CN109255512A - A kind of Course Arrangement in University method based on Monte Carlo genetic algorithm - Google Patents
A kind of Course Arrangement in University method based on Monte Carlo genetic algorithm Download PDFInfo
- Publication number
- CN109255512A CN109255512A CN201810763134.7A CN201810763134A CN109255512A CN 109255512 A CN109255512 A CN 109255512A CN 201810763134 A CN201810763134 A CN 201810763134A CN 109255512 A CN109255512 A CN 109255512A
- Authority
- CN
- China
- Prior art keywords
- class
- period
- individual
- course
- population
- 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.)
- Granted
Links
- 238000004422 calculation algorithm Methods 0.000 title claims abstract description 14
- 230000002068 genetic effect Effects 0.000 title claims abstract description 11
- 238000000034 method Methods 0.000 claims abstract description 26
- 230000014509 gene expression Effects 0.000 claims description 7
- 238000005457 optimization Methods 0.000 claims description 5
- 230000006978 adaptation Effects 0.000 claims description 4
- 230000035772 mutation Effects 0.000 claims description 4
- 230000000694 effects Effects 0.000 claims description 2
- 230000003044 adaptive effect Effects 0.000 abstract description 2
- 210000004556 brain Anatomy 0.000 description 4
- 230000006870 function Effects 0.000 description 4
- 238000000342 Monte Carlo simulation Methods 0.000 description 1
- 230000009286 beneficial effect Effects 0.000 description 1
- 238000004883 computer application Methods 0.000 description 1
- 238000005094 computer simulation Methods 0.000 description 1
- 238000002986 genetic algorithm method Methods 0.000 description 1
Classifications
-
- 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
- G06Q10/00—Administration; Management
- G06Q10/06—Resources, workflows, human or project management; Enterprise or organisation planning; Enterprise or organisation modelling
- G06Q10/063—Operations research, analysis or management
- G06Q10/0631—Resource planning, allocation, distributing or scheduling for enterprises or organisations
- G06Q10/06316—Sequencing of tasks or work
-
- 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
-
- 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
- G06Q50/10—Services
- G06Q50/20—Education
- G06Q50/205—Education administration or guidance
Landscapes
- Engineering & Computer Science (AREA)
- Business, Economics & Management (AREA)
- Human Resources & Organizations (AREA)
- Physics & Mathematics (AREA)
- Health & Medical Sciences (AREA)
- Theoretical Computer Science (AREA)
- Strategic Management (AREA)
- Biophysics (AREA)
- Life Sciences & Earth Sciences (AREA)
- Educational Administration (AREA)
- Economics (AREA)
- General Physics & Mathematics (AREA)
- Tourism & Hospitality (AREA)
- Marketing (AREA)
- Evolutionary Biology (AREA)
- Educational Technology (AREA)
- General Health & Medical Sciences (AREA)
- General Business, Economics & Management (AREA)
- Entrepreneurship & Innovation (AREA)
- Bioinformatics & Cheminformatics (AREA)
- Bioinformatics & Computational Biology (AREA)
- Biomedical Technology (AREA)
- Operations Research (AREA)
- Physiology (AREA)
- Genetics & Genomics (AREA)
- Artificial Intelligence (AREA)
- Game Theory and Decision Science (AREA)
- Computational Linguistics (AREA)
- Data Mining & Analysis (AREA)
- Evolutionary Computation (AREA)
- Development Economics (AREA)
- Molecular Biology (AREA)
- Computing Systems (AREA)
- General Engineering & Computer Science (AREA)
- Mathematical Physics (AREA)
- Software Systems (AREA)
- Quality & Reliability (AREA)
- Primary Health Care (AREA)
- Management, Administration, Business Operations System, And Electronic Commerce (AREA)
Abstract
A kind of Course Arrangement in University method based on Monte Carlo genetic algorithm, the class of acquisition, course, teacher, time and classroom information are carried out first to be spliced to form a timeline at random, then, an individual i.e. school timetable is formed by multiple timelines for meeting constraint condition, multiple individuals form a population；The individual for choosing current population adaptive optimal control degree carries out cross and variation to the individual, uses the received method of Monte Carlo probability in crossover process；It is finally converted according to the number of iterations of setting, using the optimum individual in last generation population as final prediction result.The present invention provides one kind can be in the Course Arrangement in University method of the various teaching resources of reasonable disposition.
Description
Technical field
The present invention relates to a kind of college teaching row class, intelligent optimization, computer application field more particularly to a kind of bases
In the Course Arrangement in University method of Monte Carlo genetic algorithm.
