CN110413860A - The multiple-objection optimization selection method of mysorethorn example under a kind of cloudy environment based on NSGA-II - Google Patents
The multiple-objection optimization selection method of mysorethorn example under a kind of cloudy environment based on NSGA-II Download PDFInfo
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Abstract
The multiple-objection optimization selection method of mysorethorn example is the following steps are included: Question background defines under a kind of cloudy environment based on NSGA-II provided by the invention；Coding mode；Generate initial population；Selection；Intersect；Variation；Termination condition.The present invention uses quick non-dominated ranking using NSGA-II algorithm, and obtained result reduces the complexity of entire algorithm.Secondly, in the algorithm, using crowding and crowding comparison operator, keeping population diversity, avoiding falling into local optimum.Finally, increasing excellent genes into follow-on probability using elitism strategy.
Description
Technical field
The present invention relates to the selections of cloud example types and multiple target to solve field, more particularly to a kind of based on NSGA-II's
The multiple-objection optimization selection method of mysorethorn example under cloudy environment.
Background technique
In face of public cloud existing numerous cloud example types in the market, " cloud " end is deployed in for wanting to be served by
For user, how to select suitable cloud example types and being optimal is a difficulties.It is disposed under single cloud environment
A series of problems, such as service is easy to appear some drawbacks, for example supplier locks, availability is low, data safety, privacy leakage, because
The deployment services under cloudy environment become a kind of trend in recent years for this, the optimum choice problem of cloud example types under cloudy environment
Become research hotspot.
When carrying out the optimum choice of cloud example types under cloudy environment, the case where facing, is more complicated, in need of consideration
Problem is also more complicated, other than considering the overall performance and totle drilling cost of mysorethorn example type combination scheme, it is also necessary to consider mysorethorn
The delay communicated between example type.Wherein, delay is mainly determined by geographical location, the bandwidth etc. between cloud example types.
Summary of the invention
The object of the present invention is to provide a kind of algorithms of cloud example types optimum choice problem under cloudy environment, are based on
NSGA-II solves the optimum choice problem of cloud example types under cloudy environment.
In order to achieve the above object, under the technical solution of the present invention is to provide a kind of cloudy environment based on NSGA-II
The multiple-objection optimization selection method of mysorethorn example, which comprises the following steps:
Step 1 obtains mysorethorn example type information by using crawler, and mysorethorn example type information includes mysorethorn example type combination
The delay communicated between the overall performance and totle drilling cost of scheme, cloud example types；
Step 2, when user task reach when, according to the task of user be its select cloud example types, select mysorethorn example
The Solve problems of type be multi-objective optimization question, solve the following steps are included:
Step 3, the quantity that certain cloud example types of selection are indicated using N bit, i.e. a N bit are
One gene, the quantity that every kind of cloud example types at most can choose are 2N- 1, the binary system of all mysorethorn example number of types
Coding composition item chromosome, i.e., a mysorethorn example type combination is item chromosome, and every chromosome is defined as an individual；
Step 4, the random scale that generates during initial population generates, carry out each individual for the initial population of N
The judgement of constraint condition when only individual meets constraint condition, just will form initial population, otherwise random to change gene in individual
Coding, until each individual meets constraint condition；
Step 5, the calculating that quick non-dominated ranking and crowding are carried out to population obtained in the previous step, quick non-dominant row
Sequence is for being layered population；
Step 6, according to non-dominated ranking and crowding, population is selected, if individual not in same grade, selection
The low individual of grade；If individual selects the individual that crowding is big, therefrom generates new parent population in same grade；
The common node of one step 7, random selection node as two chiasmas, carries out crossover operation to it；
Step 8 carries out mutation operation to chromosome, when chromosome is made a variation, reduces each gene high position variation
Probability carries out the judgement of constraint condition after chromosomal variation to it, if the chromosome after variation is unsatisfactory for constraint condition, changes at random
Become the coding of gene in chromosome, until it meets constraint condition；
Step 9 judges whether to reach maximum number of iterations, if being not up to, return step 4, if having reached, and algorithm
Termination obtains Pareto optimal solution set, which is the cloud example types obtained according to the task choosing of user
Assembled scheme.
