain candidate point is represented in deployment issue.
WithRepresent the relative coefficient of both " pheromones " influence and heuristic information.According to the theory of ant group algorithm,WithIllustrate previous experiences data and this relative importance between independently searching for.
（2）The calculating of heuristic information
In the individual transition probability formula of ant, except the pheromone concentration in ant systemOutward, priori is also represented
The heuristic information of effect, the calculation of the heuristic information is as follows:
Ant is individual judge working space when next step transfering node is selected in available point set (W->E whether) it is
Sky, if it is empty, then cover point set in Search-transform target, during calculating" 1 " is set to, if not empty, then in available point
Search-transform target in set,It is set to the number of the monitoring point that the node is covered.
（3）Greedy strategy in the case of monitoring point is sparse
Working space
The area of space for running algorithm that network model based on grid builds（That is grid）.
Monitoring point
Node covered by sensor node, positioned at grid lines intersection location is needed in working space.Monitoring point set
Share two tuple C (M, H) to represent, wherein M represents the set of all monitoring points, H represents the monitoring point that has been capped in M
Set, i.e. covering monitoring point set.
If the monitoring point in working space is dispersed in is more sparse, it is likely to occur in the individual transfer of ant effectively to be selected
Point set E is empty situation, cannot now calculate heuristic information, enables that algorithm finds as early as possible there is employed herein Greedy strategy
New available point.
Distance of the point to network
A little it is referred to as a little arriving the distance of network to the minimum value for sensing each sensor distance in its network in working space.
Main algorithm step is as follows：
First, distance of each point to current network in the covering point set A in evaluation work space；
Second step, finds the covering point set A maximum with current network distance；
3rd step, chooses the impact point that the maximum point of pheromone concentration is shifted as next step.
（4）The taboo list and work space information for safeguarding ant update
Available point
The point is the point in the range of the established network communication of working space, if should after placing a sensor thereon
Sensor at least covers a monitoring point arrived uncovered before, then the point is effectively to treat reconnaissance.
Ant individuality safeguards a taboo list during search, is used to avoid repeatedly be transferred to same position.Taboo
Table is initially empty, after ant makes transfer selection, it is necessary to more while sensor is added into sensor network to specified location
New taboo list.First, the position of newly-increased sensor node is added taboo list, covering in subsequent ant renewal working space by ant
Lid point set and available point set.With the addition of new node, covering point set is continuously increased, and available point set change is more multiple
Miscellaneous, new node may result in old effective point failure, while some new available points may be brought.
（5）The pheromone update strategy of ant system
The individual local release pheromone passed through at it of ant in ant group algorithm, but pheromones can over time and environment
Change and evaporate, then the Pheromone update formula on the j of working space midpoint is：
, wherein,ρThe evaporation coefficient of pheromones is represented,Represent ant by after certain mesh point
The increment of the pheromones for leaving at that point.It will thus be seen that the pheromones on good mesh point can slowly increase, conversely not
Pheromones can be reduced to disappearance on good mesh point, therefore it is more excellent that later ant can be attracted to be continued search for along good mesh point
Solution, thereforePlay a part of a guiding., wherein,QIt is a constant, it represents ant
Stayed tracking quantity.VariablesenserUsedFor representing antkCurrent iteration uses the quantity of sensor.
In order to avoid pheromone concentration value is unconfined cumulative during Pheromone update, added in this algorithm to letter
The limitation of the plain concentration of breath, to avoid Premature Convergence.Do so can both limit the pheromone concentration point high attraction individual to ant
Ability, attraction that again can be with the low point of guarantee information element concentration to ant will not be too small.Pheromone update limitation is shown below：
, wherein, use variableRepresent the pheromones of mesh point
Concentration,pher_MINWithpher_MAXRepresent the minimum value and maximum of pheromones.
