CN106845643B - A kind of cloud service system dynamic evolution method based on improved adaptive GA-IAGA - Google Patents
A kind of cloud service system dynamic evolution method based on improved adaptive GA-IAGA Download PDFInfo
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Abstract
The present invention discloses a kind of cloud service system dynamic evolution method based on improved adaptive GA-IAGA, which includes: to carry out dynamic evolution coding to the cloud service in cloud service system；The cloud service system is initialized；Fitness function is constructed, the fitness value of each candidate cloud service in i-th class candidate's cloud service is calculated；Compare each candidate's fitness value of cloud service and the size of the first given threshold in i-th class candidate's cloud service, therefrom selects the i-th class candidate cloud service corresponding more than or equal to the fitness value of the first given threshold as target cloud service；Target cloud service system is generated according to the target cloud service of all kinds of cloud services.Cloud service system dynamic evolution method provided by the invention ensure that the dynamic evolution method can be in global scope preferentially, simultaneously, it can be selected from the angle of Optimizing Search by selection operation, optimize cloud service generation by generation, can quickly and efficiently complete to cloud service it is global preferentially.
Description
Technical field
The present invention relates to dynamic evolution fields, more particularly to a kind of cloud service system dynamic based on improved adaptive GA-IAGA
Evolution method.
Background technique
With the continuous development of software technology, cloud computing is increasingly becoming the application environment of mainstream.Under cloud computing environment, service
Key concept is had become, all trend all serviced are showed.Cloud service system will become the mainstream applications form of software.So
And dynamic, opening and the complexity of cloud computing environment and the frequent variation of user demand, it is desirable that cloud service system is answered
Continuous dynamic evolution.None cloud service system can meet always user's requirement, and row of not stopping transport down.In order to adapt to
Each cloud service needs of open cloud computing environment, cloud service system are developed accordingly.Due to clock availability
The characteristics of, current research hotspot that cloud service system dynamic evolution has become.
The complexity of cloud environment determines that cloud service Population is larger, when carrying out cloud service system dynamic evolution, passes
Optimization method of uniting is to seek optimal solution from single initial value iteration, is easy to be strayed into locally optimal solution, can not quickly and efficiently be completed complete
Office preferentially develops.
Summary of the invention
The object of the present invention is to provide a kind of cloud service system dynamic evolution method based on improved adaptive GA-IAGA, it is described dynamic
The overall situation that state evolution method can quickly and efficiently complete cloud service system preferentially develops.
To achieve the above object, the present invention provides following schemes:
A kind of cloud service system dynamic evolution method based on improved adaptive GA-IAGA, the dynamic evolution method include:
Dynamic evolution coding is carried out to the cloud service in cloud service system, definition forms the cloud service of the cloud service system
Are as follows:
Si={ ni,pi,fi,fdi,fei,Ii,Idi,odi,idi,noi,Ci,Ni(0,1) },
Wherein, i indicates cloud service serial number, SiIndicate i-th of cloud service, ni、pi、fi、fdi、fei、Ii、Idi、odi、idi、
noi、CiAnd NiIt (0,1) is the encoded components of i-th of cloud service, niIndicate the corresponding title of i-th of cloud service, piIt indicates i-th
The parameter of cloud service, the parameter are the inner element of i-th of cloud service, pi=(ei1,ei2,...,eiM), wherein M is
The number of the inner element of i-th of cloud service, eikFor in i-th of cloud service, what k-th of inner element was relied on is removed k-th
The number of inner element except inner element, fiIndicate the function number of i-th of cloud service, fdiIndicate having for i-th of cloud service
The function number of defect, feiIndicate the function number of the dependence environment of i-th of cloud service, IiIndicate the number of ports of i-th of cloud service；
IdiIndicate the defective number of ports of i-th of cloud service, odiIndicate i-th of cloud service goes out the degree of coupling, idiIndicate i-th of cloud
Service enters the degree of coupling, noiIndicate the Failure count of i-th of cloud service, CiIndicate class belonging to i-th of cloud service, Ni(0,1)
Indicate that the corresponding random number of i-th of cloud service, the value interval of the random number are (0,1)；
The cloud service system is initialized, generates the initial of the i-th class cloud service in the cloud service system at random
Cloud service population；
Fitness function is constructed, the fitness value of each individual in the initial cloud service population of the i-th class is calculated；
First screening is carried out to the initial cloud service population of i-th class and determines candidate cloud service；
Compare all candidate fitness values of cloud service of i-th class and the size of the first given threshold, therefrom selects
The i-th class candidate cloud service corresponding more than or equal to the fitness value of the first given threshold is as target cloud service, if the i-th class
The target cloud service of cloud service is more than one, then is taken using probability selection method choice one as the i-th final class target cloud
Business；If the fitness value of all candidate cloud services of the i-th class cloud service is respectively less than the first given threshold, to all candidate clouds
Service carries out crossover operation and mutation operation to update i-th class candidate's cloud service；
Target cloud service system is generated according to the final goal cloud service of all kinds of cloud services.
