CN112668909B - Water resource optimal configuration method and system based on genetic algorithm - Google Patents
Water resource optimal configuration method and system based on genetic algorithm Download PDFInfo
- Publication number
- CN112668909B CN112668909B CN202011639879.6A CN202011639879A CN112668909B CN 112668909 B CN112668909 B CN 112668909B CN 202011639879 A CN202011639879 A CN 202011639879A CN 112668909 B CN112668909 B CN 112668909B
- Authority
- CN
- China
- Prior art keywords
- water
- target
- demand
- ecological
- genetic algorithm
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Active
Links
Images
Landscapes
- Management, Administration, Business Operations System, And Electronic Commerce (AREA)
Abstract
The scheme is characterized in that an economic target, a social target and an ecological target are subjected to normalization processing, so that the targets are subjected to weighted calculation under the same dimension condition, and after normalization, the weight of each target has practical significance; meanwhile, the constructed water resource optimization model has the characteristics of multivariable, multiple targets, multiple constraints and the like, and when the model is solved, the genetic algorithm is adopted in the method, so that compared with the traditional solving method, the method has great advantages in the aspects of calculation speed, precision, convergence, adaptability and mobility.
Description
Technical Field
The disclosure belongs to the technical field of water resource optimal configuration, and particularly relates to a water resource optimal configuration method and system based on a genetic algorithm.
Background
The statements in this section merely provide background information related to the present disclosure and may not necessarily constitute prior art.
The inventor finds that the existing water resource multi-objective optimization method considers incomplete influence factors, and the solving process is usually to convert a target function into a single-objective model by simple linear weighting, so that the accuracy of an optimization result is low, and the actual water resource optimization configuration cannot be guided; the water resource optimal allocation model not only needs to consider various water supply sources such as surface water, underground water and external water, but also needs to comprehensively plan multi-user types such as different industries and the like in different areas, and simultaneously needs to consider different situations such as open water years, dry water years and extra dry water years, and coordinate with the relationship between economy, society and ecology, and is a multi-objective optimization problem of a natural-social complex system, so that a high-precision multi-objective multi-water-source multi-user water resource optimal allocation method is urgently needed in the existing water resource optimal allocation.
Disclosure of Invention
Aiming at the multi-target different dimension problems of the water resource optimization model, the scheme normalizes the problems according to different requirements of each target, reasonably distributes the weights of the different targets, and carries out optimization solution through the multi-water resource optimization model with the genetic algorithm, wherein the multi-target different dimension problems are quickly and efficiently solved.
According to a first aspect of the embodiments of the present disclosure, there is provided a method for optimizing and configuring water resources based on a genetic algorithm, including:
acquiring relevant data of local water resources;
constructing a multi-target water resource optimal configuration model; the water resource optimal allocation model comprises an economic target with the minimum industrial water use benefit gap, a social target with the purpose of meeting the minimum water shortage of resident basic domestic water and industrial and agricultural production water and an ecological target with the purpose of meeting the minimum ecological water demand and water shortage; carrying out normalization processing on each target to enable each target to carry out weighting calculation under the same dimensional condition, and obtaining the multi-target water resource optimal configuration model;
and carrying out optimization solution on the multi-target water resource optimization configuration model by using a genetic algorithm to obtain an optimization result.
Further, the acquisition of the related data comprises the supply and demand analysis of water resources in the research area, namely the prediction of the domestic water demand, the industrial water demand, the agricultural water demand and the ecological water demand according to the water consumption quota; and calculating the available water supply amount of each water source of each region under different guarantee rates according to local planning data.
Further, the objective function of the economic objective is as follows:
wherein i represents the ith partition; j represents class j user (industrial) water; k represents the kth water supply source; WSijkRepresenting the water supply amount of the kth water source to the jth user of the subarea i; EWjRepresents the economic benefit (Yuan/m) brought by single water to the jth user3)；λijWeighting the water consumption of the jth class user of the partition i; n, n,m and L are the number of subareas, the number of water users and the type number of water sources respectively, and PGDP is the planned GDP of the area.
