CN113190944A - Urban rainwater drainage system automatic optimization method based on SWMM and MATLAB - Google Patents

Urban rainwater drainage system automatic optimization method based on SWMM and MATLAB Download PDF

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CN113190944A
CN113190944A CN202110485231.6A CN202110485231A CN113190944A CN 113190944 A CN113190944 A CN 113190944A CN 202110485231 A CN202110485231 A CN 202110485231A CN 113190944 A CN113190944 A CN 113190944A
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CN113190944B (en
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杜颖恩
侯精明
李轩
高徐军
杨霄
郭敏鹏
杨少雄
张文晴
周思敏
龚佳辉
张兆安
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Jiangsu Yurun Intelligent Technology Co ltd
Xian University of Technology
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Xian University of Technology
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    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F30/00—Computer-aided design [CAD]
    • G06F30/10—Geometric CAD
    • G06F30/18—Network design, e.g. design based on topological or interconnect aspects of utility systems, piping, heating ventilation air conditioning [HVAC] or cabling
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F30/00—Computer-aided design [CAD]
    • G06F30/20—Design optimisation, verification or simulation
    • G06F30/27—Design optimisation, verification or simulation using machine learning, e.g. artificial intelligence, neural networks, support vector machines [SVM] or training a model
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F30/00—Computer-aided design [CAD]
    • G06F30/20—Design optimisation, verification or simulation
    • G06F30/28—Design optimisation, verification or simulation using fluid dynamics, e.g. using Navier-Stokes equations or computational fluid dynamics [CFD]
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F8/00—Arrangements for software engineering
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    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F9/00—Arrangements for program control, e.g. control units
    • G06F9/06—Arrangements for program control, e.g. control units using stored programs, i.e. using an internal store of processing equipment to receive or retain programs
    • G06F9/44—Arrangements for executing specific programs
    • G06F9/445—Program loading or initiating
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    • G06N3/004—Artificial life, i.e. computing arrangements simulating life
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    • G06F2113/00—Details relating to the application field
    • G06F2113/08—Fluids
    • G—PHYSICS
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    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F2113/00—Details relating to the application field
    • G06F2113/14—Pipes
    • G—PHYSICS
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    • G06F—ELECTRIC DIGITAL DATA PROCESSING
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Abstract

The invention discloses an automatic optimization method of an urban rainwater drainage system based on SWMM and MATLAB, which comprises the steps of firstly completing SWMM block model building in a research area according to basic data and generating an inp format model input file; writing an interface function by performing secondary development on the SWMM, and then generating a DLL format file in a VS2013 editor for MATLAB calling; again using MATLAB programming to realize the coupling of the particle swarm optimization algorithm and the SWMM; and finally, iterative optimization is carried out through a particle swarm algorithm, an optimization design scheme which enables the overflow quantity of the nodes in the research area to be minimum is found, and automatic optimization of the urban rainwater drainage system is achieved. The method has the advantages that manual input is replaced by software programming, the artificial self-planning optimization scene is replaced by the optimization algorithm, and meanwhile LID facilities and the rainwater pipe network are optimized, so that the optimal solution can be quickly and accurately found, and the optimization integrity can be considered.

Description

Urban rainwater drainage system automatic optimization method based on SWMM and MATLAB
Technical Field
The invention belongs to the technical field of urban rainwater pipe network and LID facility layout optimization, and relates to an automatic optimization method of an urban rainwater drainage system based on SWMM and MATLAB.
Background
In recent years, with the rapid development of cities, the hardening rate of the underlying surface is rapidly improved, the urban waterlogging phenomenon is more serious, and the sponge city provides an effective way for relieving the urban waterlogging. The problem of planning and optimizing a scheme exists no matter in new city planning or old city district transformation, and at present, a numerical model is mostly adopted for relevant optimization research. However, these optimization studies mainly adopt a scenario analysis method, and by self-planning several optimization schemes and combining with model calculation, a better optimization scheme is selected according to the simulation result. The method is too dependent on the experience of designers, and the result cannot be guaranteed to be optimal; and the method needs a great deal of time and energy of designers, and the process is too tedious. When optimizing the LID facility or the rainwater pipe network, the method only considers from the LID or the rainwater pipe network, lacks of the consideration of the overall effect after the LID facility and the rainwater pipe network are combined, is not comprehensive enough, still has the defects of difficult optimal solution searching and lack of the overall optimization, and remains to be questioned whether the effect of the combination of the two independent after the unilateral optimization is optimal or not.
