Multi-target water resource optimal configuration method and system

Abstract

The invention provides a multi-target water resource optimization configuration method and system, and belongs to the technical field of water resource optimization configuration, and the method comprises the steps: obtaining multi-target resource optimization configuration data; constructing a multi-target water resource optimization model by using the multi-target resource optimization configuration data; and solving the multi-target water resource optimization model by using an eel-grouper optimization algorithm to obtain a Pareto optimization result, and completing optimal configuration of multi-target water resources. The problems of water resource shortage and low water resource utilization efficiency are solved.

Description

Technical Field

[0001] The present invention belongs to the technical field of water resource optimization configuration, and in particular relates to a multi-objective water resource optimization configuration method and system. Background Art

[0002] Optimal allocation of water resources is generally a multi-objective optimization problem. The various objectives are generally not comparable. Improving one of the objectives often causes other objectives to deteriorate, and it is impossible to achieve the optimal solution for all objectives at the same time. Most algorithms for solving the water resources optimization allocation model use older algorithms such as linear programming, nonlinear programming, dynamic programming, and genetic algorithms. Although the structure is mature, traditional optimization methods have shortcomings such as low search accuracy, slow convergence speed, and easy to fall into local optimality in multi-objective, multi-variable, and high-dimensional mathematical models. In recent years, the swarm metaheuristic optimization algorithm has been widely used in the field of multi-objective model solving due to its stable performance and high search efficiency, but it is less used in water resources allocation. Therefore, it is very necessary to design a new method for optimal allocation of water resources. Summary of the invention

[0003] In view of the above-mentioned deficiencies in the prior art, the present invention provides a multi-objective water resource optimization configuration method and system, which solves the problems of water resource shortage and low water resource utilization efficiency.

[0004] In order to achieve the above purpose, the technical solution adopted by the present invention is: a multi-objective water resources optimization configuration method, comprising the following steps:

[0005] Obtain multi-objective resource optimization configuration data;

[0006] Using multi-objective resource optimization configuration data, a multi-objective water resources optimization model is constructed;

[0007] The eel-grouper optimization algorithm is used to solve the multi-objective water resources optimization model, obtain the Pareto optimization result, and complete the optimal allocation of multi-objective water resources.

[0008] The beneficial effects of the present invention are as follows: the present invention analyzes and calculates the basic data of multi-objective water resource optimization configuration, establishes a multi-objective water resource optimization configuration model, solves the multi-objective water resource optimization configuration model based on the improved eel-grouper optimization algorithm (Eel and grouper optimizer, referred to as EGO), obtains a Pareto non-inferior solution set (a series of Pareto non-inferior solution sets are obtained after iterative calculation in step A8), and can provide decision makers with multiple reliable multi-water source joint scheduling schemes. Decision makers can select appropriate schemes for multi-water source joint scheduling and management based on the degree of preference for the objective function. The multi-objective water resource configuration optimization method provided by the present invention can realize the transfer of water resources from low-value uses to high-value uses, improve the utilization efficiency of water resources, and is of great significance for solving the water resource shortage crisis.

[0009] Furthermore, the multi-objective water resources optimization model is constructed as follows:

[0010] Using multi-objective resource optimization configuration data, construct the objective function of the multi-objective water resources optimization model;

[0011] Determine the constraints of the multi-objective water resources optimization model and complete the construction of the multi-objective water resources optimization model.

[0012] The beneficial effect of the above further plan is: based on the water demand data and water supply data of the study area, three objective functions including economic benefits, social benefits and environmental benefits are constructed, and a multi-objective water resources optimization allocation model that comprehensively considers the water supply capacity constraints of the water supply system, the water delivery capacity constraints of the water transmission system, the user water demand constraints and the non-negative constraints of variables is established. The establishment of this target water resources optimization allocation model not only promotes local economic development, but also protects the ecological environment, which is conducive to the harmonious development of social economy and environmental resources.