（6）The initialization of belief space
Individuality in belief space by receiving group space in preferably individuality obtain, belief space is a few by this
Body is compared optimization with original individuality according to certain rule, and updates the Heuristics of belief space, so as to guide group
The individual Evolutionary direction of body space ant, to improve the efficiency of evolution of group space.
After generation population generates, Cultural Algorithm can be performed to it according to rule and receive function or influence function.Execution connects
During by function, the excellent individual in population can enter the belief space of Cultural Algorithm；When performing influence function, in belief space
Some excellent individuals can replace the poor individuality in current population.
By some generations, the typically defect individual in population tends to similar i.e. algorithmic statement, or algorithm reaches maximum generation
Number, then stop continuing to produce population of new generation, and export the optimal solution for obtaining.
In this paper algorithms, with a List list as Cultural Algorithm belief space, be to complete by list entity
To the initial work of belief space.Belief space and the relatively independent presence of group space, but belief space is more empty than colony
Between life cycle it is long because the output of this paper algorithms takes optimal solution from belief space and obtains.
（7）Receiver function
Receive function and be mainly used to transmit in group space that to compare the elitist ants that obtain individual to belief space.Here I
Take the strategy for regularly updating, i.e., in the evolutionary process of group space, it is stipulated that whenever ant colony optimization algorithm operation
The multiple of AcceptStep for when, the individual of global optimum replaces worst individuality in belief space in choosing population space.Here
AcceptStep values are 2.
（8）Influence function
Influence function refers to upper strata belief space after the iteration for completing elite disaggregation updates, and feeds back to lower floor's group space
With the Evolutionary direction for instructing its follow-up.The present invention takes the strategy on dynamic select influence opportunity so that belief space is empty to colony
Between influence increase with the increase of evolution algebraically.The strategy on dynamic select influence opportunity is implemented：By setting one
The variable of dynamic change, in belief space population often run for when, by the preferable certain amount of adaptive value in belief space colony
Individuality, the more bad same number of adaptive value is individual in replacement group space.The value of InfluenceStep is calculated by following formula
Arrive：, wherein, N1, N2 are
Constant, is algorithm maximum evolution algebraically set in advance；CurrentStep is current group space evolutionary generation.Here N1, N2
Value is respectively 2 and 5.
（9）Convergence decision algorithm
Individuality in belief space periodically updates so that this list occurs most recently in representing group space
Excellent individuality, this paper algorithms are exactly that belief space is analyzed, and carry out decision algorithm convergence.Algorithm has been performed in receiver function every time
Bi Hou, followed by performs the decision algorithm.Arthmetic statement is as follows：
First, in the variances sigma of each individual evaluation of estimate in calculating belief space, i.e. calculating belief space used by wireless network
The variance of number of nodes.If the σ is less than a standard value, then process is started counting up, perform second step.
Second step, if receiving still to have σ in function implementation procedure below<Then counter i cumulative 1, performs the 3rd step；
Otherwise terminate counting process counter O reset, return to the first step.
3rd step, if counter i is more than a constant value I for setting, puts flag for false, represents algorithmic statement.
（10）Build the tree topology of sensor network
The problem to be solved herein is the initialization deployment to wireless sensor network, and the output of algorithm is a network,
Network is made up of set of node and side collection in itself, therefore in the individual tissue solution of ant, using corresponding routing mechanism, it is established that
One tree-shaped network.Route selection algorithm was performed after ant individuality performs transfer operation, and arthmetic statement is as follows：
First, scanning is the center of circle to increase node newly, the region with node communication radius as radius, if the region has other
Node, remembers that the collection of these nodes is combined into V and performs step 2, if the region is without other nodes, algorithm error；
Second step, finds the nearest node of the newly-increased node of distance, if there is unique nearest node, in newly-increased node in V
A line is set up between nearest node, step 3 is otherwise performed；
3rd step, the non-leaf nodes in tree network is found in the nearest nodal set, if finding, in newly-increased node and
Set up between nearest node and set up a line between a line, the node otherwise concentrated with nearest node at random.