Background technique
In recent years, with the implementation that state personnel plans, colleges and universities increase the demand force of student, while colleges and universities are run a school
There is phenomena such as nervous, the pressure of teaching management increases in resource.Especially in terms of Course Arrangement in University, before per term gives a course, religion
Administrator be engaged in by being the one group of instructional blocks of time appropriate of course arrangement opened up and space, that is, class using existing teaching resource
Table enables student's reasonable arrangement time, improves learning efficiency, the normal orderly function of teaching.Most of universities and colleges are still at this stage
So use artificial row's class, as colleges and universities' size of the student body gradually expands, school's subject type, professional number, class's number, course door number,
Number of student be increasing plus, artificial row's class oneself through being increasingly difficult to solve the feelings of classroom resources conflict or teacher resource conflict
The shortcomings that condition, inevitable intricate operation, workload is huge, inefficiency, is also just more and more prominent, while artificial behaviour
It is also not susceptible to that resource is made full use of to meet the needs of often changing.
Artificial row's class hour is very strong by the randomness of human brain, and not stringent work step, it is contemplated which is discharged to
Which, it is possible that attending to one thing and lose sight of another.The working method of computer is different from human brain, it does not have the diffusing thinking ability of human brain, it
All information are converted to data, the thinking of people is converted into specific rule and algorithm, this is utilized by computer language
A little algorithms carry out process data.If the thought process using computer simulation human brain carries out row's class, due to the computer speed of service
Fastly, processing capacity is strong, can quickly obtain the feasible program for meeting constraint, to compile several more scientific, accurate, uses
Optimizing scheme selected for acdemic dean, then by being manually finely adjusted, that can greatly reduce the workload of educational administration personnel, allow them
There are more times to complete other work, improves Campus MIS efficiency, and distribute various teaching resources rationally, also improve
The teaching management quality of whole school, to push the IT application process of university teaching archives.
Summary of the invention
In order to overcome artificial row's class to be difficult to the case where solving classroom resources conflict or teacher resource conflict and row's class process
In inevitable intricate operation, the shortcomings that workload is huge, inefficiency, the present invention proposes a kind of based on Monte Carlo heredity
Monte carlo method is added in most basic genetic algorithm in the Course Arrangement in University method of algorithm, this method, provides and a set of rationally matches
Set the Course Arrangement in University method of various teaching resources.
The technical solution adopted by the present invention to solve the technical problems is:
A kind of Course Arrangement in University method based on Monte Carlo genetic algorithm, the Course Arrangement in University method the following steps are included:
1) it encodes: obtaining class's essential information C={ c1,c2,c3,…,cj,…,cn, course essential information W={ w1,w2,
w3,…,wh,…,wm, teacher essential information T={ t1,t2,t3,…,tu,…,tv, class period information D={ d1,d2,
d3,…,dy,…,dlAnd classroom essential information P={ p1,p2,p3,…,pr,…,pb, wherein n indicates the total quantity of class, m table
Show course total quantity, v indicates teacher's total quantity, and l indicates the total quantity of class period, and b indicates the total quantity in classroom, cj、wh、tu、
dy、prInterior coding is made of 4 metric numbers.For period dyWhen coding, dy=dy1dy2dy3dy4, one day is divided into
Five periods, wherein dy1Indicate course this week for the first time week it is several attend class, dy2Indicate the period that course is attended class for the first time,
dy3Indicate this week of course second week it is several attend class, dy4Indicate the period that course is attended class for the second time, such as dy=1225 expression classes
The journey class period this week first time is the second section of Monday, and second of class period is Section five of Tuesday.For course whIt compiles
When code, each course has a corresponding period to attend class efficiency value wh={ wh1,wh2,wh3,wh4,wh5, wherein wh1Indicate the
The course of one period is attended class efficiency value.For teacher tuWhen coding, each teacher has a corresponding period to attend class effect
Rate value tu={ tu1,tu2,tu3,tu4,tu5}.By the analysis to class, course, teacher, class period and classroom information and divide
Class, finally with Ai=cj~wh~tu~dy~prForm is encoded, wherein AiI-th of timeline in a referred to as population, should
Timeline indicates that the individual in the classroom r professor's h course, population is made of j*h timeline by teacher u in the y period for j class；
2) shown in individual fitness function such as formula (1),