Further, the objective function of the social goal is as follows:
wherein, WRijRepresenting the water demand (ten thousand m) of class i and j users of the partition3) Including resident life and production water demand, WRA represents the total water demand (ten thousand meters) of all the regional resident life and production3)。
Further, the objective function of the ecological objective is as follows:
wherein, EWDLijThe lower limit of the ecological water demand of the jth class of users in the subarea i is shown, and the EWD represents the total ecological water demand of all subareas.
Further, the multi-target water resource optimization configuration model comprehensively considers economic targets, social targets and ecological targets, and is specifically expressed as follows:
minf＝β1g1(WS)+β2g2(WS)+β3g3(WS)
wherein, beta1、β2、β3Is a weight coefficient, beta1+β2+β3＝1。
Further, the multi-objective water resource optimization configuration model comprises the following constraint conditions: water supply capacity constraints, user water demand capacity constraints, external water regulation distribution constraints, and supply and demand relationship constraints between water resources and users.
According to a second aspect of the embodiments of the present disclosure, there is provided a water resource optimization configuration system based on genetic algorithm, including:
the data acquisition unit is configured to acquire relevant data of local water resources;
the model building unit is configured to build a multi-objective water resource optimization configuration model; the water resource optimal allocation model comprises an economic target with the minimum industrial water use benefit gap, a social target with the purpose of meeting the minimum water shortage of resident basic domestic water and industrial and agricultural production water and an ecological target with the purpose of meeting the minimum ecological water demand and water shortage; carrying out normalization processing on each target to enable each target to carry out weighting calculation under the same dimensional condition, and obtaining the multi-target water resource optimal configuration model;
and the optimization solving unit is configured to perform optimization solving on the multi-target water resource optimization configuration model by using a genetic algorithm to obtain an optimization result.
According to a third aspect of the embodiments of the present disclosure, there is provided an electronic device, including a memory, a processor, and a computer program stored in the memory and running on the memory, wherein the processor implements the method for optimizing and configuring water resources based on a genetic algorithm when executing the program.
According to a fourth aspect of the embodiments of the present disclosure, there is provided a non-transitory computer-readable storage medium, on which a computer program is stored, which when executed by a processor implements the above-mentioned method for optimizing and configuring water resources based on genetic algorithm.
Compared with the prior art, the beneficial effect of this disclosure is:
(1) aiming at the multi-target different dimension problems of the water resource optimization model, the scheme of the disclosure normalizes the water resource optimization model according to different requirements of each target, reasonably distributes weights of different targets, determines a final multi-target optimization model, and solves the problem that the accuracy of an optimization result is low because a multi-target function is simply and linearly weighted and converted into a single-target model in the prior art;
(2) according to the scheme, the economic target, the social target and the ecological target are subjected to normalization processing, so that the targets are subjected to weighting calculation under the same dimensional condition. After normalization, the weight of each target has more practical significance.
(3) The water resource optimization model has the characteristics of multivariable, multi-target, multi-constraint and the like, and when the model is solved, the genetic optimization algorithm is adopted, compared with the traditional solving method, the algorithm is applied to solving of the multi-target, multivariable and multi-constraint water resource optimization model, and has great advantages in the aspects of calculation speed, precision, convergence, adaptability and mobility.
Advantages of additional aspects of the disclosure will be set forth in part in the description which follows, and in part will be obvious from the description, or may be learned by practice of the disclosure.
Drawings
The accompanying drawings, which are included to provide a further understanding of the disclosure, illustrate embodiments of the disclosure and together with the description serve to explain the disclosure and are not to limit the disclosure.
FIG. 1 is a schematic diagram of a water resource allocation structure according to a first embodiment of the present disclosure;
fig. 2 is a flowchart of a water resource optimization configuration method according to a first embodiment of the present disclosure.
Detailed Description
The present disclosure is further described with reference to the following drawings and examples.
It should be noted that the following detailed description is exemplary and is intended to provide further explanation of the disclosure. Unless defined otherwise, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this disclosure belongs.
