CN107480813A - Basin water resources Optimal Configuration Method based on multi-Objective Chaotic genetic algorithm - Google Patents
Basin water resources Optimal Configuration Method based on multi-Objective Chaotic genetic algorithm Download PDFInfo
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Abstract
The invention discloses a kind of basin water resources Optimal Configuration Method based on multi-Objective Chaotic genetic algorithm, belong to water resource optimal allocation technical field.Its step is as follows：Obtain basin water resources essential information；Multiple target water resource optimal allocation mathematical modeling is established, and carries out allocation models parameter calibration；Using multi-Objective Chaotic genetic algorithm, water resource optimal allocation alternative collection is solved；Water resource optimal allocation optimal equalization scheme is determined finally by chaotic neural network comprehensive evaluation model.The present invention has coupled the inverting of the ergodic and genetic algorithm of chaos, improves algorithm speed, and optimal solution is stable, meets the multiple-objection optimization configuration requirement of basin water resources complication system.
Description
Technical field
The invention discloses a kind of water resource optimal allocation method based on multi-Objective Chaotic genetic algorithm, belong to water resource
Distribute technical field rationally.
Background technology
Water resource system is to utilize system, life by the water resource that water resources circulation system, mankind's activity and its influence are formed
The complex large system that state environmental system and material within and outside the region, energy, information interaction are formed.In water resource system
The complicated commutative relation of material, energy and information, the evolution of common moving system between portion, outside be present；In addition, water resource system
The ecological economy element distribution of system, demand, supply and consumption it is uneven, the lack of balance of structure be present, it is multiple between subsystem
The otherness of matter and input, the disequilibrium etc. of output, the fine difference of system action will cause different system results.Cause
This, water resource system is a complication system open, away from nonequilibrium state.The complexity of water resource system determines that water provides
Source sustainable use must use Complex System Theory and method to be analyzed and researched.Chaos is it is determined that one occurred in system
Rule, similar random phenomenon seemingly are planted, is the compound movement form of generally existing in water resources management.Using chaology,
Deterministic parsing single in traditional hydrographic water resource analysis in the past or Stochastic analysis will be broken, and set up both unifications
Chaos analysis method, be advantageous to content that is abundant and developing complicated system analysis of water resources, make water resource study to obtain new development.
Chaotic optimization algorithm is a kind of new algorithm being suggested recently as the development of chaos subject.Its basic thought is
By by mapping of the optimization problem model to Chaos Variable, i.e., the valued space of Chaos Variable Linear Mapping to optimized variable,
Chaos Variable is constructed, makes full use of Chaos Variable possessed ergodic, randomness, regularity during chaotic motion to seek
Look for the optimal solution of the overall situation.Because chaos optimization method belongs to the direct random search of no derivative, the spy of optimization object function is treated
Property require it is less, avoid and require that object function and constraints are continuously differentiable difficulties in gradient search method, carry significantly
The high efficiency of algorithm.For chaotic optimization algorithm with respect to GA, DDDP scheduling algorithms have principle simple, convenience of calculation, result precision height etc.
Advantage.(application [J] the China agriculture of the chaotic optimization algorithms such as Qiu Lin, Tian Jinghuan, Duan Chunqing in optimizing scheduling of reservoir such as Qiu Lin
Village's water conservancy and hydropower, 2005 (7):17~19.) chaotic optimization algorithm is applied in optimizing scheduling of reservoir, using chaos iteration not
Repeatability and ergodic, obtain globally optimal solution.(such as Liang Wei, Chen Shoulun, what spring member is calculated based on chaos optimization by Liang Wei, Chen Shoulun
Step hydroelectric station reservoir Optimized Operation [J] HYDROELECTRIC ENERGY science of method, 2008,26 (1):63~66.) calculated using chaos optimization
Method optimizes calculating to long-term reservoir operation problem in step power station.But chaotic optimization algorithm also has following deficiency：Only
The optimization problem of dividing value is measured suitable for unconstrained optimization and change；It is only applicable to single-object problem；Using initial value as initially
Optimal solution, iteration efficiency have much room for improvement；Ergodic means larger iterations, program runtime length.
The content of the invention
It is an object of the invention to overcome above-mentioned deficiency, there is provided a kind of basin water based on multi-Objective Chaotic genetic algorithm
Resource optimization configuration method, the inverting of the ergodic of chaos and genetic algorithm is coupled together, it is therefore an objective to overcome chaos optimization
Algorithm requires that optimization problem single goal, program runtime are long and genetic algorithm has dependence, optimal solution to problem characteristic
It is unstable, deviate it is larger the problems such as so that water resource best configuration scheme selection process is highly efficient superior.
