CN109343966A - A kind of cluster organization method and device of unmanned node - Google Patents
A kind of cluster organization method and device of unmanned node Download PDFInfo
- Publication number
- CN109343966A CN109343966A CN201811294450.0A CN201811294450A CN109343966A CN 109343966 A CN109343966 A CN 109343966A CN 201811294450 A CN201811294450 A CN 201811294450A CN 109343966 A CN109343966 A CN 109343966A
- Authority
- CN
- China
- Prior art keywords
- task
- unmanned
- node
- indicate
- fitness function
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Granted
Links
Classifications
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F9/00—Arrangements for program control, e.g. control units
- G06F9/06—Arrangements for program control, e.g. control units using stored programs, i.e. using an internal store of processing equipment to receive or retain programs
- G06F9/46—Multiprogramming arrangements
- G06F9/50—Allocation of resources, e.g. of the central processing unit [CPU]
- G06F9/5005—Allocation of resources, e.g. of the central processing unit [CPU] to service a request
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
- G06N3/00—Computing arrangements based on biological models
- G06N3/12—Computing arrangements based on biological models using genetic models
- G06N3/126—Evolutionary algorithms, e.g. genetic algorithms or genetic programming
-
- Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
- Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
- Y02D—CLIMATE CHANGE MITIGATION TECHNOLOGIES IN INFORMATION AND COMMUNICATION TECHNOLOGIES [ICT], I.E. INFORMATION AND COMMUNICATION TECHNOLOGIES AIMING AT THE REDUCTION OF THEIR OWN ENERGY USE
- Y02D10/00—Energy efficient computing, e.g. low power processors, power management or thermal management
Landscapes
- Engineering & Computer Science (AREA)
- Physics & Mathematics (AREA)
- Theoretical Computer Science (AREA)
- Software Systems (AREA)
- Health & Medical Sciences (AREA)
- Life Sciences & Earth Sciences (AREA)
- Biophysics (AREA)
- General Engineering & Computer Science (AREA)
- General Physics & Mathematics (AREA)
- Bioinformatics & Cheminformatics (AREA)
- Bioinformatics & Computational Biology (AREA)
- Evolutionary Biology (AREA)
- Physiology (AREA)
- Genetics & Genomics (AREA)
- Artificial Intelligence (AREA)
- Biomedical Technology (AREA)
- Computational Linguistics (AREA)
- Data Mining & Analysis (AREA)
- Evolutionary Computation (AREA)
- General Health & Medical Sciences (AREA)
- Molecular Biology (AREA)
- Computing Systems (AREA)
- Mathematical Physics (AREA)
- Management, Administration, Business Operations System, And Electronic Commerce (AREA)
Abstract
The invention discloses a kind of cluster organization method and devices of unmanned node, are related to computer field.This method comprises: determining the first fitness function value and the second fitness function value of each initial individuals according to fitness function when determining that meeting the quantity of the unmanned node of task restriction is zero, obtaining the first duplication population according to roulette rotary process；According to the initial individuals that first fitness function value sorts, first is replicated the multiple first duplication individuals for including in population and intersected to obtain intersection individual；It is made a variation according at least two intersection individual of mutation probability selection, and the third fitness function value of definitive variation individual；When determining that the number of iterations reaches setting value, the variation individual of the maximum third fitness function value is determined as optimal result, when the gene for determining the optimal result is encoded to 1, determine that Task league is added in unmanned node corresponding with the optimal result.
Description
Technical field
The present invention relates to computer fields, more particularly relate to a kind of cluster organization method and device of unmanned node.
Background technique
Unmanned node refers to the single unmanned systems including unmanned plane, robot, unmanned vehicle etc., unmanned node tool
There are certain communication, perception, autonomous actions, group's ability to cooperate.Unmanned node cluster is made of a large amount of unmanned nodes, has rule
It draws, self-organizing, multi-agent synergy ability, group system towards parallel multi-task.At present for the research of unmanned node cluster
Relate generally to task distribution, synergetic, cluster communication, the problems such as.However to the organisational issues of extensive unmanned node cluster
It studies fewer and fewer.With unmanned node cluster interior joint quantity be continuously increased and the dynamic change of task scene, unmanned section
The difficulty such as communication, control, the decision of point group are continuously increased, so that the problem of unmanned node cluster organization of oriented mission seems outstanding
It is important.
The unmanned node cluster tissue of parallel task driving refers to that tissue is not according to the ability and resource of different unmanned nodes
Multiple tasks are completed in same unmanned node collaboration.For the cluster organization problem of task-driven, 1) current solution includes:
The core concept of the method for based role, this method is: organizer and governor divides task, establishes role's set, role is by specific item
Three mark, ability need, income element compositions, then carry out role's recruitment, unmanned node proposes the role that expectation undertakes, by group
The ownership that manager determines role is knitted, last organizer and governor organizes these unmanned nodes to go to complete respective sub-goal；2) base
Be in the core concept of the method for auction, this method: organizer and governor divides task, assigns multiple tasks bidding documents and is auctioned,
Task bidding documents is made of task time, place, ability need, constraint four elements, and unmanned node assesses bidding documents, submits
Bid information comprising " task fitness " determines auction result by organizer and governor, and last organizer and governor organizes these nothings
People's node goes to complete respective task.
Above two method first is that unmanned node possibly can not individually complete subtask, is needed there are several common issues
Task is divided into sufficiently small；Second is that role, bid information are difficult to portray, it is difficult to realize, it is difficult to be suitable for practical application scene；
Third is that organizational efficiency is low in the case where larger, it is difficult to support the application of the unmanned node cluster of scale.
Summary of the invention
The embodiment of the present invention provides a kind of cluster organization method and device of unmanned node, parallel to solve the prior art
There are unmanned nodes to be unable to complete subtask for task-driven, it is difficult to ask suitable for practical application scene and organizational efficiency are low
Topic.
The embodiment of the present invention provides a kind of cluster organization method of unmanned node, comprising:
When determining that meeting the quantity of the unmanned node of task restriction is zero, setting one includes the first of multiple initial individuals
Beginning population, it includes the binary gene coding of multidigit that the initial individuals, which are one,；It is determined according to fitness function each described
The first fitness function value and the second fitness function value of initial individuals, according to roulette rotary process out of described initial population
Multiple initial individuals are selected, the first duplication population is obtained；
By crossover probability and the initial individuals to be sorted according to first fitness function value, described first is answered
The multiple first duplication individuals for including in production of hybrid seeds group intersect, and obtain multiple intersection individuals；According to mutation probability from multiple institutes
It states at least two intersection individuals of selection in intersection individual to make a variation, and the third fitness function of definitive variation individual
Value；
When determining that the number of iterations reaches setting value, by the variation with the maximum third fitness function value
Individual is determined as optimal result, and when the gene for determining the optimal result is encoded to 1, determination is corresponding with the optimal result
Task league is added in unmanned node.
