CN107330005A - The social network data laying method of the ultimate attainment experience of user oriented - Google Patents
The social network data laying method of the ultimate attainment experience of user oriented Download PDFInfo
- Publication number
- CN107330005A CN107330005A CN201710440739.8A CN201710440739A CN107330005A CN 107330005 A CN107330005 A CN 107330005A CN 201710440739 A CN201710440739 A CN 201710440739A CN 107330005 A CN107330005 A CN 107330005A
- Authority
- CN
- China
- Prior art keywords
- user
- data
- mrow
- msubsup
- particle
- 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.)
- Pending
Links
- 238000000034 method Methods 0.000 title claims abstract description 43
- 239000002245 particle Substances 0.000 claims abstract description 60
- 238000005457 optimization Methods 0.000 claims abstract description 19
- 238000003860 storage Methods 0.000 claims abstract description 16
- 230000003044 adaptive effect Effects 0.000 claims abstract description 4
- 238000013459 approach Methods 0.000 claims abstract description 3
- 238000013500 data storage Methods 0.000 claims description 10
- 230000006855 networking Effects 0.000 abstract description 6
- 230000005540 biological transmission Effects 0.000 description 5
- 230000008859 change Effects 0.000 description 5
- 235000013399 edible fruits Nutrition 0.000 description 3
- 238000005516 engineering process Methods 0.000 description 2
- 230000003993 interaction Effects 0.000 description 2
- 238000012804 iterative process Methods 0.000 description 2
- 238000012423 maintenance Methods 0.000 description 2
- 238000012986 modification Methods 0.000 description 2
- 230000004048 modification Effects 0.000 description 2
- 238000012546 transfer Methods 0.000 description 2
- 241000251468 Actinopterygii Species 0.000 description 1
- 238000013528 artificial neural network Methods 0.000 description 1
- 230000009286 beneficial effect Effects 0.000 description 1
- 230000033228 biological regulation Effects 0.000 description 1
- 238000004364 calculation method Methods 0.000 description 1
- 238000013523 data management Methods 0.000 description 1
- 238000013461 design Methods 0.000 description 1
- 238000011161 development Methods 0.000 description 1
- 230000000694 effects Effects 0.000 description 1
- 244000144992 flock Species 0.000 description 1
- 230000014759 maintenance of location Effects 0.000 description 1
- 230000009467 reduction Effects 0.000 description 1
- 238000011160 research Methods 0.000 description 1
- 230000004044 response Effects 0.000 description 1
- 241000894007 species Species 0.000 description 1
Classifications
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F16/00—Information retrieval; Database structures therefor; File system structures therefor
- G06F16/20—Information retrieval; Database structures therefor; File system structures therefor of structured data, e.g. relational data
- G06F16/27—Replication, distribution or synchronisation of data between databases or within a distributed database system; Distributed database system architectures therefor
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
- G06N3/00—Computing arrangements based on biological models
- G06N3/004—Artificial life, i.e. computing arrangements simulating life
- G06N3/006—Artificial life, i.e. computing arrangements simulating life based on simulated virtual individual or collective life forms, e.g. social simulations or particle swarm optimisation [PSO]
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q50/00—Information and communication technology [ICT] specially adapted for implementation of business processes of specific business sectors, e.g. utilities or tourism
- G06Q50/01—Social networking
Landscapes
- Engineering & Computer Science (AREA)
- Theoretical Computer Science (AREA)
- Physics & Mathematics (AREA)
- Computing Systems (AREA)
- General Physics & Mathematics (AREA)
- Business, Economics & Management (AREA)
- Health & Medical Sciences (AREA)
- General Engineering & Computer Science (AREA)
- General Health & Medical Sciences (AREA)
- Data Mining & Analysis (AREA)
- Databases & Information Systems (AREA)
- Marketing (AREA)
- Biomedical Technology (AREA)
- Tourism & Hospitality (AREA)
- Strategic Management (AREA)
- Primary Health Care (AREA)
- Human Resources & Organizations (AREA)
- Economics (AREA)
- Life Sciences & Earth Sciences (AREA)
- Artificial Intelligence (AREA)
- General Business, Economics & Management (AREA)
- Biophysics (AREA)
- Computational Linguistics (AREA)
- Evolutionary Computation (AREA)
- Molecular Biology (AREA)
- Mathematical Physics (AREA)
- Software Systems (AREA)
- Management, Administration, Business Operations System, And Electronic Commerce (AREA)
Abstract
The invention discloses a kind of social network data laying method of the ultimate attainment experience of user oriented, mainly solve because the problem of information interchange delay is too high between the social network user that is geographically spread out.Using two stages：Initial phase：User's social relationships data set of collection is carried out by position initialization according to social networks using adaptive approach；The data layout stage：Using output on last stage as initial input, and use particle swarm optimization algorithm for the data placement method of core to carry out data layout.Optimize the storage location of user data in social networks present invention employs particle cluster algorithm, while the storage charges that maximization reduces data is used on the basis of ensureing that network user's delay is required, an efficient data managing method is provided for social networking service supplier.