The product that wireless sensor network node Deployment Algorithm based on cultural ant group algorithm has compared with prior art
Pole effect is：
（1）The number of sensors of algorithm deployment is incremented by steadily with the increase of monitoring point quantity, and the quality of solution is better than now
There is the deployment result of algorithm.Effect is disposed in the case where impact point is sparse to become apparent from.
（2）Existing algorithm search obtain optimal solution needed for mean iterative number of time obviously higher than this algorithm, so this calculate
Method can faster search for obtain optimal solution under given conditions.
（3）Ant group algorithm can faster search globally optimal solution in this algorithm, while further increasing algorithm
Stability.
Brief description of the drawings
Fig. 1 is culture ant group algorithm basic framework figure of the invention；
Fig. 2 is working space figure of the present invention；
Fig. 3 is point set E figures effectively to be selected；
Fig. 4 is R=48 experiment effect figures；
Fig. 5 is R=72 experiment effect figures；
Fig. 6 is R=96 experiment effect figures.
Specific embodiment
The present invention is further described with reference to embodiment.The scope of the present invention is not restricted by the embodiments, this hair
Bright scope is proposed in detail in the claims.
Embodiment 1
Wireless sensor network node deployment design system based on cultural ant group algorithm, including：
（1）Population space is designed；
（2）Belief space is safeguarded；
（3）Convergence decision algorithm.
Described step（1）Comprise the following steps：
（2.A）, the calculating of heuristic information, in the individual transition probability formula of ant, except the information in ant system
Plain concentrationOutward, the heuristic information of priori effect is also represented；
（2.B）, taboo list and the work space information renewal of ant are safeguarded, ant individuality safeguards one during search
Individual taboo list, is used to avoid repeatedly be transferred to same position；Taboo list is initially empty, after ant makes transfer selection, to finger
Position while putting sensor addition sensor network, it is necessary to update taboo list, the position that will increase sensor node newly adds
Enter taboo list, then update covering point set with effective point set to be selected；
（2.C）, the local release pheromone that ant individuality passes through at it in ant group algorithm, but pheromones can be over time
Evaporated with the change of environment, then the Pheromone update formula on the j of working space midpoint is：, wherein,ρ
The evaporation coefficient of pheromones is represented, andRepresent the increment of pheromones of the ant by being left at that point after certain mesh point；
AndPlay a part of a guiding, it is defined as follows：
, wherein,QIt is a constant, it represents that ant stays tracking quantity, variablesenserUsed
For representing antkCurrent iteration uses the quantity of sensor.
Described step（2）Comprise the following steps：
（3.A）, the initialization of belief space, after generation population generates, Cultural Algorithm can be performed to it according to rule and connect
By function or influence function：When execution receives function, the excellent individual in population can enter the belief space of Cultural Algorithm；Perform
During influence function, some excellent individuals in belief space can replace the poor individuality in current population.
（3.B）, receiver function receives function and is mainly used to compare the elitist ants individuality for obtaining and arrive in transmission group space
Belief space.The present invention takes the strategy for regularly updating, i.e., in the evolutionary process of group space, it is stipulated that whenever ant group optimization is calculated
The multiple of method operation AcceptStep for when, the individual of global optimum is replaced worst in belief space in choosing population space
Body.Here AcceptStep values are 2.
（3.C）, influence function, influence function refers to upper strata belief space after the iteration for completing elite disaggregation updates, instead
Evolutionary direction of the lower floor's group space of feeding to instruct its follow-up, the opportunity to influenceing population uses dynamic strategy, by setting
One variable InfluenceStep of dynamic change, in belief space population often run the multiple of InfluenceStep for when,
The preferable certain amount of adaptive value in belief space is individual, and the more bad same number of adaptive value is individual in replacement group space,
InfluenceStep is represented by following formula：
, wherein, N1、N2
It is constant,iter maxRepresent the maximum iteration of algorithm；CurrentStep is current group space evolutionary generation.Here N1,
N2 values are respectively 2 and 5.
