CN108133257A - A kind of pumping plant optimization method based on artificial fish-swarm algorithm - Google Patents
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Abstract
The invention belongs to the technical fields of water conservancy information, and in particular to a kind of pumping plant optimization method based on artificial fish-swarm algorithm；The technical issues of solution is：There is provided it is a kind of can quickly and effectively, accurately carry out Optimal Operation of Pumping Stations, to promote the pumping plant optimization method based on artificial fish-swarm algorithm of high efficient utilization of water resources；The technical solution used for：S101, initialization systematic parameter；S102, the shoal of fish is initialized in variable allowed band, the target function value of each Artificial Fish is obtained；S103, iteration optimizing, bunch respectively to Artificial Fish execution behavior and behavior of knocking into the back, compare the functional value size of two kinds of behaviors, the larger behavior of Selection of Function value is performed；S104, judge whether iterations reach maximum allowable iterations, if so, stopping interative computation, export maximal function value, otherwise continue iteration；The present invention is suitable for Water Resources Domain.
Description
Technical field
The invention belongs to the technical fields of water conservancy information, and in particular to a kind of pumping plant optimization side based on artificial fish-swarm algorithm
Method.
Background technology
The combined dispatching Study on Problems of China's water conservancy system pumping plant is always to strengthen high efficient utilization of water resources and water environment guarantor
The critical issue of shield.Such as the large hydraulic engineering South-to-North Water Transfer Project of China all has been brought to completion and puts into effect, eastern line
Engineering is related to the scheduling problem of multi stage pumping station.At this stage south water to north east line cascade pumping station group lack it is scientific and effective control with
Management, relies primarily on the experience of people and simple technical indicator carrys out the operation of coarse manipulation pumping plant, causes accurately to exist
While completing water transfer task, pumping plant is made to be in the operating status of Economic optimization, causes the waste of resource.Therefore about pumping plant
Optimizing research just become particularly significant.
At present, the research about pumping plant optimization has had some achievements, such as based on genetic algorithm, ant group algorithm, particle
The Optimal Operation of Pumping Stations of group's algorithm etc..But artificial fish-swarm algorithm as a kind of emerging bionic intelligence optimization algorithm in pumping plant
Optimized Operation in terms of not yet apply.
Artificial fish-swarm algorithm AFSA is a kind of new random search intelligent optimization algorithm based on simulation fish school behavior.It is led
The behavioral characteristics such as look for food, bunch, knock into the back of the shoal of fish in life have been imitated, by constructing several single Artificial Fishs, have made it
Using the highest position of search of food concentration as target, the optimizing ability of algorithm is realized.The algorithm have faster convergence rate and
It does not need to accurately describe problem.Therefore, the range that has a wide range of applications and prospect.But it with its development and answers
With, it has been found that its several shortcoming：The blindness in algorithm search later stage is larger, causes algorithm optimizing due to determining the visual field and step-length
Journey becomes complicated and precision is not high enough.
Invention content
The present invention overcomes the shortcomings of the prior art, and technical problem to be solved is：There is provided it is a kind of can quickly and effectively,
Optimal Operation of Pumping Stations is accurately carried out, to promote the pumping plant optimization method based on artificial fish-swarm algorithm of high efficient utilization of water resources.
In order to solve the above-mentioned technical problem, the technical solution adopted by the present invention is：A kind of pump based on artificial fish-swarm algorithm
It stands optimization method, includes the following steps：S101, initialization systematic parameter, the parameter include：The field range of fish-swarm algorithm
Visual, moving step length Step, crowding factor delta, fish way N etc.；S102, the shoal of fish is initialized in variable allowed band
{X1,X2,…,XN, and by formulaThe target function value of each Artificial Fish is obtained；Wherein：
prFor electricity price, (unit is：Thousand yuan/kWh)；ρ be water body density, ρ=1000kg/m3；G is acceleration of gravity, g=9.80665m/
s2；QiFor the flow that draws water of i-th pump group, (unit is：m3/s)；For the net lift of i-th pump group, (unit is：m)；ηstiFor
The station efficiency of i-th pump group；S103, iteration optimizing, bunch respectively to Artificial Fish execution behavior and behavior of knocking into the back, and compare two kinds
The size of functional value obtained by behavior, the larger behavior of final Selection of Function value are performed, and record optimal iterative value；
S104, judge whether iterations reach maximum allowable iterations, if so, stopping interative computation, export maximal function value,
Otherwise it goes to step S103 and continues iteration.
