CN110458291A - A kind of best common recognition node selecting method based on genetic algorithm - Google Patents
A kind of best common recognition node selecting method based on genetic algorithm Download PDFInfo
- Publication number
- CN110458291A CN110458291A CN201910734335.9A CN201910734335A CN110458291A CN 110458291 A CN110458291 A CN 110458291A CN 201910734335 A CN201910734335 A CN 201910734335A CN 110458291 A CN110458291 A CN 110458291A
- Authority
- CN
- China
- Prior art keywords
- node
- chromosome
- common recognition
- fitness function
- best common
- 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 17
- 230000002068 genetic effect Effects 0.000 title claims abstract description 14
- 210000000349 chromosome Anatomy 0.000 claims abstract description 43
- 238000001514 detection method Methods 0.000 claims abstract description 6
- 230000006978 adaptation Effects 0.000 claims 1
- 230000000694 effects Effects 0.000 claims 1
- 238000012545 processing Methods 0.000 abstract description 2
- 230000001737 promoting effect Effects 0.000 abstract 1
- 238000012790 confirmation Methods 0.000 description 2
- 238000005516 engineering process Methods 0.000 description 2
- 238000013459 approach Methods 0.000 description 1
- 238000013461 design Methods 0.000 description 1
- 238000010586 diagram Methods 0.000 description 1
- 238000007689 inspection Methods 0.000 description 1
- 238000010187 selection method Methods 0.000 description 1
Classifications
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
- G06N3/00—Computing arrangements based on biological models
- G06N3/12—Computing arrangements based on biological models using genetic models
- G06N3/126—Evolutionary algorithms, e.g. genetic algorithms or genetic programming
Landscapes
- Engineering & Computer Science (AREA)
- Physics & Mathematics (AREA)
- Health & Medical Sciences (AREA)
- Life Sciences & Earth Sciences (AREA)
- Biophysics (AREA)
- Bioinformatics & Cheminformatics (AREA)
- Bioinformatics & Computational Biology (AREA)
- Evolutionary Biology (AREA)
- Theoretical Computer Science (AREA)
- Computational Linguistics (AREA)
- Molecular Biology (AREA)
- Biomedical Technology (AREA)
- Genetics & Genomics (AREA)
- Data Mining & Analysis (AREA)
- Evolutionary Computation (AREA)
- General Health & Medical Sciences (AREA)
- Artificial Intelligence (AREA)
- Computing Systems (AREA)
- General Engineering & Computer Science (AREA)
- General Physics & Mathematics (AREA)
- Mathematical Physics (AREA)
- Software Systems (AREA)
- Physiology (AREA)
- Management, Administration, Business Operations System, And Electronic Commerce (AREA)
Abstract
The invention discloses a kind of best common recognition node selecting method based on genetic algorithm, mainly solving the problems, such as algorithm existing in the prior art of commonly knowing together when determining best common recognition node, time-consuming there are low efficiency.Multiple participation nodes are separately encoded into corresponding chromosome including (S1) by this method, to obtain multiple chromosomes after coding；(S2) each chromosome in multiple chromosomes selected, intersected, being made a variation and feasibility detection operates；(S3) individual choice for meeting fitness function is come out, optimal solution is solved by fitness function and acquires optimal solution if reaching fitness function requirement, step (S2) is continued to execute if it is not, returning, until obtaining optimal solution.Through the above scheme, the purpose fast invention achieves trading efficiency height and processing time, has very high practical value and promotional value.
Description
Technical field
The invention belongs to Genetic Algorithm Technology fields, in particular, being to be related to a kind of best common recognition based on genetic algorithm
Node selecting method.