Description
Technical field
The invention belongs to field of data storage, the social network data of more particularly to a kind of ultimate attainment experience of user oriented is placed
Method.
Technical background
Online social network user is distributed in all over the world, can share different types of data with other people, including regarding
The data of frequency class and audio class, and the species and size of these data also rapidly increasing.Social networks supplier exists
, it is necessary to meet the low latency of data transfer when providing a user service, the need of data stability, validity and privacy
Ask, it is ensured that user obtains the data of oneself needs in an acceptable time range.If meeting user's delay requirement
Time in can not obtain the data that they want, the usage experience of user will be reduced so that the income of supplier by
Influence.At present, traditional solution, be exactly where each user data center deposit a complete data trnascription with
Ensure the low latency of data transfer, but this method can greatly improve the stored number of copy, bring very high data to deposit
Storage expense, and different user data trnascription need regularly update, being consistent property, so as to cause the maintenance workload of great number
And transmission cost.So needing balance time delay and data storage charges to use when carrying out social network data placement.
At present, many social networking service suppliers store the data of user using private data center.However, building
Vertical private data center expense is extremely expensive, and is also contemplated that data volume, the maintenance of data center and the data of rapid growth
The problems such as center energy, therefore it is not a wise selection to set up privately owned data center.In order to solve this problem, one
Suitable method is to use publicly-owned cloud data center.Such as Amazon S3, Google cloud storages, the cloud such as Microsoft Azure
Service supplier sets up different data centers all over the world.By using cloud data center, social media ISP
The user being geographically distributed can be allowed to meet delay when carrying out data exchange to require, while so that the storage charges of data is minimum
Change.If the data trnascription of user is stored in data centers all in the world, sufficiently expensive storage charges will be brought
With and update the data produced transmission cost.
In summary, data storage is a kind of data-intensive work in social networking application, and supplier is not
It can bear to set up data center in the high expense of generation all over the world, so many social networks suppliers are (for example
Dropbox) data storage is gone using cloud data center.Therefore need to design a method so that social network user is being carried out
During data interaction, allow all users can in sustainable time range (acceptable time delay be generally 250ms with
It is interior) obtain needed for data message, while allowing the storage cost minimizations of data.
The content of the invention
The present invention seeks to the weak point for existing social network data laying method, it is proposed that it is a kind of towards with
The social network data laying method of the ultimate attainment experience in family.The method is that the intelligent data using particle swarm optimization algorithm as core is placed
Algorithm, suitable position can not only be found in cloud data center and carrys out data storage, and can calculate suitable copy
Delay requirement when quantity is to ensure to carry out information interchange between social network user and their friends is while cause data storage
Cost minimization.
In order to realize above target, the present invention uses following technical scheme：A kind of social network of the ultimate attainment experience of user oriented
Network data placement method, including two stages：
Initial phase：User's social relationships data set of collection is entered by line position according to social networks using adaptive approach
Put initialization；The data layout stage：Using output on last stage as initial input, and particle swarm optimization algorithm is used for core
The data placement method of the heart carries out data layout.