[0013] Furthermore, the expression of the objective function is as follows:

[0014] f(X)=opt{f1(X),f2(X),f3(X)}

[0015]

[0016]

[0017] Among them, f(X) represents the objective function of the multi-objective water resources optimization model, opt{} represents the optimization objective function, f1(X) represents economic benefits, f2(X) represents social benefits, f3(X) represents environmental benefits, maxf1(x) represents the maximum economic benefit, which is represented by the maximum sum of the net benefits generated by the regional independent water source and public water source water supply, K represents the sub-area, J(K) represents the Jth water user in the Kth sub-area, I represents the independent water source, represents the benefit coefficient of the unit water supply from independent water source i to user j in sub-area k, represents the cost coefficient of the unit water supply from independent water source i to user j in sub-area k, represents the water supply from independent water source i to user j in sub-area k, represents the water supply order coefficient of independent water source i, C represents the public water source, represents the benefit coefficient of the unit water supply from public water source c to user j in sub-area k, represents the cost coefficient of the unit water supply from public water source c to user j in sub-area k, represents the water supply from public water source c to user j in sub-area k, represents the water supply order coefficient of the public water source c, represents the water use equity coefficient of user j in sub-area k, maxf2(x) represents the maximum social benefit, which is used to represent the minimum total water shortage of each sub-area water supply system in each level year. represents the water demand of user j in sub-area k, maxf3(x) represents the maximum environmental benefit, which is used to characterize the minimum sum of COD emissions in each sub-area in each horizontal year, J(K) represents the Jth water user in the Kth sub-area, I(K) represents the Ith independent water source in the Kth sub-area, C(K) represents the Cth public water source in the Kth sub-area, It represents the content of chemical oxygen demand (COD) in the unit wastewater discharge of user j in sub-area k. Represents the sewage discharge coefficient of user j in sub-area k.

[0018] Furthermore, the constraints include:

[0019] Water supply capacity constraints of water supply system:

[0020] Independent water source

[0021]

[0022] Among them, W i k represents the water supply of independent water source i in sub-area k, represents the water supply of the public water source c in sub-area k, W c represents the water supply of the public water source c;

[0023] Water delivery capacity constraints of water delivery system:

[0024] Independent water source

[0025] Public water source

[0026] in, and They represent the maximum water delivery capacity of the independent water source i and the public water source c in sub-area k respectively;

[0027] User water demand constraints:

[0028]

[0029] in, and They represent the minimum and maximum water demands of user j in sub-area k respectively;

[0030] Variable non-negative constraints:

[0031] Independent water source

[0032] Public water source

[0033] The beneficial effect of the above further scheme is that the boundary conditions of the multi-objective water resources optimization allocation model are stipulated, which is conducive to the solution and calculation of the model.

[0034] Furthermore, the eel-grouper optimization algorithm is used to solve the multi-objective water resources optimization model to obtain the Pareto optimization result, which is specifically:

[0035] A1. Randomly initialize the population and set the initial value of the population;

[0036] A2. Utilize multi-objective resource optimization configuration data to calculate the fitness of each search agent;

[0037] A3. Sort the initial population based on fitness;

[0038] A4. Update the best particle X based on fitness according to the sorting results Prey , Best Search Agent X Grouper and random search agent X Eel ;

[0039] A5. In response to t being less than the maximum number of iterations, update variables a and starvation_rate, where t represents the current number of iterations and starvation_rate represents the starvation rate;

[0040] A6. For each search agent, update r1, r2, r3, r4, c1, c2 and p, and set the best search agent X Grouper The best search agent in the population and the random search agent X Eel For the best search based on proxy X Grouper new random position, and update x1 and x2 respectively, where r1 and r2 both represent random numbers between 0 and 1, r3 represents a random number, and r3=(a-2)×r1+2, r4 represents a random number, and r4=100×rand, rand represents a random number between 0 and 1, c1 represents a random number in the range of [-a, a], c2 represents a random number in the range of [0, 2], p represents the probability of the prey being caught by each type of fish, x1 represents the position updated using the eel hunting model, and x2 represents the position updated using the grouper hunting model;

[0041] A7. In response to p being less than a preset threshold, the position of the current search agent is updated using the following formula:

[0042]

[0043] in, represents the position of the t+1th search agent;

[0044] A8. In response to p being greater than or equal to a preset threshold, the position of the current search agent is updated using the following formula:

[0045]

[0046] A9. In response to t being greater than or equal to the maximum number of iterations, the iterative calculation is terminated to obtain the Pareto optimization result and complete the optimal configuration of multi-objective water resources. Otherwise, return to A6.