Optionally, described to generate the initial cloud service population of the i-th class in the cloud service system at random, it specifically includes:
Using formula cijk=| (cijkmax-cijkmin)×Nij(0,1)+cijkmin| calculate each i-th class cloud service population
In j-th individual each component initial value, wherein | | be bracket function, cijkIt indicates in the i-th class cloud service population j-th
K-th of component initial value of individual, cijkmaxIndicate k-th of component of j-th of individual in the i-th class cloud service population in value model
Enclose interior maximum value, cijkminIndicate minimum of k-th of the component of j-th of individual in the i-th class cloud service population in value range
Value；The size of the initial cloud service population of the i-th class generated at random can give as needed.
Optionally, the building fitness function, specifically includes: building fitness function fij(Q)=w1i*f1ij(FC)+
w2i*f2ij(FD)+w3i*f3ij(IC)+w4i*f4ij(CP)+w5i*f5ij(CH)+w6i*f6ij(EC) it calculates in the i-th class cloud service population
The fitness of j-th of individual, wherein wriIndicate the weight of each quality index of the i-th class cloud service, r=1 ..., 6, f1ij(FC)
For the function accuracy function of j-th of individual in the i-th class cloud service population, f2ijIt (FD) is j-th in the i-th class cloud service population
The functional independence function of individual, f3ijIt (IC) is the interface correctness function of j-th of individual in the i-th class cloud service population, f4ij
It (CP) is the coupling function of j-th of individual in the i-th class cloud service population, f5ijIt (CH) is j-th in the i-th class cloud service population
The cohesion function of individual, f6ijIt (EC) is the evolution compatibility function of j-th of individual in the i-th class cloud service population.
Optionally, j-th of individual function accuracy function f in the i-th class cloud service population1ij(FC) are as follows:
f1ij(FC)=1-fdij/fij,
Wherein, fdijIn the function of j-th of individual, to detect defective function number in the i-th class cloud service population,
fijFor the function sum of j-th of individual in the i-th class cloud service population；
The functional independence function f of j-th of individual in the i-th class cloud service population2ij(FD) are as follows:
f2ij(FD)=1-feij/fij,
Wherein, feijTo detect to rely on the function number of environment in j-th of individual in the i-th class cloud service population；
The interface correctness function f of j-th of individual in the i-th class cloud service population3ij(IC) are as follows:
f3ij(IC)=1-Idij/Iij,
Wherein, IdijIn the interface of j-th of individual, to detect defective number of ports in the i-th class cloud service population,
IijFor the interface sum of j-th of individual in the i-th class cloud service population；
The coupling function f of j-th of individual in the i-th class cloud service population4ij(CP) are as follows:
f4ij(CP)=odij/(odij+idij),
Wherein, odijFor the degree of coupling out of j-th of individual in the i-th class cloud service population, idijFor the i-th class cloud service population
In j-th individual enter the degree of coupling；
The cohesion function f of j-th of individual in the i-th class cloud service population5ij(CH) are as follows:
Wherein, M is the number of j-th of individual inner element in the i-th class cloud service population, eijkFor the i-th class cloud service population
In j-th of individual the other elements number that is relied on of k-th of element；
The evolution compatibility function f of j-th of individual in the i-th class cloud service population6ij(EC) are as follows:
f6ij(EC)=1/ (noij+ 1),
Wherein, noijWhen replacing legacy version for the new version of j-th of individual in the i-th class cloud service population, what is failed is secondary
Number.
Optionally, the initial cloud service population to each i-th class cloud service carries out first screening and determines candidate cloud
Service includes:
Compare the individual corresponding fitness value of each cloud service in each i-th class cloud service initial population and the second setting threshold
The size of value therefrom selects the initial cloud serve individual corresponding greater than the fitness value of the second given threshold as i-th
Class candidate's cloud service；If the corresponding i-th class initial cloud serve individual of the fitness value for being not greater than the second given threshold,
Initialization then is re-started to the i-th class cloud service, generates the initial cloud service population of the i-th class cloud service；
Optionally, the mutation operation includes: the number for generating variation by a random number first, is then passed through every time
Mutation operator carries out random variation, and the mutation operator includes increasing function to the cloud service, removes from the cloud service
Function, the repair function from the cloud service modify the function number that environment is relied in the cloud service, introduce interface to the cloud
Service, removes interface from the cloud service, repairs interface from the cloud service, and that modifies the cloud service goes out the degree of coupling,
That modifies the cloud service enters the degree of coupling, modifies the parameter of the cloud service, modifies the Failure count of the cloud service.