Further, initial phase specifically includes following steps：
(1) data center first where the N number of user of random initializtion, and obtain the social networks between user；
(2) ratio P user is randomly choosed from N number of user, need to meet in initialization retardance 250ms with
Under；
(3) position initialization is carried out to all users, if user i needs to meet retardance requirement, from social activity
User i all friends are found out in network of personal connections, the data center that user friend i is distributed then are found out, in these data centers
Middle whole puts user i data；If user i is need not to meet the user of retardance requirement, random initializtion is carried out；
(4) all customer location initialization terminate, and obtain M feasibility solution space.
Further, ratio P is 50%, 70%, 90% or 99%.
Further, the data layout stage specifically includes following steps：
(1) M feasibility solution space is generated by initial method, each solution space is a population；
(2) in each population of random initializtion each particle speed, velocity space scope be [- 10,10]；
(3) fitness value of each particle is calculated by formula 1, and initializes with this current optimal location of particle
Pbest, gbest is designated as by optimal pbest in current population；
Wherein：
SCi=USP*SDSi*Ri
The total cost for the data storage that Cost is represented；
SCiRepresent to store expense of the user i data produced by 1 month in 1 data center；
USP represents that storage charges of the 1GB data produced by 1 month is stored in 1 data center to be used；
SDSiRepresent user i size of data；
RiRepresent the total number of user's i copies.
(4) circulation is iterated come the speed of more new particle and position using formula 2 and 3 couples of pbest and gbest, it is total to change
Algebraically is 40 times；
Wherein：Represent the d+1 times iteration particle i flying speed；
Represent the d+1 times iteration particle i position；
c1, c2Studying factors are represented, value is 2；
r1, r2Expression is evenly distributed on two random numbers between [0,1]；
W represents inertia weight, and w=0.9- (iter/Iter) * 0.5, iter represents current iteration number of times, and Iter represents to change
Generation sum；
(5) meeting delay requires is detected whether first with the constraints of formula 4 to the particle after each renewal, such as
Fruit is met then return to step 3 and fitness value is calculated using formula 1, and updates pbest and gbest；
Delay condition then return to step 4 are unsatisfactory for, when number of iterations reaches 40 times, end loop obtains gbest, are
Optimal solution.
The present invention proposes a kind of data placement method of brand-new consideration multiple target, taken into account it is traditional using storage charges as
The data Placement Problems of target, while considering the delay issue of social networking user-to-user information interaction, utilize particle colony intelligence
Two problems are integrated together by optimized algorithm, devise the social network data laying method of the ultimate attainment experience of user oriented.
Beneficial effects of the present invention：It is main to solve because of information interchange delay between the social network user that is geographically spread out
Too high the problem of.In social networks when between user carry out information exchange when because geographically distribution distance farther out the reason for, number
According to transmission inevitably produce delay, when transmission delay exceed user institute receptible time range when will influence user society
Hand over experience.Optimize the storage location of user data in social networks present invention employs particle cluster algorithm, ensureing that network uses
Family delay maximizes reduction data simultaneously storage charges on the basis of requiring is used, and not only increases the social experience and drop of user
Low social networking service supplier carrying cost, while one efficient data management side for social networking service supplier
Method.
Brief description of the drawings
Fig. 1 is user-copy distribution map.
Fig. 2 is the self-adaptive initial phase flow figure of the social network data laying method of the ultimate attainment experience of user oriented.
Fig. 3 is the social network data laying method flow chart based on particle swarm optimization algorithm.
Specific implementation method
The present invention is described in detail below in conjunction with the accompanying drawings.
Intelligent data Placement based on particle swarm optimization algorithm includes two stages, first stage：Data initialization
Stage；Second stage：The data layout stage.
1. Optimized model
Present invention provide that the data center nearest apart from each user is the primary data center of this user, each user has one
Individual primary copy is located in primary data center, several is stored in other data centers from copy, in same data center
The number of copies of same user is no more than 1.Each user is that the data of oneself, such as Fig. 1 are read from primary data center
It is shown.