Wherein fi=k1*fi1+k2*fi2+k3*fi3Indicate the fitness of i-th timeline in the individual, whereinIndicate the influence of class period spacing twice in one week,Indicate the influence of course arrangement in one week, wherein z1,z2Respectively indicate dy2And dy4The corresponding period
Index, if dy2When representing second period, z1It is then 2, thereforeThen it is expressed as wh2Efficiency value,
Indicate the influence that teacher arranges in one week, wherein x1,x2Respectively indicate dy2And dy4The index of corresponding period, if dy2It represents
When second period, x1It is then 2, thereforeThen it is expressed as tu2Efficiency value, k1,k2,k3Indicate its corresponding weight；
3) initialization of population, process are as follows:
Population scale NP, current population algebra stage=1, mutation probability mutate, maximum number of iterations N, population
Constraint condition: a teacher can only arrange a branch of instruction in school in the same period, and a class can only arrange in the same period
A branch of instruction in school, the same classroom can only arrange a subject in the same period, and the capacity in classroom should be greater than the people for the class that attends class
Number；It is random to generate the initial population Q for meeting constraint conditioninit={ S1,S2,…,SNP, wherein SNPIndicate the NP school timetable, i.e.,
Individual；
4) population is intersected, and process is as follows:
4.1) current population Q is calculatedcurrentIn each individual fitness, choose optimum individual Sbest；
4.2) in optimum individual SbestIn randomly select a timeline SbestArandom, then from population it is other individual in
Machine access time SotherAotherrandom, by the class period d in two timelinesyWith classroom prIt swaps；
4.3) in optimum individual SbestIn, whether meet constraint condition in the individual after judging exchange, if satisfied, continuing
It performs the next step, if not satisfied, then return step 4.2)；
4.4) the individual adaptation degree S after exchange is calculatedbestFbehindWith optimum individual fitness S before exchangingbestFfrontIt
Between difference DELTA Fbehind-front=SbestFbehind-SbestFfrontIf Δ Fbehind-frontIt is preferably a to indicate that intersection obtains by > 0
Body is received as next-generation individual, and other individuals are constant, ultimately generate progeny population Qreproduct；If Δ Fbehind-front< 0,
With Monte Carlo probabilityIt receives, wherein KT is temperature coefficient, if ZMonto> rand (0,1), then receive this
The exchange of secondary timeline；Otherwise, refuse the exchange of this timeline；
5) Population Variation, process are as follows:
Calculate newly-generated progeny population QreproductIn each individual fitness, choose optimum individual Sreproductbest,
A timeline S is randomly selected from optimum individualreproductbestArandomIf mutate > random (0,1), then from attending class
Class period and classroom information are randomly selected in time D and classroom information P again to timeline SreproductbestArandomIn it is upper
It is assembled between class hour with classroom information；Otherwise, it does not operate；
6) current population algebra stage=stage+1 is updated, if judgement stage < N, return step 4)；Otherwise table
Show and met maximum number of iterations, indicates that swarm optimization terminates.
Technical concept of the invention are as follows: first to the class of acquisition, course, teacher, time and classroom information carry out with
Machine is spliced to form a timeline, then, an individual i.e. school timetable is formed by multiple timelines for meeting constraint condition, multiple
Individual forms a population；The individual for choosing current population adaptive optimal control degree carries out cross and variation to the individual, in crossover process
Using the received method of Monte Carlo probability；It is finally converted according to the number of iterations of setting, in last generation population
Optimum individual is as final prediction result.
Beneficial effects of the present invention are shown: Monte Carlo probability method of reseptance being incorporated the intersection in genetic algorithm, is made
Population can preferably jump out the case where local optimum, thus closer to optimal solution.
Detailed description of the invention
Fig. 1 is the basic flow chart of Monte Carlo genetic algorithm cource arrangement method.
Fig. 2 is the school timetable figure obtained by Monte Carlo genetic algorithm cource arrangement method.
Specific embodiment
The invention will be further described below in conjunction with the accompanying drawings.
Referring to Figures 1 and 2, a kind of Course Arrangement in University method based on Monte Carlo genetic algorithm, comprising the following steps:
1) it encodes: obtaining class's essential information C={ c1,c2,c3,…,cj,…,cn, course essential information W={ w1,w2,
w3,…,wh,…,wm, teacher essential information T={ t1,t2,t3,…,tu,…,tv, class period information D={ d1,d2,
d3,…,dy,…,dlAnd classroom essential information P={ p1,p2,p3,…,pr,…,pb, wherein n indicates the total quantity of class, m table
Show course total quantity, v indicates teacher's total quantity, and l indicates the total quantity of class period, and b indicates the total quantity in classroom, cj、wh、tu、
dy、prInterior coding is made of 4 metric numbers.For period dyWhen coding, dy=dy1dy2dy3dy4, one day is divided into
Five periods, wherein dy1Indicate course this week for the first time week it is several attend class, dy2Indicate the period that course is attended class for the first time,