Optionally, the crossover operation includes: to generate the number intersected by a random number first, then random every time
The encoded components for selecting the cloud service are crosspoint, exchange the encoded components of the cloud service.
Optionally, the probability selection method include: every class cloud service each target cloud service individual selected it is general
Rate and its fitness value are proportional, if the number of the target cloud service individual of the i-th class cloud service is k, wherein target cloud service
Body SijFitness value be fij, then target cloud service individual SijIt is selected as the probability of final goal cloud service are as follows:
Wherein, f indicates that fitness value, j indicate serial number, fijIndicate j-th of target cloud service individual of the i-th class cloud service
Fitness value.
The specific embodiment provided according to the present invention, the invention discloses following technical effects: the present invention is by genetic algorithm
In selection operation, crossover operation and mutation operation be introduced into cloud service system dynamic evolution method, due to crossover operation and change
The migration rules of ETTHER-OR operation be it is random, ensure that the dynamic evolution method can be in global scope preferentially.Meanwhile with one
As the undirected search that is carried out of stochastic search methods it is different, can be carried out from a large amount of population at individual by selection operation twice
Rapid Optimum selection, makes cloud service generation by generation optimize evolution, and quickly approach optimal cloud service, to guarantee cloud service
When system dynamic evolution, the overall situation individual to a large amount of cloud services can be quickly and efficiently completed preferentially.
Detailed description of the invention
It in order to more clearly explain the embodiment of the invention or the technical proposal in the existing technology, below will be to institute in embodiment
Attached drawing to be used is needed to be briefly described, it should be apparent that, the accompanying drawings in the following description is only some implementations of the invention
Example, for those of ordinary skill in the art, without creative efforts, can also obtain according to these attached drawings
Obtain other attached drawings.
Fig. 1 is the flow chart of the dynamic evolution method of cloud service system of the embodiment of the present invention；
Fig. 2 is the cloud service code pattern before crossover operation of the embodiment of the present invention；
Fig. 3 is the cloud service code pattern after crossover operation of the embodiment of the present invention；
Fig. 4 is that itinerary of the embodiment of the present invention formulates cloud service system structure chart.
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.
The object of the present invention is to provide a kind of dynamic evolution methods of cloud service system based on improved adaptive GA-IAGA, with fast
The overall situation that speed efficiently completes cloud service system preferentially develops.
In order to make the foregoing objectives, features and advantages of the present invention clearer and more comprehensible, with reference to the accompanying drawing and specific real
Applying mode, the present invention is described in further detail.
Fig. 1 is the flow chart of the dynamic evolution method of cloud service system of the embodiment of the present invention, as shown in Figure 1, cloud service system
Steps are as follows for the dynamic evolution method of system:
Step 100: determining the species number n of cloud service in cloud service system；
Step 101: n class cloud service being encoded, definition forms the cloud service of the cloud service system are as follows:
Si={ ni,pi,fi,fdi,fei,Ii,Idi,odi,idi,noi,Ci,Ni(0,1) },
Wherein, i indicates cloud service serial number, SiIndicate i-th of cloud service, ni、pi、fi、fdi、fei、Ii、Idi、odi、idi、
noi、CiAnd NiIt (0,1) is the encoded components of i-th of cloud service, niIndicate the corresponding title of i-th of cloud service, piIt indicates i-th
The parameter of cloud service, the parameter are the inner element of i-th of cloud service, pi=(ei1,ei2,...,eiM), wherein M is
The number of the inner element of i-th of cloud service, eikFor in i-th of cloud service, what k-th of inner element was relied on is removed k-th
The number of inner element except inner element, fiIndicate the function number of i-th of cloud service, fdiIndicate having for i-th of cloud service
The function number of defect, feiIndicate the function number of the dependence environment of i-th of cloud service, IiIndicate the number of ports of i-th of cloud service；
IdiIndicate the defective number of ports of i-th of cloud service, odiIndicate i-th of cloud service goes out the degree of coupling, idiIndicate i-th of cloud
Service enters the degree of coupling, noiIndicate the Failure count of i-th of cloud service, CiIndicate class belonging to i-th of cloud service, Ni(0,1)
Indicate that the corresponding random number of i-th of cloud service, the value interval of the random number are (0,1).