Preferably, further includes:
When it is multiple for determining the quantity for meeting the unmanned node of the task restriction, multiple unmanned sections are determined
The ability of point exceeds degree, will exceed the minimum unmanned node of degree and is determined as the first unmanned node；Wherein, described first
Unmanned node is used to be responsible for the having the task restriction of the task；
Determine the ability of the unmanned node beyond degree by following equation:
CS=CL+CP
CP=(| | perceptionNi||-||perceptionT||)*w2
Wherein, CL indicate load capacity exceed degree, loadNiIndicate the load capacity value of unmanned node, loadTIt indicates
For task to the requirements of load capacity, what CP indicated sensing capability exceeds degree, | | perceptionNi| | indicate unmanned node sense
Know the total number of ability, | | perceptionT| | indicate total number of the task to sensing capability demand, Nodes={ Ni| i=1,
2,3 ... n }, Tasks={ Ti| i=1,2,3 ... m }, w1Indicate that load capacity exceeds the weight of degree, w2Indicate perception
Ability exceeds the weight of degree.
Preferably, the unmanned node is described using five-tuple:
Unmanned-Node=< Uid, Type, Capability, Location, State >
The task is described using four-tuple:
Task=< Tid, Location, Time, Capability >
Wherein, UidIndicate that the ID of unmanned node, Type indicate unmanned node type, Capability indicates unmanned node
Ability, Location indicate the maximum load capability of unmanned node, and State indicates the state of current unmanned node；TidIt indicates to appoint
Be engaged in ID, and Location indicates that the spatial positional information of task, Time indicate the time-constrain of task, and Capability indicates task
Ability need.
Preferably, first fitness function value and second that each initial individuals are determined according to fitness function
Fitness function value specifically includes:
The first fitness function value of the initial individuals is determined by following equation:
F (Q)=cost (Q)+r1*P(Q)+r2*|H(Q)|
The second fitness function value of the initial individuals is determined by following equation:
newf(Qi)=maxValue=max (f (Qi))+100
First fitness function value and the second fitness that each initial individuals are determined according to fitness function
After functional value, further includes:
The select probability of the initial individuals and the cumulative probability of the initial individuals are determined by following equation respectively:
Wherein, f (Q) indicates that the fitness of initial individuals Q, cost (Q) indicate the cost of initial individuals Q, and P (Q) is indicated just
Begin penalty value of the individual Q in terms of load capacity, and H (Q) indicates penalty value of the initial individuals Q in terms of sensing capability, newf
(Qi) initial individuals the second fitness function value, P (Qi) be the initial individuals select probability, Sum (Qi) it is described
The cumulative probability of initial individuals, r1Indicate the weight of load capacity penalty value, r2Indicate the weight of sensing capability penalty value.
Preferably, determine that unmanned node corresponding with the optimal result is added after Task league, further includes:
When only including a unmanned node in the Task league, determine the unmanned node for task connection
The administrator of alliance；Or
When the Task league includes multiple unmanned nodes, determining according to ability value has the institute of maximum capacity value
State the administrator that unmanned node is the Task league；
The ability value formula is as follows:
scorei={ loadi|perceptioni|}·{U1U2}T
Wherein, load indicates the load capacity of unmanned node, | perception | indicate of unmanned node perceived ability
Number, U1Indicate the weight of load capacity, U2Indicate the weight of sensing capability.
The embodiment of the present invention also provides a kind of cluster organization device of unmanned node, comprising:
Unit is obtained, for when determining that meeting the quantity of the unmanned node of task restriction is zero, setting one to include more
The initial population of a initial individuals, it includes the binary gene coding of multidigit that the initial individuals, which are one,；According to fitness letter
Number determines the first fitness function values and the second fitness function value of each initial individuals, according to roulette rotary process from
The multiple initial individuals of selection, obtain the first duplication population in the initial population；
First determination unit, for by crossover probability and according to the described initial of first fitness function value sequence
The multiple first duplication individuals for including in the first duplication population are intersected, obtain multiple intersection individuals by individual；According to
Mutation probability at least two intersection individuals of selection from multiple intersection individuals make a variation, and definitive variation individual
Third fitness function value；
Second determination unit, for that when determining that the number of iterations reaches setting value, will have the maximum third to adapt to
Degree functional value the variation individual be determined as optimal result, when the gene for determining the optimal result is encoded to 1, determine with
Task league is added in the corresponding unmanned node of the optimal result.
Preferably, further includes: third determination unit, for when the determining unmanned node for meeting the task restriction
When quantity is multiple, determine that the ability of multiple unmanned nodes beyond degree, will exceed the minimum unmanned node of degree
It is determined as the first unmanned node；Wherein, the described first unmanned node is used to be responsible for the having the task restriction of the task；
Determine the ability of the unmanned node beyond degree by following equation:
CS=CL+CP
CP=(| | perceptionNi||-||perceptionT||)*w2
Wherein, CL indicate load capacity exceed degree, loadNiIndicate the load capacity value of unmanned node, loadTIt indicates
For task to the requirements of load capacity, what CP indicated sensing capability exceeds degree, | | perceptionNi| | indicate unmanned node sense
Know the total number of ability, | | perceptionT| | indicate total number of the task to sensing capability demand, Nodes={ Ni| i=1,
2,3 ... n }, Tasks={ Ti| i=1,2,3 ... m }, w1 indicates that load capacity exceeds the weight of degree, w2Indicate perception
Ability exceeds the weight of degree.
Preferably, the unmanned node is described using five-tuple:
Unmanned-Node=< Uid, Type, Capability, Location, State >
The task is described using four-tuple:
Task=< Tid, Location, Time, Capability >
Wherein, UidIndicate that the ID of unmanned node, Type indicate unmanned node type, Capability indicates unmanned node
Ability, Location indicate the maximum load capability of unmanned node, and State indicates the state of current unmanned node；TidIt indicates to appoint
Be engaged in ID, and Location indicates that the spatial positional information of task, Time indicate the time-constrain of task, and Capability indicates task
Ability need.
Preferably, first determination unit is specifically used for:
The first fitness function value of the initial individuals is determined by following equation:
F (Q)=cost (Q)+r1*P(Q)+r2*|H(Q)|
The second fitness function value of the initial individuals is determined by following equation:
newf(Qi)=maxValue=max (f (Qi))+100
First fitness function value and the second fitness that each initial individuals are determined according to fitness function
After functional value, further includes:
The select probability of the initial individuals and the cumulative probability of the initial individuals are determined by following equation respectively:
Wherein, f (Q) indicates that the fitness of initial individuals Q, cost (Q) indicate the cost of initial individuals Q, and P (Q) is indicated just
Begin penalty value of the individual Q in terms of load capacity, and H (Q) indicates penalty value of the initial individuals Q in terms of sensing capability, newf
(Qi) initial individuals the second fitness function value, P (Qi) be the initial individuals select probability, Sum (Qi) it is described
The cumulative probability of initial individuals.