Background technique
Block chain is substantially the distributed account book by the sustainable growth for participating in, safeguarding in many ways jointly, it main
Feature is decentralization or weak center, i.e., without centralized control can reach between trusting relationship, this dependence
In distributed common recognition mechanism.Common common recognition algorithm can be divided into two major classes: one kind is the common recognition algorithm based on proof, if any work
Amount proves common recognition mechanism (POW), and equity proves algorithm (POS), however this kind of common recognition algorithm handling capacity is low, it is slow to go out block；It is another kind of
It is the common recognition algorithm based on ballot, most classic is practical Byzantine failure tolerance algorithm (PBFT), but there is also redundancies, consumption for this method
The problem of duration.Therefore the presence for how solving the problems, such as the prior art, is those skilled in the art's urgent problem.
Summary of the invention
The purpose of the present invention is to provide a kind of best common recognition node selecting method based on genetic algorithm mainly solves existing
The problem of time-consuming with the presence of algorithm low efficiency of commonly knowing together present in technology when determining best common recognition node.
To achieve the goals above, The technical solution adopted by the invention is as follows:
A kind of best common recognition node selecting method based on genetic algorithm, includes the following steps:
(S1) multiple participation nodes are separately encoded into corresponding chromosome, to obtain multiple chromosomes after coding；
(S2) each chromosome in multiple chromosomes selected, intersected, being made a variation and feasibility detection operates；
(S3) individual choice for meeting fitness function is come out, optimal solution is solved by fitness function, if reached
Fitness function requirement, then acquire optimal solution, step (S2) is continued to execute if it is not, returning, until obtaining optimal solution；
Wherein, f (ni) indicates that fitness function, ni indicate that a legal chromosome, N indicate the collection of all chromosomes
It closes, Fau indicates that the number that the chromosome i.e. node goes wrong, Com indicate that the chromosome i.e. node and other nodes are handed over
Easy efficiency.
It further, in a chromosome is according to one comprising m by each participation nodes encoding in the step (S1)
A coding requirement for participating in node obtains the chromosome of a m dimensional vector, this dimension, that is, all numbers for participating in node, and m is
Integer is also each number for participating in node, i.e. N={ n1, n2 ..., nm } simultaneously, wherein ni indicates i-th of node.
Further, each chromosome in multiple chromosomes selected in the step (S2), intersected, being made a variation and
Feasibility detection operation is to be grouped parent sample two-by-two, to carry out partial mapped crossover, and eliminates the digital number of conflict, appoints
Meaning chooses two points, and exchanges value number, generates new group；Verify whether any chromosome is feasible solution, is adapted to meeting
The individual choice of degree function comes out, using as parent group.
Further, the fitness function in the step (S3) is by the delay machine number of node and leading to for other nodes
The function that the trading efficiency of letter is made.
Specifically, it is to work as Fau (ni) minimum that fitness function requirement is reached in the step (S3), when Com (ni) is maximum,
Ni is optimal solution at this time, i.e., node of most preferably knowing together.
Compared with prior art, the invention has the following advantages:
The present invention is the algorithm based on PBFT, and node is divided into common recognition node and backup node, and the common recognition node is as altogether
Know node and execute the completion transaction of three stage protocols, the present invention proposes best common recognition node selecting method, only meeting fitness
Best common recognition node can be just found out in the case where function, continue to select chromosome in the case where being unsatisfactory for fitness function
It selects, intersect, making a variation and feasibility detection operates, therefore the present invention has transaction relative to the method for existing selection common recognition node and imitates
The rate high advantage fast with the processing time.
Detailed description of the invention
Fig. 1 is flow diagram of the invention.
Specific embodiment
Present invention will be further explained below with reference to the attached drawings and examples, and embodiments of the present invention include but is not limited to
The following example.
Embodiment
As shown in Figure 1, a kind of best common recognition node selecting method based on genetic algorithm, each participation node is most preferably selected
Take scheme code at a chromosome, to obtain multiple chromosomes after coding, to each chromosome of the multiple chromosome
Intersected, made a variation, feasibility inspection and selection operation, the trading efficiency that the delay machine number of node is communicated with other nodes
As the fitness function of model, optimal solution is obtained according to the fitness function, to realize the selection of best common recognition node.