[0047] The beneficial effect of the above further scheme is: based on the improved eel and grouper optimizer (Eel and grouper optimizer, referred to as EGO) algorithm, the multi-objective water resources optimization allocation model is solved to obtain a Pareto non-inferior solution set, which can provide decision makers with multiple reliable multi-water source joint scheduling schemes.

[0048] Furthermore, the expression of the position x1 updated using the eel hunting model is as follows:

[0049]

[0050] Where e represents the base of the natural logarithm function, represents the coefficient vector, Indicates the current position of the eel; Indicates the current location of the prey;

[0051] The expression of the position x2 updated using the grouper hunting model is as follows:

[0052]

[0053] in, Indicates the current position of the grouper.

[0054] The present invention also provides a multi-objective water resources optimization configuration system, comprising:

[0055] A first processing module is used to obtain multi-objective resource optimization configuration data;

[0056] The second processing module is used to construct a multi-objective water resource optimization model using multi-objective resource optimization configuration data;

[0057] The third processing module is used to solve the multi-objective water resources optimization model using the eel-grouper optimization algorithm, obtain the Pareto optimization result, and complete the optimal configuration of multi-objective water resources. BRIEF DESCRIPTION OF THE DRAWINGS

[0058] Figure 1 The figure is a flow chart of the method of the present invention.

[0059] Figure 2 It is a schematic diagram of the system structure of the present invention. DETAILED DESCRIPTION

[0060] The specific implementation modes of the present invention are described below so that those skilled in the art can understand the present invention. However, it should be clear that the present invention is not limited to the scope of the specific implementation modes. For those of ordinary skill in the art, as long as various changes are within the spirit and scope of the present invention as defined and determined by the attached claims, these changes are obvious, and all inventions and creations utilizing the concept of the present invention are protected.

[0061] Example 1

[0062] like Figure 1 As shown, the present invention provides a multi-objective water resources optimization configuration method, and its implementation method is as follows:

[0063] Obtain multi-objective resource optimization configuration data;

[0064] Using multi-objective resource optimization configuration data, a multi-objective water resources optimization model is constructed, and its implementation method is as follows:

[0065] Using multi-objective resource optimization configuration data, construct the objective function of the multi-objective water resources optimization model;

[0066] Determine the constraints of the multi-objective water resources optimization model and complete the construction of the multi-objective water resources optimization model.

[0067] In this embodiment, the objective function is established with economic benefit, social benefit and ecological benefit as the goal, which is specifically:

[0068] f(X)=opt{f1(X), f2(X), f3(X)}.

[0069] In this embodiment, objective 1 (economic benefit) is characterized by maximizing the sum of the net benefits generated by the regional independent water source and the public water source:

[0070]

[0071] In this embodiment, Goal 2 (social benefit): the total water shortage of each sub-district water supply system in each level year is minimized:

[0072]

[0073] In this example, goal 3 (environmental benefit): the sum of COD emissions in each sub-area in each level year is minimized:

[0074]