Preferably, second determination unit is also used to:
When only including a unmanned node in the Task league, determine the unmanned node for task connection
The administrator of alliance；Or
When the Task league includes multiple unmanned nodes, determining according to ability value has the institute of maximum capacity value
State the administrator that unmanned node is the Task league；
The ability value formula is as follows:
scorei={ loadi|perceptioni|}·{U1U2}T
Wherein, load indicates the load capacity of unmanned node, | perception | indicate of unmanned node perceived ability
Number, U1Indicate the weight of load capacity, U2Indicate the weight of sensing capability.
The embodiment of the present invention provides a kind of cluster organization method of unmanned node, comprising: when determination meets task restriction
When the quantity of unmanned node is zero, one initial population including multiple initial individuals of setting, the initial individuals are a packets
Include the binary gene coding of multidigit；According to fitness function determine each initial individuals the first fitness function value and
Second fitness function value selects multiple initial individuals according to roulette rotary process out of described initial population, obtains
One duplication population；By crossover probability and the initial individuals to be sorted according to first fitness function value, by described the
The multiple first duplication individuals for including in one duplication population are intersected, and multiple intersection individuals are obtained；According to mutation probability from more
At least two intersection individuals of selection make a variation in a intersection individual, and the third fitness letter of definitive variation individual
Numerical value；When determining that the number of iterations reaches setting value, by the variation with the maximum third fitness function value
Body is determined as optimal result, when the gene for determining the optimal result is encoded to 1, determines nothing corresponding with the optimal result
Task league is added in people's node.It, can by genetic algorithm when confirmation does not meet the unmanned node of mission requirements in this method
Think one or more unmanned node of task choosing, there are unmanned nodes to reach to solve existing parallel task driving
To completion subtask, it is difficult to the problem low suitable for practical application scene and organizational efficiency.
Detailed description of the invention
In order to more clearly explain the embodiment of the invention or the technical proposal in the existing technology, to embodiment or will show below
There is attached drawing needed in technical description to be briefly described, it should be apparent that, the accompanying drawings in the following description is only this
Some embodiments of invention for those of ordinary skill in the art without creative efforts, can be with
It obtains other drawings based on these drawings.
Fig. 1 is a kind of cluster organization method flow schematic diagram of unmanned node provided in an embodiment of the present invention；
Fig. 2 is the unmanned nodal method flow diagram that a kind of confirmation provided in an embodiment of the present invention meets task restriction；
Fig. 3 intersect and is shown for duplication individual 1 provided in an embodiment of the present invention and 2 two gene locations of selection of duplication individual
It is intended to；
Fig. 4 is Task league structural schematic diagram provided in an embodiment of the present invention；
Fig. 5 is a kind of cluster organization apparatus structure schematic diagram of unmanned node provided in an embodiment of the present invention.
Specific embodiment
Following will be combined with the drawings in the embodiments of the present invention, and technical solution in the embodiment of the present invention carries out clear, complete
Site preparation description, it is clear that described embodiments are only a part of the embodiments of the present invention, instead of all the embodiments.It is based on
Embodiment in the present invention, it is obtained by those of ordinary skill in the art without making creative efforts every other
Embodiment shall fall within the protection scope of the present invention.
Fig. 1 illustratively shows a kind of cluster organization method flow signal of unmanned node provided in an embodiment of the present invention
Figure, as shown in Figure 1, this method mainly comprises the steps that
Step 101, when determining that meeting the quantity of the unmanned node of task restriction is zero, setting one includes multiple initial
The initial population of individual, it includes the binary gene coding of multidigit that the initial individuals, which are one,；It is determined according to fitness function
The first fitness function value and the second fitness function value of each initial individuals, according to roulette rotary process from it is described just
The multiple initial individuals of selection, obtain the first duplication population in beginning population；
Step 102, by crossover probability and the initial individuals to be sorted according to first fitness function value, by institute
It states the multiple first duplication individuals for including in the first duplication population to be intersected, obtains multiple intersection individuals；According to mutation probability
At least two intersection individuals of selection make a variation from multiple intersection individuals, and the third of definitive variation individual adapts to
Spend functional value；
It step 103, will be with the maximum third fitness function value when determining that the number of iterations reaches setting value
The variation individual is determined as optimal result, when the gene for determining the optimal result is encoded to 1, the determining and optimal knot
Task league is added in the corresponding unmanned node of fruit.
In embodiments of the present invention, formalized description has been carried out respectively to unmanned node and task, specifically, using five yuan
Group carries out formalized description to unmanned node, carries out formalized description to task using four-tuple.
1. carrying out formalized description to unmanned node using five-tuple:
Unmanned-Node=< Uid, Type, Capability, Location, State >
Wherein, UidIndicate the ID of unmanned node；Type indicates unmanned node type, for example, 1 indicates unmanned plane, 2 indicate without
People's trolley, 3 indicate robot；Capability indicates the ability of unmanned node, and the Capability can use triple table
Show；Location indicates the maximum load capability of unmanned node, and State indicates the state of current unmanned node；
Specifically, the triple of Capability can indicate are as follows:
Capability=< Power, Loads, Perception >
Wherein, Power=< Battery, max-endurance > indicate cruising ability, and it is continuous to respectively indicate electricity, maximum
ETS estimated time of sailing；Loads indicates the maximum load capability of unmanned node, with gram for standard；
Perception=[< sensor, max-range, min-range, rate, precision > ...] indicate sense
Know ability,
Sensor indicates sensor type, max-Range and min-Range indicates minimum and maximum sensing range, rate table
Show frequency, precision indicates perceived accuracy；
2. carrying out formalized description to task using four-tuple
Task=< Tid, Location, Time, Capability >
Wherein, TidIndicate task ID；
Location=< x, y, z, radius, width, length > indicates the spatial positional information of task, parameter point
It Biao Shi not task coordinate and regional scope；Time=< LST, LFT > indicates the time-constrain of task, respectively indicates and starts at the latest
Time, latest finishing time；Capability=< Loads, Perception > indicates the ability need of task, respectively indicates
Load capacity and sensing capability are specifically described with the load in unmanned node as sensing capability description.
Table 1 is the specific descriptions form of unmanned node and task:
The description of table 1 unmanned node and task
Before step 101, it is assumed that currently have m group task and n unmanned nodes, for each task, using shown in Fig. 2
A kind of confirmation to meet the unmanned nodal method of task restriction be the one or more unmanned nodes of the task choosing, by these nothings
People's node forms an alliance, is responsible for executing the task.