Described step（3）Comprise the following steps：
（4.A）, calculate the variances sigma of each ant ideal adaptation angle value in belief space, i.e., it is every in calculating belief space
The variance of sensor node quantity needed for individual corresponding wireless sensor network, if the σ is less than a standard value, then enter
Row is counted, and performs step 2；
（4.B）If receiving still have σ in function implementation procedure below<Then counter i cumulative 1, performs step 3；It is no
Then terminate counting process counter O reset, return to step 1；
（4.C）If counter i is more than a constant value I for setting, algorithmic statement is represented, appropriate I value can be with
Effectively evade erroneous judgement in algorithm early stage.
Embodiment 2
（1）Key parameter is set
In order to verify the feasibility and validity of CA-ACA algorithms, select it is existing typically based on ant colony optimization for solving without
The Easidesign algorithms and ACO-TCAT of line sensor node deployment problem are contrasted.This algorithm is imitated using Java
Very, sizing grid as 24 is set, grid scale 20*20 produces monitoring point using pseudorandom mode.
（2）Wireless sensor network node deployment algorithm for design based on cultural ant group algorithm
One important goal of design node Deployment Algorithm aiming at rational network demand obtain it is qualified most
Excellent deployment scheme, and algorithm will have stronger optimizing ability.Here setting sensor communication radius are respectively R=48,72,96.
By these three algorithms（CA-ACA, Easidesign, ACO_TCAT）After each self-operatings of correspondence different communication radius R 20 times, obtain
Average data statistics.
In experimental result Fig. 4, Fig. 5, Fig. 6 respectively show sensor senses radius=sensor communication radius=48,72,
In the case of 96, the relation of monitoring point quantity in sensor deployment quantity and working space.Transverse axis represents the number of monitoring point, indulges
Average sensor interstitial content of the axle for needed for the node deployment that respective algorithms are searched.It can be seen that under three circumstances, CA-
The number of sensors of ACA deployment is all as the increase of monitoring point quantity is incremented by steadily, and the quality of solution is better than ACO-TCAT, more excellent
In Easidesign.It is sparse in monitoring point（As monitoring point quantity is less than or equal to 80）In the case of, Easidesign is because without relatively suitable
The mechanism answered result in cannot obtain valuable solution under the sparse environment of monitoring point, the solution and CA-ACA of ACO-TCAT in Fig. 5
Also there is obvious gap.And adding greedy strategy for sparse situation in CA-ACA so that its solution is better than other two kinds calculations
Method.And for the dense situation in monitoring point, with the increase that monitoring is counted out, gap is smaller but CA-ACA is relative between three kinds of algorithms
It is more excellent.In sum, in the case where monitoring point is sparse and dense, CA-ACA can well carry out sensor node number
Optimizing.
Embodiment 3
In an application example of the invention, such as in city to the monitoring of some flammability regimes in, with conventional method contrast
Its application effect is：
Conventional method：Wireless sensor network node Deployment Algorithm based on Basic Ant Group of Algorithm
The method of the present invention：Wireless sensor network node Deployment Algorithm based on cultural ant group algorithm
Table 1
| Impact point | Cultural ant colony | Basic ant colony |
| 40 | 54 | 97 |
| 80 | 63 | 76 |
| 120 | 66 | 72 |
| 160 | 69 | 70 |
| 200 | 73 | 73 |
Table 2
Conclusion：
（1）As shown in Table 1 and Table 2, algorithm all shows stronger stabilization under the conditions of varying environment and different parameters
Property and good performance.
（2）Interstitial content and Node distribution form are optimized when deployment strategy is designed, limited biography is efficiently make use of
Sensor Internet resources, farthest reduce deployment cost, and as shown in table 1, the inventive method is than the portion needed for conventional method
Administration's cost is much smaller, and data are presented stability and increase.