The coding of 1 cloud service system of table
Step 102: the cloud service system being initialized, the i-th class cloud in the cloud service system is generated at random and takes
The initial cloud service population of business, i=1 ..., n, the cloud service population include multiple such cloud service individuals；
Step 103: building fitness function calculates the fitness value of each individual cloud service in the i-th class cloud service population, i
=1 ..., n；
Compare the individual corresponding fitness value of each cloud service in each i-th class cloud service initial population and the second setting threshold
The size of value therefrom selects the initial cloud serve individual corresponding greater than the fitness value of the second given threshold as i-th
Class candidate's cloud service；If the corresponding i-th class initial cloud serve individual of the fitness value for being not greater than the second given threshold,
Initialization then is re-started to the i-th class cloud service, generates the initial cloud service population of the i-th class cloud service, specially step 104 arrives
Step 108.
Step 104: judging in the i-th class cloud service population whether the corresponding fitness value of each cloud service individual is greater than second and set
Determine threshold value；
Step 105: if the corresponding fitness value of cloud service individual in the i-th class cloud service population is less than or equal to second
Given threshold then abandons cloud service individual；
Step 106: if the corresponding fitness value of cloud service individual of the i-th class cloud service population is greater than the second preset value,
Then retain cloud service individual；
Step 107: if reinitializing without residue cloud service individual in the i-th class cloud service population and generating the i-th class
Cloud service population, the residue cloud service individual are the cloud service individual remained；
Step 108: if there is remaining cloud service individual in the i-th class cloud service population, by the remaining cloud service individual
As candidate cloud service, the residue cloud service individual is the cloud service individual remained；
Step 109: judging whether the fitness value of each candidate's cloud service in the i-th class cloud service population is more than or equal to first and sets
Determine threshold value；
Step 110: if the fitness value of all candidate's cloud services is respectively less than the first setting threshold in the i-th class cloud service population
Value then does crossover operation and mutation operation to all candidate cloud services, to update i-th class candidate's cloud service；
Step 111: if the fitness value that there is candidate cloud service in the i-th class cloud service population is more than or equal to the first setting
Threshold value selects the mesh in the i-th class cloud service population in all candidate's cloud services then using candidate's cloud service as target cloud service
Mark cloud service；
Step 112: judging whether the target cloud service in the i-th class cloud service population is unique；
Step 113: if the target cloud service in the i-th class cloud service population is not unique, being selected using probability selection method
One is selected as the i-th final class target cloud service；
Step 114: target cloud service system is generated according to the final goal cloud service of all kinds of cloud services.
Wherein, in step 102, the cloud service system is initialized, is generated in each cloud service system at random
The initial cloud service population of i-th class cloud service, specifically includes:
Using formula cijk=| (cijkmax-cijkmin)×Nij(0,1)+cijkmin| calculate each i-th class cloud service population
In j-th individual each component initial value, wherein | | be bracket function, cijkIt indicates in the i-th class cloud service population j-th
K-th of component initial value of individual, cijkmaxIndicate k-th of component of j-th of individual in the i-th class cloud service population in value model
Enclose interior maximum value, cijkminIndicate minimum of k-th of the component of j-th of individual in the i-th class cloud service population in value range
Value；The size of the initial cloud service population of the i-th class generated at random can give as needed.
Step 103 constructs fitness function, specifically includes:
Construct fitness function fij(Q)=w1i*f1ij(FC)+w2i*f2ij(FD)+w3i*f3ij(IC)+w4i*f4ij(CP)+
w5i*f5ij(CH)+w6i*f6ij(EC) fitness of j-th of individual in the i-th class cloud service population is calculated, wherein wriIndicate the i-th class
The weight of each quality index of cloud service, r=1 ..., 6, f1ijIt (FC) is the function of j-th of individual in the i-th class cloud service population
Correctness function, f2ijIt (FD) is the functional independence function of j-th of individual in the i-th class cloud service population, f3ijIt (IC) is the i-th class
The interface correctness function of j-th of individual, f in cloud service population4ijIt (CP) is the coupling of j-th of individual in the i-th class cloud service population
Conjunction property function, f5ijIt (CH) is the cohesion function of j-th of individual in the i-th class cloud service population, f6ijIt (EC) is the i-th class cloud service
The evolution compatibility function of j-th of individual in population.