Wherein, unmanned node can indicate are as follows: Nodes={ Ni| i=1,2,3 ... n }；
Task can indicate are as follows: Tasks={ Ti| i=1,2,3 ... n }；
Specifically, as shown in Figure 2:
Step 201, from the unmanned node of current idle, the unmanned node for meeting task ability demand is selected, i.e.,
The ability of candidate unmanned node is greater than or the ability need comprising task；
Step 202, if only one unmanned node meets, the node is selected；
Step 203, if there is multiple unmanned nodes, the ability for calculating each unmanned node of candidate according to the following equation exceeds
Degree will exceed the minimum unmanned node of degree and be determined as the first unmanned node, in embodiments of the present invention, the first unmanned node
Task for being responsible for having task restriction, i.e. first node can satisfy task restriction condition.
In embodiments of the present invention, determine the ability of unmanned node beyond degree by following equation (1):
CS=CL+CP (1)
Wherein, CL indicates that the degree that exceeds of load capacity, CP expression sensing capability exceed degree.
Specifically, load capacity is determining by following equation (2) beyond degree, and sensing capability passes through public affairs beyond degree
Formula (3) determines:
CP=(| | perceptionNi||-||perceptionT||)*w2 (3)
Wherein, loadNiIndicate the load capacity value of unmanned node, loadTExpression task to the requirements of load capacity, | |
perceptionNi| | indicate the total number of unmanned node perceived ability, | | perceptionT| | indicate that task needs sensing capability
The total number asked, Nodes={ Ni| i=1,2,3 ... n }, Tasks={ Ti| i=1,2,3 ... m }, w1Indicate load
Ability exceeds the weight of degree, w2Indicate that sensing capability exceeds the weight of degree.
In embodiments of the present invention, when the ability of unmanned node exceeds degree minimum, by being determined as the unmanned node
First node, the first node is for being responsible for the task.
It is then needed through the following steps in step 204 when none unmanned node, which can satisfy task restriction, to be needed
101, step 102 and step 103 are responsible for the task to select a unmanned node or multiple unmanned nodes.
Before in a step 101, need first to introduce several related notions:
1. gene encodes:
Using n binary codings, an alliance individual Q, Q={ a are defined1, a2..., an, gene ai=1, it indicates
Unmanned node NiTask league, otherwise, a is addedi=0 indicates unmanned node NiIt is added without Task league.
Citing: Q={ 0,1,1,0,1 } indicates that unmanned node 2,3,5 coalizes, and unmanned node 1,4 is added without.
2. fitness function designs
F (Q)=cost (Q)+r1*P(Q)+r2*|H(Q)| (3)
Wherein, f (Q) indicates that the fitness of individual Q, cost (Q) indicate the cost of individual Q, and P (Q) indicates that individual Q is being loaded
Penalty value in terms of ability, H (Q) indicate penalty value of the individual Q in terms of sensing capability, r1Indicate the power of load capacity penalty value
Weight, r2Indicate the weight of sensing capability penalty value.
Wherein, XT、YT、ZTRespectively indicate coordinate X, Y, Z of task T regional location, XNi、YNi、ZNiRespectively indicate unmanned section
Point NiPosition coordinates X, Y, Z；loadNiIndicate the load capacity value of unmanned node, loadTNeed of the expression task to load capacity
Evaluation.
Wherein, b=| | perceptionT| |, | | perceptionT| | indicate task to the total number of sensing capability demand,
ci(for example task needs shooting capacity to demand of the expression task to certain sensing capability, then ciFor shooting capacity), therefore
perceptionT(ci)=1, perceptionT(ci) indicate whether individual Q's contains sensing capability ci。
In embodiments of the present invention, fitness angle value f (Q) is lower, and individual is better, i.e., is got over by alliance's result that individual indicates
It is good.
In a step 101, above-mentioned steps 204 are connect, when determining that meeting the quantity of the unmanned node of task restriction is zero, if
Fixed one include multiple initial individuals initial population, wherein population scale is S, maximum number of iterations L.Of the invention real
It applies in example, it includes that the binary gene coding of multidigit indicates and the base when the gene is encoded to 1 that initial individuals, which are one,
It is the unmanned node that Task league can be added because encoding corresponding initial individuals；When the gene is encoded to 0, indicate with
It is the unmanned node that Task league can not be added that the gene, which encodes corresponding initial individuals,.
For example, if in initial population including multiple initial individuals, for each initial individuals, it can be set one
A several q (q ∈ [0,1]) is then randomly generated in given threshold P (P ∈ (0,1)) again, as p≤q, then can determine initial
The gene a of body Qi=1, that is, indicate that encoding corresponding initial individuals with the gene is the unmanned section that Task league can be added
Otherwise point works as ai=0, then it represents that encoding corresponding initial individuals with the gene is the nothing that Task league can not be added
People's node.
Further, the first fitness that each initial individuals for including in initial population are determined according to formula (3), in reality
In the application of border, if the first fitness is higher, then it represents that selected probability is bigger；And in embodiments of the present invention, due to
It is lower to define fitness function value, it is as a result better, it therefore, needs to carry out a step processing to existing roulette wheel selection, has
Body is as follows:
After the first fitness of each initial individuals that confirmation initial population includes, by the first of multiple initial individuals
Fitness is ranked up, and chooses maximum value therein；The maximum value of selection is handled according to following formula (9), so as to
To obtain the second fitness of initial individuals.
Specifically, formula (9) is as follows:
newf(Qi)=maxValue-f (Qi)=max (f (Qi))+100-f(Qi)
Wherein, maxValue=max (f (Qi))+100, each initial can be determined in initial population by formula (9)
The second fitness of people are as follows: { maxValue-f (Q1), maxValue-f (Q2), maxValue-f (Q3),...,maxValue-f
(Qs), it can simply be denoted as: { newf (Q1), newf (Q2), newf (Q3) ..., newf (Qs)}。
Further, it after the second fitness of confirmation initial individuals, can also be determined according to the following equation each first
Begin the select probability of individual and the cumulative probability of initial individuals:
Wherein, f (Q) indicates that the fitness of individual Q, cost (Q) indicate the cost of individual Q, and P (Q) indicates that individual Q is being loaded
Penalty value in terms of ability, H (Q) indicate penalty value of the individual Q in terms of sensing capability, newf (Qi) initial individuals
Two fitness function values, P (Qi) be the initial individuals select probability, Sum (Qi) be the initial individuals cumulative probability,
r1Indicate the weight of load capacity penalty value, r2Indicate the weight of sensing capability penalty value.
Further, random number p, p a ∈ [0,1] is generated in set interval [0,1], if Sum (Qi)≤p < Sum
(Qi+1) when, then select initial individuals Qi+1.This initial individuals of selection are replicated, so as to obtain the first duplication kind
Group.