It is noted that the terminology used herein is for the purpose of describing particular embodiments only and is not intended to be limiting of example embodiments according to the present disclosure. As used herein, the singular forms "a", "an" and "the" are intended to include the plural forms as well, and it should be understood that when the terms "comprises" and/or "comprising" are used in this specification, they specify the presence of stated features, steps, operations, devices, components, and/or combinations thereof, unless the context clearly indicates otherwise.
The embodiments and features of the embodiments in the present disclosure may be combined with each other without conflict.
The first embodiment is as follows:
the embodiment aims to provide a water resource optimal configuration method based on a genetic algorithm.
A water resource optimal configuration method based on a genetic algorithm comprises the following steps:
acquiring relevant data of local water resources;
constructing a multi-target water resource optimal configuration model; the water resource optimal allocation model comprises an economic target with the minimum industrial water use benefit gap, a social target with the purpose of meeting the minimum water shortage of resident basic domestic water and industrial and agricultural production water and an ecological target with the purpose of meeting the minimum ecological water demand and water shortage; carrying out normalization processing on each target to enable each target to carry out weighting calculation under the same dimensional condition, and obtaining the multi-target water resource optimal configuration model;
and carrying out optimization solution on the multi-target water resource optimization configuration model by using a genetic algorithm to obtain an optimization result.
Specifically, for ease of understanding, the embodiments of the present disclosure are described in detail below:
water resource allocation structure As shown in FIG. 1, the water supply source includes local surface water, underground water, external transfer water and others, and the user types include domestic water, agricultural water, industrial water and ecological water. The purpose of the water resource optimal allocation is to calculate the water amount of different water sources flowing to different user types of different partitions respectively.
(1) Firstly, the water resource in a research area is analyzed for supply and demand: predicting the domestic water demand, the industrial water demand, the agricultural water demand and the ecological water demand according to the water consumption quota; the available water supply of each water source in each region under different guarantee rates is calculated according to local government planning data.
(2) Establishing a water resource optimization configuration model
Object function (normalization of each object)
The first target is: economic goal, industrial water efficiency gap is minimum.
Wherein i represents the ith partition; j represents class j user (industrial) water; k represents the kth water supply source; WSijkRepresenting the water supply amount of the kth water source to the jth user of the subarea i; EWjRepresents the economic benefit (Yuan/m) brought by single water to the jth user3)；λijWeighting the water consumption of the jth class user of the partition i; n, m and L are the number of subareas, the number of water users and the number of water source types respectively, and PGDP is the planned GDP of the area.
And a second target: the social goal is to meet the basic domestic water of residents, improve the development quality of industry and agriculture, maximize social benefit and minimize water shortage.
Wherein, WRijRepresenting the water demand (ten thousand m) of class i and j users of the partition3) Including resident life and production water demand, WRA represents the total water demand (ten thousand meters) of all the regional resident life and production3)。
And a third target: the ecological target is the minimum water shortage amount which meets the ecological water demand.
Wherein EWDLijThe lower limit of the ecological water demand of the jth class of users in the subarea i is shown, and the EWD represents the total ecological water demand of all subareas.
The first, second and third integrated targets are as follows: beta-min ═ beta1g1(WS)+β2g2(WS)+β3g3(WS) in which1、β2、β3Is a weight coefficient, beta1+β2+β3＝1。
Constraint conditions
And (3) water supply capacity constraint:
wherein, WRPikThe upper limit of the water supply available for the kth water source of the ith division.
The water demand capacity of the user is restricted:
wherein, WRminij、WRmaxijAnd respectively representing the water requirement limit and the upper limit of the jth class user of the subarea i.
External adjustment water distribution constraints:
wherein, WAijDistributing water quantity for jth class users of the subarea for external water transfer; WAPiAnd (4) representing the maximum water distribution amount of the external mixed water obtained in the subarea i.
