CN109255501A - A kind of step library group's Long-term Optimal Dispatch algorithm based on multi-Agent artificial fish-swarm algorithm - Google Patents
A kind of step library group's Long-term Optimal Dispatch algorithm based on multi-Agent artificial fish-swarm algorithm Download PDFInfo
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Abstract
The invention belongs to hydroelectric generation and management and running field, it is related to a kind of step library group's Long-term Optimal Dispatch algorithm based on multi-Agent artificial fish-swarm algorithm, improves and the associated limitations such as take long time in the group's solution procedure of step library.The present invention realizes the completely new multi-Agent artificial fish-swarm algorithm MAAFSA of one kind to carry out model solution to step library group's Long-term Optimal Dispatch problem.MAAFSA combines the respective advantage of MAS and AFSA, by the Agent module for constructing different function, utilize the efficient collaboration, both and autonomous learning operation between Artificial Fish Agent, accelerate the convergence rate of AFSA, and the solution of Long-Term Optimal Operation of Cascade Hydropower Stations is realized from human-computer interaction angle, it is a kind of extremely innovative multi-Agent evolution algorithm.The beneficial effects of the present invention are greatly improving in the group's solution procedure of step library the associated limitations such as to take long time, a completely new solution throughway is provided for water power scheduling field.
Description
Technical field
The invention belongs to hydroelectric generations and management and running technical field, in particular to a kind of to be based on multi-Agent artificial fish-swarm
Step library group's Long-term Optimal Dispatch algorithm of algorithm.
Technical background
Over nearly more than 20 years, China's water power is constantly in the extensive operation stage, and water power is with its cleanliness without any pollution, adjustment process
Flexibly, the features such as response load is fast, is peak-frequency regulation power supply important in electric system.However, Hydropower Stations are excellent for a long time
Changing scheduling is a multivariable, high dimension, extensive multistage problem, and comprising extremely numerous and jumbled constraint condition, solution difficulty is very
Greatly.Currently need model and method for solving that research is suitable for Practical Project.This achievement introduces multiple agent (Agent) technology
In the solution procedure of step library group's Long-term Optimal Dispatch, a completely new solution throughway is provided for water power scheduling field.
Summary of the invention
The technical problem to be solved in the present invention is to provide a kind of steps for being based on multiple agent (Agent) artificial fish-swarm algorithm
Library group's Long-term Optimal Dispatch algorithm improves in the group's solution procedure of step library and the associated limitations such as takes long time.
Technical solution of the present invention are as follows:
A kind of step library group's Long-term Optimal Dispatch algorithm based on multi-Agent artificial fish-swarm algorithm, including core Agent mould
Block CA, it group Agent module GA, behavior Agent modules A A, evaluates Agent module EA and judges five moulds of Agent module J A
Block；Wherein AA includes four kinds of basic acts, and respectively foraging behavior Agent, PAA, bunch behavior Agent, SAA, behavior of knocking into the back
Agent, FAA and random movement behavior Agent, MAA；It is realized according to step (1)-(5) and solves the maximum purpose of generated energy.
(1) CA receives the group's Long-term Optimal Dispatch instruction of step library first, starts to initialize parameters.Feasible
N number of GA is generated within the scope of domain at random.Then by maximum moving step length Step, Artificial Fish absolute visual field Visual, the crowding factor
δ, the maximum parameters such as number Try-number of souning out pass to AA, and maximum number of iterations T is passed to JA；
(2) each GA carries out information exchange with four kinds of basic acts PAA, SAA, FAA and MAA respectively, to these four behaviors
Simulation execution is carried out, and the maximum value that respective behavior generates is transmitted to EA respectively；
(3) EA carries out evaluation comparison to four kinds of behaviors, selects process performing；
(4) Artificial Fish behavior is executed, oneself is updated, generates new GA；
(5) JA judges whether the number of iterations t meets t≤T or continuous several times optimal solution difference reaches required range.If full
Foot, then turn to step (2), continues iterative operation；Otherwise, terminate calculating process and export current optimal result；
In artificial fish-swarm algorithm (Artificial Fish Swarm Algorithm, AFSA), convergence exists
Determined to a certain extent by Artificial Fish foraging behavior, behavior of bunching then stabilizes convergence, knock into the back behavior and evaluation behavior it is then right
The global convergence and convergence rate of algorithm play certain impetus.On the whole, AFSA to problem Functional Quality require compared with
It is low, it is only necessary to which that evaluation update is carried out to the target function value of problem.Meanwhile also to have strong robustness, global optimizing ability strong by AFSA
The advantages that.However, the limitation of AFSA is that its convergence rate is relatively slow, in practical applications to meet Practical Project timeliness
It is required that often to improve the search efficiency of AFSA in conjunction with new technology.