For example, if having following four chromosome:
S1=13 (01101)
S2=24 (11000)
S3=8 (01000)
S4=19 (10011)
Assuming that the second fitness of above-mentioned multiple chromosomes is respectively as follows:
F (s1)=f (13)=13^2=169；
F (s2)=f (24)=24^2=576；
F (s3)=f (8)=8^2=64；
F (s4)=f (19)=19^2=361；
The select probability of chromosome is confirmed according to formula (10):
The cumulative probability of chromosome is confirmed according to formula (11):
If from 4 random numbers are generated in section [0,1]:
R1=0.450126,
R2=0.110347,
R3=0.572496,
R4=0.98503；
Then situation shown in available following table 1:
| Chromosome | Second fitness | Select probability | Cumulative probability | Choose number |
| S1=01101 | 169 | 0.14 | 0.14 | 1 |
| S2=11000 | 576 | 0.49 | 0.63 | 2 |
| S3=01000 | 64 | 0.06 | 0.69 | 0 |
| S4=10011 | 361 | 0.31 | 1.00 | 1 |
According to table 1 it was determined that can choose chromosome s11 times, chromosome s22 times, chromosome s41 times, to obtain
First duplication population.
In a step 102, according to the first fitness function value, by the multiple initial individuals for including in initial population from greatly to
It is small to be ranked up, the duplication individual in population, which is replicated, by first respectively according to the sequence after sequence carries out pairing intersection, so as to
To obtain intersecting individual；It is matched for example, replicating first and second in population for first, then according to crossover probability
Intersected, is that two gene locations of random selection are intersected intersect.Fig. 3 is duplication provided in an embodiment of the present invention
Individual 1 and duplication individual 2 select two gene locations to carry out intersection schematic diagram, as shown in figure 3, will duplication individual 1 and duplication
Body 2 is matched, and has been randomly choosed the 2nd and the 5th and has been intersected, that is, selects the 2nd and the 5th two gene location, by individual 1
Genic value with individual 2 in the two positions is exchanged with each other, and obtains two new individuals.
Further, according to mutation probability, at least two intersection individual of selection makes a variation from multiple intersection individuals, presses
According to the individual chosen, randomly chooses two gene locations and make a variation.
After variation individual has been determined, according to the third fitness function of fitness function formula definitive variation individual
Value.
In step 103, it is assumed that after the number of iterations of above-mentioned algorithm reaches the maximum times of setting, then above-mentioned algorithm stops
It only runs, so that it is determined that the third fitness of each variation individual, the first of determining third fitness and initial individuals is fitted
Response compares, if third fitness is greater than the first fitness, it is determined that selection variation individual corresponding with third fitness；
If third fitness is less than the first fitness, it is determined that selection initial individuals corresponding with the first fitness；
Further, a variation with maximum adaptation degree is selected from the variation individual of selection or initial individuals
Body or initial individuals are as optimal result.
After determining optimal result, need to carry out genetic decoding to the optimal result, if genetic decoding result is 1,
Then determine that unmanned node corresponding with the optimal result can be added in Task league；If genetic decoding result is 0, really
Fixed unmanned node corresponding with the optimal result cannot be added in Task league.
In embodiments of the present invention, the quantity that the unmanned node of Task league is added can be one, also may include having
It is multiple.By above-mentioned algorithm, it can choose multiple unmanned nodes and be added in Task league, and it includes more for working as in Task league
When a unmanned node, then the administrator for selecting a unmanned node as the Task league for each Task league is needed.Specifically
Ground, only one unmanned node in single task alliance, then the unmanned node is the administrator of the alliance；Have in Task league multiple
Unmanned node determines have the unmanned node of maximum capacity value for the administrator of the Task league according to ability value.
Ability value formula is such as shown in (12):
scorei={ loadi|perceptioni|}·{U1U2}T (12)
Wherein, load indicates the load capacity of unmanned node, | perception | indicate of unmanned node perceived ability
Number, U1Indicate the weight of load capacity, U2Indicate the weight of sensing capability.
It should be noted that in practical applications, if being worth the management that maximum unmanned node is the alliance in selective power
Member then randomly chooses one if there is the identical unmanned node of multiple ability value sizes.
When all Task league administrators selection finishes, at this point, unmanned node cluster forms structure as shown in Figure 4.
Based on the same inventive concept, the embodiment of the invention provides a kind of cluster organization devices of unmanned node, due to this
The principle that device solves technical problem is similar to a kind of cluster organization method of unmanned node, therefore the implementation of the device can be joined
The implementation of square method, overlaps will not be repeated.
Fig. 5 is a kind of cluster organization apparatus structure schematic diagram of unmanned node provided in an embodiment of the present invention, such as Fig. 5 institute
Show, which includes obtaining unit 501, the first determination unit 502, the second determination unit 503 and third determination unit 504.
Unit 501 is obtained, for when determining that meeting the quantity of the unmanned node of task restriction is zero, setting one to include
The initial population of multiple initial individuals, it includes the binary gene coding of multidigit that the initial individuals, which are one,；According to fitness
Function determines the first fitness function value and the second fitness function value of each initial individuals, according to roulette rotary process
Multiple initial individuals are selected out of described initial population, obtain the first duplication population；
First determination unit 502, for by crossover probability and according to first fitness function value sequence
The multiple first duplication individuals for including in the first duplication population are intersected, obtain multiple intersection individuals by initial individuals；
According to mutation probability, at least two intersection individuals of selection make a variation from multiple intersection individuals, and definitive variation
The third fitness function value of body；
Second determination unit 503, for that when determining that the number of iterations reaches setting value, will have the maximum third to fit
The variation individual of response functional value is determined as optimal result, when the gene for determining the optimal result is encoded to 1, determines
Task league is added in unmanned node corresponding with the optimal result.
Preferably, further includes: third determination unit 504, for when the determining unmanned section for meeting the task restriction
When the quantity of point is multiple, determine the abilities of multiple unmanned nodes beyond degree, will exceed degree it is minimum it is described nobody
Node is determined as the first unmanned node；Wherein, the described first unmanned node is used to be responsible for the having the task restriction of the task；
Determine the ability of the unmanned node beyond degree by following equation:
CS=CL+CP
CP=(| | perceptionNi||-||perceptionT||)*w2
Wherein, CL indicate load capacity exceed degree, loadNiIndicate the load capacity value of unmanned node, loadTIt indicates
For task to the requirements of load capacity, what CP indicated sensing capability exceeds degree, | | perceptionNi| | indicate unmanned node sense
Know the total number of ability, | | perceptionT| | indicate total number of the task to sensing capability demand, Nodes={ Ni| i=1,
2,3 ... n }, Tasks={ Ti| i=1,2,3 ... m }, w1 indicates that load capacity exceeds the weight of degree, w2Indicate perception
Ability exceeds the weight of degree.
Preferably, the unmanned node is described using five-tuple:
Unmanned-Node=< Uid, Type, Capability, Location, State >
The task is described using four-tuple:
Task=< Tid, Location, Time, Capability >
Wherein, UidIndicate that the ID of unmanned node, Type indicate unmanned node type, Capability indicates unmanned node
Ability, Location indicate the maximum load capability of unmanned node, and State indicates the state of current unmanned node；TidIt indicates to appoint
Be engaged in ID, and Location indicates that the spatial positional information of task, Time indicate the time-constrain of task, and Capability indicates task
Ability need.
