CN109272137A - A kind of scheduling of resource optimization method based on the global artificial bee colony algorithm of intersection - Google Patents
A kind of scheduling of resource optimization method based on the global artificial bee colony algorithm of intersection Download PDFInfo
- Publication number
- CN109272137A CN109272137A CN201810768826.0A CN201810768826A CN109272137A CN 109272137 A CN109272137 A CN 109272137A CN 201810768826 A CN201810768826 A CN 201810768826A CN 109272137 A CN109272137 A CN 109272137A
- Authority
- CN
- China
- Prior art keywords
- bee
- scheduling
- colony algorithm
- optimization method
- resource
- 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 29
- 238000005457 optimization Methods 0.000 title claims abstract description 29
- 241000256844 Apis mellifera Species 0.000 claims abstract description 116
- 241000894007 species Species 0.000 claims description 11
- 238000004364 calculation method Methods 0.000 claims description 8
- 235000013379 molasses Nutrition 0.000 claims description 6
- 241000256837 Apidae Species 0.000 claims description 3
- 229910002056 binary alloy Inorganic materials 0.000 claims description 3
- 235000012907 honey Nutrition 0.000 claims 1
- 230000007547 defect Effects 0.000 abstract description 4
- 230000000694 effects Effects 0.000 abstract description 4
- 241000257303 Hymenoptera Species 0.000 abstract description 3
- 235000013305 food Nutrition 0.000 abstract description 3
- 238000010586 diagram Methods 0.000 description 1
- 230000006872 improvement Effects 0.000 description 1
- 230000007246 mechanism Effects 0.000 description 1
- 230000004048 modification Effects 0.000 description 1
- 238000012986 modification Methods 0.000 description 1
- 239000011664 nicotinic acid Substances 0.000 description 1
- 230000008569 process Effects 0.000 description 1
- 238000010845 search algorithm Methods 0.000 description 1
- 230000007704 transition Effects 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
- G06N3/004—Artificial life, i.e. computing arrangements simulating life
- G06N3/006—Artificial life, i.e. computing arrangements simulating life based on simulated virtual individual or collective life forms, e.g. social simulations or particle swarm optimisation [PSO]
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q10/00—Administration; Management
- G06Q10/04—Forecasting or optimisation specially adapted for administrative or management purposes, e.g. linear programming or "cutting stock problem"
-
- 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
- G06Q10/06315—Needs-based resource requirements planning or analysis
-
- 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
- G06Q10/0635—Risk analysis of enterprise or organisation activities
-
- 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
- G06Q50/26—Government or public services
Landscapes
- Business, Economics & Management (AREA)
- Engineering & Computer Science (AREA)
- Human Resources & Organizations (AREA)
- Economics (AREA)
- Strategic Management (AREA)
- Theoretical Computer Science (AREA)
- Physics & Mathematics (AREA)
- Tourism & Hospitality (AREA)
- Entrepreneurship & Innovation (AREA)
- General Physics & Mathematics (AREA)
- Marketing (AREA)
- General Business, Economics & Management (AREA)
- Development Economics (AREA)
- Quality & Reliability (AREA)
- Operations Research (AREA)
- Educational Administration (AREA)
- Game Theory and Decision Science (AREA)
- Health & Medical Sciences (AREA)
- General Health & Medical Sciences (AREA)
- Computational Linguistics (AREA)
- Computing Systems (AREA)
- Artificial Intelligence (AREA)
- Data Mining & Analysis (AREA)
- Evolutionary Computation (AREA)
- Biomedical Technology (AREA)
- Molecular Biology (AREA)
- Biophysics (AREA)
- General Engineering & Computer Science (AREA)
- Mathematical Physics (AREA)
- Software Systems (AREA)
- Life Sciences & Earth Sciences (AREA)
- Primary Health Care (AREA)
- Medicines Containing Material From Animals Or Micro-Organisms (AREA)
Abstract
The invention discloses a kind of based on the scheduling of resource optimization method for intersecting global artificial bee colony algorithm, it is firstly introduced into nectar source, it is re-introduced into three kinds of bees: gathering honey bee, observation bee, search bee, gathering honey bee links together with specific nectar source, gathering honey bee passes through swing and other honeybee sharing informations, observation bee etc. stays in dancing area and is made a choice by sharing the information of gathering honey bee to food source, and the effect of search bee is one new position of random search.Of the invention is a kind of based on the scheduling of resource optimization method for intersecting global artificial bee colony algorithm, overcomes the defect that basic artificial bee colony algorithm is negligent of exploitation, enhances algorithm directionality, substantially increase development ability of the artificial bee colony algorithm near optimal solution.At the same time, it introduces coefficient cr to be affected for the exploring ability and development ability of algorithm, satisfactory solution can be obtained by adjusting the value of cr for particular problem, enhance algorithm for the adaptability of various optimization problems.