dy3Indicate this week of course second week it is several attend class, dy4Indicate the period that course is attended class for the second time, such as dy=1225 expression classes
The journey class period this week first time is the second section of Monday, and second of class period is Section five of Tuesday.For course whIt compiles
When code, each course has a corresponding period to attend class efficiency value wh:{wh1,wh2,wh3,wh4,wh5, wherein wh1Indicate first
The course of a period is attended class efficiency value.For teacher tuWhen coding, each teacher has a corresponding period to attend class efficiency
Value tu:{tu1,tu2,tu3,tu4,tu5}.By the analysis and classification to class, course, teacher, class period and classroom information, most
Eventually with Ai=cj~wh~tu~dy~prForm is encoded, wherein AiI-th of timeline in a referred to as population, the timeline
Indicate that the individual in the classroom r professor's h course, population is made of j*h timeline by teacher u in the y period for j class；
2) shown in individual fitness function such as formula (1),
Wherein fi=k1*fi1+k2*fi2+k3*fi3Indicate the fitness of i-th timeline in the individual, whereinIndicate the influence of class period spacing twice in one week,Indicate the influence of course arrangement in one week, wherein z1,z2Respectively indicate dy2And dy4The corresponding period
Index, if dy2When representing second period, z1It is then 2, thereforeThen it is expressed as wh2Efficiency value,
Indicate the influence that teacher arranges in one week, wherein x1,x2Respectively indicate dy2And dy4The index of corresponding period, if dy2It represents
When second period, x1It is then 2, therefore tuz1Then it is expressed as tu2Efficiency value, k1,k2,k3Indicate its corresponding weight；
3) initialization of population, process are as follows:
Population scale NP, current population algebra stage=1, mutation probability mutate, maximum number of iterations N, population
Constraint condition: a teacher can only arrange a branch of instruction in school in the same period, and a class can only arrange in the same period
A branch of instruction in school, the capacity in classroom should be greater than the number for the class that attends class；It is random to generate the initial population Q for meeting constraint conditioninit
={ S1,S2,…,SNP, wherein SNPIndicate the NP school timetable, i.e., it is individual；
4) population is intersected, and process is as follows:
4.1) current population Q is calculatedcurrentIn each individual fitness, choose optimum individual Sbest；
4.2) in optimum individual SbestIn randomly select a timeline SbestArandom, then from population it is other individual in
Machine access time SotherAotherrandom, by the class period d in timelineyWith classroom prIt swaps；
4.3) in optimum individual SbestIn, whether meet constraint condition in the timeline individual after judging exchange, if full
Foot continues to execute in next step, if not satisfied, then return step 4.2)；
4.4) the individual adaptation degree S to calculate after exchangingbestFbehindWith optimum individual fitness S before exchangingbestFfrontIt
Between difference DELTA Fbehind-front=SbestFbehind-SbestFfrontIf Δ Fbehind-frontIt is preferably a to indicate that intersection obtains by > 0
Body is received as next-generation individual, and other individuals are constant, ultimately generate progeny population Qreproduct；If Δ Fbehind-front< 0,
With Monte Carlo probabilityIt receives, wherein KT is temperature coefficient, is a constant, if ZMonto> rand (0,
1) exchange of current timeline, is then received；Otherwise, refuse the exchange of this timeline；
5) Population Variation, process are as follows:
Calculate the i.e. newly-generated progeny population Q of current populationreproductThe fitness of total each individual, chooses optimum individual
Sreproductbest, a timeline S is randomly selected from optimum individualreproductbestArandomIf mutate > random (0,
1), then class period and classroom information are randomly selected again to timeline from class period D and classroom information P
SreproductbestArandomIn class period and classroom information assembled；Otherwise, it does not operate；
6) current population algebra stage=stage+1 is updated, if judgement stage < N, return step 4)；Otherwise table
Show and met maximum number of iterations, indicates that swarm optimization terminates.
This example is using Hangzhou institute of colleges and universities as example, a kind of Course Arrangement in University side based on Monte Carlo genetic algorithm
Method, comprising the following steps:
1) it encodes: the class of acquisition, course, teacher, class period and classroom information is encoded, wherein class
It encodes C={ 1101,1102,1103,1104,1105,1106,1107,1108,1109,1110,1111,1112 }, course is basic
It encodes W={ 1101,1102,1103,1104,1105,1106,1107,1108,1109,1110 }, the coding T=of teacher
{ 1101,1102,1103,1104,1105,1106,1107,1108,1109,1110 }, the encoding D of class period=1121,
1122,1123,1124,1125,1131,1132,1133,1134,1135,1141,1142,1143,1144,1145,1151,
1152,1153,1154,1155,1221,1222,1223,1224,1225,1231,1232,1233,1234,1235,1241,
1242,1243,1244,1245,1251,1252,1253,1254,1255,1321,1322,1323,1324,1325,1331,
1332,1333,1334,1335,1341,1342,1343,1344,1345,1351,1352,1353,1354,1355,1421,
1422,1423,1424,1425,1431,1432,1433,1434,1435,1441,1442,1443,1444,1445,1451,