Preferably, in step 5, when carrying out quick non-dominated ranking, the fitness function value the high more easily becomes non-branch
With solution；The calculation basis fitness function of the crowding is solved.
Due to the adoption of the above technical solution, compared with prior art, the present invention having the following advantages that and actively imitating
Fruit: first, the present invention uses quick non-dominated ranking using NSGA-II algorithm, and obtained result reduces answering for entire algorithm
Miscellaneous degree.Secondly, in the algorithm, using crowding and crowding comparison operator, keeping population diversity, avoid falling into part most
It is excellent.Finally, increasing excellent genes into follow-on probability using elitism strategy.This algorithm obtains Pareto optimal solution set,
The case where specifying constraint condition for user can recommend the highest mysorethorn example type group of performance for meeting constraint condition for user
Conjunction scheme；The case where not specifying constraint condition for user, the default value being arranged using the present invention, solving Pareto disaggregation is simultaneously
User recommends Pareto disaggregation, is therefrom selected by user according to its preference.The present invention has rapidly and efficiently, fitting demand etc.
Advantage.The invention is for wanting how to select mysorethorn example type combination scheme under cloudy environment and solve excellent similar to multiple target
The user of change problem has general applicability.Promotion and application can be carried out in the practical operation of enterprise, and there is stronger society
And commercial value.
Detailed description of the invention
Fig. 1 is overall flow figure of the invention；
Fig. 2 is NSGA-II algorithm overall flow figure in the present invention.
Specific embodiment
Present invention will be further explained below with reference to specific examples.It should be understood that these embodiments are merely to illustrate the present invention
Rather than it limits the scope of the invention.In addition, it should also be understood that, after reading the content taught by the present invention, those skilled in the art
Member can make various changes or modifications the present invention, and such equivalent forms equally fall within the application the appended claims and limited
Range.
Embodiments of the present invention are related to a kind of multiple-objection optimization selection of mysorethorn example under the cloudy environment based on NSGA-II
Method, as shown in Figure 1, comprising the following steps: the definition of A. Question background；B. coding mode；C. initial population is generated；D. it selects；
E. intersect；F. it makes a variation；G. termination condition.
Wherein, step A is specifically included:
A1. mysorethorn example type information is obtained by using crawler, main includes the overall performance of mysorethorn example type combination scheme
The delay communicated between totle drilling cost, cloud example types；
A2. cloud example types select permeability is defined, is it according to the task of user when the task of user reaches
Cloud example types are selected, different subtasks may select the cloud example types of different regions.For the problem, target is not only
It is to maximize overall performance and minimize overall price, also pays close attention to and minimize the delay that geographical location generates, this is
One multi-objective optimization question carries out trade-off between multiple optimization aims of foundation, is then based on NSGA-II algorithm,
Finally obtain a Pareto optimal solution set.
Step B is specifically included: indicating to select the quantity of certain cloud example types, i.e. a position N two using N bit
The main reason for system number is a gene, is done so is to limit each selectable maximum quantity of cloud example types, i.e., often
Kind cloud example types at most can choose 2N- 1.The binary coding of all mysorethorn example number of types forms item chromosome,
I.e. a mysorethorn example type combination is item chromosome (individual).
Step C is specifically included: random to generate the population that scale is N when initial；It, can be to every during random generate
Individual carries out the judgement of constraint condition, when only individual meets constraint condition, just will form initial population；Otherwise will change at random
The coding for becoming gene in individual, until each individual meets constraint condition.
Step D is specifically included:
D1. the calculating of quick non-dominated ranking and crowding is carried out to newborn population.The purpose of non-dominated ranking is pair
Population is layered.When carrying out quick non-dominated ranking, the fitness function value the high more easily becomes non-domination solution；Crowding
The calculation basis fitness function of Nd is solved；
D2. according to non-dominated ranking and crowding, population is selected, if individual is not in same grade, selection etc.
The low individual of grade；If individual selects the individual that crowding is big, therefrom generates new parent population in same grade.
Step E is specifically included: common node of one node of random selection as two chiasmas hands over it
Fork operation.