Preferably, in step S103, the behavior of bunching includes the following steps：State is XiArtificial Fish search neighborhood in
Artificial Fish number nf, and find out its center Xc, if(wherein：YiFor XiCorresponding functional value, YcFor XcIt is right
The functional value answered), show that there is more food at the shoal of fish center and crowding is little, then the Artificial Fish is to center direction
Shifting moves a step, and otherwise performs foraging behavior.
Preferably, in step S103, the behavior of knocking into the back includes the following steps：State is XiArtificial Fish search neighborhood in
Respective value Y in all Artificial FishsjMaximum Artificial Fish XjIf(wherein：YiFor XiCorresponding functional value, nfFor in neighborhood
Artificial Fish number), show Artificial Fish XjHave higher food concentration and its around it is less crowded, just select towards the direction
It takes a step forward, otherwise performs foraging behavior.
Preferably, the foraging behavior includes the following steps：The current state of known Artificial Fish is Xi, corresponding function
It is worth for Yi, a state X is randomly being found within sweep of the eyej, and judge the functional value Y corresponding to the statejWhether it is better than
Current function value Yi, if so, Artificial Fish takes a step forward to the direction；Otherwise, NextState is found at random again；If it tastes repeatedly
Try try_number (wherein：Try_number is maximum attempts) it is secondary after be still unsatisfactory for advance condition, then random movement one
Step.
Preferably, Artificial Fish is when performing the foraging behavior, if making repeated attemptsIt is still unsatisfactory for after secondary
Advance condition directly takes the direction of wherein optimal value point to move.
Preferably, if when Artificial Fish be unsatisfactory for bunching behavior, knock into the back behavior and foraging behavior when, just at it within sweep of the eye
A state is randomly choosed, is then moved to the direction.
The present invention has the advantages that compared with prior art：
1st, the present invention innovatively applies to fish-swarm algorithm in pumping plant optimization, and first systematic parameter is initialized, is asked
Go out the target function value of each Artificial Fish, then bunch respectively to Artificial Fish execution behavior and behavior of knocking into the back, and compare two kinds of behaviors
The size of the functional value of gained, the larger behavior of final Selection of Function value are performed；With traditional intelligent optimization algorithm as lost
Propagation algorithm compares, and the present invention allows its visual field and step-length into Mobile state by being improved to traditional artificial fish-swarm algorithm
Variation, in the final stage of algorithm, step-length and the visual field taper into, it are made more accurately to search out optimal value, and then can
Quickly and effectively, the Optimized Operation of pumping plant is accurately carried out, promotes high efficient utilization of water resources.
2nd, in the present invention, Artificial Fish is when performing foraging behavior, if making repeated attemptsIt is still unsatisfactory for after secondary
Advance condition directly takes the direction of wherein optimal value point to move, not only increases the ability of looking for food of Artificial Fish in this way, it is thus also avoided that
Foraging behavior in traditional fish-swarm algorithm occupies drawback caused by vast resources in system.
Description of the drawings
The present invention will be further described in detail below in conjunction with the accompanying drawings；
Fig. 1 is a kind of flow signal for pumping plant optimization method based on artificial fish-swarm algorithm that the embodiment of the present invention one provides
Figure；
Fig. 2 is artificial fish-swarm algorithm that the embodiment of the present invention one provides to be respectively adopted and traditional genetic algorithm is come to pumping plant
The optimization process figure optimized.
Specific embodiment
Purpose, technical scheme and advantage to make the embodiment of the present invention are clearer, below in conjunction with the embodiment of the present invention
In attached drawing, the technical solution in the embodiment of the present invention is clearly and completely described, it is clear that described embodiment is
The part of the embodiment of the present invention, instead of all the embodiments；Based on the embodiments of the present invention, ordinary skill people
Member's all other embodiments obtained without creative efforts, shall fall within the protection scope of the present invention.
The fish lived in waters has the characteristics of apparent in this way：They often occur in groups, even if some fishes fall list, but
Be that it also can be quickly by trailing, looking for food, other shoals of fish are found in behavior of bunching, and the bigger place of the shoal of fish is exactly that food is dense
The higher place of degree.Artificial fish-swarm algorithm is exactly these features according to the shoal of fish, constructs Artificial Fish to imitate the various lifes of fish
Optimizing is realized in living behavior.The behavior of the shoal of fish has following several：Foraging behavior, behavior of bunching, knock into the back behavior and random behavior.