1452,1453,1454,1455,1521,1522,1523,1524,1525,1531,1532,1533,1534,1535,1541,
1542,1543,1544,1545,1551,1552,1553,1554,1555,2131,2132,2133,2134,2135,2141,
2142,2143,2144,2145,2151,2152,2153,2154,2155,2231,2232,2233,2234,2235,2241,
2242,2243,2244,2245,2251,2252,2253,2254,2255,2331,2332,2333,2334,2335,2341,
2342,2343,2344,2345,2351,2352,2353,2354,2355,2431,2432,2433,2434,2435,2441,
2442,2443,2444,2445,2451,2452,2453,2454,2455,2531,2532,2533,2534,2535,2541,
2542,2543,2544,2545,2551,2552,2553,2554,2555,3141,3142,3143,3144,3145,3151,
3152,3153,3154,3155,3241,3242,3243,3244,3245,3251,3252,3253,3254,3255,3341,
3342,3343,3344,3345,3351,3352,3353,3354,3355,3441,3442,3443,3444,3445,3451,
3452,3453,3454,3455,3541,3542,3543,3544,3545,3551,3552,3553,3554,3555,4151,
4152,4153,4154,4155,4251,4252,4253,4254,4255,4351,4352,4353,4354,4355,4451,
4452,4453,4454,4455,4551,4552,4553,4554,4555 } and the coding P=in classroom 0114,0125,0134,
0211,0224,0234,0335,0334,0341 }, wherein for 1101 in class's coding, the corresponding institute of first expression, the
The corresponding profession of two expressions, behind two then indicate class, for 1101 in course coding, first expressions correspondence institute,
Second indicates corresponding profession, behind two then indicate course, for 1101 in teacher's coding, corresponding of first expression
Institute, second indicate corresponding profession, behind two then indicate teacher's work number, when encoding for period D, be divided into five for one day
Period, such as dy=1225 indicate that the course class period this week first time is the second section of Monday, and second of class period is week
Section five of two.In course coding, each course has efficiency value such as course 1101 of attending class of the corresponding period corresponding
Efficiency value 1101:{ 0.5,0.25,0,0.25,0.5 }, indicate attend class benefit value of 1101 courses in 5 periods.In teacher
In coding, it is 1101 corresponding efficiency values 1101 that each teacher, which has efficiency value such as teacher's number of attending class of the corresponding period:
{1,0,0.25,0.25,0.5}.Finally carry out class, course, teacher, class period and classroom coding to be integrally formed the time
Item；
2) shown in individual fitness function such as formula (1),
Wherein fi=k1*fi1+k2*fi2+k3*fi3Indicate the fitness of i-th timeline in the individual, whereinIndicate the influence of class period spacing twice in one week,Indicate the influence of course arrangement in one week, wherein z1,z2Respectively indicate dy2And dy4The corresponding period
Index, if dy2When representing second period, z1It is then 2, thereforeThen it is expressed as wh2Efficiency value,
Indicate the influence that teacher arranges in one week, wherein x1,x2Respectively indicate dy2And dy4The index of corresponding period, if dy2It represents
When second period, x1It is then 2, thereforeThen it is expressed as tu2Efficiency value, wherein k1=0.6, k2=0.3, k3=0.1；
3) initialization of population, process are as follows:
Population scale 50, current population algebra stage=1, mutation probability 0.1, maximum number of iterations 1000, population
Constraint condition: a teacher can only arrange a branch of instruction in school in the same period, and a class can only arrange in the same period
A branch of instruction in school, the capacity in classroom should be greater than the number for the class that attends class；It is random to generate the initial population Q for meeting constraint conditioninit
={ S1,S2,…,S50}；
4) population is intersected, and process is as follows:
4.1) current population Q is calculatedcurrentIn each individual fitness, choose optimum individual Sbest；
4.2) in optimum individual SbestIn randomly select a timeline SbestArandom, then from population it is other individual in
Machine access time SotherAotherrandom, by the class period d in timelineyWith classroom prIt swaps；
4.3) in optimum individual SbestIn, whether meet constraint condition in the timeline individual after judging exchange, if full
Foot continues to execute in next step, if not satisfied, then return step 4.2)；
4.4) the individual adaptation degree S to calculate after exchangingbestFbehindWith optimum individual fitness S before exchangingbestFfrontIt
Between difference DELTA Fbehind-front=SbestFbehind-SbestFfrontIf Δ Fbehind-frontIt is preferably a to indicate that intersection obtains by > 0
Body is received as next-generation individual, and other individuals are constant, ultimately generate progeny population Qreproduct；If Δ Fbehind-front< 0,
With Monte Carlo probabilityIt receives, wherein KT=2, is a constant, if ZMonto> rand (0,1), then connect
Receive the exchange of current timeline；Otherwise, refuse the exchange of this timeline；
5) Population Variation, process are as follows:
Calculate the i.e. newly-generated progeny population Q of current populationreproductThe fitness of total each individual, chooses optimum individual
Sreproductbest, a timeline S is randomly selected from optimum individualreproductbestArandomIf mutate > random (0,
1), then class period and classroom information are randomly selected again to timeline from class period D and classroom information P
SreproductbestArandomIn class period and classroom information assembled；Otherwise, it does not operate；
6) current population algebra stage=stage+1 is updated, if judgement stage < N, return step 4；Otherwise table
Show and met maximum number of iterations, indicates that swarm optimization terminates.