Description
Technical field
The present invention relates to scheduling of resource to optimize field, and in particular, to a kind of based on intersecting global artificial bee colony algorithm
Scheduling of resource optimization method.
Background technique
With industrialization and the aggravation of globalization process, the transition of social structure, various Large Scale Natural Disasters and public
The world of our existence is more and more continually invaded in the emergency events such as security incident, and huge property loss and personnel is caused to hurt
It dies.
After emergency event occurs, emergency resources dispatch the important link as contingency management, run through entire emergency management and rescue work
The implementation phase of work is the important behaviour that rescue value is realized in contingency management.To emergency scheduling of resource research be in order to faster,
Emergency management and rescue work is preferably completed, improves the efficiency of emergency resources scheduling to greatest extent, event is avoided to continue to deteriorate and produce
Raw unfavorable chain reaction.
Artificial bee colony algorithm is a kind of modern times heuristic intelligent search algorithm, is had become to its theoretical research and application new
Hot spot has become a kind of important optimization algorithm of bionic intelligence calculating field since it is in very various excellent performances.People
Work ant colony algorithm is simply easily achieved, strong robustness.Compared with other Swarm Intelligence Algorithms, the outstanding advantages of artificial bee colony algorithm
It is all to carry out global and local search in each iteration.Therefore, the probability for finding optimal solution greatly increases, and largely
On avoid local optimum.
Basic artificial bee colony algorithm does not have global optimum to remember and participate in algorithmic procedure, and the algorithm is caused to detect because of the overall situation
Scarce capacity and fall into locally optimal solution.The present invention is using global artificial bee colony (CGABC) algorithm based on crossover operation to money
Source scheduling optimizes, and CGABC algorithm overcomes the defect that artificial bee colony algorithm is negligent of exploitation, increase by introducing crossover mechanism
Strong algorithm directionality, substantially increases development ability of the artificial bee colony algorithm near optimal solution.At the same time, it introduces and is
Number cr is affected for the exploring ability and development ability of algorithm, can be obtained by adjusting the value of cr for particular problem
Satisfactory solution enhances algorithm for the adaptability of various optimization problems.
Summary of the invention
It is an object of the present invention in view of the above-mentioned problems, propose a kind of based on the resource for intersecting global artificial bee colony algorithm
Method for optimizing scheduling, to realize the situation for improving basic artificial bee colony algorithm and easily falling into locally optimal solution, in convergence precision and receipts
Hold back the advantages of significant raising has been obtained in speed.
To achieve the above object, the technical solution adopted by the present invention is that: it is a kind of based on intersecting global artificial bee colony algorithm
Scheduling of resource optimization method, specifically includes that
Step S1: initialization honeybee populations parameter；
Step S2: gathering honey bee carries out neighborhood search in the search incipient stage, calculates initial fitness value fitness (xi),
And record global optimum GlobalValue and global optimum solution vector GlobalMin；
Step S3: binomial is intersected in conjunction with artificial bee colony algorithm；
Step S4: it calculates observation bee and follows probability, observation bee is converted into gathering honey bee and carries out neighborhood search, and carries out intersection behaviour
Make, new nectar source is selected according to greedy criterion, and retain global optimum；
Step S5: if it is more than neighborhood maximum search number limit that gathering honey bee, observation bee, which search number (nectar source stop), still
The nectar source is then abandoned in the nectar source for not finding higher fitness so, while the role of honeybee is converted by gathering honey bee or observation bee
For search bee, and a new nectar source is randomly generated；
Step S6: the optimal nectar source that current all honeybees are found is recorded, and skips to step S2, until meeting greatest iteration time
Number maxCycle is less than output global optimum position when optimizing error.
Further, the scheduling of resource optimization method further include:
The determination formula of initial time, population position is as follows:
In formula:For the upper bound of search space；For the lower bound of search space.