The function accuracy function f of j-th of individual in the i-th class cloud service population1ij(FC) are as follows:
f1ij(FC)=1-fdij/fij,
Wherein, fdijIn the function of j-th of individual, to detect defective function number in the i-th class cloud service population,
fijFor the function sum of j-th of individual in the i-th class cloud service population；
The functional independence function f of j-th of individual in the i-th class cloud service population2ij(FD) are as follows:
f2ij(FD)=1-feij/fij,
Wherein, feijTo detect to rely on the function number of environment in j-th of individual in the i-th class cloud service population；
The interface correctness function f of j-th of individual in the i-th class cloud service population3ij(IC) are as follows:
f3ij(IC)=1-Idij/Iij,
Wherein, IdijIn the interface of j-th of individual, to detect defective number of ports in the i-th class cloud service population,
IijFor the interface sum of j-th of individual in the i-th class cloud service population；
The coupling function f of j-th of individual in the i-th class cloud service population4ij(CP) are as follows:
f4ij(CP)=odij/(odij+idij),
Wherein, odijFor the degree of coupling out of j-th of individual in the i-th class cloud service population, idijFor the i-th class cloud service population
In j-th individual enter the degree of coupling；
The cohesion function f of j-th of individual in the i-th class cloud service population5ij(CH) are as follows:
Wherein, M is the number of j-th of individual inner element in the i-th class cloud service population, eijkFor the i-th class cloud service population
In j-th of individual the other elements number that is relied on of k-th of element；
The evolution compatibility function f of j-th of individual in the i-th class cloud service population6ij(EC) are as follows:
f6ij(EC)=1/ (noij+ 1),
Wherein, noijWhen replacing legacy version for the new version of j-th of individual in the i-th class cloud service population, what is failed is secondary
Number.
The mutation operation in step 110 includes: the number for generating variation by a random number first, then every time
Random variation is carried out by mutation operator, the mutation operator includes increasing function to the cloud service, from the cloud service
Function is removed, the repair function from the cloud service modifies the function number that environment is relied in the cloud service, introduces interface to institute
Cloud service is stated, removes interface from the cloud service, repairs interface from the cloud service, that modifies the cloud service goes out coupling
Degree, that modifies the cloud service enters the degree of coupling, modifies the parameter of the cloud service, modifies the Failure count of the cloud service.
The crossover operation in step 110 includes: to generate the number intersected by a random number first, then every time
The encoded components for randomly choosing the cloud service are crosspoint, exchange the encoded components of the cloud service.
Specifically, selection operation twice is carried out by step 108,111 and 113 pairs of each cloud service individuals.
The purpose of selection operation is to quickly preferentially screen to the progress of a large amount of cloud service individuals.By step 108 carry out
First time selection operation is screened for the first time from a large amount of individuals of every class cloud service population, is selected fitness value and is set greater than second
Determine candidate cloud service of the cloud service individual as such cloud service of threshold value；Second of selection behaviour is carried out by step 111 and 113
Make, selecting cloud service individual conduct of the fitness value more than or equal to the first given threshold from every class candidate cloud service first should
The target cloud service of class cloud service is led in all target cloud services of such cloud service if target cloud service is not unique
The final goal cloud service that probability selection method selects unique cloud service as such cloud service is crossed, if such cloud service
The fitness value of all candidate's cloud services is respectively less than the first given threshold, then carries out mutation operation and friendship to all candidate cloud services
Fork operation is to carry out next-generation heredity.The final goal cloud service of every class cloud service forms optimal cloud as optimal cloud service
Service system, i.e. target cloud service system.In the probability selection method, each target cloud service individual of every class cloud service
The probability that is selected and its fitness value are proportional, if the number of the target cloud service individual of the i-th class cloud service is k, wherein mesh
Mark cloud service individual SijFitness value be fij, then target cloud service individual SijIt is selected as the general of final goal cloud service
Rate are as follows:
Wherein, f indicates that fitness value, j indicate serial number, fijIndicate j-th of target cloud service individual of the i-th class cloud service
Fitness value.
Specifically, the mutation operator in step 110 refers to certain to an existing cloud service progress in cloud service system
Change operation, so that a new cloud service is formed, to achieve the purpose that increase or change characteristic.The variation that the present embodiment defines
Operator is as follows:
Increase a function FkTo cloud service Si；
From cloud service SiMiddle removal function Fi；
Repair cloud service SiIn defective function Fj；
Introduce an interface IkTo cloud service Si；
From cloud service SiMiddle removal interface Ii；
Repair cloud service SiIn defective interface Ij；
Modify cloud service SiGo out the degree of coupling；
Modify cloud service SiEnter the degree of coupling；
Modify cloud service SiParameter；
Modify cloud service SiFailure count.