In the present invention, the state vector X=(x of Artificial Fish1,x2,...,xn) represent, wherein：xi(i=1 ..., n) table
Show and be intended to optimizing variable；Artificial Fish present position food concentration is：Y=f (X), Y are target function value；Artificial Fish individual
Between distance be：di,j=| | Xi-Xj||；The perceived distance (i.e. field range) of Artificial Fish is：Visual；The maximum of Artificial Fish moves
Dynamic step-length is：Step；The crowding factor is：δ；
Fig. 1 is a kind of flow signal for pumping plant optimization method based on artificial fish-swarm algorithm that the embodiment of the present invention one provides
Figure, as shown in Figure 1, a kind of pumping plant optimization method based on artificial fish-swarm algorithm, includes the following steps：
S101, initialization systematic parameter, the parameter include：The field range Visual of fish-swarm algorithm, moving step length
Step, crowding factor delta, fish way N etc..
The visual field and step-length suffer from the complexity of convergence speed of the algorithm and precision and system important influence.Visual field model
It encloses larger, is conducive to Artificial Fish and realizes global optimizing, but consume more resources；Field range is smaller, then Artificial Fish
Global optimizing ability be restricted.Step-length is larger, and algorithm can realize quick convergence, but precision reduces；Step-length compared with
Hour, situation is exactly the opposite.
Visual=Visual × α
Step=Step × α
Wherein, gen represents current iterations, and MAXGEN represents the total degree of iteration.
S102, the shoal of fish { X is initialized in variable allowed band1,X2,…,XN, and by formulaThe target function value of each Artificial Fish is obtained.
Wherein：prFor electricity price, (unit is：Thousand yuan/kWh)；ρ be water body density, ρ=1000kg/m3；G is acceleration of gravity,
G=9.80665m/s2；QiFor the flow that draws water of i-th pump group, (unit is：m3/s)；Net lift for i-th pump group is (single
Position is：m)；ηstiStation efficiency for i-th pump group.
S103, iteration optimizing, bunch respectively to Artificial Fish execution behavior and behavior of knocking into the back, and compare obtained by two kinds of behaviors
The size of functional value, the larger behavior of final Selection of Function value are performed, and record optimal iterative value；
S104, judge whether iterations reach maximum allowable iterations, if so, stopping interative computation, output is most
Otherwise big functional value goes to step S103 and continues iteration.
Compared with traditional intelligent optimization algorithm such as genetic algorithm, the present invention is by carrying out traditional artificial fish-swarm algorithm
It improves, its visual field and step-length is allow to carry out dynamic change, in the final stage of algorithm, step-length and the visual field taper into, and make it
Optimal value more can be accurately searched out, and then can quickly and effectively, accurately carry out the Optimized Operation of pumping plant, promotes water resource
Efficiently utilize.
Specifically, in step S103, the behavior of bunching includes the following steps：
State is XiArtificial Fish search neighborhood in Artificial Fish number nf, and find out its center XcIf(wherein：YiFor XiCorresponding functional value, YcFor XcCorresponding functional value), show that there is more food at the shoal of fish center
Object and crowding is little, then the Artificial Fish move and move a step to center direction, otherwise perform foraging behavior.
Specifically, in step S103, the behavior of knocking into the back includes the following steps：
State is XiArtificial Fish search neighborhood in respective value Y in all Artificial FishsjMaximum Artificial Fish XjIf
(wherein：YiFor XiCorresponding functional value, nfNumber for the Artificial Fish in neighborhood), show Artificial Fish XjThere is higher food
Concentration and its around it is less crowded, just select to take a step forward towards the direction, otherwise perform foraging behavior.
Specifically, the foraging behavior includes the following steps：
The current state of known Artificial Fish is Xi, corresponding functional value is Yi, one is randomly being found within sweep of the eye
A state Xj, and judge the functional value Y corresponding to the statejWhether current function value Y is better thani, if so, Artificial Fish is to the party
Forward further；Otherwise, NextState is found at random again；If make repeated attempts try_number (wherein：Try_number is most
Big number of attempt) it is secondary after be still unsatisfactory for advance condition, then one step of random movement.