And the main target of MAS is the small letter for being reduced to contact, cooperate, coordinate each other by complicated large scale system
Single system.MAS has the superior functions such as independence, autonomy and adaptability, is very suitable to solve the energy root such as reservoir dispatching
According to the large-scale optimization problem of space-time and function division.Compared with traditional AFSA, the multi-Agent artificial fish-swarm algorithm of MAS is combined
(Multi-Agent Artificial Fish Swarm Algorithm, MAAFSA) merges together multiple Agent, leads to
Cross communication, coordination and cooperation come combine solve the problems, such as single Agent it is helpless, than single Agent have widely appoint
Business field and higher efficiency.
The present invention compares the prior art and has the advantages that: the present invention is a kind of based on multi-Agent artificial fish-swarm algorithm
Step library group's Long-term Optimal Dispatch algorithm, on the basis of traditional AFSA, design realizes a kind of completely new multi-Agent Artificial Fish
Group's algorithm to carry out model solution to step library group's Long-term Optimal Dispatch problem.It is respective excellent that MAAFSA combines MAS and AFSA
Gesture, using the efficient collaboration, both and autonomous learning operation between Artificial Fish Agent, is added by constructing the Agent module of different function
The fast convergence rate of AFSA, and realize from human-computer interaction angle the solution of Long-Term Optimal Operation of Cascade Hydropower Stations.It compares existing
Technology, the present invention greatly improve in the group's solution procedure of step library and the associated limitations such as take long time.
Detailed description of the invention
Fig. 1 is the system assumption diagram of MAAFSA.
Fig. 2 is the solution flow chart of MAAFSA.
Fig. 3 (a) is the power station A water level process.
Fig. 3 (b) is A output of power station process.
Fig. 3 (c) is the power station B water level process.
Fig. 3 (d) is B output of power station process.
Fig. 3 (e) is the power station C water level process.
Fig. 3 (f) is C output of power station process.
Fig. 4 (a) is low flow year each output of power station load diagram.
Fig. 4 (b) is each output of power station load diagram of normal flow year.
Fig. 4 (c) is high flow year each output of power station load diagram.
Fig. 5 is different Typical Year lower step total power generation change procedures.
Specific embodiment
The present invention will be further described with reference to the accompanying drawings and examples.
The present invention is conducted a research using the most common generated energy maximum model in Long-Term Optimal Operation of Cascade Hydropower Stations, i.e., known
Day part section flow in the whole story water level and schedule periods of each reservoir in step hydropower station is sought water level of each power station in day part and is gone out
Power change procedure, to reach the maximum purpose of total power generation in schedule periods.
The objective function of the generated energy maximum model of Long-Term Optimal Operation of Cascade Hydropower Stations are as follows:
Pi,t=AiQi,tHi,t (2)
In formula: E is each power station total power generation of step in schedule periods, kWh；N is step hydropower station number；I is power station serial number, i
=1,2 ..., N；T is scheduling slot sum；T is period serial number, t=1,2 ..., T；Pi,tFor power station i averaging out in the t period
Power, kW；ΔtFor the hourage of period t, h；AiFor the power factor of power station i；Qi,tGenerating flow for power station i in period t, m3/
s；Hi,tProductive head for power station i in period t, m.
In order to guarantee the safety and stability of maximum generating watt and power station, the generated energy maximum norm of Long-Term Optimal Operation of Cascade Hydropower Stations
Type need to meet following constraint condition:
A. water balance constrains
Vi,t+1=Vi,t+3600×(qi,t-Qi,t-di,t)×Δt (3)
In formula: Vi,t+1And Vi,tRespectively storage capacity of the power station i in period t+1 and period t, m3；qi,t、Qi,t、di,tRespectively
Power station i is in the reservoir inflow of t period, generating flow, abandoning water flow, m3/s；ΔtFor t period corresponding hourage, h.
B. restriction of water level
In formula: Zi,tReservoir level for power station i in period t, m；Zi,tRespectively power station i is on the reservoir level of period t
Limit and lower limit, m.
C. whole story water level control constrains
In formula:For the initial water level of power station i, m；For the scheduling end of term water level of power station i, m.