Described above is the excellent results that one embodiment that the present invention provides shows, it is clear that the present invention not only fits
Above-described embodiment is closed, it can under the premise of without departing from essence spirit of the present invention and without departing from content involved by substantive content of the present invention
Many variations are done to it to be implemented.
Claims (1)
1. a kind of Course Arrangement in University method based on Monte Carlo genetic algorithm, which is characterized in that the Course Arrangement in University method includes
Following steps:
1) it encodes: obtaining class's essential information C={ c1,c2,c3,…,cj,…,cn, course essential information W={ w1,w2,
w3,…,wh,…,wm, teacher essential information T={ t1,t2,t3,…,tu,…,tv, class period information D={ d1,d2,
d3,…,dy,…,dlAnd classroom essential information P={ p1,p2,p3,…,pr,…,pb, wherein n indicates the total quantity of class, m table
Show course total quantity, v indicates teacher's total quantity, and l indicates the total quantity of class period, and b indicates the total quantity in classroom, cj、wh、tu、
dy、prInterior coding is made of 4 metric numbers.For period dyWhen coding, dy=dy1dy2dy3dy4, one day is divided into
Five periods, wherein dy1Indicate course this week for the first time week it is several attend class, dy2Indicate the period that course is attended class for the first time,
dy3Indicate this week of course second week it is several attend class, dy4Indicate the period that course is attended class for the second time, such as dy=1225 expression classes
The journey class period this week first time is the second section of Monday, and second of class period is Section five of Tuesday.For course whIt compiles
When code, each course has a corresponding period to attend class efficiency value wh={ wh1,wh2,wh3,wh4,wh5, wherein wh1Indicate the
The course of one period is attended class efficiency value.For teacher tuWhen coding, each teacher has a corresponding period to attend class effect
Rate value tu={ tu1,tu2,tu3,tu4,tu5}.By the analysis to class, course, teacher, class period and classroom information and divide
Class, finally with Ai=cj~wh~tu~dy~prForm is encoded, wherein AiI-th of timeline in a referred to as population, should
Timeline indicates that the individual in the classroom r professor's h course, population is made of j*h timeline by teacher u in the y period for j class；
2) shown in individual fitness function such as formula (1),
Wherein fi=k1*fi1+k2*fi2+k3*fi3Indicate the fitness of i-th timeline in the individual, whereinIndicate the influence of class period spacing twice in one week,Indicate the influence of course arrangement in one week, wherein z1,z2Respectively indicate dy2And dy4The corresponding period
Index, if dy2When representing second period, z1It is then 2, thereforeThen it is expressed as wh2Efficiency value,
Indicate the influence that teacher arranges in one week, wherein x1,x2Respectively indicate dy2And dy4The index of corresponding period, if dy2It represents
When second period, x1It is then 2, thereforeThen it is expressed as tu2Efficiency value, k1,k2,k3Indicate its corresponding weight；
3) initialization of population, process are as follows:
Population scale NP, current population algebra stage=1, mutation probability mutate, maximum number of iterations N, the pact of population
Beam condition: a teacher can only arrange a branch of instruction in school in the same period, and a class can only arrange one in the same period
Course, the same classroom can only arrange a subject in the same period, and the capacity in classroom should be greater than the number for the class that attends class；With
Machine generates the initial population Q for meeting constraint conditioninit={ S1,S2,…,SNP, wherein SNPIndicate the NP school timetable, i.e., it is individual；
4) population is intersected, and process is as follows:
4.1) current population Q is calculatedcurrentIn each individual fitness, choose optimum individual Sbest；
4.2) in optimum individual SbestIn randomly select a timeline SbestArandom, then from population it is other individual in select at random
Take timeline SotherAotherrandom, by the class period d in two timelinesyWith classroom prIt swaps；
4.3) in optimum individual SbestIn, whether meet constraint condition in the individual after judging exchange, if satisfied, continuing to execute
In next step, if not satisfied, then return step 4.2)；
4.4) the individual adaptation degree S after exchange is calculatedbestFbehindWith optimum individual fitness S before exchangingbestFfrontBetween
Difference DELTA Fbehind-front=SbestFbehind-SbestFfrontIf Δ Fbehind-frontIt is preferably individual to indicate that intersection obtains by > 0,