The crossover operation of step 110 is usually realized by certain members in exchange cloud service, the purpose of crossover operation
It is to combine the optimal properties in two cloud services.For example, it is assumed that cloud service individual G before crossover operation1And G2Coding as scheme
Shown in 2, if selecting the defective function number of cloud service respectively and defective number of ports is crosspoint, intersect behaviour twice
The cloud service individual obtained after work is respectively G1' and G2', the cloud service individual G after crossover operation twice1' and G2' coding
As shown in Figure 3.
The genetic manipulation that cloud service system develops includes selection operation, mutation operation and crossover operation.The present embodiment setting
Cloud service evolution termination condition are as follows: when all kinds of cloud service populations select only one fitness value more than or equal to the first threshold
When final cloud service individual (the i.e. optimal cloud service individual) of value, genetic algorithm is terminated.
As specific embodiments of the present invention, Fig. 4 is that itinerary of the embodiment of the present invention formulates cloud service system structure chart,
As shown in figure 4, it includes five class cloud services that an itinerary, which formulates cloud service system, it is travelling planning service S respectively1, make a reservation for
Aircraft/train ticket services S2, predetermined food and beverage sevice S3, predetermined hotel service S4With tourist service S5。
The dynamic evolution that itinerary formulates cloud service system is carried out below, the specific steps are as follows:
1) cloud service for formulating cloud service system to itinerary encodes, and formulates cloud as shown in table 2 for itinerary
The cloud service of service system encodes.
The coding of 2 itinerary of table formulation cloud service system
2) cloud service system is initialized, generates the initial of all kinds of cloud services in the cloud service system at random
Population.
Assuming that the size of every class cloud service initialization population is 10, their individual cloud service generate at random after it is specific
Supplemental characteristic is as shown in table 3 below:
The quality index of the initial all kinds of cloud service populations of table 3
3) Proper treatment is constructed, the appropriateness of each cloud service individual in five class cloud service populations is calculated separately using Proper treatment
Value；Proper treatment is fij(Q)=w1i*f1ij(FC)+w2i*f2ij(FD)+w3i*f3ij(IC)+w4i*f4ij(CP)+w5i*f5ij(CH)+
w6i*f6ij(EC)。
It is illustrated by taking travelling planning service as an example below:
With the individual S in planning service of travelling11For, calculate individual S116 quality index value:
f111(FC)=1-fd11/f11=1-2/5=0.6,
f211(FD)=1-fe11/f11=1-1/5=0.8,
f311(IC)=1-Id11/I11=1-1/5=0.8,
f411(CP)=od11/(od11+id11)=2/ (2+1)=2/3,
f611(EC)=1/ (no11+ 1)=1/ (2+1)=1/3,
The appropriateness value of individual: f is calculated according to appropriateness value calculation formula11(Q)=0.2*0.6+0.15*0.8+0.2*0.8+
0.15*2/3+0.15*0.5+0.15*1/3=0.625 is calculated separately out in tourism planning service using same calculation method
Other 9 individual appropriateness values.
4) judge whether the appropriate value of 10 cloud service individuals in travelling planning service population is greater than the second given threshold, such as
Fruit is less than or equal to the second given threshold, then abandons cloud service individual；If it is greater than the second given threshold, then judge that the cloud takes
Whether remaining cloud service individual is had in business population, if reinitializing without remaining cloud service individual and generating such cloud clothes
Business population；If there is remaining cloud service individual, then using remaining cloud service individual as candidate cloud service.
5) judge whether the fitness value of each candidate cloud service in travelling planning service is more than or equal to the first given threshold,
If the fitness value of all candidate cloud services is respectively less than the first given threshold in travelling planning service population, to all candidates
Crossover operation and mutation operation are done in cloud service, to update the candidate cloud service for planning service of travelling；If travelling planning service kind
There are the candidate cloud services that fitness value is more than or equal to the first given threshold in group, then take using this candidate cloud service as target cloud
The target cloud service in travelling planning service population in all candidate cloud services is selected in business.
6) judge whether the target cloud service in travelling planning service population is unique, if in travelling planning service population
Target cloud service is not unique, then using probability selection method choice one as such final target cloud service.
The target cloud service of travelling planning service in cloud service has been obtained by above step.It is identical as above-mentioned steps, it can
To obtain the target cloud service of other four classes cloud services, target cloud service is generated according to the final goal cloud service of five class cloud services
System.