Multi-Objective Chaotic genetic Optimization Algorithm (Multi-objective Chaotic Genetic Algorithm,
MCGA it is) to be coupled chaos optimization, genetic algorithm and multi-objective decision-making.Realize that the algorithm there are two kinds of approach, it is a kind of
It is that multi-objective optimization question is converted into single-objective problem using multi-objective decision-making, is solved using Chaos Genetic Algorithm；Separately
A kind of is the collective search characteristic using genetic algorithm, using multiple individual methods in processing colony simultaneously, to search space
In multiple solutions assessed.The optimal solution obtained using Chaos Genetic Algorithm keeps stable optimum state, and each run obtains
The achievement arrived is substantially close, is all sufficiently close to optimal solution, and the optimal solution obtained using traditional genetic algorithm have it is unstable
Property, although its solution is all to approach optimal solution, sometimes deviate larger.Therefore, the ergodic and genetic algorithm of chaos are utilized
The Chaos Genetic Algorithm that inverting is coupled together undoubtedly has more superiority in searching process than simple genetic algorithm.
The object of the present invention is achieved like this：
The present invention is achieved by the following technical solutions：A kind of basin water resources optimization based on multi-Objective Chaotic genetic algorithm
Collocation method, comprise the following steps：
Step 1：Basin water resources system essential information is obtained, including：Basin basic condition and water resources characteristic, basin subregion
Generalization, basin gross water requirement, basin total supply；
Step 2：Establish and Optimality Criteria, water balance constraint, water are up to society, economic and ecological environment comprehensive benefit
Reservoir filling capacity consistency, the constraint of reservoir letdown flow, the constraint of water source available water, the constraint of water source conveyance power of water, user need water energy
Force constraint, variable nonnegativity restrictions are the multiple target water resource optimal allocation mathematical modeling of constraints, and carry out allocation models ginseng
Digit rate is determined；
Step 3：Multi-Objective Chaotic genetic algorithm is applied to above-mentioned water resource optimal allocation mathematical modeling, generation Pareto is most
Excellent disaggregation, i.e. water resource optimal allocation alternative collection；
Step 4：On the basis of water resource optimal allocation alternative collection, determined using chaotic neural network comprehensive evaluation model
Water resource optimal allocation optimal equalization scheme.
Further, the step 3 specifically includes：
Step 3-1：Restriction condition treat, using non-differentiability Exact Penalty Function Method, certain penalty factor is selected, constructs penalty term,
Restricted problem is converted into unconstrained optimization problem, now obtains the optimization problem of continuous object：
min f(x1,x2,L,xN)xi∈[ai,bi] i=1,2, L, N (3)
In formula, xiFor decision variable, ai、biFor decision variable value bound；
Step 3-2：Parameter setting, determine the number N of the decision variable and span [a of variablei,bi], determine genetic algorithm
Initial population scale M, termination iterations T, the crossover probability P of genetic algorithmcWith mutation probability Pm；
Step 3-3：Population is initialized, chooses n different initial values, it is different to can obtain n track by Logistic mappings
Chaos Variable sequence εi,j, i=1,2, L, N, j=1,2, L, M, Logistic mapping：
εi,k+1=μ εi,k(1-εi,k) (4)
In formula, μ is controling parameter, if 0≤εI, 1During≤1, μ=4, system is completely in chaos state, has the institute of chaotic motion
There is feature；Again by Logistic map caused by chaos sequence be amplified to by formula (3) span of optimized variable, in this, as
Initial population：
xi,j=ai+(bi-ai)εi,j(i=1,2, L, N, j=1,2, L, M) (5)
In formula, εi,jFor Chaos Variable sequence；
Step 3-4：External storage archives are constructed, determine the maximum-norm of archives, the external storage archives, for advancing for
Fitness value is preferably individual, avoids it from being destroyed because of the strong randomness of genetic algorithm, finally therefrom obtains Noninferior Solution Set；
Step 3-5：According to target the number of function is split to population, distributes a sub- object function to each sub-group, respectively
Individual sub- object function independently carries out selection operation in corresponding sub-group；
Step 3-6：Appropriate fitness function is selected, calculates the fitness value of specific item scalar functions corresponding to each sub-group；
Step 3-7：Select the high individual of fitness value to form new sub-group, by the colony of all new compositions be merged into one it is complete
Whole colony, is intersected and mutation operation, forms population of future generation；
Step 3-8：Calculate new fitness value and be adjusted, then colony is ranked up by fitness value, will wherein fit
Response is worst 10% to be replaced, and carries out File Maintenance；
Step 3-9：If meeting to terminate iterated conditional, Pareto optimal solution sets are exported, otherwise gives and works as fitness in former generation colony
Corresponding optimized variable adds a chaotic disturbance, and is mapped as optimized variable by carrier method, is transferred to step 3-6.