1) fitness function of the intelligent data Placement of cost model-particle group optimizing
Storage charges produced by the expense refers to all data trnascriptions in all data centers is used.Assuming that there is N number of use
Family, each user from copy number be Ri, then the calculation formula 1 of storage charges is as follows：
Wherein：
SCi=USP*SDSi*Ri
The total cost for the data storage that Cost is represented；
SCiRepresent to store expense of the user i data produced by 1 month in 1 data center；
USP represents that storage charges of the 1GB data produced by 1 month is stored in 1 data center to be used；
SDSiRepresent user i size of data；
RiRepresent the total number of user's i copies.
2) constraints of the intelligent data Placement of delay model-particle group optimizing
Network delay between user and data center is weighed using geographic distance, but can not be obtained from network
The more specific location information of user is obtained, therefore random site can only be generated according to the general longitude and latitude of user.If user leads with it
Data center is in the same area, then it is 20ms to provide the network delay between user and its primary data center.User counts with other
Obtained according to the network delay between center by equation below：
This formula is that the one-way latency being located at by ping between 260 main frames in the U.S. finds that this linear relationship is truly deposited
.Each user reads data from the data center of data needed for the storage away from its nearest neighbours, when average response time is little
When 250ms, the delay requirement that this network delay meets user is considered as.
2. the first stage：Initial phase
The stage proposes a kind of adaptive initial method, and idiographic flow is shown in Fig. 2.Social networks between user is closed
It is that data set comes from the Facebook websites True Data of 2015, wherein having 4039 users and 88234 social networks
Data set.The data set of collection is subjected to position initialization, the calculating for making it meet particle swarm optimization algorithm according to social networks
Rule.In social networks, because user group is huge, want that it is substantially not to allow all users to meet network delay requirement
Reality, therefore the reliability and efficiency of the method will be detected on the premise of the user for meeting different proportion postpones requirement.
Ratio setting into 50%, is tested to verify the reliability and validity of this method by this method under this ratio.
3. second stage：The data layout stage
In this stage, it is proposed that the social network data laying method based on particle swarm optimization algorithm, idiographic flow is shown in figure
3.The algorithm is using the output of second stage as initial input, and the formula 1 of first stage is fitness function, and formula 2 is about
Beam condition carrys out iteration and updates each result.The expense that the generation of 1GB data is monthly stored in each data center is beautiful for 0.125
Member, each user monthly stores 27MB data, and network delay needs to ensure in below 250ms.Due to combining the first stage
Self-adaptive initial method, the operation efficiency of the social network data laying method based on particle swarm optimization algorithm carried
Rise, and convergence rate is faster.The particle rapidity of social network data laying method based on particle swarm optimization algorithm and position
It is updated by formula 3 and 4, iterations is set to 40 times, and each iteration records local optimum pbest and global optimum
Gbest, until iteration terminates, obtains optimal value.
It is to dispose the Matlab 2014 used under experimental situation, the systems of Windows 7 as development environment first.
The problem of what this method was mainly solved is quantity and the deposit position of data trnascription in social networks, it is ensured that Yong Hu
Time delay during progress information interchange within the acceptable range, while making data storage cost minimization, optimizes social network
Expense of the network service supplier in cloud resource distribution, the expense does not include the update cost of transmission cost and data.
1. initial phase
The particular flow sheet of the step is shown in Fig. 2；
Step1：Data center first where the N number of user of random initializtion is (by gathering the number from Facebook websites
The social networks between each user are obtained according to collection)；
Step2：Certain proportion P (50%, 70%, 90%, 99%) user is randomly choosed from N number of user, these use
Family needs to meet retardance in below 250ms in initialization, remaining to meet the user of retardance requirement, Ke Yisui
Machine is initialized, then P must be not less than by meeting user's ratio of retardance requirement after so initializing in population；(such as need
The user's ratio for ensureing to meet retardance requirement is 50%, then N*0.5 user is just randomly choosed from N number of user)；
Step3：Then each user is initialized respectively；
Step4：Loop iteration is carried out, sum is user's number N；
Step5：If user i needs to meet retardance requirement；
Step6：User i all friends are found out from social networks net, then the number that user friend i is distributed are being found out
According to center, user i data are all put in these data centers；(concrete operations are shown in Table 1)；
Step7：If user i is need not to meet the user of retardance requirement；
Step8：Can be with random initializtion；
Step9：Step 5 is returned to, when iteration result reaches n times, end loop；
Step10：Obtain final result.