Preferably, first determination unit 502 is specifically used for:
The first fitness function value of the initial individuals is determined by following equation:
F (Q)=cost (Q)+r1*P(Q)+r2*|H(Q)|
The second fitness function value of the initial individuals is determined by following equation:
newf(Qi)=maxValue=max (f (Qi))+100
First fitness function value and the second fitness that each initial individuals are determined according to fitness function
After functional value, further includes:
The select probability of the initial individuals and the cumulative probability of the initial individuals are determined by following equation respectively:
Wherein, f (Q) indicates that the fitness of initial individuals Q, cost (Q) indicate the cost of initial individuals Q, and P (Q) is indicated just
Begin penalty value of the individual Q in terms of load capacity, and H (Q) indicates penalty value of the initial individuals Q in terms of sensing capability, newf
(Qi) initial individuals the second fitness function value, P (Qi) be the initial individuals select probability, Sum (Qi) it is described
The cumulative probability of initial individuals, r1Indicate the weight of load capacity penalty value, r2Indicate the weight of sensing capability penalty value.
Preferably, second determination unit 503 is also used to:
When only including a unmanned node in the Task league, determine the unmanned node for task connection
The administrator of alliance；Or
When the Task league includes multiple unmanned nodes, determining according to ability value has the institute of maximum capacity value
State the administrator that unmanned node is the Task league；
The ability value formula is as follows:
scorei={ loadi|perceptioni|}·{U1U2}T
Wherein, load indicates the load capacity of unmanned node, | perception | indicate of unmanned node perceived ability
Number, U1Indicate the weight of load capacity, U2Indicate the weight of sensing capability.
It should be appreciated that the cluster organization device of the unmanned node of one of the above include unit only according to the apparatus it is real
The logical partitioning that existing function carries out in practical application, can carry out the superposition or fractionation of said units.And the embodiment mentions
The function and a kind of collection of unmanned node provided by the above embodiment that a kind of cluster organization device of the unmanned node supplied is realized
Group's organization method corresponds, for the more detailed process flow that the device is realized, in above method embodiment one
It has been be described in detail that, be not described in detail herein.
It should be understood by those skilled in the art that, the embodiment of the present invention can provide as method, system or computer program
Product.Therefore, complete hardware embodiment, complete software embodiment or reality combining software and hardware aspects can be used in the present invention
Apply the form of example.Moreover, it wherein includes the computer of computer usable program code that the present invention, which can be used in one or more,
The computer program implemented in usable storage medium (including but not limited to magnetic disk storage, CD-ROM, optical memory etc.) produces
The form of product.
The present invention be referring to according to the method for the embodiment of the present invention, the process of equipment (system) and computer program product
Figure and/or block diagram describe.It should be understood that every one stream in flowchart and/or the block diagram can be realized by computer program instructions
The combination of process and/or box in journey and/or box and flowchart and/or the block diagram.It can provide these computer programs
Instruct the processor of general purpose computer, special purpose computer, Embedded Processor or other programmable data processing devices to produce
A raw machine, so that being generated by the instruction that computer or the processor of other programmable data processing devices execute for real
The device for the function of being specified in present one or more flows of the flowchart and/or one or more blocks of the block diagram.
These computer program instructions, which may also be stored in, is able to guide computer or other programmable data processing devices with spy
Determine in the computer-readable memory that mode works, so that it includes referring to that instruction stored in the computer readable memory, which generates,
Enable the manufacture of device, the command device realize in one box of one or more flows of the flowchart and/or block diagram or
The function of being specified in multiple boxes.
These computer program instructions also can be loaded onto a computer or other programmable data processing device, so that counting
Series of operation steps are executed on calculation machine or other programmable devices to generate computer implemented processing, thus in computer or
The instruction executed on other programmable devices is provided for realizing in one or more flows of the flowchart and/or block diagram one
The step of function of being specified in a box or multiple boxes.
Although preferred embodiments of the present invention have been described, it is created once a person skilled in the art knows basic
Property concept, then additional changes and modifications may be made to these embodiments.So it includes excellent that the following claims are intended to be interpreted as
It selects embodiment and falls into all change and modification of the scope of the invention.
Obviously, various changes and modifications can be made to the invention without departing from essence of the invention by those skilled in the art
Mind and range.In this way, if these modifications and changes of the present invention belongs to the range of the claims in the present invention and its equivalent technologies
Within, then the present invention is also intended to include these modifications and variations.
Claims (10)
1. a kind of cluster organization method of unmanned node characterized by comprising
When determining that meeting the quantity of the unmanned node of task restriction is zero, initial kind including multiple initial individuals is set
Group, it includes the binary gene coding of multidigit that the initial individuals, which are one,；It is determined according to fitness function each described initial
The first fitness function value and the second fitness function value of individual, select out of described initial population according to roulette rotary process
Multiple initial individuals obtain the first duplication population；
By crossover probability and the initial individuals to be sorted according to first fitness function value, by first duplication kind
The multiple first duplication individuals for including in group are intersected, and multiple intersection individuals are obtained；According to mutation probability from multiple friendships
At least two intersection individuals of selection make a variation in fork individual, and the third fitness function value of definitive variation individual；
When determining that the number of iterations reaches setting value, by the variation individual with the maximum third fitness function value
Be determined as optimal result, when the gene for determining the optimal result is encoded to 1, determine it is corresponding with the optimal result nobody
Task league is added in node.
2. the method as described in claim 1, which is characterized in that further include:
When it is multiple for determining the quantity for meeting the unmanned node of the task restriction, multiple unmanned nodes are determined
Ability exceeds degree, will exceed the minimum unmanned node of degree and is determined as the first unmanned node；Wherein, described first nobody
Node is used to be responsible for the having the task restriction of the task；
Determine the ability of the unmanned node beyond degree by following equation:
CS=CL+CP
CP=(| | perceptionNi||-||perceptionT||)*w2
Wherein, CL indicate load capacity exceed degree, loadNiIndicate the load capacity value of unmanned node, loadTExpression task
To the requirements of load capacity, CP expression sensing capability exceeds degree, | | perceptionNi| | indicate unmanned node perceived energy
The total number of power, | | perceptionT| | indicate total number of the task to sensing capability demand, Nodes={ Ni| i=1,2,
3 ... n }, Tasks={ Ti| i=1,2,3 ... m }, w1 indicates that load capacity exceeds the weight of degree, w2Indicate perception energy
Power exceeds the weight of degree.