It receives as next-generation individual, other individuals are constant, ultimately generate progeny population Qreproduct；If Δ Fbehind-front< 0, with illiteracy
Special Carlow probabilityIt receives, wherein KT is temperature coefficient, if ZMonto> rand (0,1), then when receiving current
Between item exchange；Otherwise, refuse the exchange of this timeline；
5) Population Variation, process are as follows:
Calculate newly-generated progeny population QreproductIn each individual fitness, choose optimum individual Sreproductbest, from most
A timeline S is randomly selected in excellent individualreproductbestArandomIf mutate > random (0,1), then from class period D
With class period and classroom information are randomly selected in the information P of classroom again to timeline SreproductbestArandomIn upper class hour
Between and classroom information assembled；Otherwise, it does not operate；
6) current population algebra stage=stage+1 is updated, if judgement stage < N, return step 4)；Otherwise it indicates
Met maximum number of iterations, indicates that swarm optimization terminates.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201810763134.7A CN109255512B (en) | 2018-07-12 | 2018-07-12 | University course arrangement method based on Monte Carlo genetic algorithm |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201810763134.7A CN109255512B (en) | 2018-07-12 | 2018-07-12 | University course arrangement method based on Monte Carlo genetic algorithm |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN109255512A true CN109255512A (en) | 2019-01-22 |
| CN109255512B CN109255512B (en) | 2021-08-03 |
Family
ID=65051680
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN201810763134.7A Active CN109255512B (en) | 2018-07-12 | 2018-07-12 | University course arrangement method based on Monte Carlo genetic algorithm |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN109255512B (en) |
Cited By (9)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN110020805A (en) * | 2019-04-11 | 2019-07-16 | 广东工业大学 | A kind of task calendar formulating method, device and medium |
| CN110020831A (en) * | 2019-03-29 | 2019-07-16 | 北京工业大学 | New college entrance examination timetabling algorithm based on particle swarm algorithm |
| CN110610239A (en) * | 2019-08-13 | 2019-12-24 | 浙江工业大学 | Method for solving balanced transportation problem based on dynamic genetic algorithm |
| CN110738461A (en) * | 2019-10-10 | 2020-01-31 | 青岛海信商用显示股份有限公司 | Course arrangement method and equipment |
| CN111091344A (en) * | 2019-11-15 | 2020-05-01 | 珠海丰实科技有限责任公司 | Course arrangement method based on hill climbing algorithm and parallel disturbance mixed search |
| CN111161112A (en) * | 2019-12-27 | 2020-05-15 | 广东宜教通教育有限公司 | Intelligent course scheduling method and system for administrative classes, computer equipment and storage medium |
| CN111178751A (en) * | 2019-12-27 | 2020-05-19 | 广东宜教通教育有限公司 | Intelligent class scheduling method, system, computer equipment and storage medium for new college entrance examination shift |
| CN111815279A (en) * | 2020-07-08 | 2020-10-23 | 广州魔晶教育科技有限公司 | Intelligent course arrangement method, device, terminal and storage medium based on genetic algorithm |
| CN114399191A (en) * | 2022-01-11 | 2022-04-26 | 西安建筑科技大学 | College course arrangement system and method based on building energy conservation |
Citations (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN104751388A (en) * | 2015-03-01 | 2015-07-01 | 江西科技学院 | Genetic algorithm based college course arranging method |
| CN106127355A (en) * | 2016-07-19 | 2016-11-16 | 焦点科技股份有限公司 | The cource arrangement method of a kind of high efficiency smart and system |
| CN107204133A (en) * | 2017-06-15 | 2017-09-26 | 西安交通大学 | A kind of automatic teaching and examination robot |
-
2018
- 2018-07-12 CN CN201810763134.7A patent/CN109255512B/en active Active
Patent Citations (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN104751388A (en) * | 2015-03-01 | 2015-07-01 | 江西科技学院 | Genetic algorithm based college course arranging method |
| CN106127355A (en) * | 2016-07-19 | 2016-11-16 | 焦点科技股份有限公司 | The cource arrangement method of a kind of high efficiency smart and system |
| CN107204133A (en) * | 2017-06-15 | 2017-09-26 | 西安交通大学 | A kind of automatic teaching and examination robot |
Non-Patent Citations (5)
| Title |
|---|
| MASSARU TEZUKA等: "Maintenance Schedule Optimization Based on Failure Probability Distribution", 《PROCEEDING OF THE 2015 INTERNATIONAL CONFERENCE ON INDUSTRIAL ENGINEERING AND OPERATIONS MANAGEMENT》 * |
| 唐勇等: "基于遗传算法的排课系统", 《计算机应用》 * |
| 张贵军等: "模块化柔性制造综合实训教学管理系统开发", 《实验技术与管理》 * |
| 滕姿等: "基于遗传算法的排课系统的设计与实现", 《计算机应用》 * |
| 董玉锁等: "利用改进遗传算法破解排课难题", 《中国教育技术装备》 * |
Cited By (14)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN110020831A (en) * | 2019-03-29 | 2019-07-16 | 北京工业大学 | New college entrance examination timetabling algorithm based on particle swarm algorithm |