More specifically, Artificial Fish is when performing the foraging behavior, if making repeated attemptsIt is still discontented after secondary
Sufficient advance condition directly takes the direction of wherein optimal value point to move.
When operation is looked for food in execution, the random point found in the visual field is the discovery that than oneself current location basic fish-swarm algorithm
More preferably point is then moved to the direction, and the number found is often below the value of Try_number.Therefore it is currently searched out
Point is frequently not the optimum point in the visual field, has thus slackened the ability of looking for food of Artificial Fish significantly, while occupy in system again
Vast resources.For these features, it is contemplated that Artificial Fish is made directly to be taken wherein optimal after half Try_number is performed
The direction movement of value point.The ability of looking for food of Artificial Fish is not only increased in this way, it is thus also avoided that the row of looking for food in traditional fish-swarm algorithm
To occupy drawback caused by vast resources in system.
Specifically, if when Artificial Fish be unsatisfactory for bunching behavior, knock into the back behavior and foraging behavior when, just at it within sweep of the eye
A state is randomly choosed, is then moved to the direction.
Fig. 2 is artificial fish-swarm algorithm that the embodiment of the present invention one provides to be respectively adopted and traditional genetic algorithm is come to pumping plant
The optimization process figure optimized, this legend journey use six directions M350HD-10 models mixed-flow pump (wherein one spare), total flow
Q=1.5m3/ s, electricity price pr=0.5 yuan/kWh, unit traffic constraints Qi∈ [0.3,0.5], pumping plant lift H ∈ [9,14], water
The way η >=0.80 is obtained through polyfit Function Fittings in MATLAB：Lift flow curve H (Q)=- 92.4257Q2+
32.6487Q+13.9505, efficiency flow curve η (Q)=- 8.6176Q2+ 7.2438Q-0.6679, since object function is asks
Minimum is solved, simply takes its value negative, then problem is converted into solution maximum problem.This figure compare based on genetic algorithm and
The pumping plant optimization method performance of improved artificial fish-swarm algorithm proposed by the present invention.As shown in Figure 2, improved artificial fish-swarm is calculated
Method can obtain optimal result, but convergence rate is faster with genetic algorithm.
The present invention has carried out basic fish-swarm algorithm certain improvement in existing theoretical foundation, makes its visual field and step
Length can dynamic change, obtain direction movement towards optimal solution within sweep of the eye when performing foraging behavior, significantly improve the algorithm
Convergence rate and precision, and its application in terms of pumping plant optimization also achieves good effect, superior performance, and easily
In realization, therefore with prominent substantive distinguishing features and significant progress.
Finally it should be noted that：The above embodiments are only used to illustrate the technical solution of the present invention., rather than its limitations；To the greatest extent
Pipe is described in detail the present invention with reference to foregoing embodiments, it will be understood by those of ordinary skill in the art that：Its according to
Can so modify to the technical solution recorded in foregoing embodiments either to which part or all technical features into
Row equivalent replacement；And these modifications or replacement, various embodiments of the present invention technology that it does not separate the essence of the corresponding technical solution
The range of scheme.
Claims (6)
1. a kind of pumping plant optimization method based on artificial fish-swarm algorithm, it is characterised in that：Include the following steps：
S101, initialization systematic parameter, the parameter include：The field range Visual of fish-swarm algorithm, moving step length Step are gathered around
Squeeze degree factor delta, fish way N etc.；
S102, the shoal of fish { X is initialized in variable allowed band1,X2,…,XN, and by formula
The target function value of each Artificial Fish is obtained；
Wherein：prFor electricity price, (unit is：Thousand yuan/kWh)；ρ be water body density, ρ=1000kg/m3；G is acceleration of gravity, g=
9.80665m/s2；QiFor the flow that draws water of i-th pump group, (unit is：m3/s)；Net lift (unit for i-th pump group
For：m)；ηstiStation efficiency for i-th pump group；
S103, iteration optimizing, bunch respectively to Artificial Fish execution behavior and behavior of knocking into the back, and compare the function obtained by two kinds of behaviors
The size of value, the larger behavior of final Selection of Function value are performed, and record optimal iterative value；
S104, judge whether iterations reach maximum allowable iterations, if so, stopping interative computation, export maximum letter
Otherwise numerical value goes to step S103 and continues iteration.
