CN108021658A - A kind of big data intelligent search method and system based on whale optimization algorithm - Google Patents
A kind of big data intelligent search method and system based on whale optimization algorithm Download PDFInfo
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Abstract
The invention discloses a kind of big data intelligent search method and system based on whale optimization algorithm, optimize algorithm to big data intelligent search problem Optimization Solution using whale, so as to rapidly obtain the search result closest to user demand, the accuracy and efficiency of intelligent searching engine are further improved；The present invention can find the feasible solution of big data intelligent search problem high quality in acceptable time cost, according to the search condition of user, obtain inputting the relevant big data of keyword with user from engine database, constantly given a mark by user to search result, be progressively met the search effect of the demand of user individual.The present invention carries out big data intelligent search using whale optimization algorithm, establishing one based on main body characteristic has intelligentized search engine, the search result for best suiting user's needs is quickly and efficiently found, solves the problems, such as that present search engine cannot provide information of interest to the user well and search efficiency is high.
Description
Technical field
The invention belongs to big data and the cross-application field of intelligence computation, is related to a kind of intelligent optimization algorithm in big data
The application in field, more particularly to a kind of solution method in big data intelligent search problem, and in particular to one kind is based on whale
Optimize the big data intelligent search method and system of algorithm.
Background technology
In recent years, big data was grown rapidly in the whole world, while caused the height of academic, industry and national governments
Degree is paid attention to.By effectively managing big data and obtaining its value by analyzing, using the teaching of the invention it is possible to provide the application and service of high added value,
Realize more economy and social value.But the arriving in big data epoch, not only brought great opportunity to develop but also brought
Technological challenge.Traditional computing technique can face many technical difficulties when solving big data issue handling.Therefore, it is necessary to grind
Study carefully and find new effective technology method, found with completing the analyzing and processing of big data and value.
Different from traditional data base querying, a feature of web search is that user tends not to complete table quickly
Up to the demand of oneself, but by the multiple interaction with search engine, Step wise approximation, the mesh that user asks can be just basically reached
Mark.It can so cause efficiency very low.Search system can just pass through big data analysis when user proposes inquiry request for the first time
See clearly its implication, do semantic extension automatically to query expression, once in be just greatly improved efficiency, mitigate burden for users.
Network search engines are a strong instruments of network information inquiry, are the key technologies of networked information retrieval.
Traditional search engines mainly include：Catalogue class search engine, full-text search engine, META Search Engine, aggregation type search engine and
Vertical search engine.With the variation of information format and the surge of information content, traditional search engines are faced with huge choose
War, it cannot meet, and user is more personalized, intelligent and diversified needs.From the tissue for collecting information of information
The various aspects of traditional search engines are being continued to optimize with index and retrieval and the user interface of information, intelligent searching engine.
Intelligent searching engine mainly includes：The intelligent searching engine and base of intelligent searching engine based on body, knowledge based storehouse system
In the intelligent searching engine of semantic association.Intelligent searching engine is to improve the traditional search engines retrieved based on keyword aspect
To knowledge based or concept aspect come the search engine retrieved, it can understand word from the angle of knowledge and concept, performance
Go out stronger intelligent and personality, it with certain Knowledge Base Techniques basis, have very high natural language understanding with
Knowledge process ability.
Big data search engine have passed through the development of more than 20 years, all have greatly improved in search speed and accuracy, but
Be its basic framework and technology all without too big change, this has also resulted in its limitation.For different application occasion
Search engine, it is necessary to information be also multifarious.Non intelligentization retrieval lacks the ability of identification user interest information, and
And sortord cannot make corresponding adjustment according to different users.There is provided for different people with targetedly retrieval clothes
Business, is one of road of future searches engine development, thus thus various intelligentized search technologies are born.Artificial intelligence and calculating
The birth of the subjects such as intelligence, is that researchers attempt to gain enlightenment from the certain law of human thinking and living nature, creates phase
The computation model answered, is applied in information science.Artificial neural network, swarm intelligence, gene calculating etc. are all to apply to work as
Some successful examples in preceding big data challenge.There is intelligentized search engine to establish one with reference to main body characteristic, both solved
The problem of current search engine of having determined cannot provide information of interest to the user well, while also improve search efficiency.