And (3) restricting the supply and demand relationship between a water source and a user: as supply and demand relationship matrix Rk＝(rijk) When the number of the water sources is 1, the water sources of the kth class supply water to the class j users of the partition i; rk＝(rijk) When the value is 0, the relation between the k k th class water source and the j class users of the partition i is represented
(3) Solving models using genetic algorithms
With the rise and development of intelligent algorithms, a powerful new approach is provided for solving the multi-objective optimization problem. The genetic algorithm is a global optimization search algorithm and has the remarkable characteristics of simplicity, universality, strong robustness, suitability for parallel processing, wide application range and the like. The intelligent optimization algorithm is a highly effective random search algorithm developed by taking advantage of natural selection and evolution mechanisms in the biology world, is a product of cross fusion of mathematics and biology, and fully embodies the ideas of evolutionary theory and genetics theory in the process and principle of the algorithm.
The flow of the genetic algorithm is as follows:
determining a coding strategy. The solution space of the problem must first be encoded using a genetic algorithm to achieve mapping of the solution space to the genetic algorithm search space. Coding methods can be divided into three categories: binary coding method, real number coding method, symbol coding method. The model adopts real number coding.
Define the fitness function. The genetic algorithm is based on a fitness function in evolutionary search, and the fitness value of each individual in a population is used for commemorative search. The model directly takes the transformation of the objective function as a fitness function.
And thirdly, determining genetic parameters including population size, evolution algebra, selection and mutation methods, cross probability, mutation probability and the like.
And fourthly, randomly generating an initialization population.
Calculating the fitness and evaluating the population.
Sixthly, generating a next generation group. And performing crossover and mutation operations through random pairing to generate a next generation group.
And seventhly, evolution iteration. Repeating the fifth to sixth steps until the optimization standard is met, outputting the result and terminating the calculation.
(4) Plan setting
The water resource optimal allocation scheme with different water supply guarantee rates comprises the following steps:
the first scheme is as follows: the forecast year is the open water year (p is 50%), and the optimal allocation of water resources is realized by comprehensively considering three targets of economy, society and ecology.
Scheme II: the forecast year is the dry year (p is 75%), and the optimal allocation of water resources is realized by comprehensively considering three targets of economy, society and ecology.
The third scheme is as follows: the forecast year is the extra-dry water year (p is 95%), and the optimal allocation of water resources is realized by comprehensively considering three targets of economy, society and ecology.
According to the scheme disclosed by the invention, after the objective function in the water resource optimal configuration model of the multi-objective multi-water-source multi-user is normalized, the economic, life and ecological optimal configuration of the water resource is more consistent with the actual planning objective. The efficiency of solving the model based on the genetic algorithm is higher, taking the mansion area as an example, the utilization rate of water resources of the optimized configuration scheme reaches more than 95 percent, and the method has stronger applicability and can be transferred to the water resource optimization configuration scheme of other areas.
Example two:
the embodiment aims to provide a water resource optimal configuration system based on a genetic algorithm.
A water resource optimal configuration system based on genetic algorithm comprises:
the data acquisition unit is configured to acquire relevant data of local water resources;
the model building unit is configured to build a multi-objective water resource optimization configuration model; the water resource optimal allocation model comprises an economic target with the minimum industrial water use benefit gap, a social target with the purpose of meeting the minimum water shortage of resident basic domestic water and industrial and agricultural production water and an ecological target with the purpose of meeting the minimum ecological water demand and water shortage; carrying out normalization processing on each target to enable each target to carry out weighting calculation under the same dimensional condition, and obtaining the multi-target water resource optimal configuration model;
and the optimization solving unit is configured to perform optimization solving on the multi-target water resource optimization configuration model by using a genetic algorithm to obtain an optimization result.
Example three:
the embodiment aims at providing an electronic device.
An electronic device comprising a memory, a processor and a computer program stored in the memory for execution, wherein the processor implements a method for optimal allocation of water resources based on genetic algorithm when executing the program, comprising: acquiring relevant data of local water resources;
constructing a multi-target water resource optimal configuration model; the water resource optimal allocation model comprises an economic target with the minimum industrial water use benefit gap, a social target with the purpose of meeting the minimum water shortage of resident basic domestic water and industrial and agricultural production water and an ecological target with the purpose of meeting the minimum ecological water demand and water shortage; carrying out normalization processing on each target to enable each target to carry out weighting calculation under the same dimensional condition, and obtaining the multi-target water resource optimal configuration model;
and carrying out optimization solution on the multi-target water resource optimization configuration model by using a genetic algorithm to obtain an optimization result.