D. generating flow constrains
In formula: Qi,tGenerating flow for power station i in period t, m3/s；Qi,tRespectively power generation of the power station i in period t
Flow rate upper limit and lower limit, m3/s。
E. storage outflow constrains
In formula: Qi,t、di,tRespectively power station i the t period generating flow, abandon water flow, m3/s；Oi,tIt is respectively electric
It stands storage outflow upper and lower bound of the i in period t, m3/s。
F. output of power station constrains
In formula: Pi,tAverage output for power station i in the t period, kW；Pi,tRespectively power station i is in the power output of period t
Limit and lower limit, kW.
G. stepped system units limits
In formula: Pi,tAverage output for power station i in the t period, kW；htPower output lower limit for stepped system in period t, kW.
MAAFSA algorithm is utilized in multi-Agent artificial fish-swarm algorithm model construction based on step generated energy maximum model,
Essence is the Agent module of multiple and different functions to be constructed from the visual angle of distributed computing, and then AFSA solution procedure is divided into
Several subproblems.According to the coordination of each Agent intermodule, cooperation and intelligent interaction hierarchical solving subproblem.MAAFSA is constructed
The Agent module of five kinds of different function, including core Agent (Core Agent, CA), group Agent (Group Agent,
GA), behavior Agent (Action Agent, AA), evaluation and judge Agent (Judge at Agent (Evaluate Agent, EA)
Agent,JA).Fig. 1 is the system assumption diagram of MAAFSA.Fig. 2 is the solution flow chart of MAAFSA.
The function of CA is to create the living environment of artificial fish-swarm Agent, is completed to Artificial Fish Agent scale N, Artificial Fish most
The setting of the parameters such as big visual field Visual, and initialize N number of GA.Each GA indicates Artificial Fish Agent, GA a state by step
Reservoir level X before each power station damiIt indicates.Wherein,D indicates Cascaded Hydro-power Stations
Power station number, i indicate GA serial number,Represent reservoir level before the dam in kth grade power station.
AA is mainly made of four kinds of basic acts, including foraging behavior Agent (Prey Action Agent, PAA), poly-
Group behavior Agent (Swarm Action Agent, SAA), the behavior Agent that knocks into the back (Follow Action Agent, FAA) and
Random movement behavior Agent (Move Action Agent, MAA).The function of AA be complete the looking for food of each GA, clustering, knock into the back and
Random movement behavior.
The task of EA is to evaluate the state of current each GA, then selects process performing according to optimal rules, and realize GA's
It updates.
The state evaluation of GA determines by the objective function of step generated energy maximum model, the shape of the more big then GA of target function value
State is then more excellent.
In formula: E is each power station total power generation of step in schedule periods, kWh；N is step hydropower station number；I is power station serial number, i
=1,2 ..., N；T is scheduling slot sum；T is period serial number, t=1,2 ..., T；Pi,tFor power station i averaging out in the t period
Power, kW；ΔtFor the hourage of period t, h.
JA is responsible for the stopping and output optimal result operation of iterative calculation.
Each behavior of Artificial Fish Agent is described as follows substantially:
A. foraging behavior
The state for initializing Artificial Fish Agent is Xi, its pass through within sweep of the eye short distance optimization technology find it is another
A state Xj, XjIt is determined by following formula:
In formula, Xmax=(x1,max,x2,max,…,xk,max,…,xd,max) indicate problem feasible solution upper limit value, xk,maxTable
Show the reservoir level upper limit of power station k；Xmin=(x1,min,x2,min,…,xk,min,…,xd,min) indicate problem feasible solution lower limit value,
xk,minIndicate the reservoir level lower limit of power station k；R is short distance search radius, r ∈ [0,1]；Rand (1-r, 1+r) indicates (1-r, 1+
R) random number between.
Determine XjAfterwards, adaptive value g (X is calculatedi) and g (Xj), if g (Xj)≥g(Xi), then XiTo XjDirection be moved to it is new
State completes foraging behavior
In formula,Respectively state of the Artificial Fish at current and new position；XjFor the Artificial Fish field range
Interior optimal companion；Step is moving step length；Rand () is the random number between 0~1.
Otherwise, another new state is found again by short distance optimization technology, judges whether to meet above-mentioned condition, attempted
After Try-number operation, if being still not carried out foraging behavior, random movement behavior is executed.