Further, in above-mentioned steps 3-1, the non-differentiability Exact Penalty Function Method, restricted problem is converted into without about
Beam optimization problem, avoid calculate on sequential property, directly make constrained optimization solution and penalty function some minimal point accurately
Unanimously, conversion formula is：
P (x)=f (x)+σ (c (x)) (2)
In formula, f (x) is the object function of former problem, and σ (c (x)) is penalty term, and c (x) is the constraints of former problem.
Further, in above-mentioned steps 3-8, the File Maintenance is specially：When the number solved in archives reaches regulation
During value, archives are safeguarded, to determine that can new explanation be added in archives, and if new explanation is added in archives after, from it
For middle which solution of removal to ensure that archives size is no more than setting all the time, external storage File Maintenance mainly passes through external storage
Collection increase rule and external storage collection reduce rule and determine that can new explanation be added in archives.
Wherein, external storage collection increase rule be approximation theory non-domination solution precision index be advantageous to reality
One of both existing population diversity indexs just have an opportunity to be added into external storage collection better than his father's individual；
It is to concentrate each individual to assign age attribute to external storage that the external storage collection, which reduces rule, is assigned to when adding first
0, circulate and remain in the set if completing one and developing, the age increases by 1, if certain individual age exceedes max age
Limitation, or certain individual adaptive value are minimum, then reduce rule and concentrate the deletion individual from external storage.
Further, in above-mentioned steps 3-9, the termination iterated conditional is：The average value of fitness and maximum it
Difference is less than allowable error, reaches termination iterations, until the difference of the front and rear fitness average value calculated twice is less than in advance
The small positive number of some given.
In step 4, water resource optimal allocation optimal equalization side is determined using chaotic neural network comprehensive evaluation model
Case, specifically include following steps：
Step 4-1：Each parameter of neutral net is set：Input layer, hidden layer and output layer neuron node number N1、N2And N3；
Step 4-2：Water resource optimal allocation Effect Evaluation Index System is established, standardization is done to each index, by each scheme
Input value R=[r of the quantized value of evaluation index property value as neutral netp], and determine neutral net desired output B=
[b1,b2,L,bp]T, i.e. input pattern (R, B), wherein, p is scheme number；
Step 4-3：By neutral net and each scheme input value and desired output of determination, the neutral net overall situation is determined most
Excellent weights, i.e., each most reasonable weights of evaluation index；
Step 4-4：With the most reasonable weights of each evaluation index, chaotic neural network comprehensive evaluation model is built, then each scheme is referred to
Evaluations matrix R=[r after mark standardizationp] input chaotic neural network comprehensive evaluation model calculated, obtain each scheme
Value of utility { PVk(k=1,2, L, p), then on this basis, according to Preference Theory, synthesis is ranked up to scheme preferably, i.e.,
PV*=max { PVkCorresponding to scheme be preferred plan.
In step 4-2, the determination of the neutral net desired output：Respect former evaluation expertise and evaluation is tied
Fruit, or determined using the evaluation method of multi-attribute-utility theories of value and Preference Theory method.
In step 4-3, the determination of neutral net global optimum weights：Mapped using Logistic and produce chaos change
Amount, obtain network weight vectorInput (R, B) and network weight coefficientAccording to the Mechanics in Chaotic Neural Networks calculating network phase
Hope output bpWith network reality output op；The correction error of output layer is calculated by error function method, and it is reverse using network error
Propagate, constantly modification adjustment weight parameter, to obtain neutral net global optimum weights, error function E：
In formula, EpFor scheme p error；bpFor scheme p desired output, opFor neutral net network output valve；X is sample,
W is weighted value；F (x, W) is the nonlinear function of neutral net description, in the case where network structure determines, error letter in formula
Number E is the energy function using weight as primary variables.