The cloud service system dynamic evolution method based on improved adaptive GA-IAGA that the present invention passes through is by the choosing in genetic algorithm
It selects operation, crossover operation and mutation operation to be introduced into cloud service system dynamic evolution method, due to crossover operation and mutation operation
Migration rules be it is random, ensure that the dynamic evolution method can be in global scope preferentially.Meanwhile with it is general with
The undirected search that machine searching method is carried out is different, can be carried out from a large amount of population at individual by selection operation twice quickly excellent
Change selection, so that cloud service generation by generation is optimized evolution, and quickly approach optimal cloud service, to guarantee that cloud service system is dynamic
When state develops, the overall situation individual to a large amount of cloud services can be quickly and efficiently completed preferentially.
Each embodiment in this specification is described in a progressive manner, the highlights of each of the examples are with other
The difference of embodiment, the same or similar parts in each embodiment may refer to each other.
Used herein a specific example illustrates the principle and implementation of the invention, and above embodiments are said
It is bright to be merely used to help understand method and its core concept of the invention；At the same time, for those skilled in the art, foundation
Thought of the invention, there will be changes in the specific implementation manner and application range.In conclusion the content of the present specification is not
It is interpreted as limitation of the present invention.
Claims (8)
1. a kind of cloud service system dynamic evolution method based on improved adaptive GA-IAGA, which is characterized in that the dynamic evolution side
Method includes:
Dynamic evolution coding is carried out to the cloud service in cloud service system, definition forms the cloud service of the cloud service system are as follows:
Si={ ni,pi,fi,fdi,fei,Ii,Idi,odi,idi,noi,Ci,Ni(0,1) },
Wherein, i indicates cloud service serial number, SiIndicate i-th of cloud service, ni、pi、fi、fdi、fei、Ii、Idi、odi、idi、noi、Ci
And NiIt (0,1) is the encoded components of i-th of cloud service, niIndicate the corresponding title of i-th of cloud service, piIndicate i-th of cloud service
Parameter, the parameter be i-th of cloud service inner element, pi=(ei1,ei2,...,eiM), wherein M is i-th of cloud
The number of the inner element of service, eikFor in i-th of cloud service, what k-th of inner element was relied on removes k-th of inner element
Except inner element number, fiIndicate the function number of i-th of cloud service, fdiIndicate the defective function of i-th of cloud service
Energy number, feiIndicate the function number of the dependence environment of i-th of cloud service, IiIndicate the number of ports of i-th of cloud service；IdiIndicate i-th
The defective number of ports of a cloud service, odiIndicate i-th of cloud service goes out the degree of coupling, idiIndicate i-th of cloud service enters coupling
It is right, noiIndicate the Failure count of i-th of cloud service, CiIndicate class belonging to i-th of cloud service, Ni(0,1) i-th of cloud is indicated
Corresponding random number is serviced, the value interval of the random number is (0,1)；
The cloud service system is initialized, generates the initial cloud clothes of the i-th class cloud service in the cloud service system at random
Business population；
Fitness function is constructed, the fitness value of each individual in the initial cloud service population of the i-th class is calculated；
First screening is carried out to the initial cloud service population of i-th class and determines candidate cloud service；
Compare all candidate fitness values of cloud service of i-th class and the size of the first given threshold, therefrom selects and be greater than
Or the i-th class candidate cloud service corresponding equal to the fitness value of the first given threshold is as target cloud service, if the i-th class cloud takes
The target cloud service of business is more than one, then using probability selection method choice one as the i-th final class target cloud service；Such as
The fitness value of all candidate cloud services of fruit the i-th class cloud service is respectively less than the first given threshold, then to all candidate cloud services
Crossover operation and mutation operation are carried out to update i-th class candidate's cloud service；
Target cloud service system is generated according to the final goal cloud service of all kinds of cloud services.
2. the dynamic evolution method of cloud service system according to claim 1, which is characterized in that described in the random generation
The initial cloud service population of i-th class in cloud service system, specifically includes:
Using formula cijk=| (cijkmax-cijkmin)×Nij(0,1)+cijkmin| calculate jth in each i-th class cloud service population
The initial value of each component of individual, wherein | | it is bracket function, cijkIndicate j-th of individual in the i-th class cloud service population
K-th of component initial value, cijkmaxIndicate k-th of component of j-th of individual in the i-th class cloud service population in value range
Maximum value, cijkminIndicate minimum value of k-th of the component of j-th of individual in the i-th class cloud service population in value range；
The size of the initial cloud service population of the i-th class generated at random can give as needed.