Step F is specifically included: the cost in order to reduce optimum combination reduces each base when chromosome is made a variation
Because of the probability of high position variation, the quantity to avoid the selection of cloud example types is excessive, so that it is total to reach reduction mysorethorn example assembled scheme
The purpose of cost.The judgement of constraint condition is carried out after chromosomal variation to it, deterministic process is identical with deterministic process in B.
Step G is specifically included: loop iteration step C, D, E, F, and when number reaches specified quantity, algorithm is terminated, iteration knot
After beam, available Pareto optimal solution set.
It is not difficult to find that the present invention carries out the generation of Pareto optimal solution set using NSGA-II algorithm, obtained result compares intelligence
Energy optimization algorithm is more complete, and is better able to meet the needs of users.This algorithm obtains Pareto optimal solution set, for user
The case where specified constraint condition, can recommend the highest mysorethorn example type combination scheme of performance for meeting constraint condition for user；
The case where not specifying constraint condition for user, the default value being arranged using the present invention are solved Pareto disaggregation and simultaneously pushed away for user
Pareto disaggregation is recommended, is therefrom selected by user according to its preference.The present invention has many advantages, such as rapidly and efficiently, to be bonded demand.
The invention is for wanting how to select mysorethorn example type combination scheme under cloudy environment and solve similar multiple-objection optimization to ask
The user of topic has general applicability.Promotion and application can be carried out in the practical operation of enterprise, and there is stronger society and quotient
Industry value.
Claims (2)
1. the multiple-objection optimization selection method of mysorethorn example under a kind of cloudy environment based on NSGA-II, which is characterized in that including with
Lower step:
Step 1 obtains mysorethorn example type information by using crawler, and mysorethorn example type information includes mysorethorn example type combination scheme
Overall performance and totle drilling cost, cloud example types between the delay that communicates；
Step 2, when user task reach when, according to the task of user be its select cloud example types, select cloud example types
Solve problems be multi-objective optimization question, solve the following steps are included:
Step 3, the quantity that certain cloud example types of selection are indicated using N bit, i.e. a N bit is one
Gene, the quantity that every kind of cloud example types at most can choose are 2N- 1, the binary coding of all mysorethorn example number of types
Item chromosome is formed, i.e., a mysorethorn example type combination is item chromosome, and every chromosome is defined as an individual；
Step 4, the random scale that generates during initial population generates, constrain each individual for the initial population of N
The judgement of condition when only individual meets constraint condition, just will form initial population, otherwise the random volume for changing gene in individual
Code, until each individual meets constraint condition；
Step 5, the calculating that quick non-dominated ranking and crowding are carried out to population obtained in the previous step, quick non-dominated ranking are used
It is layered in population；
Step 6, according to non-dominated ranking and crowding, population is selected, if individual not in same grade, selects grade
Low individual；If individual selects the individual that crowding is big, therefrom generates new parent population in same grade；
The common node of one step 7, random selection node as two chiasmas, carries out crossover operation to it；
Step 8 carries out mutation operation to chromosome, when chromosome is made a variation, reduces the probability of each gene high position variation,
The judgement of constraint condition is carried out after chromosomal variation to it, it is random to change dye if the chromosome after variation is unsatisfactory for constraint condition
The coding of gene in colour solid, until it meets constraint condition；
Step 9 judges whether to reach maximum number of iterations, if not up to, return step 4, if having reached, algorithm is terminated
Pareto optimal solution set is obtained, which is the group for being the cloud example types obtained according to the task choosing of user
Conjunction scheme.
2. the multiple-objection optimization selection method of mysorethorn example under a kind of cloudy environment based on NSGA-II as described in claim 1,
It is characterized in that, when carrying out quick non-dominated ranking, the fitness function value the high more easily becomes non-domination solution in step 5；
The calculation basis fitness function of the crowding is solved.
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Cited By (2)
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| CN113225370A (en) * | 2021-03-08 | 2021-08-06 | 河北工业大学 | Block chain multi-objective optimization method based on Internet of things |
| CN114690038A (en) * | 2022-06-01 | 2022-07-01 | 华中科技大学 | Motor fault identification method and system based on neural network and storage medium |
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