The content of the invention
In order to solve the problems, such as big data intelligent search, the present invention proposes a kind of big data intelligence based on whale optimization algorithm
Can searching method and system.
Technical solution is used by the method for the present invention：A kind of big data intelligent search side based on whale optimization algorithm
Method, it is characterised in that comprise the following steps：
Step 1：The search condition of user is read in, is obtained according to the search condition of user from engine database defeated with user
Enter the matched big data of keyword, each big data is a whale, i-th of whale current location X in whale groupi, initialization
The position of whale group：N represents dimension, and N represents whale group's size；
Step 2：The parameter needed for whale optimization algorithm, including whale group size N, logarithmic spiral shape constant b are initialized, when
Preceding iterations j, maximum iteration M, whole Jing Qun global optimums position are G；
Step 3：The fitness function value of the initial position of whale group in whale optimization algorithm is calculated, fitness function value is commented
The highest big data of valency is as the individual best spatial location of current whale group
Step 4：Design factor vector A and C；
Step 5：The random number p that a value range is [0,1] is produced, and different renewal whales is selected according to the value of p
The mode of group space position；
Step 6：The position vector of whale group after renewal is decoded into corresponding big data and is presented to user, user is according to oneself
Search condition, for obtain big data marking, as fitness function value；Find and preserve optimal whale group in current group
Body X*；
Step 7：By compare renewal before and after whale group the corresponding fitness function value of position vector, determine the next generation whale group
Position；
Step 8：It is globally optimal solution G and its fitness letter to record the corresponding whale group position of the highest big data of degree of conformity
Numerical value；
Step 9：Judge whether user have found the text document of needs in engine；
Step 4 is performed if it is not, then making j=j+1 and turning round；
If so, then export optimal whale group's ideal adaptation angle value and location X*Corresponding big data.
Technical solution is used by the system of the present invention：It is characterized in that：Including at the beginning of input module, whale optimization algorithm
Beginningization module, fitness function value module, coefficient vector computing module, whale group space location updating mode selecting module, renewal
Whale group space position vector fitness value calculation module afterwards, the position determination module of whale group of future generation, whale group position are the overall situation
Optimal solution G and its fitness function value logging modle, judgment module；
The input module：For reading in the search condition of user, according to the search condition of user from engine database
Obtain inputting the big data of Keywords matching with user, each big data is a whale, i-th of whale present bit in whale group
Put Xi, the position of initialization whale group：N represents dimension, and N represents whale group's size；
The whale optimizes algorithm initialization module：For initializing the parameter needed for whale optimization algorithm, including whale group
Size N, logarithmic spiral shape constant b, current iteration number j, maximum iteration M, whole Jing Qun global optimums position are G；
The fitness function value module：For calculating the fitness function of the initial position of whale group in whale optimization algorithm
Value, highest big data is evaluated as the individual best spatial location of current whale group using fitness function value
The coefficient vector computing module：For design factor vector A and C；
The whale group space location updating mode selecting module：For producing the random number that a value range is [0,1]
P, and the different modes for updating whale group space position is selected according to the value of p；
Whale group space position vector fitness value calculation module after the renewal：For by after renewal whale group position to
Amount is decoded into corresponding big data and is presented to user, and user according to the search condition of oneself, give a mark by the big data to obtain, as
Fitness function value；Find and preserve optimal whale group's individual X in current group*；
The position determination module of the next generation whale group：For by compare renewal before and after whale group position vector it is corresponding
Fitness function value, determines the position of next generation whale group；
The whale group position is globally optimal solution G and its fitness function value logging modle：For recording degree of conformity most
The corresponding whale group position of high big data is globally optimal solution G and its fitness function value；
The judgment module：For judging whether user have found in engine the text document of needs；
Step 4 is performed if it is not, then making j=j+1 and turning round；
If so, then export optimal whale group's ideal adaptation angle value and location X*Corresponding big data.