3. the dynamic evolution method of cloud service system according to claim 1, which is characterized in that the building fitness letter
Number, specifically includes: building fitness function fij(Q)=w1i*f1ij(FC)+w2i*f2ij(FD)+w3i*f3ij(IC)+w4i*f4ij
(CP)+w5i*f5ij(CH)+w6i*f6ij(EC) fitness of j-th of individual in the i-th class cloud service population is calculated, wherein wriIt indicates
The weight of each quality index of i-th class cloud service, r=1 ..., 6, f1ijIt (FC) is j-th of individual in the i-th class cloud service population
Function accuracy function, f2ijIt (FD) is the functional independence function of j-th of individual in the i-th class cloud service population, f3ij(IC) it is
The interface correctness function of j-th of individual, f in i-th class cloud service population4ijIt (CP) is j-th in the i-th class cloud service population
The coupling function of body, f5ijIt (CH) is the cohesion function of j-th of individual in the i-th class cloud service population, f6ijIt (EC) is the i-th class
The evolution compatibility function of j-th of individual in cloud service population.
4. the dynamic evolution method of cloud service system according to claim 3, which is characterized in that
The function accuracy function f of j-th of individual in the i-th class cloud service population1ij(FC) are as follows:
f1ij(FC)=1-fdij/fij,
Wherein, fdijIn the function of j-th of individual, to detect defective function number, f in the i-th class cloud service populationijFor
The function sum of j-th of individual in i-th class cloud service population；
The functional independence function f of j-th of individual in the i-th class cloud service population2ij(FD) are as follows:
f2ij(FD)=1-feij/fij,
Wherein, feijTo detect to rely on the function number of environment in j-th of individual in the i-th class cloud service population；
The interface correctness function f of j-th of individual in the i-th class cloud service population3ij(IC) are as follows:
f3ij(IC)=1-Idij/Iij,
Wherein, IdijIn the interface of j-th of individual, to detect defective number of ports, I in the i-th class cloud service populationijFor
The interface sum of j-th of individual in i-th class cloud service population；
The coupling function f of j-th of individual in the i-th class cloud service population4ij(CP) are as follows:
f4ij(CP)=odij/(odij+idij),
Wherein, odijFor the degree of coupling out of j-th of individual in the i-th class cloud service population, idijFor jth in the i-th class cloud service population
Individual enters the degree of coupling；
The cohesion function f of j-th of individual in the i-th class cloud service population5ij(CH) are as follows:
Wherein, M is the number of j-th of individual inner element in the i-th class cloud service population, eijkIt is in the i-th class cloud service population
The other elements number that k-th of element of j individual is relied on；
The evolution compatibility function f of j-th of individual in the i-th class cloud service population6ij(EC) are as follows:
f6ij(EC)=1/ (noij+ 1),
Wherein, noijFor the number when new version replacement legacy version of j-th of individual, to fail in the i-th class cloud service population.
5. the dynamic evolution method of cloud service system according to claim 1, which is characterized in that described to i-th class
The initial cloud service population of cloud service carries out first screening and determines that candidate cloud service includes:
Compare the corresponding fitness value of each cloud service individual and the second given threshold in each i-th class cloud service initial population
Size is therefrom selected the initial cloud serve individual corresponding greater than the fitness value of the second given threshold and is waited as the i-th class
Select cloud service；If the corresponding i-th class initial cloud serve individual of the fitness value for being not greater than the second given threshold, right
I-th class cloud service re-starts initialization, generates the initial cloud service population of the i-th class cloud service.
6. the dynamic evolution method of cloud service system according to claim 1, which is characterized in that the mutation operation packet
It includes: generating the number of variation by a random number first, random variation, the variation are then carried out by mutation operator every time
Operator includes increasing function to the cloud service, removes function from the cloud service, the repair function from the cloud service is repaired
Change the function number for relying on environment in the cloud service, introduces interface to the cloud service, remove interface from the cloud service, from
Interface is repaired in the cloud service, that modifies the cloud service goes out the degree of coupling, modifies the degree of coupling that enters of the cloud service, modifies institute
The parameter for stating cloud service modifies the Failure count of the cloud service.
7. the dynamic evolution method of cloud service system according to claim 1, which is characterized in that the crossover operation packet
It includes: the number intersected being generated by a random number first, then randomly chooses a coding of the cloud service every time
Component is crosspoint, exchanges the encoded components of the cloud service.
8. the dynamic evolution method of cloud service system according to claim 1, which is characterized in that the probability selection method
Include: every class cloud service the probability that is selected of each target cloud service individual and its fitness value it is proportional, if the i-th class cloud takes
The number of the target cloud service individual of business is k, wherein target cloud service individual SijFitness value be fij, then target cloud service
Individual SijIt is selected as the probability of final goal cloud service are as follows:
Wherein, f indicates that fitness value, j indicate serial number, fijIndicate the adaptation of j-th of target cloud service individual of the i-th class cloud service
Angle value.
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