2. a kind of pumping plant optimization method based on artificial fish-swarm algorithm according to claim 1, it is characterised in that：Step
In S103, the behavior of bunching includes the following steps：
State is XiArtificial Fish search neighborhood in Artificial Fish number nf, and find out its center XcIf(its
In：YiFor XiCorresponding functional value, YcFor XcCorresponding functional value), show that there are more food and crowding in the shoal of fish center
Less, then the Artificial Fish moves a step to the shifting of center direction, otherwise performs foraging behavior.
3. a kind of pumping plant optimization method based on artificial fish-swarm algorithm according to claim 1, it is characterised in that：Step
In S103, the behavior of knocking into the back includes the following steps：
State is XiArtificial Fish search neighborhood in respective value Y in all Artificial FishsjMaximum Artificial Fish XjIf(its
In：YiFor XiCorresponding functional value, nfNumber for the Artificial Fish in neighborhood), show Artificial Fish XjThere is higher food concentration
And it is less crowded around it, it just selects to take a step forward towards the direction, otherwise performs foraging behavior.
4. according to a kind of any pumping plant optimization method based on artificial fish-swarm algorithm of Claims 2 or 3, feature exists
In：The foraging behavior includes the following steps：
The current state of known Artificial Fish is Xi, corresponding functional value is Yi, a shape is randomly being found within sweep of the eye
State Xj, and judge the functional value Y corresponding to the statejWhether current function value Y is better thani, if so, Artificial Fish is before the direction
Further；Otherwise, NextState is found at random again；If make repeated attempts try_number (wherein：Try_number is tasted for maximum
Examination number) it is secondary after be still unsatisfactory for advance condition, then one step of random movement.
5. a kind of pumping plant optimization method based on artificial fish-swarm algorithm according to claim 4, it is characterised in that：Artificial Fish
When performing the foraging behavior, if making repeated attemptsAdvance condition is still unsatisfactory for after secondary, is directly taken wherein most
The direction movement of figure of merit point.
6. a kind of pumping plant optimization method based on artificial fish-swarm algorithm according to claim 4, it is characterised in that：If work as people
Work fish be unsatisfactory for bunching behavior, knock into the back behavior and foraging behavior when, just randomly choose a state within sweep of the eye at it, then
It is moved to the direction.
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Cited By (5)
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| CN109057776A (en) * | 2018-07-03 | 2018-12-21 | 东北大学 | A kind of oil well fault diagnostic method based on improvement fish-swarm algorithm |
| CN110008653A (en) * | 2019-02-26 | 2019-07-12 | 西北工业大学 | A kind of aviation centrifugal pump blade profile optimum design method |
| CN110442995A (en) * | 2019-08-13 | 2019-11-12 | 江苏师范大学 | A kind of LCL filter parameter optimization method based on artificial fish-swarm algorithm |
| CN112070375A (en) * | 2020-08-27 | 2020-12-11 | 宁波市电力设计院有限公司 | Power transmission equipment optimization model selection method based on improved artificial fish swarm algorithm |
| CN113688488A (en) * | 2021-08-17 | 2021-11-23 | 南京信息工程大学 | Power grid line planning method based on improved artificial fish swarm algorithm |
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Cited By (7)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN109057776A (en) * | 2018-07-03 | 2018-12-21 | 东北大学 | A kind of oil well fault diagnostic method based on improvement fish-swarm algorithm |
| CN110008653A (en) * | 2019-02-26 | 2019-07-12 | 西北工业大学 | A kind of aviation centrifugal pump blade profile optimum design method |
| CN110008653B (en) * | 2019-02-26 | 2022-06-03 | 西北工业大学 | Blade profile optimization design method for aviation centrifugal pump |
| CN110442995A (en) * | 2019-08-13 | 2019-11-12 | 江苏师范大学 | A kind of LCL filter parameter optimization method based on artificial fish-swarm algorithm |
| CN112070375A (en) * | 2020-08-27 | 2020-12-11 | 宁波市电力设计院有限公司 | Power transmission equipment optimization model selection method based on improved artificial fish swarm algorithm |
| CN113688488A (en) * | 2021-08-17 | 2021-11-23 | 南京信息工程大学 | Power grid line planning method based on improved artificial fish swarm algorithm |
| CN113688488B (en) * | 2021-08-17 | 2023-05-30 | 南京信息工程大学 | Power grid line planning method based on improved artificial fish swarm algorithm |
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