Further, honeybee populations parameter described in step S1 includes:
(gathering honey bee and the number for observing bee are honeybee populations scale S), maximum number of iterations maxCycle, neighborhood most
Big searching times limit andThe feasible solution X of a D dimensioni=(xi1,xi2,…,xiD)T, i=1,2 ..., S, i.e. nectar source；
Nectar source and gathering honey bee correspond, i.e. nectar source number is
N number of species resource individual in population S is encoded, all resource individuals form a population.
Further, N number of species resource individual in population S, which encode, includes:
N number of species resource individual in population S is encoded, each individual indicates to dispatch from i-th of resource provisioning point
To the jth kind resource quantity of demand point, wherein j ∈ N.
Further, N number of species resource individual in population S encodes, further includes:
Binary system or real number is taken to encode resource individual.
Further, neighborhood search described in step S2 specifically includes:
Neighborhood search formula is as follows:
In formula: the value of β is 0~1.5；Random number between α ∈ [- 1,1].
Further, fitness value described in step S2 specifically includes:
Wherein, fitness value calculation formula is as follows:
In formula: f (xi) it is function to be optimized；fitness(xi) be function fitness value.
Further, the intersection of binomial described in step S3 specifically includes:
Binomial is intersected, equally distributed random value rand between one 0~1 is generated to each component, if
Rand < cr (crossover operator), then receive target component, otherwise retains the respective components of current individual；
The optimal solution of solution and iteration generation after neighborhood search is carried out crossover operation by gathering honey bee, calculates new fitness value
(the nectar amount in nectar source), if new nectar source nectar amount is better than green molasses source, honeybee replaces green molasses source by greedy criterion, with new nectar source,
Otherwise constant.
Further, crossover operation described in step S3 specifically includes:
Crossover operation formula is as follows:
In formula: the general value of crossover operator cr is 0.3~0.6；The value of β is 0~1.5.
Further, probability is followed to specifically include described in step S4:
Follow probability calculation formula as follows:
Advantageous effects of the invention:
Scheduling of resource optimization method provided in an embodiment of the present invention based on the global artificial bee colony algorithm of intersection, is firstly introduced into
Nectar source, it represents the various possible solutions in solution space, and and function value is related, measures nectar source with fitness function value, is re-introduced into
Three kinds of bees: gathering honey bee, observation bee, search bee, gathering honey bee link together with specific nectar source, and gathering honey bee passes through swing and its
Its honeybee sharing information, observation bee etc. stay in dancing area and are made a choice by sharing the information of gathering honey bee to food source, search bee
Effect be one new position of random search.Compared with basic artificial bee colony algorithm, CGABC algorithm is by introducing intersecting machine
System overcomes the defect that basic artificial bee colony algorithm is negligent of exploitation, enhances algorithm directionality, substantially increases artificial bee colony calculation
Development ability of the method near optimal solution.At the same time, coefficient cr is introduced for the exploring ability and development ability shadow of algorithm
Sound is larger, can obtain satisfactory solution by adjusting the value of cr for particular problem, enhance algorithm for various optimization problems
Adaptability.
Other features and advantages of the present invention will be illustrated in the following description, also, partly becomes from specification
It obtains it is clear that understand through the implementation of the invention.
Below by drawings and examples, technical scheme of the present invention will be described in further detail.
Detailed description of the invention
Attached drawing is used to provide further understanding of the present invention, and constitutes part of specification, with reality of the invention
It applies example to be used to explain the present invention together, not be construed as limiting the invention.In the accompanying drawings:
Fig. 1 is provided in an embodiment of the present invention a kind of based on the scheduling of resource optimization method for intersecting global artificial bee colony algorithm
Flow diagram.
Specific embodiment
Hereinafter, preferred embodiments of the present invention will be described with reference to the accompanying drawings, it should be understood that preferred reality described herein
Apply example only for the purpose of illustrating and explaining the present invention and is not intended to limit the present invention.