Beneficial effect
Compared with prior art, advantageous effects of the invention are：
(1) the multiple-objection optimization configuration requirement of basin water resources complication system is met；
(2) take full advantage of the ergodic of chaotic optimization algorithm, randomness, it is regular find the optimal solution of the overall situation, chaos is excellent
Change method belongs to the direct random search of no derivative, and it is less to treat the characteristic requirements of optimization object function, avoids gradient search
Require that object function and constraints are continuously differentiable difficulties in method, substantially increase the efficiency of algorithm；
(3) multi-Objective Chaotic genetic algorithm is proposed, has coupled inverting and the multiple target of the ergodic, genetic algorithm of chaos optimization
Decision-making technic, solves chaotic optimization algorithm requirement optimization problem single goal without constraint and iterations multiprogram long operational time
And genetic algorithm has the problems such as dependence, optimal solution are unstable, deviation is larger to problem characteristic；
(4) noninferior solution of algorithm acquisition is retained using the external archive of Dynamic Updating Mechanism, passing through external storage collection increases rule
Reduce rule with external storage collection and determine that can new explanation be added in archives, and if new explanation is added to archives after, which is therefrom removed
A little solutions ensure that archives size is no more than setting all the time, ensure that noninferior solution individual is evenly distributed with this, have good diversity,
Accelerate global convergence；
(5) propose that chaotic neural network comprehensive evaluation model concentrates selection preferred plan from water resource optimal allocation alternative,
Enhance the ability of making decisions on one's own of Programming for Multiobjective Water Resources configuration system.
Brief description of the drawings
Fig. 1 is flow chart of the method for the present invention；
Fig. 2 is multi-Objective Chaotic genetic algorithm calculation flow chart of the present invention；
Fig. 3 is chaotic neural network overall merit flow chart of the present invention；
Fig. 4 lifts current domain water resource system subregion schematic diagram for the present invention；
Fig. 5 lifts current domain water resource system sketch network figure for the present invention.
Embodiment
With reference to specific embodiment, clear, complete description is carried out to technical solution of the present invention.
The present invention requires optimization problem single goal without constraint and iterations multiprogram for traditional chaotic optimization algorithm
Long operational time and traditional genetic algorithm have the problems such as dependence, optimal solution are unstable, deviation is larger to problem characteristic, carry
For a kind of basin water resources Optimal Configuration Method based on multi-Objective Chaotic genetic algorithm, this method has fully coupled chaos optimization
Ergodic, genetic algorithm inverting and multi-objective decision-making so that optimal solution keeps stable optimum state, realizes and calculates
Method ability of searching optimum, and on the basis of water resource optimal allocation alternative collection, using chaotic neural network overall merit mould
Type independently selects water resource optimal equalization allocation plan, realizes that basin water resources multiple-objection optimization configures, referring to Fig. 1 to Fig. 3, its
In, Fig. 1 is flow chart of the method for the present invention；Fig. 2 is multi-Objective Chaotic genetic algorithm calculation flow chart of the present invention；Fig. 3 is this hair
Bright chaotic neural network overall merit flow chart.
A kind of basin water resources Optimal Configuration Method based on multi-Objective Chaotic genetic algorithm of the present invention, including following step
Suddenly：
Step 1：Basin water resources system essential information data are obtained, including：Basin basic condition and water resources characteristic, basin
Subregion is generally changed, basin gross water requirement, basin total supply.
Wherein, basin basic condition includes physical geography, social economy, hydrometeorology, trunk river reservoir etc.；Water resource
Feature includes distribution of water resources feature, situation of water resource use；Basin gross water requirement is divided into economic society water requirement and ecological ring
Border water requirement, economic society water requirement includes life again needs water, agricultural to need water, industrial water demand, construction industry and the tertiary industry to need water,
Ecological environment water demand includes ecology narration and water demand for natural service outside river course in river course, and water demand for natural service includes ecological water demand of rivers outside river course
With urban ecological demand water；Basin total supply is divided into earth's surface water supply and groundwater supply, and terrestrial reference output includes water storage project and supplied water
Measure and draw water lift engineering water supply amount.
Step 2：Establish and Optimality Criteria is up to society, economic and ecological environment comprehensive benefit, water balance is about
Beam, reservoir filling capacity consistency, the constraint of reservoir letdown flow, the constraint of water source available water, the constraint of water source conveyance power of water, Yong Huxu
Outlet capacity constraint, the multiple target water resource optimal allocation mathematical modeling that variable nonnegativity restrictions is constraints：
In formula, opt represents that optimization direction, including minimax direction, n represent the number of targets of water resource optimal allocation, F (x) tables
Society, the object function of economic and ecological environment comprehensive benefit maximum are shown as, i, k, j are respectively independent water source, basin
Region and user's ordinal number, xijFor user's output；Vt、Vt+1Respectively at the beginning of the reservoir t periods and period Mo pondage；
QintFor reservoir t periods average Incoming water quantity；QouttFor the average outbound water of t periods reservoir, including supply water, irrigate, generating electricity and
Abandon water；QstWater, including evaporation and seepage are lost for the reservoir t periods；Respectively the reservoir t periods allow most
Small and maximum water-storage；qminFor the minimum vent flow (such as shipping, ecology water) of mining under reservoir requirement；For independent water source
I available waters；For the maximum conveyance power of water at k sub-district i water sources；Represent minimum, the maximum of k sub-district j users
Water requirement.