Example four:
it is an object of the present embodiments to provide a non-transitory computer-readable storage medium.
According to a fourth aspect of the embodiments of the present disclosure, there is provided a non-transitory computer-readable storage medium, on which a computer program is stored, the computer program, when executed by a processor, implementing the method for optimal configuration of water resources based on genetic algorithm, including:
acquiring relevant data of local water resources;
constructing a multi-target water resource optimal configuration model; the water resource optimal allocation model comprises an economic target with the minimum industrial water use benefit gap, a social target with the purpose of meeting the minimum water shortage of resident basic domestic water and industrial and agricultural production water and an ecological target with the purpose of meeting the minimum ecological water demand and water shortage; carrying out normalization processing on each target to enable each target to carry out weighting calculation under the same dimensional condition, and obtaining the multi-target water resource optimal configuration model;
and carrying out optimization solution on the multi-target water resource optimization configuration model by using a genetic algorithm to obtain an optimization result.
The embodiment provides a water resource optimal configuration method and system based on a genetic algorithm, which can be realized and has wide application prospect.
The above description is only a preferred embodiment of the present disclosure and is not intended to limit the present disclosure, and various modifications and changes may be made to the present disclosure by those skilled in the art. Any modification, equivalent replacement, improvement and the like made within the spirit and principle of the present disclosure should be included in the protection scope of the present disclosure.
Although the present disclosure has been described with reference to specific embodiments, it should be understood that the scope of the present disclosure is not limited thereto, and those skilled in the art will appreciate that various modifications and changes can be made without departing from the spirit and scope of the present disclosure.
Claims (6)
1. A water resource optimal allocation method based on genetic algorithm is characterized by comprising the following steps:
acquiring relevant data of local water resources;
constructing a multi-target water resource optimal configuration model; the water resource optimal allocation model comprises an economic target with the minimum industrial water use benefit gap, a social target with the purpose of meeting the minimum water shortage of resident basic domestic water and industrial and agricultural production water and an ecological target with the purpose of meeting the minimum ecological water demand and water shortage; carrying out normalization processing on each target to enable each target to carry out weighting calculation under the same dimensional condition, and obtaining the multi-target water resource optimal configuration model;
optimizing and solving the multi-target water resource optimization configuration model by using a genetic algorithm to obtain an optimization result;
the objective function of the economic objective is as follows:
wherein i represents the ith partition; j represents the j-th type of user industrial water; k represents the kth water supply source; WSijkRepresenting the water supply amount of the kth water source to the jth user of the subarea i; EWjRepresents the economic benefit brought by single water for the jth class of users, and the unit is element/m3(ii) a n, m and L are the number of subareas, the number of water users and the number of water source types respectively, and PGDP is a region planning GDP;
the objective function of the social goal is as follows:
wherein, WRijThe water demand of the jth class of users of the partition i is expressed in ten thousandm3(ii) a Including resident living and production water demand; WRA represents the total water demand of life and production of all subareas of residents and has the unit of ten thousand meters3；
The objective function of the ecological objective is as follows:
wherein λ isijFor partition i class j user water usage weight, EWDLijRepresenting the ecological water demand lower limit of class i and j users of the subarea, wherein the EWD represents the ecological total water demand of all subareas;
the multi-target water resource optimal configuration model comprehensively considers an economic target, a social target and an ecological target, and is specifically expressed as follows:
min f＝β1g1(WS)+β2g2(WS)+β3g3(WS)
wherein, beta1、β2、β3Is a weight coefficient, beta1+β2+β3＝1。
2. The method for optimal allocation of water resources based on genetic algorithm as claimed in claim 1, wherein the obtaining of the relevant data comprises performing supply and demand analysis on water resources in research area, i.e. predicting domestic water demand, industrial water demand, agricultural water demand and ecological water demand according to water usage quota; and calculating the available water supply amount of each water source of each region under different guarantee rates according to local planning data.