The user's copy of table 1-data center's placement schemes
The 1st row represents that data center numbers in table 1, and the 1st row represent the numbering of user, by taking user 1 as an example, if user 1
To need to meet the user of retardance requirement, the friend that user 1 is obtained by social network relationships net be distributed in data center 4,
7th, the region where 8,10, user 1 is located at the region where data center 2, then respectively put in data center 2,4,7,8,10
A copy is put, copy is not placed by other data centers, and the delay of such user 1 and user 1 friend can all be less than
250ms；Such 1st user initialization is completed；If some user need not meet retardance requirement, then just random initial
Change.
2. the data layout stage
For explanation technical scheme definitely, first to the present invention based on particle swarm optimization algorithm enter
Row is simple to be introduced.
Particle swarm optimization algorithm (Particle Swarm Optimization, PSO) be derived to flock of birds in nature and
The research of the compoundanimal motor behavior such as shoal of fish, is a kind of evolutionary computing based on swarm intelligence method.Particle group optimizing
The basic thought of algorithm is to find optimal solution by the cooperation and information sharing between individual in population, and this algorithm is simple, searches
Rope efficiency high, can be good at solving the problems, such as local optimum by the regulation to parameter.Knot can not only be ensured using this algorithm
The convergence rate of fruit, and optimal result can be obtained within the shorter time.In James Kennedy and Russell
(Kennedy J, Eberhart R.Particle swarm in the particle swarm optimization algorithm that Eberhart is proposed
optimization.Proceedings of The IEEE international conference on neural
networks(Perth,Australia),1942–1948.Piscataway,NJ:IEEE Service Center；1995),
The potential solution of each optimization problem can be imagined as a point on search space, be referred to as " particle " (Particle), often
Individual particle has position and the speed of oneself, and the position of particle in space represents a solution in solution space, and speed then generation
Table direction and the distance of particle flight, and this speed is dynamically adjusted according to the flying experience of itself and the flying experience of companion
It is whole.All particles have a fitness value determined by object function (fitness value), and record oneself and arrive
The desired positions (particle best, be designated as pbest) found so far and current location, this can be regarded as particle
The flying experience of oneself.In addition, the desired positions that all particles are found in up to the present whole population are also recorded
(global best, be designated as gbest), this can be regarded as the experience of particle companion.
Stage beginning determines the position of each particle, then the velocity information of each particle of random initializtion, Ran Hou
Particle updates oneself by pbest and gbest in later iterative process, while in order to avoid particle swarm optimization algorithm is run counter to
Ensure the original intention of time delay requirement.Therefore in each iterative process, the position where the primary copy of each user will not become
Change, it is necessary to be stored in primary data center.The position of particle and speed more new formula are respectively as shown in formula 3 and formula 4.
Wherein：
Represent the d+1 times iteration particle i flying speed
Represent the d+1 times iteration particle i position
c1, c2Represent to take 2 in Studying factors, this method
r1, r2It is generally evenly distributed in two random numbers between [0,1]
W represents inertia weight, and w=0.9- (iter/Iter) * 0.5, iter represents current iteration number of times in the method,
Iter represents iteration sum.This inertia weight can dynamically adjust the hunting zone of solution space, with the increase of iterations,
Inertia weight constantly reduces, so that particle swarm optimization algorithm has stronger global convergence ability in the early stage, and in the later stage
With stronger local convergence ability.
The flow of social network data laying method based on particle swarm optimization algorithm is as follows, the particular flow sheet of the step
See Fig. 3.