Disclosure of Invention
The invention aims to provide a rainwater pipe network and LID facility optimization method based on SWMM, and solves the problems that in the prior art, the optimal solution cannot be found by depending on actual experience of designers too much, and manual modification and input are complicated.
According to the method, automatic modification of LID and rainwater pipe network parameter input in the model can be conveniently realized through software programming, the modified model can automatically run, and an optimal solution is found by combining an intelligent optimization algorithm.
The method adopts the technology that an SWMM and MATLAB-based automatic optimization method for the urban rainwater drainage system is used for firstly completing SWMM block model building in a research area according to basic data and generating an inp-format model input file; writing an interface function by performing secondary development on the SWMM, and then generating a DLL format file in a VS2013 editor for MATLAB calling; again using MATLAB programming to realize the coupling of the particle swarm optimization algorithm and the SWMM; and finally, iterative optimization is carried out through a particle swarm algorithm, an optimization design scheme which enables the overflow quantity of the nodes in the research area to be minimum is found, and automatic optimization of the urban rainwater drainage system is achieved.
The method comprises the following specific steps:
step 1, completing construction of an SWMM model by utilizing information such as land parcel data (or CAD (computer-aided design) and satellite images) given by a research area, pipe network and rainwater well data, LID layout data and the like, completing parameter calibration on the model, and finally generating a text file in an inp format;
step 2, performing secondary development on the SWMM source code, writing an interface function called by the MATLAB to the SWMM dynamic link library into the SWMM source code, and then generating a DLL file from the SWMM source code written with the interface function by using a VS2013 editor;
step 3, writing a particle swarm algorithm program by using MATLAB, constructing a target optimization function by taking the total overflow amount of the nodes in the research area as an optimization target, and simultaneously completing the calling and modifying of the inp file and writing of a calling function of the DLL;
and 4, operating the particle swarm optimization algorithm program written by MATLAB in the step 3, taking the inp file in the step 1 as an input file, taking the function constructed in the step 3 as a target function, calculating the fitness value of the particle swarm optimization by using the inp file in the step 3 and a call function of the DLL dynamic link library, and finally obtaining the optimal rainwater pipe network and LID layout optimization scheme through continuous iteration.
The SWMM dynamic link library generated in step 2 is specifically as follows:
the dynamic link library is generated in a C + + language environment based on SWMM source codes, and can be called through MATLAB to realize file input and output and program operation of SWMM and read of SWMM simulation results.
In step 3, the specific construction method of the objective function is as follows:
the objective function of the invention is to control the overflow amount of the nodes by designing pipelines with different diameters and changing the layout area of LID in different areas so as to minimize the total overflow amount of the nodes in the areas of study, therefore, the cost and overflow amount control objective function of the design of the rainwater drainage system can be expressed as:
Figure BDA0003050010590000031
the limiting conditions are as follows:
[Violationall]＝[Violationp]+[Violationh]+[Violations] (2)
Figure BDA0003050010590000032
Ek∈Ω (4)
Figure BDA0003050010590000033
Figure BDA0003050010590000034
Ip＝(Hp→u-Hu→p)/Lp×100％ (7)
Dp∈B (8)
Dp≥max{Dp→u} (9)
Figure BDA0003050010590000041
in the formula: qkIndicating the overflow amount of the kth node; j represents the total number of nodes in the study area; k represents the total LID monomer number in the study area; p represents the total pipe network number of the research area;
[Violationall]all penalty terms are represented; [ Violation ]p]Representing a penalty function corresponding to a rain drainage system cost limit; [ Violation ]h]Representing a secondary penalty function corresponding to the hydraulic limitation of the pipeline; [ Violation ]s]A penalty function corresponding to the area ratio limit of the land where the LID facility is located is represented;
C(Dp) Represents the cost per unit length of the pipeline, where DpThe pipe diameters of different pipelines; c (E)k) Representing the cost of the corresponding LID facility per unit area,
Ekdifferent LID facility types; skRepresenting the area of different types of LID installations; Ω represents all types of constructed LIDs in the research area; fD、FERespectively representing the upper limit of investment cost for the construction of a pipe network and an LID facility; alpha represents [ Violation ]p]The penalty coefficient of the penalty item is a positive integer; vpIndicating the pipe full flow rate; n represents a pipe wall roughness coefficient; rpRepresents the hydraulic radius of the pipeline; i ispRepresents the pipeline grade; l ispRepresents the length of the pipe; hp→u、Hu→pRespectively represent a conduit DpThe bottom elevations of the upstream and downstream nodes; b represents a total set of pipeline specifications that may be purchased locally; dp→uRepresenting a pipe DpMaximum diameter of the upstream pipe; vp,max、Vp,minRespectively representing the maximum flow rate and the minimum flow rate allowed for the pipeline; eta represents [ Violation ]h]The secondary punishment coefficient of the punishment item is a positive integer; pkRepresenting the proportion of the LID facility area to the land parcel; pk,maxRepresenting the area proportion of the land parcel occupied by the LID facility; theta represents [ Violation ]s]And the penalty coefficient of the penalty item is a positive integer.