（3）The present invention realizes the number that nodes are optimized under conditions of coverage rate and connectedness is ensured.
Claims (1)
1. it is a kind of based on cultural ant group algorithm wireless sensor network node deployment method for designing, it is characterised in that including with
Lower step：
（1）Population space is designed；
（2）Belief space is safeguarded；
（3）Convergence decision algorithm；
Described step（1）Comprise the following steps：
（1.A）, the calculating of heuristic information, in the individual transition probability formula of ant, except the pheromones in ant system are dense
DegreeIt is outer to also have the heuristic information for representing priori effect
（1.B）, taboo list and the work space information renewal of ant are safeguarded, ant individuality safeguards a taboo during search
Avoid table, be used to avoid repeatedly be transferred to same position；Taboo list is initially empty, after ant makes transfer selection, to specific bit
Put while sensor is added into sensor network, it is necessary to update taboo list, the position that will increase sensor node newly adds taboo
Avoid table, then update covering point set with effective point set to be selected；
（1.C）, the individual local release pheromone passed through at it of ant in ant group algorithm, but pheromones can over time and ring
The change in border and evaporate, then working space midpointOn Pheromone update formula be：Wherein,Represent
The evaporation coefficient of pheromones,Represent antThe increment of the pheromones by being left at that point after certain mesh point；And
Play a part of a guiding, it is defined as follows：
, wherein,It is a constant, it represents that ant stays tracking quantity, variableFor table
Show antCurrent iteration uses the quantity of sensor；
Described step（2）Comprise the following steps：
（2.A）, the initialization of belief space, after generation population generates, Cultural Algorithm can perform letter of acceptance according to rule to it
Number or influence function：When execution receives function, the excellent individual in population can enter the belief space of Cultural Algorithm；Perform influence
During function, some excellent individuals in belief space can replace the poor individuality in current population；
（2.B）, receive function, receive function and be mainly used to transmit in group space that to compare the elitist ants that obtain individual to conviction
The strategy for regularly updating is taken in space, i.e., in the evolutionary process of group space, it is stipulated that whenever ant colony optimization algorithm operation
The multiple of AcceptStep for when, the individual of global optimum replaces worst individuality in belief space in choosing population space;
Here AcceptStep values are 2；
（2.C）, influence function, influence function refers to upper strata belief space after the iteration for completing elite disaggregation updates, and is fed back to
Evolutionary direction of lower floor's group space to instruct its follow-up, the opportunity to influenceing population uses dynamic strategy, by setting one
The variable InfluenceStep of dynamic change, in belief space population often run the multiple of InfluenceStep for when, will believe
The preferable certain amount of adaptive value is individual in reading space, and the more bad same number of adaptive value is individual in replacement group space,
InfluenceStep is represented by following formula：
, wherein,
It is constant,Represent the maximum iteration of algorithm；CurrentStep is that current group space is evolved generation
Number;
Here N1, N2 value are respectively 2 and 5；Described step（3）Comprise the following steps：
（3.A）, calculate the variance of each ant ideal adaptation angle value in belief spaceI.e. per each and every one in calculating belief space
The variance of sensor node quantity needed for the corresponding wireless sensor network of body, if shouldLess than a standard valueThen counted
Number, performs step 2；
（3.B）If receiving still to have in function implementation procedure belowThen counter m cumulative 1, performs step 3；Otherwise tie
Beam counting process counter O reset, return to step 1；
（3.C）If counter m is more than a constant value I for setting, algorithmic statement is represented, an appropriate I value can calculated
Method early stage effectively evades erroneous judgement.
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| CN108055636A (en) * | 2017-12-20 | 2018-05-18 | 杭州电子科技大学 | A kind of method of the 3D wireless sensor networks positioning based on unmanned plane auxiliary |
| CN108738031B (en) * | 2018-04-16 | 2021-07-16 | 大连理工大学 | Cooperative perception-oriented multi-sensor joint deployment model construction method |
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