Then allocation models parameter calibration is carried out, specifically depending on the situation of basin.
Step 3：Water resource optimal allocation mathematical modeling is solved using multi-Objective Chaotic genetic algorithm, generation Pareto is most
Excellent disaggregation, i.e. water resource optimal allocation alternative collection.Specifically include (see Fig. 2)：
Step 3-1：The constraints that multiple target water resource optimal allocation mathematical modeling is established in step 2 is handled.Utilize
Non-differentiability Exact Penalty Function Method, certain penalty factor is selected, construct penalty term, restricted problem is converted into unconstrained optimization and asked
Topic, conversion formula are：
P (x)=f (x)+σ (c (x)) (2)
In formula, f (x) is the object function of former problem, and σ (c (x)) is penalty term, and c (x) is the constraints of former problem.
Now obtain the optimization problem of continuous object：
min f(x1,x2,L,xN)xi∈[ai,bi] i=1,2, L, N (3)
In formula, xiFor decision variable, ai、biFor decision variable value bound.
Step 3-2：Setup parameter.Determine the number N of the decision variable and span [a of variablei,bi], it is determined that heredity is calculated
The initial population scale M of method, termination iterations T, the crossover probability P of genetic algorithmcWith mutation probability Pm。
Step 3-3：Population is initialized, chooses n different initial values, n track can obtain not by Logistic mappings
Same Chaos Variable sequence εi,j, i=1,2, L, N, j=1,2, L, M, Logistic mapping：
εi,k+1=μ εi,k(1-εi,k) (4)
In formula, μ is controling parameter, if 0≤εI, 1During≤1, μ=4, system is completely in chaos state, has the institute of chaotic motion
There is feature, therefore can be as the Chaos Variable iterative equation in optimized algorithm.
Again by Logistic map caused by chaos sequence be amplified to by formula (5) span of optimized variable, made with this
For initial population：
xi,j=ai+(bi-ai)εi,j(i=1,2, L, N, j=1,2, L, M) (5)
In formula, εi,jFor Chaos Variable sequence.
Step 3-4：External storage archives are constructed, determine the maximum-norm of archives, external storage archives are used to advance for
Fitness value is preferably individual, avoids it from being destroyed because of the strong randomness of genetic algorithm, finally therefrom obtains Noninferior Solution Set.
Step 3-5：According to target the number of function is split to population, and a sub-goal letter is distributed to each sub-group
Number, each specific item scalar functions independently carry out selection operation in corresponding sub-group.
Step 3-6：Appropriate fitness function is selected, calculates the fitness value of specific item scalar functions corresponding to each sub-group.
Step 3-7：Select the high individual of fitness value to form new sub-group, the colony of all new compositions is merged into one
Individual complete colony, is intersected and mutation operation, forms population of future generation.
Step 3-8：Calculate new fitness value and carry out File Maintenance.Specifically include：Calculate new fitness value and progress
Adjustment, is then ranked up by fitness value to colony, 10% is replaced wherein fitness is worst, and carry out archives dimension
Shield.Wherein File Maintenance is specially：When the number solved in archives reaches setting, archives are safeguarded, it is new to determine
Can solution be added in archives, and if new explanation is added in archives after, remove which solution therefrom to ensure archives size all the time
No more than setting.External storage File Maintenance mainly reduces rule by external storage collection increase rule and external storage collection
Determine that can new explanation be added in archives；
Wherein, external storage collection increase rule be approximation theory non-domination solution precision index with to be advantageously implemented population various
Property one of both indexs better than his father's individual, just have an opportunity to be added into external storage collection；
It is to concentrate each individual to assign age attribute to external storage that external storage collection, which reduces rule, and 0 is assigned to when adding first,
Circulate and remain in the set if completing one and developing, age increase by 1, if certain individual age is more than max age limit
System, or certain individual adaptive value are minimum, then are concentrated from external storage and delete the individual.
Step 3-9：If meet to terminate iterated conditional：The average value of fitness and the difference of maximum are less than allowable error,
Reach termination iterations, until the difference of the front and rear fitness average value calculated twice is small just less than some previously given
Number, then Pareto optimal solution sets are exported, otherwise give and work as fitness in former generation colony and correspond to optimized variable and add a chaotic disturbance, and lead to
Cross carrier method and be mapped as optimized variable, be transferred to step 3-6.