The beneficial effects of the invention are as follows：Whale optimization algorithm have adjustment parameter is few, fast convergence rate, low optimization accuracy are high,
Global optimizing ability is strong and restrains the characteristics of stability is good, and optimizing algorithm using whale optimizes big data intelligent search problem
Solve, so as to rapidly obtain the search result closest to user demand, there is good accuracy and efficiency.
Brief description of the drawings
Fig. 1 is the method flow diagram of the embodiment of the present invention.
Embodiment
Understand for the ease of those of ordinary skill in the art and implement the present invention, with reference to the accompanying drawings and embodiments to this hair
It is bright to be described in further detail, it will be appreciated that implementation example described herein is merely to illustrate and explain the present invention, not
For limiting the present invention.
Referring to Fig.1, a kind of big data intelligent search method based on whale optimization algorithm provided by the invention, including it is following
Step：
Step 1：The search condition of user is read in, is obtained according to the search condition of user from engine database defeated with user
Enter the matched big data of keyword, each big data is a whale, i-th of whale current location X in whale groupi, initialization
The position of whale group：N represents dimension, and N represents whale group's size；
Step 2：Whale is set to optimize the parameter needed for algorithm, the parameter bag needed for the initialization whale optimization algorithm
Include whale group size N, logarithmic spiral shape constant b, current iteration number j, maximum iteration M, whole Jing Qun global optimums position
It is set to G.
Step 3：The initial position vector decoding that whale is optimized to whale group in algorithm is presented to user into corresponding big data,
User is fitness function value for obtained big data marking according to the search condition of oneself.Engine is by fitness function value
Highest big data is evaluated as the individual best spatial location of current whale group
Step 4：Design factor vector A and C.
The calculation formula of coefficient vector A is：
A=2a × r-a
Wherein, M is maximum iteration, and r is random vector of the value range between [0,1].
The calculation formula of coefficient vector C is：
C=2r
Wherein, r is random vector, and value range is [0,1].
Step 5：A random number p is produced, value range is [0,1], and different renewal whale groups is selected according to the value of p
The mode of locus.
As p ＜ 0.5, if A ＜ 1, the formula for updating the locus of current whale group individual is：
Xj+1=Xj-A×D
Wherein, j is current iterations, XjFor current whale group's individual space position, A and C are coefficient vector,To work as
The preceding individual best spatial location of whale group.
As p ＜ 0.5, if A >=1, whale group body position X is randomly choosed from current grouprand, and update current whale
The locus of group's individual.Updating the individual locus formula of current whale group is：
X=Xrand-A×D
D=| C × Xrand,j-Xj|
Wherein, XrandFor randomly selected position in current whale group, i.e., random whale group individual；Xrand,jFor in current whale group
Jth ties up randomly selected position；
As p >=0.5, the locus formula for updating current whale group individual is：
Wherein, D ' arrives the distance between prey for i-th preceding optimum position of Cetacea of whale group, and b is the logarithmic spiral shape of definition
Shape constant, random numbers of the l between [- 1,1], XjFor current whale group individual space position,It is optimal empty for current whale group's individual
Between position.
Step 6：The position vector of whale group after renewal is decoded into corresponding big data and is presented to user, user is according to oneself
Search condition, for obtain big data marking, as fitness function value.Find and preserve optimal whale group in current group
Body X*。
Step 7：By compare renewal before and after whale group the corresponding fitness function value of position vector, determine the next generation whale group
Position.Definite rule is：Before if the corresponding fitness function value of position vector of the whale group after renewal is higher than renewal, replace
Original whale group；Otherwise, the whale group before renewal is retained.The computational methods of fitness function value are the same as step 3.
Step 8：It is globally optimal solution G and its fitness letter to record the corresponding whale group position of the highest big data of degree of conformity
Numerical value.
Step 9：Judge whether user have found the text document of needs in engine；
Step 4 is performed if it is not, then making j=j+1 and turning round；
If so, then export optimal whale group's ideal adaptation angle value and location X*Corresponding big data.
The present invention optimizes algorithm to big data intelligent search problem Optimization Solution by using whale, so as to rapidly obtain
Closest to the search result of user demand, this method can be used in big data and intelligence computation correlative technology field.
It should be appreciated that the part that this specification does not elaborate belongs to the prior art.