The first step encodes all participation nodes:
The chromosome of a m dimensional vector is obtained according to a coding requirement comprising m participation node, m is integer, this
Dimension, that is, all numbers for participating in node, while being also their number.That is N={ n1, n2 ..., nm }, wherein ni indicates i-th
A node.
Second step the operation such as is selected, intersects, makes a variation to each chromosome of multiple chromosomes:
Parent sample is grouped two-by-two, to carry out partial mapped crossover, and eliminates the digital number of conflict, arbitrarily chooses two
It is a, and value number is exchanged, generate new group；Verify whether any chromosome is feasible solution, and fitness letter will be met
Several individual choices come out, using as parent group.
Third step, setting fitness function seek optimal solution:
Wherein: f (ni) is fitness function, and ni represents a legal chromosome, and N then represents the collection of all chromosomes
It closes, Fau represents the number that the chromosome i.e. node goes wrong, and Com represents the chromosome i.e. node and other nodes are handed over
Easy efficiency.
If reaching fitness function requirement, i.e. Fau (ni) is minimum, and Com (ni) is maximum, then meets fitness function, should
Ni is optimal solution (most preferably common recognition node), second step is continued to execute if it is not, returning, until obtaining the optimal solution i.e. most happy festival time
Point.
Concrete application based on selection method of the present invention:
The A1 pre-preparation stage:
It trades after having chosen common recognition node according to the above genetic algorithms approach.Common recognition node is received client
Request sequence n:<<PRE-PREPARE, v, n, d>, m>, wherein v indicates view number, and the request that m indicates that client is sent disappears
Breath, d indicate the eap-message digest of m, and pre-preparation information is broadcast to backup node by node of then knowing together.
The A2 preparation stage:
The backup node pre-preparation message sent of node that will know together is verified.Priori signed certificate name and its correct of d of making a summary
Property, guarantee not to be tampered, whether consistent then verify view number, and checks that the backup node never received in view v
Serial number n but d different message m of making a summary.
For pre-preparation message after verifying, which enters the preparation stage, and to the broadcast of all backup nodes <
PREPARE, v, n, d, i >, i is backup node number, while by pre-preparation message and being prepared in message write-in message logging.
A3 confirmation stage:
Backup node checks received preparation message (digital signature, view number, message sequence number), verifies errorless
The message is written in message logging afterwards.If backup node has had received send from different backup nodes and pre-preparation
The preparation message that message matches has had 2f, which enters confirmation stage, while true to the broadcast of all backup nodes
Recognize message<COMMIT, v, n, d, i>.
Above-described embodiment is merely a preferred embodiment of the present invention, and it is not intended to limit the protection scope of the present invention, as long as using
Design principle of the invention, and the non-creative variation worked and made is carried out on this basis, it should belong to of the invention
Within protection scope.
Claims (5)
1. a kind of best common recognition node selecting method based on genetic algorithm, which comprises the steps of:
(S1) multiple participation nodes are separately encoded into corresponding chromosome, to obtain multiple chromosomes after coding；
(S2) each chromosome in multiple chromosomes selected, intersected, being made a variation and feasibility detection operates；
(S3) individual choice for meeting fitness function is come out, optimal solution is solved by fitness function, if reaching adaptation
Function requirements are spent, then acquire optimal solution, step (S2) are continued to execute if it is not, returning, until obtaining optimal solution；
Wherein, f (ni) indicates that fitness function, ni indicate that a legal chromosome, N indicate the set of all chromosomes, Fau
Indicate that the number that the chromosome i.e. node goes wrong, Com indicate the effect of the chromosome i.e. node and the transaction of other nodes
Rate.
2. a kind of best common recognition node selecting method based on genetic algorithm according to claim 1, which is characterized in that institute
State in step (S1) by it is each participation nodes encoding in a chromosome be according to one comprising m participate in node coding requirement
The chromosome of a m dimensional vector is obtained, this dimension, that is, all numbers for participating in node, m is that integer is also each participation simultaneously
The number of node, i.e. N={ n1, n2 ..., nm }, wherein ni indicates i-th of node.