Step 4：On the basis of water resource optimal allocation alternative collection, using chaotic neural network comprehensive evaluation model
Determine water resource optimal allocation optimal equalization scheme.Specifically include following steps (see Fig. 3)：
Step 4-1：Each parameter of neutral net is set：Input layer, hidden layer and output layer neuron node number N1、N2And N3。
Step 4-2：Water resource optimal allocation Effect Evaluation Index System is established, standardization is done to each index, will be each
Input value of the quantized value of evaluating indexesto scheme property value as neutral netAnd determine neutral net desired output
Value B=[b1,b2,L,bp]T, i.e. input pattern (R, B), wherein, p is scheme number.Neutral net desired output is usually to respect
Original evaluation expertise and evaluation result, it can also use the evaluation method of multi-attribute-utility theories of value and Preference Theory method true
It is fixed.
Step 4-3：By neutral net and each scheme input value and desired output of determination, determine that neutral net is complete
Office's best initial weights, i.e., each most reasonable weights of evaluation index；Further comprise：Mapped using Logistic and produce Chaos Variable, obtained
To network weight vectorInput (R, B) and network weight coefficientAccording to neural computing network desired output bpAnd network
Reality output op；The correction error of output layer is calculated by error function method, and utilizes network error backpropagation, constantly modification
Weight parameter is adjusted, to obtain neutral net global optimum weights, error function E：
In formula, EpFor scheme p error；bpFor scheme p desired output, opFor neutral net network output valve；X is sample,
W is weighted value；F (x, W) is the nonlinear function of neutral net description, in the case where network structure determines, error letter in formula
Number E is the energy function using weight as primary variables.
Step 4-4：With the most reasonable weights of each evaluation index, chaotic neural network comprehensive evaluation model is built, then by each side
Evaluations matrix after case criterionInput chaotic neural network collective model is calculated, and obtains the effect of each scheme
With value { PVk, then on this basis, according to Preference Theory, comprehensive preferred, i.e. PV is ranked up to scheme*=max { PVkRight
The scheme answered is preferred plan.
Embodiment
Now lifted by Hubei Province exemplified by current domains Programming for Multiobjective Water Resources distributes rationally, illustrate the validity and rationally of inventive method
Property.It is the tributary of the Changjiang river one to lift current domains, mainstream total length 170.4km, drainage area 4367.6km2.It is existing large, medium and small to lift current domain
293, all kinds of reservoirs of type, the m of aggregate storage capacity 15.25 hundred million3, the m of effective storage 8.64 hundred million3, including small reservoir, output account for storage draw carry it is total
The 74.2% of output.Current domain will be lifted in this research and is generalized as 9 intake areas (see Fig. 4), current domain water resource of mutually attending an imperial examination
System sketch network figure (see Fig. 5).According to current domain water source supply area situation is lifted, across the basin water source in this area is public
Water mainstream is is lifted in water source, and independent water source can be divided into two kinds of surface water and groundwater, and water user is urban life, life in the countryside, agriculture
Industry water, industry, construction industry and the tertiary industry and the class of Eco-environmental Water Consumption six.
The present invention is using each user's output of normal flow year (fraction P=90%) as decision variable, economic, social, ecological ring
Border comprehensive benefit is up to target, constrained with water balance, reservoir filling capacity consistency, reservoir letdown flow constraint, water source can
Output constraint, water source conveyance power of water constraint, user need outlet capacity constraint, variable nonnegativity restrictions be constraints, using more mesh
Mark Chaos Genetic Algorithm carries out lifting current domain water resource optimal allocation.
Objectives function is as follows：
(1) economic benefit target：Water resource is maximum using the net contribution value of GDP in watershed in basin,
In formula, GDPw(k) the net contribution value for water resource optimal allocation to kth sub-district GDP, using between GDP and water consumption
Linear relationship represents；
(2) social benefit target：Water supply fairness degrees of coordination is maximum in basin,
In formula, f2' it is department's water supply Gini coefficient, reflect the interdepartmental fairness of water resource optimal allocation；f2" be
Basin water supply Gini coefficient, reflect the fairness between each sub-district of water resource optimal allocation；
(3) ecological environment benefit target：Each sub-district BOD discharge capacitys sum in region is minimum,
In formula,For the content of k sub-district j Subscriber Unit wastewater discharge mesophytization oxygen demands (BOD), mg/L；For k sub-districts j
User's sewage discharge coefficient.
Parameter rating of the model is carried out, calculates and lifts current domain gross water requirement and total available water.In Matlab software programming rings
Solved under border.Four Water Resources Allocation sides corresponding to finally being randomly selected from the water resource optimal allocation scheme noninferior set of generation
Case V1、V2、V3、V4Optionally, composition lifts current domain water resource optimal allocation alternative collection (being shown in Table 1).
Table 1 lifts current domain water resource optimal allocation alternative collection (P=90%) unit：Hundred million m3
On the basis of above-mentioned water resource optimal allocation alternative collection, determined using chaotic neural network comprehensive evaluation model
Water Resources Allocation preferred plan, specific achievement of evaluating are shown in Table 2.