3. The method for optimal allocation of water resources based on genetic algorithm as claimed in claim 1, wherein the multi-objective water resource optimal allocation model includes the following constraints: water supply capacity constraints, user water demand capacity constraints, external water regulation distribution constraints, and supply and demand relationship constraints between water resources and users.
4. A water resource optimal configuration system based on genetic algorithm comprises:
the data acquisition unit is configured to acquire relevant data of local water resources;
the model building unit is configured to build a multi-objective water resource optimization configuration model; the water resource optimal allocation model comprises an economic target with the minimum industrial water use benefit gap, a social target with the purpose of meeting the minimum water shortage of resident basic domestic water and industrial and agricultural production water and an ecological target with the purpose of meeting the minimum ecological water demand and water shortage; carrying out normalization processing on each target to enable each target to carry out weighting calculation under the same dimensional condition, and obtaining the multi-target water resource optimal configuration model;
the optimization solving unit is configured to utilize a genetic algorithm to carry out optimization solving on the multi-target water resource optimization configuration model to obtain an optimization result;
the objective function of the economic objective is as follows:
wherein i represents the ith partition; j represents the j-th type of user industrial water; k represents the kth water supply source; WSijkRepresenting the water supply amount of the kth water source to the jth user of the subarea i; EWjRepresents the economic benefit brought by single water for the jth class of users, and the unit is element/m3(ii) a n, m and L are the number of subareas, the number of water users and the number of water source types respectively, and PGDP is a region planning GDP;
the objective function of the social goal is as follows:
wherein, WRijThe water demand of class i and j users of the partition is expressed in units of ten thousand meters3(ii) a Including resident living and production water demand; WRA represents the total water demand of life and production of all subareas of residents and has the unit of ten thousand meters3；
The objective function of the ecological objective is as follows:
wherein λ isijFor partition i class j user water usage weight, EWDLijRepresenting the ecological water demand lower limit of class i and j users of the subarea, wherein the EWD represents the ecological total water demand of all subareas; the multi-target water resource optimal configuration model comprehensively considers an economic target, a social target and an ecological target, and is specifically expressed as follows:
min f＝β1g1(WS)+β2g2(WS)+β3g3(WS)
wherein, beta1、β2、β3Is a weight coefficient, beta1+β2+β3＝1。
5. An electronic device comprising a memory, a processor and a computer program stored in the memory for execution, wherein the processor implements a method for optimal allocation of water resources based on genetic algorithm according to any one of claims 1 to 3.
6. A non-transitory computer readable storage medium, on which a computer program is stored, which when executed by a processor implements a method for optimal configuration of water resources based on genetic algorithms according to any of claims 1-3.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202011639879.6A CN112668909B (en) | 2020-12-31 | 2020-12-31 | Water resource optimal configuration method and system based on genetic algorithm |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202011639879.6A CN112668909B (en) | 2020-12-31 | 2020-12-31 | Water resource optimal configuration method and system based on genetic algorithm |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN112668909A CN112668909A (en) | 2021-04-16 |
| CN112668909B true CN112668909B (en) | 2022-04-01 |
Family
ID=75413977
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202011639879.6A Active CN112668909B (en) | 2020-12-31 | 2020-12-31 | Water resource optimal configuration method and system based on genetic algorithm |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN112668909B (en) |
Families Citing this family (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN113689108B (en) * | 2021-08-19 | 2023-11-28 | 崔莉 | Multi-objective optimization method and device based on ecological banking system |
| CN114154421A (en) * | 2021-12-08 | 2022-03-08 | 中国科学院地理科学与资源研究所 | Regional water resource control management method, device and system |