3. a kind of best common recognition node selecting method based on genetic algorithm according to claim 2, which is characterized in that institute
State in step (S2) each chromosome in multiple chromosomes is selected, is intersected, is made a variation and feasibility detection operation be by
Parent sample is grouped two-by-two, to carry out partial mapped crossover, and eliminates the digital number of conflict, arbitrarily chooses two points, and mutually
Value number is changed, new group is generated；Verify whether any chromosome is feasible solution, the individual choice for meeting fitness function is gone out
Come, using as parent group.
4. a kind of best common recognition node selecting method based on genetic algorithm according to claim 3, which is characterized in that institute
Stating the fitness function in step (S3) is made by the trading efficiency of the communication of the delay machine number and other nodes of node
Function.
5. a kind of best common recognition node selecting method based on genetic algorithm according to claim 3, which is characterized in that institute
State that reach fitness function requirement in step (S3) be when Fau (ni) is minimum, and Com (ni) is maximum, ni is optimal solution at this time, i.e.,
Best common recognition node.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201910734335.9A CN110458291A (en) | 2019-08-09 | 2019-08-09 | A kind of best common recognition node selecting method based on genetic algorithm |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201910734335.9A CN110458291A (en) | 2019-08-09 | 2019-08-09 | A kind of best common recognition node selecting method based on genetic algorithm |
Publications (1)
| Publication Number | Publication Date |
|---|---|
| CN110458291A true CN110458291A (en) | 2019-11-15 |
Family
ID=68485625
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN201910734335.9A Pending CN110458291A (en) | 2019-08-09 | 2019-08-09 | A kind of best common recognition node selecting method based on genetic algorithm |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN110458291A (en) |
Cited By (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN110990353A (en) * | 2019-12-11 | 2020-04-10 | 深圳证券交易所 | Log extraction method, log extraction device and storage medium |
Citations (7)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN108108815A (en) * | 2017-12-13 | 2018-06-01 | 北京邮电大学 | Mapping method of virtual network and device based on improved adaptive GA-IAGA |
| CN108665365A (en) * | 2018-05-16 | 2018-10-16 | 四川大学 | A kind of mixing block chain architecture system, processing method and processing system |
| CN108737175A (en) * | 2018-05-19 | 2018-11-02 | 上海分布信息科技有限公司 | A kind of node administration method and its realize system |
| US20190068380A1 (en) * | 2017-08-24 | 2019-02-28 | Alibaba Group Holding Limited | Blockchain consensus node selection |
| WO2019072263A2 (en) * | 2018-11-07 | 2019-04-18 | Alibaba Group Holding Limited | Facilitating practical byzantine fault tolerance blockchain consensus and node synchronization |
| CN109800071A (en) * | 2019-01-03 | 2019-05-24 | 华南理工大学 | A kind of cloud computing method for scheduling task based on improved adaptive GA-IAGA |
| CN110047004A (en) * | 2019-04-23 | 2019-07-23 | 重庆金窝窝网络科技有限公司 | Block chain accounting nodes selection method and device |
-
2019
- 2019-08-09 CN CN201910734335.9A patent/CN110458291A/en active Pending
Patent Citations (8)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20190068380A1 (en) * | 2017-08-24 | 2019-02-28 | Alibaba Group Holding Limited | Blockchain consensus node selection |
| CN108108815A (en) * | 2017-12-13 | 2018-06-01 | 北京邮电大学 | Mapping method of virtual network and device based on improved adaptive GA-IAGA |
| CN108665365A (en) * | 2018-05-16 | 2018-10-16 | 四川大学 | A kind of mixing block chain architecture system, processing method and processing system |
| CN108737175A (en) * | 2018-05-19 | 2018-11-02 | 上海分布信息科技有限公司 | A kind of node administration method and its realize system |
| WO2019072263A2 (en) * | 2018-11-07 | 2019-04-18 | Alibaba Group Holding Limited | Facilitating practical byzantine fault tolerance blockchain consensus and node synchronization |