It should be appreciated that the above-mentioned description for preferred embodiment is more detailed, can not therefore be considered to this
The limitation of invention patent protection scope, those of ordinary skill in the art are not departing from power of the present invention under the enlightenment of the present invention
Profit is required under protected ambit, can also be made replacement or deformation, be each fallen within protection scope of the present invention, this hair
It is bright scope is claimed to be determined by the appended claims.
Claims (7)
1. a kind of big data intelligent search method based on whale optimization algorithm, it is characterised in that comprise the following steps：
Step 1：The search condition of user is read in, obtains inputting with user from engine database according to the search condition of user and closes
The matched big data of keyword, each big data are a whales, i-th of whale current location X in whale groupi, initialization whale group
Position：I=1,2 ..., N, n represent dimension, N represent whale group size；
Step 2：The parameter needed for whale optimization algorithm, including whale group size N, logarithmic spiral shape constant b are initialized, currently repeatedly
Generation number j, maximum iteration M, whole Jing Qun global optimums position are G；
Step 3：The fitness function value of the initial position of whale group in whale optimization algorithm is calculated, by fitness function value evaluation most
High big data is as the individual best spatial location of current whale group
Step 4：Design factor vector A and C；
Step 5：The random number p that a value range is [0,1] is produced, and selects different renewal whale groups empty according to the value of p
Between position mode；
Step 6：The position vector of whale group after renewal is decoded into corresponding big data and is presented to user, user searches according to oneself
Rope condition, the big data to obtain is given a mark, as fitness function value；Find and preserve optimal whale group individual in current group
X*；
Step 7：By compare renewal before and after whale group the corresponding fitness function value of position vector, determine the next generation whale group position
Put；
Step 8：It is globally optimal solution G and its fitness function to record the corresponding whale group position of the highest big data of degree of conformity
Value；
Step 9：Judge whether user have found the text document of needs in engine；
Step 4 is performed if it is not, then making j=j+1 and turning round；
If so, then export optimal whale group's ideal adaptation angle value and location X*Corresponding big data.
2. the big data intelligent search method according to claim 1 based on whale optimization algorithm, it is characterised in that：Step
In 3, fitness function value calculation is that the position vector of whale group is decoded into corresponding big data to be presented to user, Yong Hugen
According to the search condition of oneself, for obtained big data marking.
3. the big data intelligent search method according to claim 1 based on whale optimization algorithm, it is characterised in that step
The calculation formula of coefficient vector A described in 4 is：
A=2a × r-a
<mrow>
<mi>a</mi>
<mo>=</mo>
<mn>2</mn>
<mo>-</mo>
<mfrac>
<mrow>
<mn>2</mn>
<mi>j</mi>
</mrow>
<mi>M</mi>
</mfrac>
</mrow>
Wherein, r is random vector of the value range between [0,1].
4. the big data intelligent search method according to claim 1 based on whale optimization algorithm, it is characterised in that step
The calculation formula of coefficient vector C described in 4 is：
C=2r
Wherein, r is random vector, and value range is [0,1].