| CN117829559A (en) * | 2024-03-06 | 2024-04-05 | 陕西省水利电力勘测设计研究院 | Multi-water source-to-multi-user water resource integrated allocation method |
Citations (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN107480813A (en) * | 2017-07-27 | 2017-12-15 | 河海大学 | Basin water resources Optimal Configuration Method based on multi-Objective Chaotic genetic algorithm |
| CN107944603A (en) * | 2017-11-09 | 2018-04-20 | 中山大学 | Water resource optimal allocation newsboy's method based on water total amount control |
| CN111160430A (en) * | 2019-12-19 | 2020-05-15 | 广东工业大学 | Water resource optimization configuration method based on artificial intelligence algorithm |
Family Cites Families (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US7668788B2 (en) * | 2005-06-06 | 2010-02-23 | Wren William E | Resource assignment optimization using direct encoding and genetic algorithms |
-
2020
- 2020-12-31 CN CN202011639879.6A patent/CN112668909B/en active Active
Patent Citations (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN107480813A (en) * | 2017-07-27 | 2017-12-15 | 河海大学 | Basin water resources Optimal Configuration Method based on multi-Objective Chaotic genetic algorithm |
| CN107944603A (en) * | 2017-11-09 | 2018-04-20 | 中山大学 | Water resource optimal allocation newsboy's method based on water total amount control |
| CN111160430A (en) * | 2019-12-19 | 2020-05-15 | 广东工业大学 | Water resource optimization configuration method based on artificial intelligence algorithm |
Also Published As
| Publication number | Publication date |
|---|---|
| CN112668909A (en) | 2021-04-16 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN112668909B (en) | Water resource optimal configuration method and system based on genetic algorithm | |
| Cai et al. | Predicting the energy consumption of residential buildings for regional electricity supply-side and demand-side management | |
| Ma et al. | Forecasting iron ore import and consumption of China using grey model optimized by particle swarm optimization algorithm | |
| CN102629106B (en) | Water supply control method and water supply control system | |
| CN111160430A (en) | Water resource optimization configuration method based on artificial intelligence algorithm | |
| Li et al. | Water resources optimal allocation based on large-scale reservoirs in the upper reaches of Yangtze River | |
| Li et al. | Comprehensive entropy weight observability-controllability risk analysis and its application to water resource decision-making | |
| CN108564205A (en) | A kind of load model and parameter identification optimization method based on measured data | |
| Wu et al. | Intelligent optimization framework of near zero energy consumption building performance based on a hybrid machine learning algorithm | |
| CN104636834B (en) | A kind of improved joint probability plan model system optimization method | |
| CN110570041A (en) | AP clustering-based prospective year typical daily load prediction method | |
| CN109118120A (en) | Consider the Multiobjective Decision Making Method of Reservoir Operation Scheme Substantial evaluation | |
| Zhang et al. | Multi-source big data dynamic compressive sensing and optimization method for water resources based on IoT | |
| Yuan et al. | A SAFSA-and metabolism-based nonlinear grey Bernoulli model for annual water consumption prediction | |
| Li et al. | Construction and application of a refined model for the optimal allocation of water resources—Taking Guantao County, China as an example | |
| Li et al. | Comprehensive evaluation and scenario simulation of water resources carrying capacity: a case study in Xiong’an New Area, China | |
| CN109214610A (en) | A kind of saturation Methods of electric load forecasting based on shot and long term Memory Neural Networks | |
| CN113673141B (en) | Energy router modeling and optimization control method based on data driving | |
| Mrówczyńska et al. | Research on energy consumption in the city. A system of modelling potential for energy effectiveness using artificial intelligence | |
| He et al. | Solving water resource scheduling problem through an improved artificial fish swarm algorithm | |
| CN103440540B (en) | A kind of parallel method of land utilization space layout artificial immunity Optimized model | |
| Zheng et al. | Intelligent control system integration and optimization for zero energy buildings to mitigate urban heat island | |
| Lu et al. | Application of GA optimized wavelet neural networks for carrying capacity of water resources prediction | |
| CN112465214B (en) | Coupling degree calculation method for comprehensive energy system full life cycle | |
| Qu et al. | Differential evolution-optimized general regression neural network and application to forecasting water demand in Yellow River Basin |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| GR01 | Patent grant | ||
| GR01 | Patent grant |