The embodiment of the present invention provides a kind of based on the scheduling of resource optimization method for intersecting global artificial bee colony algorithm, such as Fig. 1
It is shown, comprising:
Step S1, initializing honeybee populations scale S, (gathering honey bee and the number for observing bee are), maximum number of iterations
MaxCycle, neighborhood maximum search number limit andThe feasible solution X of a D dimensioni=(xi1,xi2,…,xiD)T, i=1,2 ...,
S, i.e. nectar source.Nectar source and gathering honey bee correspond, i.e. nectar source number isN number of species resource individual in population S is compiled
Code, all resource individuals form a population；
Step S2, gathering honey bee carries out neighborhood search in the search incipient stage, calculates initial fitness value fitness (xi),
And record global optimum GlobalValue and global optimum solution vector GlobalMin；
Wherein, neighborhood search formula is as follows:
In formula: the value of β is 0~1.5；Random number between α ∈ [- 1,1]；
Wherein, fitness value calculation formula is as follows:
In formula: f (xi) it is function to be optimized；fitness(xi) be function fitness value；
Step S3, binomial is intersected in conjunction with artificial bee colony algorithm, binomial is intersected, one is generated to each component
Otherwise equally distributed random value rand between a 0~1 retains and works as if rand < cr (crossover operator), receives target component
The respective components of preceding individual.The optimal solution of solution and iteration generation after neighborhood search is carried out crossover operation by gathering honey bee, is calculated new
Fitness value (the nectar amount in nectar source), if new nectar source nectar amount is better than green molasses source, honeybee is taken by greedy criterion with new nectar source
It is otherwise constant for green molasses source；
Wherein, crossover operation formula is as follows:
In formula: the general value of crossover operator cr is 0.3~0.6；The value of β is 0~1.5；
Step S4, it calculates observation bee and follows probability, observation bee is converted into gathering honey bee and carries out neighborhood search, and carries out intersection behaviour
Make, new nectar source is selected according to greedy criterion, and retain global optimum；
Wherein, follow probability calculation formula as follows:
If it is more than neighborhood maximum search number limit that step S5, gathering honey bee, observation bee, which search number (nectar source stop), still
The nectar source is then abandoned in the nectar source for not finding higher fitness so, while the role of honeybee is converted by gathering honey bee or observation bee
For search bee, and a new nectar source is randomly generated.
Step S6, the optimal nectar source (i.e. globally optimal solution) that current all honeybees are found is recorded, and skips to step S2, until
Meet maximum number of iterations maxCycle or exports global optimum position when being less than optimization error.
The determination formula of optimization method proposed by the present invention, initial time, population position is as follows:
In formula:For the upper bound of search space；For the lower bound of search space.
Wherein, it includes: to N number of type money in population S that N number of species resource individual in population S, which carries out coding,
Source individual is encoded, and each individual indicates the jth kind resource quantity that demand point is dispatched to from i-th of resource provisioning point, wherein
j∈N。
It includes taking binary system or real number to resource individual that N number of species resource individual in population S, which carries out coding,
It is encoded.
At least can achieve it is following the utility model has the advantages that
Scheduling of resource optimization method provided in an embodiment of the present invention based on the global artificial bee colony algorithm of intersection, is firstly introduced into
Nectar source, it represents the various possible solutions in solution space, and and function value is related, measures nectar source with fitness function value, is re-introduced into
Three kinds of bees: gathering honey bee, observation bee, search bee, gathering honey bee link together with specific nectar source, and gathering honey bee passes through swing and its
Its honeybee sharing information, observation bee etc. stay in dancing area and are made a choice by sharing the information of gathering honey bee to food source, search bee
Effect be one new position of random search.Compared with basic artificial bee colony algorithm, CGABC algorithm is by introducing intersecting machine
System overcomes the defect that basic artificial bee colony algorithm is negligent of exploitation, enhances algorithm directionality, substantially increases artificial bee colony calculation
Development ability of the method near optimal solution.At the same time, coefficient cr is introduced for the exploring ability and development ability shadow of algorithm
Sound is larger, can obtain satisfactory solution by adjusting the value of cr for particular problem, enhance algorithm for various optimization problems
Adaptability.
Finally, it should be noted that the foregoing is only a preferred embodiment of the present invention, it is not intended to restrict the invention,
Although the present invention is described in detail referring to the foregoing embodiments, for those skilled in the art, still may be used
To modify the technical solutions described in the foregoing embodiments or equivalent replacement of some of the technical features.
All within the spirits and principles of the present invention, any modification, equivalent replacement, improvement and so on should be included in of the invention
Within protection scope.