B. bunch behavior
Artificial Fish Agent executes behavior of bunching and mainly scrupulously abides by two principles: first is that making great efforts the centre bit towards companion in the visual field
Direction travelling is set, second is that preventing excessive congestion.Now Artificial Fish Agent behavior of bunching is described: setting certain Artificial Fish Agent shape
State is Xi, search for companion's number n in the visual fieldfWith the center X of companioncenter, wherein the definition of companion need to meet d in the visual fieldij
< Vaisul, wherein dij=| | Xi-Xj| |, indicate the distance between two Artificial Fish Agent.If meeting g (Xcenter)/nf≥δg
(Xi), illustrate that companion center does not have excessive congestion, then XiTo XcenterDirection is moved to new positionMove mode by
Formula (14) determines.If Artificial Fish Agent does not meet the principle for behavior of bunching, foraging behavior is executed.
In formula,Respectively state of the Artificial Fish at current and new position；XcenterFor the centre bit of companion
It sets；Step is moving step length；Rand () is the random number between 0~1.
C. it knocks into the back behavior
If Artificial Fish Agent state is Xi, optimal companion is X in the visual fieldjIf meeting g (Xj)/nf≥δg(Xi), then
Illustrate optimal companion not congestion nearby, XiTo XjThe mobile new position in directionCompletion is knocked into the back behavior.Move mode by
Formula (15) determines.If Artificial Fish Agent does not meet the behavior condition that knocks into the back, foraging behavior is executed:
In formula,Respectively state of the Artificial Fish at current and new position；XjFor the Artificial Fish field range
Interior optimal companion；Step is moving step length；Rand () is the random number between 0~1.
D. random movement behavior
Random movement behavior refers to Artificial Fish Agent XiOne step of random movement is to new state, move mode in the visual field
It is determined by following formula:
In formula,Respectively state of the Artificial Fish at current and new position；Visual Artificial Fish absolute visual field；
Step is moving step length；Rand () is the random number between 0~1.
According to above-mentioned thought, a kind of algorithm model and solution based on the maximum step library group Long-term Optimal Dispatch of generated energy
Method, (1)-(5) are achieved as steps described below:
(1) CA receives the group's Long-term Optimal Dispatch instruction of step library first, starts to initialize parameters.Feasible
N number of GA is generated within the scope of domain at random.Then by maximum moving step length Step, Artificial Fish absolute visual field Visual, the crowding factor
δ, the maximum parameters such as number Try-number of souning out pass to AA, and maximum number of iterations T is passed to JA；
(2) each GA carries out information exchange with four kinds of AA (PAA, SAA, FAA and MAA) respectively, carries out to these four behaviors
Simulation executes, and the maximum value that respective behavior generates is transmitted to EA respectively；
(3) EA carries out evaluation comparison to four kinds of behaviors, selects process performing；
(4) Artificial Fish behavior is executed, oneself is updated, generates new GA；
(5) JA judges whether the number of iterations t meets t≤T or continuous several times optimal solution difference reaches a certain range.If full
Foot, then turn to step (2), continues iterative operation；Otherwise, terminate calculating process and export current optimal result；
It is now studied using the power station A, B, C, D in a certain basin as example, this four power station total installations of generating capacity reach 3145MW.
Wherein the power station D belongs to daily regulated powerstaion, is usually only necessary to consider the power generation of its utilized head in the calculating of step Long-term Optimal Dispatch
Effect, Reservoir Operation effect are ignored.Power station sequence is followed successively by A, B, D, C.
As shown in Table 1, when being solved using MAAFSA, the generated energy in low flow year, normal flow year and high flow year entire step is distinguished
Reach 89.47 hundred million kWh, 112.49 hundred million kWh and 129.81 hundred million kWh, is more conform with the actual schedule result of its step hydropower station.It says
When the bright Optimal Operation of Cascade Reservoirs for solving complexity using MAAFSA, the global optimum of problem can be found under certain condition
Solution solves quality and meets Practical Project scheduling requirement.In terms of solving the time, by taking normal flow year as an example, MAAFSA, which is solved, to be needed greatly
About 29.3s, and traditional AFSA is solved and is probably needed 34.5s or so.Illustrate in terms of computational efficiency, the calculating effect of MAAFSA
It can satisfy practical engineering application demand, also illustrate between each Functions Agent module through cooperation cooperation, can play very
Positive effect.Therefore, solving different Typical Year condition lower step library group Long-term Optimal Dispatchs using MAAFSA can obtain
It is ideal as a result, it is possible to which the Long-term Optimal Dispatch for other Cascade Hydropower Stations on River Basin provides calculating reference.