Step 1：Generate 30 feasibility solution spaces at random by initial phase, each solution space is a population；
Step 2：The velocity space in each population of random initializtion in the speed of each particle, this method for [- 10,
10]；
Step 3：The fitness value of each particle is calculated by formula 1, and initializes with this current optimal location of particle
pbest；
Step 4：Optimal pbest is designated as gbest in current population；
Step 5：Circulation is iterated, total number of iterations is 40 times；
Step 6：Using formula (3) (4) come the speed of more new particle and position；
Step 7：Meeting delay requires is detected whether first with constraints (2) to the particle after each renewal, such as
Fruit is met and fitness value is then calculated using formula 1, and updates pbest and gbest；
Step 8：Return to Step 6, when number of iterations reaches 40 times, end loop；
Step 9：Obtain gbest, as optimal solution.
The above described is only a preferred embodiment of the present invention, not doing any type of limitation to the present invention.It is every
Any simple modification, equivalent variations and modification that technology and method according to the present invention are substantially made to above example, still
In the range of the technology and method scheme that belong to the present invention.
Claims (4)
1. a kind of social network data laying method of the ultimate attainment experience of user oriented, it is characterised in that including two stages：
Initial phase：Using adaptive approach by user's social relationships data set of collection according to social networks carry out position at the beginning of
Beginningization；
The data layout stage：Using output on last stage as initial input, and particle swarm optimization algorithm is used for core
Data placement method carries out data layout.
2. social network data laying method according to claim 1, it is characterised in that：The initial phase is specifically wrapped
Include following steps：
(1) data center first where the N number of user of random initializtion, and obtain the social networks between user；
(2) ratio P user is randomly choosed from N number of user, needs to meet retardance in below 250ms in initialization；
(3) position initialization is carried out to all users, if user i needs to meet retardance requirement, from social networks
User i all friends are found out in net, the data center that user friend i is distributed then is found out, it is complete in these data centers
Put user i data in portion；If user i is need not to meet the user of retardance requirement, random initializtion is carried out；
(4) all customer location initialization terminate, and obtain M feasibility solution space so that have certain proportion P's in each solution
User meets delay and required.
3. social network data laying method according to claim 2, it is characterised in that：The ratio P be 50%,
70%th, 90% or 99%.
4. social network data laying method according to claim 2, it is characterised in that：The data layout stage is specific
Comprise the following steps：
(1) M feasibility solution space is generated by initial method, each solution space is a population；
(2) in each population of random initializtion each particle speed, velocity space scope be [- 10,10]；
(3) fitness value of each particle is calculated by formula 1, and the current optimal location pbest of particle is initialized with this, will
Optimal pbest is designated as gbest in current population；
<mrow>
<mi>C</mi>
<mi>o</mi>
<mi>s</mi>
<mi>t</mi>
<mrow>
<mo>(</mo>
<mi>$</mi>
<mo>)</mo>
</mrow>
<mo>=</mo>
<munderover>
<mo>&Sigma;</mo>
<mrow>
<mi>i</mi>
<mo>=</mo>
<mn>1</mn>
</mrow>
<mi>N</mi>
</munderover>
<msub>
<mi>SC</mi>
<mi>i</mi>
</msub>
<mo>-</mo>
<mo>-</mo>
<mo>-</mo>
<mrow>
<mo>(</mo>
<mn>1</mn>
<mo>)</mo>
</mrow>
</mrow>
Wherein：
SCi=USP*SDSi*Ri
The total cost for the data storage that Cost is represented；
SCiRepresent to store expense of the user i data produced by 1 month in 1 data center；
USP represents that storage charges of the 1GB data produced by 1 month is stored in 1 data center to be used；
SDSiRepresent user i size of data；
RiRepresent the total number of user's i copies.