| CN110020805A (en) * | 2019-04-11 | 2019-07-16 | 广东工业大学 | A kind of task calendar formulating method, device and medium |
| CN110020805B (en) * | 2019-04-11 | 2023-11-07 | 广东工业大学 | Task schedule making method, device and medium |
| CN110610239A (en) * | 2019-08-13 | 2019-12-24 | 浙江工业大学 | Method for solving balanced transportation problem based on dynamic genetic algorithm |
| CN110738461B (en) * | 2019-10-10 | 2022-09-30 | 青岛海信商用显示股份有限公司 | Course arrangement method and equipment |
| CN110738461A (en) * | 2019-10-10 | 2020-01-31 | 青岛海信商用显示股份有限公司 | Course arrangement method and equipment |
| CN111091344A (en) * | 2019-11-15 | 2020-05-01 | 珠海丰实科技有限责任公司 | Course arrangement method based on hill climbing algorithm and parallel disturbance mixed search |
| CN111091344B (en) * | 2019-11-15 | 2023-09-22 | 珠海丰实科技有限责任公司 | Course arrangement method based on mountain climbing algorithm and parallel disturbance mixed search |
| CN111161112A (en) * | 2019-12-27 | 2020-05-15 | 广东宜教通教育有限公司 | Intelligent course scheduling method and system for administrative classes, computer equipment and storage medium |
| CN111178751A (en) * | 2019-12-27 | 2020-05-19 | 广东宜教通教育有限公司 | Intelligent class scheduling method, system, computer equipment and storage medium for new college entrance examination shift |
| CN111178751B (en) * | 2019-12-27 | 2024-01-23 | 广东宜教通教育有限公司 | New college entrance examination shift intelligent course arrangement method, system, computer equipment and storage medium |
| CN111815279A (en) * | 2020-07-08 | 2020-10-23 | 广州魔晶教育科技有限公司 | Intelligent course arrangement method, device, terminal and storage medium based on genetic algorithm |
| CN114399191A (en) * | 2022-01-11 | 2022-04-26 | 西安建筑科技大学 | College course arrangement system and method based on building energy conservation |
| CN114399191B (en) * | 2022-01-11 | 2024-05-07 | 西安建筑科技大学 | University course arrangement system and method based on building energy conservation |
Also Published As
| Publication number | Publication date |
|---|---|
| CN109255512B (en) | 2021-08-03 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN109255512A (en) | A kind of Course Arrangement in University method based on Monte Carlo genetic algorithm | |
| CN110020831A (en) | New college entrance examination timetabling algorithm based on particle swarm algorithm | |
| CN109165799A (en) | The class's of walking education course arrangement system based on genetic algorithm | |
| CN108122179A (en) | Delamination Teaching cource arrangement method and device, delamination Teaching curricula-variable method and system | |
| CN108280549A (en) | A kind of senior middle school cource arrangement method under the class's of walking system based on improved adaptive GA-IAGA | |
| CN109961189A (en) | New college entrance examination timetabling algorithm based on genetic algorithm | |
| Wen-jing | Improved Adaptive Genetic Algorithm for Course Scheduling in Colleges and Universities. | |
| Modupe et al. | Development of a university lecture timetable using modified genetic algorithms approach | |
| Ahmad et al. | A heuristics approach for classroom scheduling using genetic algorithm technique | |
| CN116957869A (en) | Education platform based on resource pattern matching and cooperation | |
| Spielauer | What is dynamic social science microsimulation | |
| CN109472410A (en) | A kind of dynamic and intelligent is put into several classes cource arrangement method | |
| Perzina et al. | Self-learning genetic algorithm for a timetabling problem with fuzzy constraints | |
| Matsuda et al. | Benchmark for Personalized Federated Learning | |
| Junaedi et al. | Solving curriculum-based course timetabling problem with artificial bee colony algorithm | |
| El-Sherbiny et al. | Genetic algorithm for solving course timetable problems | |
| Austero et al. | Solving course timetabling problem using Whale Optimization Algorithm | |
| CN114328609A (en) | Course arrangement method based on combination of meta-heuristic algorithm and greedy algorithm | |
| Gao et al. | Evaluation method of creative dance teaching quality based on fuzzy comprehensive evaluation | |
| CN114781806A (en) | Multi-combination course arrangement method and device based on multi-objective evolutionary algorithm and readable medium | |
| Spielauer | Microsimulation approaches | |
| Bahargam et al. | Team formation for scheduling educational material in massive online classes | |
| Hakrama | Artificial Economy and the usage of ACE | |
| Ferdoushi et al. | Highly constrained university course scheduling using modified hybrid particle swarm optimization | |
| Agahian et al. | Adaptation and use of artificial bee colony algorithm to solve curriculum-based course time-tabling problem |
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 | ||
| GR01 | Patent grant | ||
| GR01 | Patent grant |