Claims (10)
1. a kind of based on the scheduling of resource optimization method for intersecting global artificial bee colony algorithm, which is characterized in that specifically include that
Step S1: initialization honeybee populations parameter；
Step S2: gathering honey bee carries out neighborhood search in the search incipient stage, calculates initial fitness value fitness (xi), and record
Global optimum GlobalValue and global optimum solution vector GlobalMin；
Step S3: binomial is intersected in conjunction with artificial bee colony algorithm；
Step S4: calculating observation bee and follow probability, and observation bee is converted into gathering honey bee and carries out neighborhood search, and carries out crossover operation,
New nectar source is selected according to greedy criterion, and retains global optimum；
Step S5: if it is more than neighborhood maximum search number limit that gathering honey bee, observation bee, which search number (nectar source stop), still do not have
There is the nectar source for finding higher fitness, then abandons the nectar source, while the role of honeybee is converted into and is detectd by gathering honey bee or observation bee
Bee is examined, and a new nectar source is randomly generated；
Step S6: the optimal nectar source that current all honeybees are found is recorded, and skips to step S2, until meeting maximum number of iterations
MaxCycle is less than output global optimum position when optimizing error.
2. it is according to claim 1 a kind of based on the scheduling of resource optimization method for intersecting global artificial bee colony algorithm, it is special
Sign is, the scheduling of resource optimization method further include:
The determination formula of initial time, population position is as follows:
In formula:For the upper bound of search space；For the lower bound of search space.
3. it is according to claim 1 a kind of based on the scheduling of resource optimization method for intersecting global artificial bee colony algorithm, it is special
Sign is that honeybee populations parameter described in step S1 includes:
(gathering honey bee and the number for observing bee are honeybee populations scale S), maximum number of iterations maxCycle, neighborhood most wantonly search for
Rope number limit andThe feasible solution X of a D dimensioni=(xi1,xi2,…,xiD)T, i=1,2 ..., S, i.e. nectar source；
Nectar source and gathering honey bee correspond, i.e. nectar source number is
N number of species resource individual in population S is encoded, all resource individuals form a population.
4. it is according to claim 3 a kind of based on the scheduling of resource optimization method for intersecting global artificial bee colony algorithm, it is special
Sign is that N number of species resource individual in population S carries out coding and includes:
N number of species resource individual in population S is encoded, each individual is indicated to be dispatched to from i-th of resource provisioning point and be needed
Seek jth kind resource quantity a little, wherein j ∈ N.
5. it is according to claim 3 a kind of based on the scheduling of resource optimization method for intersecting global artificial bee colony algorithm, it is special
Sign is that N number of species resource individual in population S encodes, further includes:
Binary system or real number is taken to encode resource individual.
6. it is according to claim 1 a kind of based on the scheduling of resource optimization method for intersecting global artificial bee colony algorithm, it is special
Sign is that neighborhood search described in step S2 specifically includes:
Neighborhood search formula is as follows:
In formula: the value of β is 0~1.5；Random number between α ∈ [- 1,1].
7. it is according to claim 1 a kind of based on the scheduling of resource optimization method for intersecting global artificial bee colony algorithm, it is special
Sign is that fitness value described in step S2 specifically includes:
Wherein, fitness value calculation formula is as follows:
In formula: f (xi) it is function to be optimized；fitness(xi) be function fitness value.
8. it is according to claim 1 a kind of based on the scheduling of resource optimization method for intersecting global artificial bee colony algorithm, it is special
Sign is that the intersection of binomial described in step S3 specifically includes:
Binomial is intersected, equally distributed random value rand between one 0~1 is generated to each component, if rand < cr
(crossover operator) then receives target component, otherwise retains the respective components of current individual；
The optimal solution of solution and iteration generation after neighborhood search is carried out crossover operation by gathering honey bee, calculates new fitness value (honey
The nectar amount in source), if new nectar source nectar amount is better than green molasses source, honeybee replaces green molasses source by greedy criterion, with new nectar source, otherwise
It is constant.
9. it is according to claim 1 a kind of based on the scheduling of resource optimization method for intersecting global artificial bee colony algorithm, it is special
Sign is that crossover operation described in step S3 specifically includes:
Crossover operation formula is as follows:
In formula: the general value of crossover operator cr is 0.3~0.6；The value of β is 0~1.5.