(4) circulation is iterated come the speed of more new particle and position, total number of iterations using formula 2 and 3 couples of pbest and gbest
For 40 times；
<mrow>
<msubsup>
<mi>v</mi>
<mi>i</mi>
<mrow>
<mi>d</mi>
<mo>+</mo>
<mn>1</mn>
</mrow>
</msubsup>
<mo>=</mo>
<mi>w</mi>
<mo>*</mo>
<msubsup>
<mi>v</mi>
<mi>i</mi>
<mi>d</mi>
</msubsup>
<mo>+</mo>
<msub>
<mi>c</mi>
<mn>1</mn>
</msub>
<mo>*</mo>
<msub>
<mi>r</mi>
<mn>1</mn>
</msub>
<mo>*</mo>
<mrow>
<mo>(</mo>
<msub>
<mi>pbest</mi>
<mi>i</mi>
</msub>
<mo>-</mo>
<msubsup>
<mi>x</mi>
<mi>i</mi>
<mi>d</mi>
</msubsup>
<mo>)</mo>
</mrow>
<mo>+</mo>
<msub>
<mi>c</mi>
<mn>2</mn>
</msub>
<mo>*</mo>
<mo>*</mo>
<mrow>
<mo>(</mo>
<msub>
<mi>gbest</mi>
<mi>i</mi>
</msub>
<mo>-</mo>
<msubsup>
<mi>x</mi>
<mi>i</mi>
<mi>d</mi>
</msubsup>
<mo>)</mo>
</mrow>
<mo>-</mo>
<mo>-</mo>
<mo>-</mo>
<mrow>
<mo>(</mo>
<mn>2</mn>
<mo>)</mo>
</mrow>
</mrow>
<mrow>
<msubsup>
<mi>x</mi>
<mi>i</mi>
<mrow>
<mi>d</mi>
<mo>+</mo>
<mn>1</mn>
</mrow>
</msubsup>
<mo>=</mo>
<msubsup>
<mi>x</mi>
<mi>i</mi>
<mi>d</mi>
</msubsup>
<mo>+</mo>
<msubsup>
<mi>v</mi>
<mi>i</mi>
<mi>d</mi>
</msubsup>
<mo>-</mo>
<mo>-</mo>
<mo>-</mo>
<mrow>
<mo>(</mo>
<mn>3</mn>
<mo>)</mo>
</mrow>
</mrow>
Wherein：Represent the d+1 times iteration particle i flying speed；
Represent the d+1 times iteration particle i position；
c1, c2Studying factors are represented, value is 2；
r1, r2Expression is evenly distributed on two random numbers between [0,1]；
W represents inertia weight, and w=0.9- (iter/Iter) * 0.5, iter represents current iteration number of times, and Iter represents that iteration is total
Number；
(5) meeting delay requires is detected whether first with constraints (4) to the particle after each renewal, if meeting
Return to step 3 calculates fitness value using formula 1, and updates pbest and gbest；
Delay condition then return to step 4 are unsatisfactory for, when number of iterations reaches 40 times, end loop obtains gbest, as optimal
Solution.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201710440739.8A CN107330005A (en) | 2017-06-13 | 2017-06-13 | The social network data laying method of the ultimate attainment experience of user oriented |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201710440739.8A CN107330005A (en) | 2017-06-13 | 2017-06-13 | The social network data laying method of the ultimate attainment experience of user oriented |
Publications (1)
| Publication Number | Publication Date |
|---|---|
| CN107330005A true CN107330005A (en) | 2017-11-07 |
Family
ID=60194214
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN201710440739.8A Pending CN107330005A (en) | 2017-06-13 | 2017-06-13 | The social network data laying method of the ultimate attainment experience of user oriented |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN107330005A (en) |
Cited By (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN111124762A (en) * | 2019-12-30 | 2020-05-08 | 航天科工网络信息发展有限公司 | Dynamic copy placing method based on improved particle swarm optimization |
Citations (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN103699446A (en) * | 2013-12-31 | 2014-04-02 | 南京信息工程大学 | Quantum-behaved particle swarm optimization (QPSO) algorithm based multi-objective dynamic workflow scheduling method |
| CN104200264A (en) * | 2014-09-25 | 2014-12-10 | 国家电网公司 | Two-stage particle swarm optimization algorithm including independent global search |
| US20160203419A1 (en) * | 2013-03-09 | 2016-07-14 | Bigwood Technology, Inc. | Metaheuristic-guided trust-tech methods for global unconstrained optimization |
-
2017
- 2017-06-13 CN CN201710440739.8A patent/CN107330005A/en active Pending
Patent Citations (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20160203419A1 (en) * | 2013-03-09 | 2016-07-14 | Bigwood Technology, Inc. | Metaheuristic-guided trust-tech methods for global unconstrained optimization |