3. method according to claim 2, which is characterized in that the unmanned node is described using five-tuple:
Unmanned-Node=< Uid, Type, Capability, Location, State >
The task is described using four-tuple:
Task=< Tid, Location, Time, Capability >
Wherein, UidIndicate that the ID of unmanned node, Type indicate unmanned node type, Capability indicates the energy of unmanned node
Power, Location indicate the maximum load capability of unmanned node, and State indicates the state of current unmanned node；TidExpression task
ID, Location indicate that the spatial positional information of task, Time indicate the time-constrain of task, and Capability indicates task
Ability need.
4. the method as described in claim 1, which is characterized in that described to determine each initial individuals according to fitness function
The first fitness function value and the second fitness function value, specifically include:
The first fitness function value of the initial individuals is determined by following equation:
F (Q)=cost (Q)+r1*P(Q)+r2*|H(Q)|
The second fitness function value of the initial individuals is determined by following equation:
newf(Qi)=maxValue-f (Qi)=max (f (Qi))+100-f(Qi)
First fitness function value and the second fitness function that each initial individuals are determined according to fitness function
After value, further includes:
The select probability of the initial individuals and the cumulative probability of the initial individuals are determined by following equation respectively:
Wherein, f (Q) indicates that the fitness of initial individuals Q, cost (Q) indicate the cost of initial individuals Q, and P (Q) indicates initial
Penalty value of the body Q in terms of load capacity, H (Q) indicate penalty value of the initial individuals Q in terms of sensing capability, newf (Qi) institute
State the second fitness function value of initial individuals, P (Qi) be the initial individuals select probability, Sum (Qi) it is described initial
The cumulative probability of body, r1Indicate the weight of load capacity penalty value, r2Indicate the weight of sensing capability penalty value.
5. the method as described in claim 1, which is characterized in that determine that unmanned node corresponding with the optimal result is added and appoint
It is engaged in after alliance, further includes:
When only including a unmanned node in the Task league, determine that the unmanned node is the Task league
Administrator；Or
When the Task league includes multiple unmanned nodes, determining according to ability value has the nothing of maximum capacity value
People's node is the administrator of the Task league；
The ability value formula is as follows:
scorei={ loadi|perceptioni|}·{U1 U2}T
Wherein, load indicates the load capacity of unmanned node, | perception | indicate the number of unmanned node perceived ability, U1
Indicate the weight of load capacity, U2Indicate the weight of sensing capability.
6. a kind of cluster organization device of unmanned node characterized by comprising
Unit is obtained, for when determining that meeting the quantity of the unmanned node of task restriction is zero, setting one to include multiple first
Begin individual initial population, and it includes the binary gene coding of multidigit that the initial individuals, which are one,；It is true according to fitness function
The first fitness function value and the second fitness function value of fixed each initial individuals, according to roulette rotary process from described
The multiple initial individuals of selection, obtain the first duplication population in initial population；
First determination unit, for by crossover probability and according to the described initial a of first fitness function value sequence
The multiple first duplication individuals for including in the first duplication population are intersected, obtain multiple intersection individuals by body；According to change
Different probability at least two intersections individuals of selection from multiple intersections individuals make a variation, and definitive variation it is individual the
Three fitness function values；
Second determination unit, for that when determining that the number of iterations reaches setting value, will have the maximum third fitness letter
The variation individual of numerical value is determined as optimal result, when the gene for determining the optimal result is encoded to 1, it is determining with it is described
Task league is added in the corresponding unmanned node of optimal result.
7. device as claimed in claim 6, which is characterized in that further include: third determination unit determines described in satisfaction for working as
When the quantity of the unmanned node of task restriction is multiple, determine that the ability of multiple unmanned nodes beyond degree, will surpass
The minimum unmanned node of degree is determined as the first unmanned node out；Wherein, the described first unmanned node is for being responsible for
State the task of task restriction；
Determine the ability of the unmanned node beyond degree by following equation:
CS=CL+CP
CP=(| | perceptionNi||-||perceptionT||)*w2
Wherein, CL indicate load capacity exceed degree, loadNiIndicate the load capacity value of unmanned node, loadTExpression task
To the requirements of load capacity, CP expression sensing capability exceeds degree, | | perceptionNi| | indicate unmanned node perceived energy
The total number of power, | | perceptionT| | indicate total number of the task to sensing capability demand, Nodes={ Ni| i=1,2,
3 ... n }, Tasks={ Ti| i=1,2,3 ... m }, w1Indicate that load capacity exceeds the weight of degree, w2Indicate perception energy
Power exceeds the weight of degree.
8. device as claimed in claim 7, which is characterized in that the unmanned node is described using five-tuple:
Unmanned-Node=< Uid, Type, Capability, Location, State >
The task is described using four-tuple:
Task=< Tid, Location, Time, Capability >
Wherein, UidIndicate that the ID of unmanned node, Type indicate unmanned node type, Capability indicates the energy of unmanned node
Power, Location indicate the maximum load capability of unmanned node, and State indicates the state of current unmanned node；TidExpression task
ID, Location indicate that the spatial positional information of task, Time indicate the time-constrain of task, and Capability indicates task
Ability need.
9. device as claimed in claim 6, which is characterized in that first determination unit is specifically used for:
The first fitness function value of the initial individuals is determined by following equation:
F (Q)=cost (Q)+r1*P(Q)+r2*|H(Q)|
The second fitness function value of the initial individuals is determined by following equation:
newf(Qi)=maxValue=max (f (Qi))+100
First fitness function value and the second fitness function that each initial individuals are determined according to fitness function
After value, further includes:
The select probability of the initial individuals and the cumulative probability of the initial individuals are determined by following equation respectively:
Wherein, f (Q) indicates that the fitness of initial individuals Q, cost (Q) indicate the cost of initial individuals Q, and P (Q) indicates initial
Penalty value of the body Q in terms of load capacity, H (Q) indicate penalty value of the initial individuals Q in terms of sensing capability, newf (Qi) institute
State the second fitness function value of initial individuals, P (Qi) be the initial individuals select probability, Sum (Qi) it is described initial
The cumulative probability of body, r1Indicate the weight of load capacity penalty value, r2Indicate the weight of sensing capability penalty value.