10. it is according to claim 1 a kind of based on the scheduling of resource optimization method for intersecting global artificial bee colony algorithm, it is special
Sign is, follows probability to specifically include described in step S4:
Follow probability calculation formula as follows:
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201810768826.0A CN109272137A (en) | 2018-07-13 | 2018-07-13 | A kind of scheduling of resource optimization method based on the global artificial bee colony algorithm of intersection |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201810768826.0A CN109272137A (en) | 2018-07-13 | 2018-07-13 | A kind of scheduling of resource optimization method based on the global artificial bee colony algorithm of intersection |
Publications (1)
| Publication Number | Publication Date |
|---|---|
| CN109272137A true CN109272137A (en) | 2019-01-25 |
Family
ID=65153060
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN201810768826.0A Pending CN109272137A (en) | 2018-07-13 | 2018-07-13 | A kind of scheduling of resource optimization method based on the global artificial bee colony algorithm of intersection |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN109272137A (en) |
Cited By (12)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN110013658A (en) * | 2019-04-30 | 2019-07-16 | 西南大学 | A kind of dedicated tennis of tennis training pushes service control system and method |
| CN110020710A (en) * | 2019-03-08 | 2019-07-16 | 华南理工大学 | A kind of beam direction and weight Multipurpose Optimal Method based on artificial bee colony algorithm |
| CN110083195A (en) * | 2019-03-29 | 2019-08-02 | 广东工业大学 | A kind of Poewr control method based on the wave-power device for improving ant colony algorithm |
| CN110598946A (en) * | 2019-09-20 | 2019-12-20 | 浙江树人学院（浙江树人大学） | Flood prevention material rescue distribution method based on non-dominated artificial bee colony |
| CN111325306A (en) * | 2020-02-05 | 2020-06-23 | 河北工程大学 | ABC-PSO hybrid algorithm-based water supply pump set scheduling method |
| CN111674795A (en) * | 2020-05-27 | 2020-09-18 | 浙江工业大学 | Task scheduling method of cross-layer and cross-roadway shuttle storage system |
| CN112202538A (en) * | 2020-09-21 | 2021-01-08 | 齐齐哈尔大学 | Artificial bee colony OFDMA resource allocation method for guaranteeing fairness threshold value |
| CN112698637A (en) * | 2021-01-13 | 2021-04-23 | 广东轻工职业技术学院 | Cooperative resource scheduling algorithm for multi-task bee colony |
| CN113365282A (en) * | 2021-06-22 | 2021-09-07 | 成都信息工程大学 | WSN obstacle area covering deployment method adopting artificial bee colony algorithm of problem features |
| CN113537439A (en) * | 2020-04-17 | 2021-10-22 | 中国石油化工股份有限公司 | Improved artificial bee colony optimization algorithm |
| CN114465216A (en) * | 2022-01-17 | 2022-05-10 | 沈阳工程学院 | Active power distribution network fault recovery method based on MO-CGABC |
| CN116882304A (en) * | 2023-09-08 | 2023-10-13 | 山东工商学院 | Closed bus temperature fault monitoring method based on multi-gradient descent bee colony algorithm |
-
2018
- 2018-07-13 CN CN201810768826.0A patent/CN109272137A/en active Pending
Cited By (16)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN110020710A (en) * | 2019-03-08 | 2019-07-16 | 华南理工大学 | A kind of beam direction and weight Multipurpose Optimal Method based on artificial bee colony algorithm |
| CN110020710B (en) * | 2019-03-08 | 2023-10-27 | 华南理工大学 | Beam direction and weight multi-objective optimization method based on artificial bee colony algorithm |
| CN110083195B (en) * | 2019-03-29 | 2021-08-24 | 广东工业大学 | Power control method of wave power generation device based on improved bee colony algorithm |
| CN110083195A (en) * | 2019-03-29 | 2019-08-02 | 广东工业大学 | A kind of Poewr control method based on the wave-power device for improving ant colony algorithm |
| CN110013658A (en) * | 2019-04-30 | 2019-07-16 | 西南大学 | A kind of dedicated tennis of tennis training pushes service control system and method |
| CN110598946A (en) * | 2019-09-20 | 2019-12-20 | 浙江树人学院（浙江树人大学） | Flood prevention material rescue distribution method based on non-dominated artificial bee colony |
| CN111325306A (en) * | 2020-02-05 | 2020-06-23 | 河北工程大学 | ABC-PSO hybrid algorithm-based water supply pump set scheduling method |
| CN113537439A (en) * | 2020-04-17 | 2021-10-22 | 中国石油化工股份有限公司 | Improved artificial bee colony optimization algorithm |