[0075] Among them, f(X) represents the objective function of the multi-objective water resources optimization model, opt{} represents the optimization objective function, f1(X) represents economic benefits, f2(X) represents social benefits, f3(X) represents environmental benefits, maxf1(x) represents the maximum economic benefit, which is represented by the maximum sum of the net benefits generated by the regional independent water source and public water source water supply, K represents the sub-area, J(K) represents the Jth water user in the Kth sub-area, I represents the independent water source, represents the benefit coefficient of the unit water supply from independent water source i to user j in sub-area k, represents the cost coefficient of the unit water supply from independent water source i to user j in sub-area k, represents the water supply from independent water source i to user j in sub-area k, represents the water supply order coefficient of independent water source i, C represents the public water source, represents the benefit coefficient of the unit water supply from public water source c to user j in sub-area k, represents the cost coefficient of the unit water supply from public water source c to user j in sub-area k, represents the water supply from public water source c to user j in sub-area k, represents the water supply order coefficient of the public water source c, represents the water use equity coefficient of user j in sub-area k, maxf2(x) represents the maximum social benefit, which is used to represent the minimum total water shortage of each sub-area water supply system in each level year. represents the water demand of user j in sub-area k, maxf3(x) represents the maximum environmental benefit, which is used to characterize the minimum sum of COD emissions in each sub-area in each horizontal year, J(K) represents the Jth water user in the Kth sub-area, I(K) represents the Ith independent water source in the Kth sub-area, C(K) represents the Cth public water source in the Kth sub-area, It represents the content of chemical oxygen demand (COD) in the unit wastewater discharge of user j in sub-area k. Represents the sewage discharge coefficient of user j in sub-area k.

[0076] In this embodiment, the constraint conditions are determined, which are specifically:

[0077] Water supply capacity constraints of water supply system:

[0078] Independent water source

[0079] Public water source

[0080] Among them, W i k represents the water supply of independent water source i in sub-area k, represents the water supply of the public water source c in sub-area k, W c Represents the water supply of the public water source c.

[0081] Water delivery capacity constraints of water delivery system:

[0082] Independent water source

[0083] Public water source

[0084] in, and They represent the maximum water delivery capacity of the independent water source i and the public water source c in sub-area k respectively;

[0085] User water demand constraints:

[0086]

[0087] in, and They represent the minimum and maximum water demands of user j in sub-area k respectively;

[0088] Variable non-negative constraints:

[0089] Independent water source

[0090] Public water source

[0091] The eel-grouper optimization algorithm is used to solve the multi-objective water resources optimization model, obtain the Pareto optimization result, and complete the optimal configuration of multi-objective water resources, which is specifically:

[0092] A1. Randomly initialize the population and set the initial value of the population;

[0093] A2. Utilize multi-objective resource optimization configuration data to calculate the fitness of each search agent;

[0094] A3. Sort the initial population based on fitness;

[0095] A4. Update the best particle X based on fitness according to the sorting results Prey , Best Search Agent X Grouper and random search agent X Eel ;

[0096] A5. In response to t being less than the maximum number of iterations, update variables a and starvation_rate, where t represents the current number of iterations and starvation_rate represents the starvation rate;

[0097] A6. For each search agent, update r1, r2, r3, r4, c1, c2 and p, and set the best search agent X Grouper The best search agent in the population and the random search agent X Eel For the best search based on proxy X Grouper new random positions, and update x1 and x2 respectively, where r1 and r2 both represent random numbers between 0 and 1, r3 represents a random number, and r3=(a-2)×r1+2, r4 represents a random number, and r4=100×rand, rand represents a random number between 0 and 1, c1 represents a random number in the range of [-a, a], c2 represents a random number in the range of [0, 2], p represents the probability of the prey being caught by each type of fish, such as eel or grouper, x1 represents the position updated using the eel hunting model, and x2 represents the position updated using the grouper hunting model;

[0098] The expression for the updated position x1 using the eel hunting model is as follows:

[0099]

[0100] Where e represents the base of the natural logarithm function, represents the coefficient vector, Indicates the current position of the eel, Indicates the current location of the prey;

[0101] The expression of the position x2 updated using the grouper hunting model is as follows:

[0102]

[0103] in, Indicates the current position of the grouper;

[0104] A7. In response to p being less than a preset threshold, the position of the current search agent is updated using the following formula:

[0105]

[0106] in, represents the position of the t+1th search agent;

[0107] A8. In response to p being greater than or equal to a preset threshold, the position of the current search agent is updated using the following formula:

[0108]

[0109] A9. In response to t being greater than or equal to the maximum number of iterations, the iterative calculation is terminated to obtain the Pareto optimization result and complete the optimal configuration of multi-objective water resources. Otherwise, return to A6.