In step 3, the particle swarm optimization algorithm comprises the following specific steps:
step 3.1, initializing particle population algorithm parameters, setting maximum iteration times, the number of independent variables of a target function and upper and lower thresholds of the position and the speed of the particles, and setting the particle swarm size;
step 3.2, initializing the speed and position of each particle by adopting a Latin hypercube sampling method;
step 3.3, taking the inp file in the step 1 as an input file, calculating the fitness value of each particle in the research area by using the call and running functions of a DLL file compiled by MATLAB and taking the overflow amount of the node as an optimization target, and updating the individual optimal position of the particle and the historical optimal position of the group;
and 3.4, updating the position and the speed of each particle, wherein the calculation formula is as follows:
Figure BDA0003050010590000051
in the formula:
omega represents an inertia factor which is a non-negative number, the global optimization capability is strong when the inertia factor is large, and the local optimization capability is strong when the inertia factor is small; c1、C2Respectively representing the learning factor of the particle individual and the social learning factor of the particle;
rand (0,1) denotes the interval [0,1]]A random number of (c); pidDimension d representing the individual mechanism of the ith variable;
Pgda d-dimension representing a global optimal solution; xid、VidRespectively representing the position and the speed of the ith variable dimension of the particle;
step 3.5, calculating the fitness value of each particle after updating by adopting the method in the step 3.3, and updating the individual optimal position of the particle and the historical optimal position of the group;
and 3.6, judging whether the maximum iteration times is reached, if so, jumping out of iteration and outputting an optimal solution, otherwise, returning to the step 3.4.
The invention has the beneficial effects that:
the invention combines a hypercube sampling method and a particle swarm optimization algorithm, replaces manual input with software programming, adopts the optimization algorithm to replace manual self-simulation optimization scene, and simultaneously optimizes LID facilities and a rainwater network, thereby quickly and accurately finding the optimal solution and considering the integrity of optimization.
By MATLAB software programming, automatic modification of LID and rainwater pipe network parameter input in the model can be conveniently realized, the model is automatically operated, and finally an optimal solution is found to realize the automatic optimization process of the urban rainwater drainage system; the method can replace designers to carry out optimization design by depending on actual experience, can solve the problems of complicated manual modification input and the like, ensures that the found solution is the optimal solution, and can guide the designers to carry out optimization design on the urban rainwater drainage system.
Drawings
FIG. 1 is a flow chart of an implementation of an automatic optimization scheme of an urban rainwater drainage system based on SWMM and MATLAB according to the present invention;
FIG. 2 is a SWMM model of a certain area to be optimized, which contains rainwater pipe network and LID facilities, based on SWMM and MATLAB automatic optimization scheme of the urban rainwater drainage system of the present invention;
fig. 3 is a diagram of the optimization results of the example in fig. 2 of an automatic optimization scheme of an urban rainwater drainage system based on SWMM and MATLAB according to the present invention.
Detailed Description
The present invention will be described in detail below with reference to the accompanying drawings and specific embodiments.
An automatic optimization method of an urban rainwater drainage system based on SWMM and MATLAB is disclosed, as shown in figure 1, firstly, SWMM block model building in a research district is completed according to basic data, and an inp format model input file is generated; writing an interface function by performing secondary development on the SWMM, and then generating a DLL format file in a VS2013 editor for MATLAB calling; again using MATLAB programming to realize the coupling of the particle swarm optimization algorithm and the SWMM; and finally, iterative optimization is carried out through a particle swarm algorithm, an optimization design scheme which enables the overflow quantity of the nodes in the research area to be minimum is found, and the automatic optimization process of the urban rainwater drainage system is realized.