Fig. 3 (a)~Fig. 3 (f) is each output of power station process.Fig. 4 (a)~Fig. 4 (c) is low flow year, normal flow year and high flow year
Each output of power station load diagram under the conditions of three kinds of Typical Years.Fig. 5 is different Typical Year lower step total power generation change procedures.For dragon
For the A in head power station, for step overall efficiency, not generating electricity substantially in flood season, the main task of reservoir is to participate in compensation adjustment,
Flood season gradually raising of water level so as to abundant water storage.Withered phase water level, which slowly disappears, to be fallen, and main task is can be to while power generation
Lower reservoir moisturizing, to increase the generated energy of entire Cascaded Hydro-power Stations.The power station B maintains always after flood season stores rapidly completely
It generates electricity under high water head state.As the D of daily regulated hydroplant, water level is held essentially constant, and comes that how much water generates how many electricity.C electricity
Installed capacity of standing is larger, has reached 1250MW.Therefore within entire dispatching cycle, range of stage is smaller, maintains height as far as possible
Head is run to reduce water consume, increment life insurance.
Optimum results compare under the conditions of the different Typical Years of table 1
Claims (2)
1. a kind of step library group's Long-term Optimal Dispatch algorithm based on multi-Agent artificial fish-swarm algorithm, including core Agent module
CA, it group Agent module GA, behavior Agent modules A A, evaluates Agent module EA and judges five modules of Agent module J A；
Wherein AA includes four kinds of basic acts, and respectively foraging behavior Agent, PAA, bunch behavior Agent, SAA, behavior of knocking into the back
Agent, FAA and random movement behavior Agent, MAA；It is characterized in that, steps are as follows:
(1) CA receives the group's Long-term Optimal Dispatch instruction of step library first, starts to initialize parameters；In feasible zone model
It encloses and interior generates N number of GA at random；Then by maximum moving step length Step, Artificial Fish absolute visual field Visual, crowding factor delta and most
The big number Try-number that sounds out passes to AA, and maximum number of iterations T is passed to JA；
(2) each GA carries out information exchange with four kinds of basic acts PAA, SAA, FAA and MAA respectively, carries out mould to four kinds of behaviors
It is quasi- to execute, and the maximum value that respective behavior generates is transmitted to EA respectively；
(3) EA carries out evaluation comparison to four kinds of behaviors, selects process performing；
A. foraging behavior
In formula,Respectively state of the Artificial Fish at current and new position；XjIt is within the vision for the Artificial Fish
Optimal companion；Step is moving step length；Rand () is the random number between 0~1；
Calculate adaptive value g (Xi) and g (Xj), if g (Xj)≥g(Xi), then XiTo XjDirection be moved to new state, complete to look for food
Otherwise behavior finds another new state again by short distance optimization technology, judge whether to meet above-mentioned condition, attempts
After Try-number operation, if being still not carried out foraging behavior, random movement behavior is executed；
B. bunch behavior
In formula,Respectively state of the Artificial Fish at current and new position；XcenterFor the center of companion；
Step is moving step length；Rand () is the random number between 0~1；
If Artificial Fish Agent state is Xi, search for companion's number n in the visual fieldfWith the center X of companioncenter, wherein in the visual field
The definition of companion need to meet dij< Vaisul, wherein dij=| | Xi-Xj| |, indicate the distance between two Artificial Fish Agent；If
Meet g (Xcenter)/nf≥δg(Xi), illustrate that companion center does not have excessive congestion, then XiTo XcenterDirection is moved to new
PositionIf Artificial Fish Agent does not meet the principle for behavior of bunching, foraging behavior is executed；
C. it knocks into the back behavior
In formula,Respectively state of the Artificial Fish at current and new position；XjIt is within the vision for the Artificial Fish
Optimal companion；Step is moving step length；Rand () is the random number between 0~1；
If Artificial Fish Agent state is Xi, optimal companion is X in the visual fieldjIf meeting g (Xj)/nf≥δg(Xi), then illustrate
Optimal companion not congestion nearby, XiTo XjThe mobile new position in directionCompletion is knocked into the back behavior；If Artificial Fish Agent
The behavior condition that knocks into the back is not met, then executes foraging behavior；
D. random movement behavior
In formula,Respectively state of the Artificial Fish at current and new position；Visual Artificial Fish absolute visual field；Step
For moving step length；Rand () is the random number between 0~1；
(4) Artificial Fish behavior is executed, oneself is updated, generates new GA；
(5) JA judges whether the number of iterations t meets t≤T or continuous several times optimal solution difference reaches required range；If it is satisfied,
It then turns to step (2), continues iterative operation；Otherwise, terminate calculating process and export current optimal result.