[0110] In summary, the present invention establishes a multi-objective water resource optimization configuration model through the analysis and calculation of the basic data of multi-objective water resource optimization configuration, and solves the multi-objective water resource optimization configuration model based on the improved eel-grouper optimization algorithm (Eel and grouperoptimizer, referred to as EGO), and obtains a Pareto non-inferior solution set, which can provide decision makers with multiple reliable multi-water source joint scheduling schemes. Decision makers can select appropriate schemes for multi-water source joint scheduling and management based on the degree of preference for the objective function. The multi-objective water resource configuration optimization method provided by the present invention can realize the transfer of water resources from low-value uses to high-value uses, improve the utilization efficiency of water resources, and is of great significance for solving the water resource shortage crisis.

[0111] Example 2

[0112] like Figure 2 As shown, the present invention also provides a multi-objective water resources optimization configuration system, comprising:

[0113] A first processing module is used to obtain multi-objective resource optimization configuration data;

[0114] The second processing module is used to construct a multi-objective water resource optimization model using multi-objective resource optimization configuration data;

[0115] The third processing module is used to solve the multi-objective water resources optimization model using the eel-grouper optimization algorithm, obtain the Pareto optimization result, and complete the optimal configuration of multi-objective water resources.

[0116] like Figure 2 The multi-objective water resource optimization configuration system provided in the illustrated embodiment can execute the technical solution shown in the multi-objective water resource optimization configuration method in the above-mentioned method embodiment 1, and its implementation principle and beneficial effects are similar and will not be repeated here.

[0117] In this embodiment, the present application can divide the functional units according to the multi-objective water resources optimization configuration method. For example, each function can be divided into each functional unit, or two or more functions can be integrated into one processing unit. The above integrated unit can be implemented in the form of hardware or in the form of software functional units. It should be noted that the division of units in the present invention is schematic and is only a logical division. There may be other division methods in actual implementation.

[0118] In this embodiment, in order to realize the principles and beneficial effects of the multi-objective water resource optimization configuration method, the multi-objective water resource optimization configuration system includes hardware structures and / or software modules corresponding to the execution of each function. Those skilled in the art should easily realize that, in combination with the schematic units and algorithm steps described in the embodiments disclosed in the present invention, the present invention can be implemented in the form of hardware and / or a combination of hardware and computer software. Whether a function is executed in a hardware or computer software driven manner depends on the specific application and design constraints of the technical solution. Different methods can be used for each specific application to implement the described function, but such implementation should not be considered to exceed the scope of this application.

Claims

1. A multi-objective water resources optimization allocation method, characterized in that: The following steps are involved: Obtain multi-objective resource optimization configuration data; Using multi-objective resource optimization configuration data, a multi-objective water resources optimization model is constructed; The eel-grouper optimization algorithm is used to solve the multi-objective water resources optimization model, obtain the Pareto optimization result, and complete the optimal allocation of multi-objective water resources.

2. The multi-objective water resources optimization allocation method according to claim 1 is characterized in that: The multi-objective water resources optimization model is constructed as follows: Using multi-objective resource optimization configuration data, construct the objective function of the multi-objective water resources optimization model; Determine the constraints of the multi-objective water resources optimization model and complete the construction of the multi-objective water resources optimization model.

3. The multi-objective water resources optimization allocation method according to claim 2 is characterized in that: The expression of the objective function is as follows: f(X)=opt{f1(X),f2(X),f3(X)} Among them, f(X) represents the objective function of the multi-objective water resources optimization model, opt{} represents the optimization objective function, f1(X) represents economic benefits, f2(X) represents social benefits, f3(X) represents environmental benefits, maxf1(x) represents the maximum economic benefit, which is represented by the maximum sum of the net benefits generated by the regional independent water source and public water source water supply, K represents the sub-area, J(K) represents the Jth water user in the Kth sub-area, I represents the independent water source, represents the benefit coefficient of the unit water supply from independent water source i to user j in sub-area k, represents the cost coefficient of the unit water supply from independent water source i to user j in sub-area k, represents the water supply from independent water source i to user j in sub-area k, represents the water supply order coefficient of independent water source i, C represents the public water source, represents the benefit coefficient of the unit water supply from public water source c to user j in sub-area k, represents the cost coefficient of the unit water supply from public water source c to user j in sub-area k, represents the water supply from public water source c to user j in sub-area k, represents the water supply order coefficient of the public water source c, represents the water use equity coefficient of user j in sub-area k, maxf2(x) represents the maximum social benefit, which is used to represent the minimum total water shortage of each sub-area water supply system in each level year. represents the water demand of user j in sub-area k, maxf3(x) represents the maximum environmental benefit, which is used to characterize the minimum sum of COD emissions in each sub-area in each horizontal year, J(K) represents the Jth water user in the Kth sub-area, I(K) represents the Ith independent water source in the Kth sub-area, C(K) represents the Cth public water source in the Kth sub-area, It represents the content of chemical oxygen demand (COD) in the unit wastewater discharge of user j in sub-area k. Represents the sewage discharge coefficient of user j in sub-area k.