The method comprises the following specific steps:
step 1, completing construction of an SWMM model by utilizing information such as land parcel data (or CAD (computer-aided design) and satellite images) given by a research area, pipe network and rainwater well data, LID layout data and the like, completing parameter calibration on the model, and finally generating a text file in an inp format;
step 2, performing secondary development on the SWMM source code, writing an interface function called by the MATLAB to the SWMM dynamic link library into the SWMM source code, and then generating a DLL file from the SWMM source code written with the interface function by using a VS2013 editor;
step 3, writing a particle swarm algorithm program by using MATLAB, constructing a target optimization function by taking the total overflow amount of the nodes in the research area as an optimization target, and simultaneously completing the calling and modifying of the inp file and writing of a calling function of the DLL;
and 4, in the SWMM model containing the rainwater pipe network and the LID facility to be optimized in the graph 2, operating the particle swarm optimization algorithm program written by MATLAB in the step 3, taking the inp file in the step 1 as an input file, taking the function constructed in the step 3 as a target function, calculating the fitness value of the particle swarm optimization by using the inp file in the step 3 and a calling function of the DLL dynamic link library, and finally obtaining the optimal rainwater pipe network and LID layout optimization scheme through continuous iteration.
The specific functions of the SWMM dynamic link library generated in step 2 are as follows:
the dynamic link library is generated in a C + + language environment based on SWMM source codes, and can be called through MATLAB to realize file input and output and program operation of SWMM and read of SWMM simulation results.
The specific construction method of the objective function in the step 3 is as follows:
the objective function of the invention is to control the overflow amount of the nodes by designing pipelines with different diameters and changing the layout area of LID in different areas so as to minimize the total overflow amount of the nodes in the areas of study, therefore, the cost and overflow amount control objective function of the design of the rainwater drainage system can be expressed as:
Figure BDA0003050010590000081
the limiting conditions are as follows:
[Violationall]＝[Violationp]+[Violationh]+[Violations] (2)
Figure BDA0003050010590000082
Ek∈Ω (4)
Figure BDA0003050010590000083
Figure BDA0003050010590000084
Ip＝(Hp→u-Hu→p)/Lp×100％ (7)
Dp∈B (8)
Dp≥max{Dp→u} (9)
Figure BDA0003050010590000091
in the formula:
Qk-representing the overflow volume of the kth node;
j-represents the total number of nodes in the area of interest;
k-represents the total LID monomer number in the study area;
p represents the total network number of the research area;
[Violationall]-representing all penalty terms;
[Violationp]-representing a penalty function corresponding to a rain drainage system cost limit;
[Violationh]-representing a quadratic penalty function corresponding to the hydraulic restriction of the pipeline;
[Violations]-a penalty function corresponding to the area occupancy limit of the parcel in which the LID facility is located;
C(Dp) -expressing the cost per unit length of the pipeline, wherein DpThe pipe diameters of different pipelines;
C(Ek) Representing the cost of the corresponding LID facility per unit area, where EkDifferent LID facility types;
Sk-representing the area of different types of LID facilities;
Ω — represents all types of LID that have been constructed in the study area;
FD、FErespectively representing the upper limit of investment cost for the construction of the pipe network and the LID facility;
alpha-means [ Violation ]p]The penalty coefficient of the penalty item is a positive integer;
Vp-representing a pipe full flow rate;
n-represents the pipe wall roughness coefficient;
Rp-representing the hydraulic radius of the pipeline;
Ip-representing the pipe gradient;
Lp-representing the length of the pipe;
Hp→u、Hu→prespectively representing the conduits DpThe bottom elevations of the upstream and downstream nodes;
b-represents the total set of pipe specifications that are available for purchase locally;
Dp→uto represent a conduit DpMaximum diameter of the upstream pipe;
Vp,max、Vp,min-representing the maximum and minimum flow rates allowed for the pipe, respectively;
eta-represents [ Violation ]h]The secondary punishment coefficient of the punishment item is a positive integer;
Pk-representing the proportion of the area of the LID facility in the plot;
Pk,max-representing the proportion of area of the plot occupied by the LID installation;
theta-represents [ Violation ]s]And the penalty coefficient of the penalty item is a positive integer.