| CA3041463A1 (en) * | 2018-11-07 | 2019-04-18 | Alibaba Group Holding Limited | Facilitating practical byzantine fault tolerance blockchain consensus and node synchronization |
| CN109800071A (en) * | 2019-01-03 | 2019-05-24 | 华南理工大学 | A kind of cloud computing method for scheduling task based on improved adaptive GA-IAGA |
| CN110047004A (en) * | 2019-04-23 | 2019-07-23 | 重庆金窝窝网络科技有限公司 | Block chain accounting nodes selection method and device |
Non-Patent Citations (1)
| Title |
|---|
| 王俊等: "遗传算法优化的BP神经网络在EDXRF中对钛铁元素含量的预测", 《原子能科学技术》 * |
Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN110990353A (en) * | 2019-12-11 | 2020-04-10 | 深圳证券交易所 | Log extraction method, log extraction device and storage medium |
| CN110990353B (en) * | 2019-12-11 | 2023-10-13 | 深圳证券交易所 | Log extraction method, log extraction device and storage medium |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN110011814B (en) | A kind of DPoS common recognition method and its system that can verify that | |
| US10838595B2 (en) | Stranded blockchain | |
| CN109150598A (en) | A kind of BFT common recognition algorithm bandwidth utilization rate improved method based on block piece | |
| CN107147735B (en) | Distributed account book system based on hierarchical structure | |
| CN107733855B (en) | A kind of block catenary system and application method that can support publicly-owned chain, alliance's chain and privately owned chain simultaneously | |
| CN109819003A (en) | A kind of layering common recognition method and system of block chain | |
| CN108848056A (en) | Block chain common recognition method based on verifying | |
| CN109522362A (en) | Incomplete markets synchronous method, system and equipment based on block chain data | |
| CN111899002B (en) | Method for efficiently performing cross-chain information transaction interaction in blockchain | |
| CN109409887B (en) | Method for realizing block chain consensus mechanism | |
| JP5640910B2 (en) | Verification device and verification program | |
| CN110766551B (en) | Alliance chain based on improved Kafka consensus mechanism and transaction method | |
| CN111464633B (en) | Consensus method and system for transaction information of block chain | |
| CN110022231A (en) | Block chain common recognition election method | |
| CN112232619A (en) | Block output and sequencing method, node and block chain network system of alliance chain | |
| CN110210655A (en) | Goods delivery method and device | |
| CN110458291A (en) | A kind of best common recognition node selecting method based on genetic algorithm | |
| CN109041010A (en) | Vehicle-mounted Delay Tolerant Network data forwarding method based on semi-naive Bayes classifier | |
| CN111798234B (en) | Lightweight block chain system and construction method | |
| CN112016919A (en) | Data processing method and device for calculating settlement amount | |
| CN114449476A (en) | Block link point consensus method for safety communication in Internet of vehicles | |
| WO2024066665A1 (en) | Snowman consensus-based consortium blockchain multi-chain system and interaction and deployment method therefor | |
| CN107103360A (en) | A kind of reliability application distribution distributed genetic algorithm based on mixed cloud | |
| Elek | Asia pacific economic co-operation (APEC) | |
| CN108834058A (en) | A kind of indoor positioning signal source Optimization deployment method based on heredity with fireworks combinational 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 | ||
| TA01 | Transfer of patent application right |
Effective date of registration: 20230510 Address after: No. 337, Jinhai 3rd road, Wenzhou Economic and Technological Development Zone, Wenzhou, Zhejiang 325000 Applicant after: Wenzhou Institute of Technology Address before: Three road 610000 Sichuan city of Chengdu province Chenghua District Erxian Qiaodong No. 1 Applicant before: Chengdu University of Technology |
|
| TA01 | Transfer of patent application right | ||
| RJ01 | Rejection of invention patent application after publication |
Application publication date: 20191115 |
|
| RJ01 | Rejection of invention patent application after publication |