| CN111674795A (en) * | 2020-05-27 | 2020-09-18 | 浙江工业大学 | Task scheduling method of cross-layer and cross-roadway shuttle storage system |
| CN112202538A (en) * | 2020-09-21 | 2021-01-08 | 齐齐哈尔大学 | Artificial bee colony OFDMA resource allocation method for guaranteeing fairness threshold value |
| CN112202538B (en) * | 2020-09-21 | 2023-08-22 | 齐齐哈尔大学 | Artificial bee colony OFDMA resource allocation method for guaranteeing fairness threshold |
| CN112698637A (en) * | 2021-01-13 | 2021-04-23 | 广东轻工职业技术学院 | Cooperative resource scheduling algorithm for multi-task bee colony |
| CN113365282A (en) * | 2021-06-22 | 2021-09-07 | 成都信息工程大学 | WSN obstacle area covering deployment method adopting artificial bee colony algorithm of problem features |
| CN114465216A (en) * | 2022-01-17 | 2022-05-10 | 沈阳工程学院 | Active power distribution network fault recovery method based on MO-CGABC |
| CN116882304A (en) * | 2023-09-08 | 2023-10-13 | 山东工商学院 | Closed bus temperature fault monitoring method based on multi-gradient descent bee colony algorithm |
| CN116882304B (en) * | 2023-09-08 | 2023-12-05 | 山东工商学院 | Closed bus temperature fault monitoring method based on multi-gradient descent bee colony algorithm |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN109272137A (en) | A kind of scheduling of resource optimization method based on the global artificial bee colony algorithm of intersection | |
| CN106251001A (en) | A kind of based on the photovoltaic power Forecasting Methodology improving fuzzy clustering algorithm | |
| CN108242807B (en) | A kind of reconstruction method of power distribution network containing photovoltaic considering multidimensional security constraint | |
| CN105447510B (en) | Fluctuating wind speed prediction technique based on artificial bee colony optimization LSSVM | |
| CN106484512A (en) | The dispatching method of computing unit | |
| CN113268931B (en) | Method and system for reconstructing photovoltaic array based on multi-target slime optimization algorithm | |
| Camp et al. | Using density surface models to estimate spatio‐temporal changes in population densities and trend | |
| CN109255513A (en) | A kind of power telecom network scene work order dispatching method | |
| Null et al. | Fish habitat optimization to prioritize river restoration decisions | |
| CN109888840A (en) | Scene-garbage power energy comprehensive utilization system method for optimizing scheduling and device | |
| CN111950297A (en) | Abnormal event oriented relation extraction method | |
| CN110163420A (en) | A kind of multi-objective ecological operation method and system based on decomposition cultural volution algorithm | |
| Jordaan et al. | Seasonal fission and fusion of killer whale, Orcinus orca, social structure at sub-Antarctic Marion Island | |
| CN109842614A (en) | Network inbreak detection method based on data mining | |
| Pfeiffer et al. | Cliff characteristics, neighbour requirements and breeding success of the colonial Cape Vulture Gyps coprotheres | |
| CN111832836A (en) | Power distribution network reconstruction method and system considering load power utilization characteristics | |
| Liang et al. | A virus‐evolutionary differentiated‐PSO approach for short‐term generation scheduling with uncertainties | |
| CN106406082A (en) | System control method and device, controller and control system | |
| CN113537439A (en) | Improved artificial bee colony optimization algorithm | |
| CN108805254A (en) | Parameter Optimization System for time series forecasting | |
| Chen et al. | A derivative-free hybrid optimization model for short-term operation of a multi-objective reservoir system under uncertainty | |
| CN115034608A (en) | Distribution network risk assessment method based on distribution network element and neural network | |
| Ya | AWNG-BP prediction technique study based on nonlinear combination–A case study of prediction of food supply chain in rural areas of Hubei Province, China | |
| CN105426910B (en) | A kind of adaptive clustering scheme based on improvement ABC algorithm and DE Mutation Strategy | |
| CN111756819A (en) | Internet of things resource scheduling optimization operation method based on improved immune heredity |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| RJ01 | Rejection of invention patent application after publication | ||
| RJ01 | Rejection of invention patent application after publication |
Application publication date: 20190125 |