The Mechanics in Chaotic Neural Networks of table 2 is to allocation plan overall merit achievement
To each scheme, water supply of each water source to each user can be sub-divided into water resources regionalization.Detained among total supply water
Except ecological environment supplies water in river course, obtain under the conditions of lifting current domain P=90%, act current domain water resource optimal allocation achievement (see
Table 3).
Table 3 lifts current domain water resource optimal allocation achievement (P=90%-V1 schemes) unit：Ten thousand m3
Although an embodiment of the present invention has been shown and described, it will be understood by those skilled in the art that not taking off
In the case of principle and objective from the present invention a variety of change, modification, replacement and modification, this hair can be carried out to these embodiments
Bright scope is limited by claim and its equivalent.
Claims (9)
1. a kind of basin water resources Optimal Configuration Method based on multi-Objective Chaotic genetic algorithm, it is characterised in that including following
Step：
Step 1：Basin water resources system essential information is obtained, including：Basin basic condition and water resources characteristic, basin subregion
Generalization, basin gross water requirement, basin total supply；
Step 2：Establish and Optimality Criteria, water balance constraint, water are up to society, economic and ecological environment comprehensive benefit
Reservoir filling capacity consistency, the constraint of reservoir letdown flow, the constraint of water source available water, the constraint of water source conveyance power of water, user need water energy
Force constraint, variable nonnegativity restrictions are the multiple target water resource optimal allocation mathematical modeling of constraints, and carry out allocation models ginseng
Digit rate is determined；
Step 3：Multi-Objective Chaotic genetic algorithm is applied to above-mentioned water resource optimal allocation mathematical modeling, generation Pareto is most
Excellent disaggregation, i.e. water resource optimal allocation alternative collection；
Step 4：On the basis of water resource optimal allocation alternative collection, determined using chaotic neural network comprehensive evaluation model
Water resource optimal allocation optimal equalization scheme.
2. the basin water resources Optimal Configuration Method based on multi-Objective Chaotic genetic algorithm as claimed in claim 1, it is special
Sign is that the step 3 specifically includes：
Step 3-1：Restriction condition treat, using non-differentiability Exact Penalty Function Method, certain penalty factor is selected, constructs penalty term,
Restricted problem is converted into unconstrained optimization problem, now obtains the optimization problem of continuous object：
minf(x1,x2,L,xN)xi∈[ai,bi] i=1,2, L, N (3)
In formula, xiFor decision variable, ai、biFor decision variable value bound；
Step 3-2：Parameter setting, determine the number N of the decision variable and span [a of variablei,bi], determine genetic algorithm
Initial population scale M, genetic algorithm termination iterations T, crossover probability PcWith mutation probability Pm；
Step 3-3：Population is initialized, chooses n different initial values, it is different to can obtain n track by Logistic mappings
Chaos Variable sequence εi,j, i=1,2, L, N, j=1,2, L, M, Logistic mapping：
εi,k+1=μ εi,k(1-εi,k) (4)
In formula, μ is controling parameter, if 0≤εI, 1During≤1, μ=4, system is completely in chaos state, has the institute of chaotic motion
There is feature；Again by Logistic map caused by chaos sequence be amplified to by formula (5) span of optimized variable, in this, as
Initial population：
xi,j=ai+(bi-ai)εi,j(i=1,2, L, N, j=1,2, L, M) (5)
In formula, εi,jFor Chaos Variable sequence；
Step 3-4：External storage archives are constructed, determine the maximum-norm of archives, the external storage archives, for advancing for
Fitness value is preferably individual, avoids it from being destroyed because of the strong randomness of genetic algorithm, finally therefrom obtains Noninferior Solution Set；
Step 3-5：According to target the number of function is split to population, distributes a sub- object function to each sub-group, respectively
Individual sub- object function independently carries out selection operation in corresponding sub-group；
Step 3-6：Appropriate fitness function is selected, calculates the fitness value of specific item scalar functions corresponding to each sub-group；
Step 3-7：Select the high individual of fitness value to form new sub-group, by the colony of all new compositions be merged into one it is complete
Whole colony, is intersected and mutation operation, forms population of future generation；
Step 3-8：Calculate new fitness value and be adjusted, then colony is ranked up by fitness value, will wherein fit
Response is worst 10% to be replaced, and carries out File Maintenance；
Step 3-9：If meeting to terminate iterated conditional, Pareto optimal solution sets are exported, otherwise gives and works as fitness in former generation colony
Corresponding optimized variable adds a chaotic disturbance, and is mapped as optimized variable by carrier method, is transferred to step 3-6.