The specific steps of the particle group optimization algorithm in step 3 are as follows:
and 3.1, initializing the parameters of the particle population algorithm. Setting the maximum iteration times, the number of independent variables of the objective function and upper and lower thresholds of the position and the speed of the particles, and setting the particle swarm size;
step 3.2, initializing the speed and position of each particle by adopting a Latin hypercube sampling method;
step 3.3, taking the inp file in the step 1 as an input file, calculating the fitness value of each particle in the research area by using the call and running functions of a DLL file compiled by MATLAB and taking the overflow amount of the node as an optimization target, and updating the individual optimal position of the particle and the historical optimal position of the group;
and 3.4, updating the position and the speed of each particle, wherein the calculation formula is as follows:
Figure BDA0003050010590000101
in the formula:
omega represents an inertia factor, is a non-negative number, has strong global optimization capability when being large and has strong local optimization capability when being small;
C1、C2-representing the learning factor of the individual particle and the social learning factor of the particle, respectively;
rand (0,1) -a random number representing the interval [0,1 ];
Pidthe d-dimension of the individual mechanism representing the ith variable;
Pgd-d-dimension representing a global optimal solution;
Xid、Vid-representing the position and velocity, respectively, of the d-dimension of the ith variable of the particle;
step 3.5, calculating the fitness value of each particle after updating by adopting the method in the step 3.3, and updating the individual optimal position of the particle and the historical optimal position of the group;
and 3.6, judging whether the maximum iteration times is reached, if so, jumping out of iteration and outputting an optimal solution, otherwise, returning to the step 3.4.
According to the invention, a hypercube sampling method and a particle swarm optimization algorithm are combined, manual input is replaced by software programming, an optimization algorithm is used for replacing a manual self-simulation optimization scenario, and meanwhile, LID facilities and a rainwater pipe network are optimized, so that an optimal solution can be quickly and accurately found, and the integrity of optimization can be considered.
By MATLAB software programming, automatic modification of LID and rainwater pipe network parameter input in the model can be conveniently realized, the model is automatically operated, and finally an optimal solution is found to realize the automatic optimization process of the urban rainwater drainage system; the optimization effect is shown in fig. 3, the optimized overflow amount is obviously reduced, the design can replace designers to carry out optimization design by depending on actual experience, the problems of complicated manual modification input and the like can be solved, the found solution is the optimal solution, and the designers can be guided to carry out the optimization design of the urban rainwater drainage system.

Claims (5)

1. An automatic optimization method of an urban rainwater drainage system based on SWMM and MATLAB is characterized in that firstly, SWMM block model building in a research area is completed according to basic data, and an inp format model input file is generated; writing an interface function by performing secondary development on the SWMM, and then generating a DLL format file in a VS2013 editor for MATLAB calling; again using MATLAB programming to realize the coupling of the particle swarm optimization algorithm and the SWMM; and finally, iterative optimization is carried out through a particle swarm algorithm, an optimization design scheme which enables the overflow quantity of the nodes in the research area to be minimum is found, and automatic optimization of the urban rainwater drainage system is achieved.
2. The method for automatically optimizing an urban rainwater drainage system based on SWMM and MATLAB according to claim 1, comprising the following steps:
step 1, completing construction of an SWMM model by utilizing information such as land parcel data, pipe network and rainwater well data, LID layout data and the like given by a research area, completing parameter calibration on the model, and finally generating a text file in an inp format;
step 2, performing secondary development on the SWMM source code, writing an interface function called by the MATLAB to the SWMM dynamic link library into the SWMM source code, and then generating a DLL file from the SWMM source code written with the interface function by using a VS2013 editor;
step 3, writing a particle swarm algorithm program by using MATLAB, constructing a target optimization function by taking the total overflow amount of the nodes in the research area as an optimization target, and simultaneously completing the calling and modifying of the inp file and writing of a calling function of the DLL;
and 4, operating the particle swarm optimization algorithm program written by MATLAB in the step 3, taking the inp file in the step 1 as an input file, taking the function constructed in the step 3 as a target function, calculating the fitness value of the particle swarm optimization by using the inp file in the step 3 and a call function of the DLL dynamic link library, and finally obtaining the optimal rainwater pipe network and LID layout optimization scheme through continuous iteration.
3. The method for automatically optimizing an urban rainwater drainage system based on SWMM and MATLAB according to claim 1, wherein the SWMM dynamic link library generated in the step 2 is specifically as follows:
the dynamic link library is generated in a C + + language environment based on SWMM source codes, and can be called through MATLAB to realize file input and output and program operation of SWMM and read of SWMM simulation results.