4. The multi-objective water resources optimization allocation method according to claim 3 is characterized in that: The constraints include: Water supply capacity constraints of water supply system: in, represents the water supply of independent water source i in sub-area k, represents the water supply of the public water source c in sub-area k, W c represents the water supply of the public water source c; Water delivery capacity constraints of water delivery system: Independent water source Public water source in, and They represent the maximum water delivery capacity of the independent water source i and the public water source c in sub-area k respectively; User water demand constraints: in, and They represent the minimum and maximum water demands of user j in sub-area k respectively; Variable non-negative constraints: Independent water source Public water source 5. The multi-objective water resources optimization allocation method according to claim 3 is characterized in that: The multi-objective water resources optimization model is solved by using the eel-grouper optimization algorithm to obtain the Pareto optimization result, which is specifically: A1. Randomly initialize the population and set the initial value of the population; A2. Utilize multi-objective resource optimization configuration data to calculate the fitness of each search agent; A3. Sort the initial population based on fitness; A4. Update the best particle X based on fitness according to the sorting results Prey , Best Search Agent X Grouper and random search agent X Eel ; A5. In response to t being less than the maximum number of iterations, update variables a and starvation_rate, where t represents the current number of iterations and starvation_rate represents the starvation rate; A6. For each search agent, update r1, r2, r3, r4, c1, c2 and p, and set the best search agent X Grouper The best search agent in the population and the random search agent X Eel For the best search based on proxy X Grouper new random position, and update x1 and x2 respectively, where r1 and r2 both represent random numbers between 0 and 1, r3 represents a random number, and r3=(a-2)×r1+2, r4 represents a random number, and r4=100×rand, rand represents a random number between 0 and 1, c1 represents a random number in the range of [-a, a], c2 represents a random number in the range of [0, 2], p represents the probability of the prey being caught by each type of fish, x1 represents the position updated using the eel hunting model, and x2 represents the position updated using the grouper hunting model; A7. In response to p being less than a preset threshold, the position of the current search agent is updated using the following formula: in, represents the position of the t+1th search agent; A8. In response to p being greater than or equal to a preset threshold, the position of the current search agent is updated using the following formula: A9. In response to t being greater than or equal to the maximum number of iterations, the iterative calculation is terminated to obtain the Pareto optimization result and complete the optimal configuration of multi-objective water resources. Otherwise, return to A6.

6. The multi-objective water resources optimization allocation method according to claim 5 is characterized in that: The expression for the position x1 updated using the eel hunting model is as follows: Where e represents the base of the natural logarithm function, represents the coefficient vector, Indicates the current position of the eel, Indicates the current location of the prey; The expression of the position x2 updated using the grouper hunting model is as follows: in, Indicates the current position of the grouper.

7. A multi-objective water resources optimization allocation system, characterized in that: The multi-objective water resources optimization configuration system is used to execute the multi-objective water resources optimization configuration method, including: A first processing module is used to obtain multi-objective resource optimization configuration data; The second processing module is used to construct a multi-objective water resource optimization model using multi-objective resource optimization configuration data; The third processing module is used to solve the multi-objective water resources optimization model using the eel-grouper optimization algorithm, obtain the Pareto optimization result, and complete the optimal configuration of multi-objective water resources.

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