| CN103699446A (en) * | 2013-12-31 | 2014-04-02 | 南京信息工程大学 | Quantum-behaved particle swarm optimization (QPSO) algorithm based multi-objective dynamic workflow scheduling method |
| CN104200264A (en) * | 2014-09-25 | 2014-12-10 | 国家电网公司 | Two-stage particle swarm optimization algorithm including independent global search |
Non-Patent Citations (2)
| Title |
|---|
| HOURIEH KHALAJZADEH等: "Improving cloud-based online social network data placement and replication", 《2016 IEEE 9TH INTERNATIONAL CONFERENCE ON CLOUD COMPUTING》 * |
| 曹盟盟等: "基于改进粒子群算法的虚拟机放置算法", 《软件》 * |
Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN111124762A (en) * | 2019-12-30 | 2020-05-08 | 航天科工网络信息发展有限公司 | Dynamic copy placing method based on improved particle swarm optimization |
| CN111124762B (en) * | 2019-12-30 | 2023-11-14 | 航天科工网络信息发展有限公司 | Dynamic copy placement method based on improved particle swarm optimization |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN110851272B (en) | Cloud task scheduling method based on phagocytic particle swarm genetic hybrid algorithm | |
| Raju et al. | A bio inspired Energy-Aware Multi objective Chiropteran Algorithm (EAMOCA) for hybrid cloud computing environment | |
| Fu et al. | Task scheduling of cloud computing based on hybrid particle swarm algorithm and genetic algorithm | |
| Alfarrarjeh et al. | Scalable spatial crowdsourcing: A study of distributed algorithms | |
| CN108170530B (en) | Hadoop load balancing task scheduling method based on mixed element heuristic algorithm | |
| Gao et al. | An energy-aware ant colony algorithm for network-aware virtual machine placement in cloud computing | |
| Chen et al. | An efficient new hybrid ICA-PSO approach for solving large scale non-convex multi area economic dispatch problems | |
| CN109613914B (en) | Robot path planning method of spider social algorithm | |
| Ji et al. | Particle swarm optimization for mobile ad hoc networks clustering | |
| Banharnsakun et al. | ABC-GSX: A hybrid method for solving the Traveling Salesman Problem | |
| CN113885555A (en) | Multi-machine task allocation method and system for power transmission line dense channel routing inspection | |
| CN109670655B (en) | Multi-target particle swarm optimization scheduling method for electric power system | |
| CN113485409B (en) | Geographic fairness-oriented unmanned aerial vehicle path planning and distribution method and system | |
| Mohan et al. | Energy aware and energy efficient routing protocol for adhoc network using restructured artificial bee colony system | |
| CN106155785B (en) | A kind of data migration method across data center's cloud computing system | |
| Shah et al. | Depth based routing protocol using smart clustered sensor nodes in underwater WSN | |
| CN112685138A (en) | Multi-workflow scheduling method based on multi-population hybrid intelligent optimization in cloud environment | |
| Wang et al. | Cost-effective and latency-minimized data placement strategy for spatial crowdsourcing in multi-cloud environment | |
| CN107330005A (en) | The social network data laying method of the ultimate attainment experience of user oriented | |
| CN106295791A (en) | For the method finding travelling salesman's optimal path | |
| CN107257356B (en) | Social user data optimal placement method based on hypergraph segmentation | |
| CN105959368B (en) | A kind of method of social activity cloud hot point resource prediction and deployment | |
| Medhat et al. | An Ant Algorithm for cloud task scheduling | |
| Xiaoguang et al. | Research on cloud computing schedule based on improved hybrid PSO | |
| CN116339973A (en) | Digital twin cloud platform computing resource scheduling method based on particle swarm optimization algorithm |
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 | ||
| RJ01 | Rejection of invention patent application after publication | ||
| RJ01 | Rejection of invention patent application after publication |
Application publication date: 20171107 |