4. The method for automatically optimizing an urban rainwater drainage system based on SWMM and MATLAB according to claim 1, wherein in the step 3, the specific construction method of the objective function is as follows:
the objective function of the invention is to control the overflow amount of the nodes by designing pipelines with different diameters and changing the layout area of LID in different areas so as to minimize the total overflow amount of the nodes in the areas of study, therefore, the cost and overflow amount control objective function of the design of the rainwater drainage system can be expressed as:
Figure FDA0003050010580000021
the limiting conditions are as follows:
[Violationall]＝[Violationp]+[Violationh]+[Violations] (2)
Figure FDA0003050010580000022
Ek∈Ω (4)
Figure FDA0003050010580000023
Figure FDA0003050010580000031
Ip＝(Hp→u-Hu→p)/Lp×100％ (7)
Dp∈B (8)
Dp≥max{Dp→u} (9)
Figure FDA0003050010580000032
in the formula: qkIndicating the overflow amount of the kth node; j represents the total number of nodes in the study area; k represents the total LID monomer number in the study area; p represents the total pipe network number of the research area;
[Violationall]all penalty terms are represented; [ Violation ]p]Representing a penalty function corresponding to a rain drainage system cost limit; [ Violation ]h]Representing a secondary penalty function corresponding to the hydraulic limitation of the pipeline; [ Violation ]s]A penalty function corresponding to the area ratio limit of the land where the LID facility is located is represented;
C(Dp) Represents the cost per unit length of the pipeline, where DpThe pipe diameters of different pipelines; c (E)k) Representing the cost of the corresponding LID facility per unit area,
Ekdifferent LID facility types; skRepresenting the area of different types of LID installations; Ω represents all types of constructed LIDs in the research area; fD、FERespectively representing the upper limit of investment cost for the construction of a pipe network and an LID facility; alpha represents [ Violation ]p]The penalty coefficient of the penalty item is a positive integer; vpIndicating the pipe full flow rate; n represents a pipe wall roughness coefficient; rpRepresents the hydraulic radius of the pipeline; i ispRepresents the pipeline grade; l ispRepresents the length of the pipe; hp→u、Hu→pRespectively represent a conduit DpThe bottom elevations of the upstream and downstream nodes; b represents a total set of pipeline specifications that may be purchased locally; dp→uRepresenting a pipe DpMaximum diameter of the upstream pipe; vp,max、Vp,minRespectively representing the maximum flow rate and the minimum flow rate allowed for the pipeline; eta represents [ Violation ]h]The secondary punishment coefficient of the punishment item is a positive integer; pkRepresenting the proportion of the LID facility area to the land parcel; pk,maxRepresenting the area proportion of the land parcel occupied by the LID facility; theta represents [ Violation ]s]And the penalty coefficient of the penalty item is a positive integer.
5. The automatic urban rainwater drainage system optimization method based on SWMM and MATLAB according to claim 1, wherein in the step 3, the particle swarm optimization algorithm comprises the following specific steps:
step 3.1, initializing particle population algorithm parameters, setting maximum iteration times, the number of independent variables of a target function and upper and lower thresholds of the position and the speed of the particles, and setting the particle swarm size;
step 3.2, initializing the speed and position of each particle by adopting a Latin hypercube sampling method;
step 3.3, taking the inp file in the step 1 as an input file, calculating the fitness value of each particle in the research area by using the call and running functions of a DLL file compiled by MATLAB and taking the overflow amount of the node as an optimization target, and updating the individual optimal position of the particle and the historical optimal position of the group;
and 3.4, updating the position and the speed of each particle, wherein the calculation formula is as follows:
Figure FDA0003050010580000041
in the formula:
omega represents an inertia factor which is a non-negative number, the global optimization capability is strong when the inertia factor is large, and the local optimization capability is strong when the inertia factor is small; c1、C2Respectively representing the learning factor of the particle individual and the social learning factor of the particle;
rand (0,1) denotes the interval [0,1]]A random number of (c); pidDimension d representing the individual mechanism of the ith variable;
Pgdto representDimension d of the global optimal solution; xid、VidRespectively representing the position and the speed of the ith variable dimension of the particle;
step 3.5, calculating the fitness value of each particle after updating by adopting the method in the step 3.3, and updating the individual optimal position of the particle and the historical optimal position of the group;
and 3.6, judging whether the maximum iteration times is reached, if so, jumping out of iteration and outputting an optimal solution, otherwise, returning to the step 3.4.
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