2. step library group's Long-term Optimal Dispatch algorithm according to claim 1 based on multi-Agent artificial fish-swarm algorithm,
It is characterized in that, target, the objective function of the generated energy maximum model of Long-Term Optimal Operation of Cascade Hydropower Stations is up to generated energy are as follows:
Pi,t=AiQi,tHi,t
In formula: E is each power station total power generation of step in schedule periods, kWh；N is step hydropower station number；I be power station serial number, i=1,
2 ..., N；T is scheduling slot sum；T is period serial number, t=1,2 ..., T；Pi,tAverage output for power station i in the t period,
kW；ΔtFor the hourage of period t, h；AiFor the power factor of power station i；Qi,tGenerating flow for power station i in period t, m3/s；
Hi,tProductive head for power station i in period t, m；
The constraint that the generated energy maximum model of Long-Term Optimal Operation of Cascade Hydropower Stations needs to meet is as follows:
A. water balance constrains
Vi,t+1=Vi,t+3600×(qi,t-Qi,t-di,t)×Δt
In formula: Vi,t+1And Vi,tRespectively storage capacity of the power station i in period t+1 and period t, m3；qi,t、Qi,t、di,tRespectively power station i
In the reservoir inflow of t period, generating flow, abandon water flow, m3/s；ΔtFor t period corresponding hourage, h；
B. restriction of water level
In formula: Zi,tReservoir level for power station i in period t, m； Z i,tRespectively power station i period t the reservoir level upper limit and
Lower limit, m；
C. whole story water level control constrains
In formula:For the initial water level of power station i, m；For the scheduling end of term water level of power station i, m；
D. generating flow constrains
In formula: Qi,tGenerating flow for power station i in period t, m3/s； Q i,tRespectively generating flow of the power station i in period t
Upper and lower bound, m3/s；
E. storage outflow constrains
In formula: Qi,t、di,tRespectively power station i the t period generating flow, abandon water flow, m3/s； O i,tRespectively power station i
In the storage outflow upper and lower bound of period t, m3/s；
F. output of power station constrains
In formula: Pi,tAverage output for power station i in the t period, kW； P i,tRespectively power station i period t the power output upper limit and
Lower limit, kW；
G. stepped system units limits
In formula: Pi,tAverage output for power station i in the t period, kW；htPower output lower limit for stepped system in period t, kW.
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Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN103532133A (en) * | 2013-09-29 | 2014-01-22 | 天津理工大学 | Load transfer system and method used in case of failure of 35kV power distribution network on basis of MAS (Multi-Agents) |
| CN105676890A (en) * | 2016-01-22 | 2016-06-15 | 长江水利委员会长江科学院 | Dynamic operation water level control method for 3D or higher cascaded reservoirs in flood season |
| CN106527373A (en) * | 2016-12-05 | 2017-03-22 | 中国科学院自动化研究所 | Workshop automatic scheduling system and method based on mutli-intelligent agent |
| CN107055116A (en) * | 2017-06-07 | 2017-08-18 | 大连大学 | Automated container dock coordinated operation system and unloading method for searching shortest route |
-
2018
- 2018-10-29 CN CN201811268417.0A patent/CN109255501B/en active Active
Patent Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN103532133A (en) * | 2013-09-29 | 2014-01-22 | 天津理工大学 | Load transfer system and method used in case of failure of 35kV power distribution network on basis of MAS (Multi-Agents) |
| CN105676890A (en) * | 2016-01-22 | 2016-06-15 | 长江水利委员会长江科学院 | Dynamic operation water level control method for 3D or higher cascaded reservoirs in flood season |
| CN106527373A (en) * | 2016-12-05 | 2017-03-22 | 中国科学院自动化研究所 | Workshop automatic scheduling system and method based on mutli-intelligent agent |
| CN107055116A (en) * | 2017-06-07 | 2017-08-18 | 大连大学 | Automated container dock coordinated operation system and unloading method for searching shortest route |
Non-Patent Citations (3)
| Title |
|---|
| LIANGUO WANG等: "A Multiagent Artificial Fish Swarm Algorithm", 《2008 7TH WORLD CONGRESS ON INTELLIGENT CONTROL AND AUTOMATION》 * |
| 彭勇等: "基于改进人工鱼群算法的梯级水库群优化调度", 《系统工程理论与实践》 * |
| 马小建: "基于MAS的电力系统二级电压控制研究", 《中国优秀硕士学位论文全文数据库 工程科技II辑》 * |
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