5. the big data intelligent search method according to claim 1 based on whale optimization algorithm, it is characterised in that step
The mode of whale group space position is updated described in 5：
As p ＜ 0.5, if A ＜ 1, the formula for updating the locus of current whale group individual is：
Xj+1=Xj-A×D
<mrow>
<mi>D</mi>
<mo>=</mo>
<mi>C</mi>
<mo>&times;</mo>
<msubsup>
<mi>X</mi>
<mi>j</mi>
<mo>*</mo>
</msubsup>
<mo>-</mo>
<msub>
<mi>X</mi>
<mi>j</mi>
</msub>
</mrow>
Wherein, j is current iterations, XjFor current whale group's individual space position, A and C are coefficient vector,For current whale
The individual best spatial location of group；
As p ＜ 0.5, if A >=1, whale group body position X is randomly choosed from current grouprand, and update current whale group
The locus of body；Updating the individual locus formula of current whale group is：
X=Xrand-A×D
D=| C × Xrand,j-Xj|
Wherein, XrandFor randomly selected position in current whale group, i.e., random whale group individual；Xrand,jTieed up for jth in current whale group
Randomly selected position；
As p >=0.5, the locus formula for updating current whale group individual is：
<mrow>
<msub>
<mi>X</mi>
<mrow>
<mi>j</mi>
<mo>+</mo>
<mn>1</mn>
</mrow>
</msub>
<mo>=</mo>
<msup>
<mi>D</mi>
<mo>&prime;</mo>
</msup>
<msup>
<mi>e</mi>
<mrow>
<mi>b</mi>
<mi>l</mi>
</mrow>
</msup>
<mi>c</mi>
<mi>o</mi>
<mi>s</mi>
<mrow>
<mo>(</mo>
<mn>2</mn>
<mi>&pi;</mi>
<mi>l</mi>
<mo>)</mo>
</mrow>
<mo>+</mo>
<msubsup>
<mi>X</mi>
<mi>j</mi>
<mo>*</mo>
</msubsup>
</mrow>
<mrow>
<msup>
<mi>D</mi>
<mo>&prime;</mo>
</msup>
<mo>=</mo>
<mrow>
<mo>|</mo>
<mrow>
<msubsup>
<mi>X</mi>
<mi>j</mi>
<mo>*</mo>
</msubsup>
<mo>-</mo>
<msub>
<mi>X</mi>
<mi>j</mi>
</msub>
</mrow>
<mo>|</mo>
</mrow>
</mrow>
Wherein, D ' arrives the distance between prey for i-th preceding optimum position of Cetacea of whale group, and b is that the logarithmic spiral shape of definition is normal
Number, random numbers of the l between [- 1,1].
6. the big data intelligent search method based on whale optimization algorithm according to claim 1-5 any one, it is special
Sign is that the rule that the position of next generation whale group is determined described in step 7 is：If the position vector of the whale group after renewal is corresponding
Before fitness function value is higher than renewal, then original whale group is replaced；Otherwise, the whale group before renewal is retained；Wherein fitness function
The computational methods of value are the same as step 3.
A kind of 7. big data intelligent searching system based on whale optimization algorithm, it is characterised in that：It is excellent including input module, whale
Change algorithm initialization module, fitness function value module, coefficient vector computing module, whale group space location updating mode and select mould
The position determination module of whale group space position vector fitness value calculation module, whale group of future generation after block, renewal, whale group position
For globally optimal solution G and its fitness function value logging modle, judgment module；
The input module：For reading in the search condition of user, obtained according to the search condition of user from engine database
The big data of Keywords matching is inputted with user, each big data is a whale, i-th of whale current location in whale group
Xi, the position of initialization whale group：I=1,2 ..., N, n represent dimension, N represent whale group size；
The whale optimizes algorithm initialization module：For initializing the parameter needed for whale optimization algorithm, including whale group's size
N, logarithmic spiral shape constant b, current iteration number j, maximum iteration M, whole Jing Qun global optimums position are G；
The fitness function value module：For calculating the fitness function value of the initial position of whale group in whale optimization algorithm,
Fitness function value is evaluated into highest big data as the individual best spatial location of current whale group
The coefficient vector computing module：For design factor vector A and C；
The whale group space location updating mode selecting module：For producing the random number p that a value range is [0,1], and
The mode of different renewal whale group space positions is selected according to the value of p；
Whale group space position vector fitness value calculation module after the renewal：For by after renewal whale group position vector solution
Code is presented to user into corresponding big data, and user is according to the search condition of oneself, and the big data to obtain is given a mark, as adaptation
Spend functional value；Find and preserve optimal whale group's individual X in current group*；
The position determination module of the next generation whale group：For by compare renewal before and after whale group the corresponding adaptation of position vector
Functional value is spent, determines the position of next generation whale group；
The whale group position is globally optimal solution G and its fitness function value logging modle：It is highest for recording degree of conformity
The corresponding whale group position of big data is globally optimal solution G and its fitness function value；
The judgment module：For judging whether user have found in engine the text document of needs；
Step 4 is performed if it is not, then making j=j+1 and turning round；
If so, then export optimal whale group's ideal adaptation angle value and location X*Corresponding big data.
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