3. the basin water resources Optimal Configuration Method based on multi-Objective Chaotic genetic algorithm as claimed in claim 2, its feature
It is, in step 3-1, the non-differentiability Exact Penalty Function Method, restricted problem is converted into unconstrained optimization problem, avoids counting
The sequential property counted in, directly make constrained optimization solution and penalty function some minimal point it is accurately consistent, conversion formula is：
P (x)=f (x)+σ (c (x)) (2)
In formula, f (x) is the object function of former problem, and σ (c (x)) is penalty term, and c (x) is the constraints of former problem.
4. the basin water resources Optimal Configuration Method based on multi-Objective Chaotic genetic algorithm as claimed in claim 2, its feature
It is, in step 3-8, the File Maintenance is specially：When the number solved in archives reaches setting, archives are carried out
Safeguard, to determine that can new explanation be added in archives, and if new explanation is added in archives after, remove which solution therefrom to ensure
Archives size is no more than setting all the time, and external storage File Maintenance mainly increases rule by external storage collection and outside is deposited
Preserve reduction rule and determine that can new explanation be added in archives.
5. the basin water resources Optimal Configuration Method based on multi-Objective Chaotic genetic algorithm as claimed in claim 4, its feature
Be, external storage collection increase rule be approximation theory non-domination solution precision index with to be advantageously implemented population more
One of both sample indexs just have an opportunity to be added into external storage collection better than his father's individual；
It is to concentrate each individual to assign age attribute to external storage that the external storage collection, which reduces rule, is assigned to when adding first
0, circulate and remain in the set if completing one and developing, the age increases by 1, if certain individual age exceedes max age
Limitation, or certain individual adaptive value are minimum, then reduce rule and concentrate the deletion individual from external storage.
6. the basin water resources Optimal Configuration Method based on multi-Objective Chaotic genetic algorithm as claimed in claim 2, its feature
It is, in step 3-9, the termination iterated conditional is：The average value of fitness and the difference of maximum are less than allowable error, reach
To iterations is terminated, until the difference of the front and rear fitness average value calculated twice is less than the small positive number of some previously given.
7. the basin water resources Optimal Configuration Method based on multi-Objective Chaotic genetic algorithm as claimed in claim 1, its feature
It is, in step 4, water resource optimal allocation optimal equalization scheme is determined using chaotic neural network comprehensive evaluation model, has
Body comprises the following steps：
Step 4-1：Each parameter of neutral net is set：Input layer, hidden layer and output layer neuron node number N1、N2And N3；
Step 4-2：Water resource optimal allocation Effect Evaluation Index System is established, standardization is done to each index, by each scheme
Input value R=[r of the quantized value of evaluation index property value as neutral netp], and determine neutral net desired output B=
[b1,b2,L,bp]T, i.e. input pattern (R, B), wherein, p is scheme number；
Step 4-3：By nerve network system and each scheme input value and desired output of determination, determine that neutral net is complete
Office's best initial weights, i.e., each most reasonable weights of evaluation index；
Step 4-4：With the most reasonable weights of each evaluation index, chaotic neural network comprehensive evaluation model is built, then each scheme is referred to
Evaluations matrix R=[r after mark standardizationp] input chaotic neural network comprehensive evaluation model calculated, obtain each scheme
Value of utility { PVk(k=1,2, L, p), then on this basis, according to Preference Theory, synthesis is ranked up to scheme preferably, i.e.,
PV*=max { PVkCorresponding to scheme be preferred plan.
8. the basin water resources Optimal Configuration Method based on multi-Objective Chaotic genetic algorithm as claimed in claim 7, its feature
It is, in step 4-2, the determination of the neutral net desired output：Respect former evaluation expertise and evaluation result, or
Determined using the evaluation method of multi-attribute-utility theories of value and Preference Theory method.
9. the basin water resources Optimal Configuration Method based on multi-Objective Chaotic genetic algorithm as claimed in claim 7, its feature
It is, in step 4-3, the determination of neutral net global optimum weights：Mapped using Logistic and produce Chaos Variable,
Obtain network weight vectorInput (R, B) and network weight coefficientIt is expected according to Mechanics in Chaotic Neural Networks calculating network defeated
Go out bpWith network reality output op；The correction error of output layer is calculated by error function method, and is reversely passed using network error
Broadcast, constantly modification adjustment weight parameter, to obtain neutral net global optimum weights, error function E：
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In formula, EpFor scheme p error；bpFor scheme p desired output, opFor neutral net network output valve；X is sample,
W is weighted value；F (x, W) is the nonlinear function of neutral net description, in the case where network structure determines, error letter in formula
Number E is the energy function using weight as primary variables.
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