10. device as claimed in claim 6, which is characterized in that second determination unit is also used to:
When only including a unmanned node in the Task league, determine that the unmanned node is the Task league
Administrator；Or
When the Task league includes multiple unmanned nodes, determining according to ability value has the nothing of maximum capacity value
People's node is the administrator of the Task league；
The ability value formula is as follows:
scorei={ loadi|perceptioni|}·{U1 U2}T
Wherein, load indicates the load capacity of unmanned node, | perception | indicate the number of unmanned node perceived ability, U1
Indicate the weight of load capacity, U2Indicate the weight of sensing capability.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201811294450.0A CN109343966B (en) | 2018-11-01 | 2018-11-01 | Cluster organization method and device for unmanned nodes |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201811294450.0A CN109343966B (en) | 2018-11-01 | 2018-11-01 | Cluster organization method and device for unmanned nodes |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN109343966A true CN109343966A (en) | 2019-02-15 |
| CN109343966B CN109343966B (en) | 2023-04-07 |
Family
ID=65313273
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN201811294450.0A Active CN109343966B (en) | 2018-11-01 | 2018-11-01 | Cluster organization method and device for unmanned nodes |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN109343966B (en) |
Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN110321938A (en) * | 2019-06-20 | 2019-10-11 | 西北工业大学 | A kind of state space construction method and device of Intelligent unattended cluster |
| CN111007874A (en) * | 2019-09-18 | 2020-04-14 | 合肥工业大学 | Unmanned aerial vehicle and vehicle cooperative power inspection method and device |
Citations (8)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20070005522A1 (en) * | 2005-06-06 | 2007-01-04 | Wren William E | Resource assignment optimization using direct encoding and genetic algorithms |
| US20130308570A1 (en) * | 2012-05-17 | 2013-11-21 | Beijing University Of Posts And Telecommunications | Method for joint optimization of schedule and resource allocation based on the genetic algorithm |
| CN104573820A (en) * | 2014-12-31 | 2015-04-29 | 中国地质大学（武汉） | Genetic algorithm for solving project optimization problem under constraint condition |
| CN105704255A (en) * | 2016-04-29 | 2016-06-22 | 浙江理工大学 | Server load balancing method based on genetic algorithm |
| WO2016165392A1 (en) * | 2015-04-17 | 2016-10-20 | 华南理工大学 | Genetic algorithm-based cloud computing resource scheduling method |
| CN106934459A (en) * | 2017-02-03 | 2017-07-07 | 西北工业大学 | A kind of self-adapted genetic algorithm based on Evolution of Population process |
| CN107329831A (en) * | 2017-06-29 | 2017-11-07 | 北京仿真中心 | A kind of artificial resource dispatching method based on improved adaptive GA-IAGA |
| WO2018036282A1 (en) * | 2016-08-24 | 2018-03-01 | 深圳市中兴微电子技术有限公司 | Task scheduling method, device and computer storage medium |
-
2018
- 2018-11-01 CN CN201811294450.0A patent/CN109343966B/en active Active
Patent Citations (8)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20070005522A1 (en) * | 2005-06-06 | 2007-01-04 | Wren William E | Resource assignment optimization using direct encoding and genetic algorithms |
| US20130308570A1 (en) * | 2012-05-17 | 2013-11-21 | Beijing University Of Posts And Telecommunications | Method for joint optimization of schedule and resource allocation based on the genetic algorithm |
| CN104573820A (en) * | 2014-12-31 | 2015-04-29 | 中国地质大学（武汉） | Genetic algorithm for solving project optimization problem under constraint condition |
| WO2016165392A1 (en) * | 2015-04-17 | 2016-10-20 | 华南理工大学 | Genetic algorithm-based cloud computing resource scheduling method |
| CN105704255A (en) * | 2016-04-29 | 2016-06-22 | 浙江理工大学 | Server load balancing method based on genetic algorithm |
| WO2018036282A1 (en) * | 2016-08-24 | 2018-03-01 | 深圳市中兴微电子技术有限公司 | Task scheduling method, device and computer storage medium |
| CN106934459A (en) * | 2017-02-03 | 2017-07-07 | 西北工业大学 | A kind of self-adapted genetic algorithm based on Evolution of Population process |
| CN107329831A (en) * | 2017-06-29 | 2017-11-07 | 北京仿真中心 | A kind of artificial resource dispatching method based on improved adaptive GA-IAGA |
Non-Patent Citations (3)
| Title |
|---|
| HYUNJIN CHOI ET AL: "Genetic Algorithm Based Decentralized Task Assignment for Multiple", 《IJASS》 * |
| 孔凡兴: "无线传感器网络节点感知进化计算模型研究", 《计算机仿真》 * |
| 徐健锐等: "基于自适应惩罚函数的云工作流调度协同进化遗传算法", 《计算机科学》 * |
Cited By (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN110321938A (en) * | 2019-06-20 | 2019-10-11 | 西北工业大学 | A kind of state space construction method and device of Intelligent unattended cluster |
| CN110321938B (en) * | 2019-06-20 | 2022-10-11 | 西北工业大学 | State space construction method and device of intelligent unmanned cluster |
| CN111007874A (en) * | 2019-09-18 | 2020-04-14 | 合肥工业大学 | Unmanned aerial vehicle and vehicle cooperative power inspection method and device |
| CN111007874B (en) * | 2019-09-18 | 2022-07-19 | 合肥工业大学 | Unmanned aerial vehicle and vehicle cooperative power inspection method and device |
Also Published As
| Publication number | Publication date |
|---|---|
| CN109343966B (en) | 2023-04-07 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| Simaria et al. | A genetic algorithm based approach to the mixed-model assembly line balancing problem of type II | |
| US7537523B2 (en) | Dynamic player groups for interest management in multi-character virtual environments | |
| CN103229487A (en) | Partition balance method, device and server in distributed storage system | |
| Mistry et al. | Committed activists and the reshaping of status-quo social consensus | |
| CN110334919B (en) | Production line resource matching method and device | |
| CN101702655A (en) | Layout method and system of network topological diagram | |
| CN109343966A (en) | A kind of cluster organization method and device of unmanned node | |
| CN103455888A (en) | Method and device for configuring flow permission | |
| CN111784211A (en) | Cluster-based group multitask allocation method and storage medium | |
| CN105631750A (en) | Civil aviation passenger group discovery method | |
| CN111078380B (en) | Multi-target task scheduling method and system | |
| CN110097190A (en) | A kind of intelligent perception method for allocating tasks based on dual-time limitation | |
| Khodar et al. | New scheduling approach for virtual machine resources in cloud computing based on genetic algorithm | |
| Billard et al. | Effects of delayed communication in dynamic group formation | |
| CN109753501A (en) | A kind of data display method of off-line state, device, equipment and storage medium | |
| Kaveh et al. | Chaotic vibrating particles system for resource-constrained project scheduling problem | |
| CN113865607A (en) | Path planning method, device, equipment and storage medium | |
| CN108282523A (en) | A kind of SiteServer LBS based on SDN | |
| CN110011971A (en) | A kind of manual configuration method of network security policy | |
| CN106780722B (en) | A kind of differential mode scale Forest Scene construction method of the same race and system | |
| CN106779186B (en) | Energy supply scale determination method and device based on energy consumption main bodies in different business states | |
| CN115271821A (en) | Dot distribution processing method, dot distribution processing device, computer equipment and storage medium | |
| CN115358532A (en) | Work order generation method and device for equipment operation and computer equipment | |
| DE102022110762A1 (en) | Content management process, content management system, metaverse and computer program product | |
| CN111882457A (en) | Transformer substation site selection method and device |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| GR01 | Patent grant | ||
| GR01 | Patent grant |