CN110084443B - QPSO optimization algorithm-based power change station operation optimization model analysis method - Google Patents
QPSO optimization algorithm-based power change station operation optimization model analysis method Download PDFInfo
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Abstract
The invention discloses a QPSO optimization algorithm-based power change station operation optimization model analysis method, which comprises the following steps of: the method comprises the following steps: establishing a power exchange station service model; step two: defining an optimization objective function of a battery swapping service model: the wind-solar load deviation rate, the wind-solar storage purchase and operation cost, the service life of an energy storage battery and the proportion of new energy power generation in the charging electric energy of the electric automobile are reduced; step three: and obtaining a power swapping station dynamic operation strategy under a power swapping station service model based on a QPSO optimization algorithm according to the objective function. According to the analysis method provided by the invention, the wind-solar energy storage capacity configuration and the operation strategy of the expressway power change station in the remote area in the electric power system are optimized, and as a result, a theoretical basis is provided for the construction and transformation scheme of the expressway independent micro-grid containing the power change station in the electric power system after the electric vehicle rapidly generates electricity, and meanwhile, a theoretical basis is provided for the follow-up work of the electric power system such as power change scheduling of the power change station.
Description
Technical Field
The invention belongs to the field of electric vehicle charging and battery replacing infrastructure construction in an electric power system, relates to an analysis method for researching wind-solar storage capacity ratio and optimized operation of a battery replacing station, and particularly relates to a battery replacing station operation optimization model analysis method based on a quantum-behaved particle swarm (QPSO) optimization algorithm.
Background
Under the background of the development of the automobile industry and the increasing occupation of automobiles, the consumption of people on petroleum resources is increased, and the severity of environmental pollution is increased. The consumption of the traditional fossil energy is reduced, renewable energy is fully utilized to generate electricity, and the popularization of the use of electric automobiles becomes an important means for energy conservation and emission reduction.
Meanwhile, at present, the Chinese expressway is developing towards a green high speed, an ecological high speed and an intelligent high speed, and due to the fact that green wind and light such as wild animals and plants, natural protection areas and the like exist around the expressway in remote areas, how to implement measures for ecological protection and water and soil loss prevention and control along the highway while ensuring traffic reduces damage to the ecological environment, the wild animals and habitats of aquatic organisms to the maximum extent, effectively eliminates potential hazards of geological disasters along the highway and avoids water and soil loss is further needed to be researched.
In conclusion, in order to avoid the situation that the expressway in the remote area becomes the bottleneck of future electric vehicle development and furthest realize the low carbon theme of the expressway in the remote area, the wind-light storage independent micro-grid comprising the electric vehicle charging and replacing power station is established on the expressway in the remote area, and important practical value and significance are undoubtedly achieved.
Disclosure of Invention
The invention provides a quick change power station operation optimization model analysis method based on a QPSO optimization algorithm, which aims to establish an independent micro-grid on a highway in a remote area and meet the requirement of quick-speed running electric automobile battery change. According to the analysis method, the wind-solar energy storage capacity configuration and the operation strategy of the expressway power change station in the remote area in the electric power system are optimized, and as a result, after the electric vehicle rapidly generates electricity, a theoretical basis is provided for the scheme for building and modifying the expressway independent micro-grid including the power change station in the electric power system, and meanwhile, a theoretical basis is provided for the follow-up work of the electric power system on power change scheduling and the like of the power change station.
The purpose of the invention is realized by the following technical scheme:
a QPSO optimization algorithm-based power change station operation optimization model analysis method comprises the following steps:
the method comprises the following steps: establishing a power exchange station service model;
step two: defining an optimization objective function of a battery swapping service model: the wind-solar load deviation rate, the purchasing and operating cost of wind-solar energy storage, the service life of an energy storage battery and the proportion of new energy power generation in the charging electric energy of the electric automobile are reduced;
step three: and obtaining a power swapping station dynamic operation strategy under a power swapping station service model based on a QPSO optimization algorithm according to the objective function.
Compared with the prior art, the invention has the following advantages:
1. the invention combines the electric automobile power exchange station, considers the characteristics of small load, high grid-connected cost, high environmental protection requirements of a plurality of wild animals and plants and natural scenic spots, and considers the flow characteristics of the high-speed highway vehicles in the remote areas and the power exchange requirements of actual users.
2. Under the condition that the battery replacement is not matched with the centralized charging of the battery replacement station, the capacity configuration and operation scheme of the battery replacement station is finally obtained through the quantum-behavior particle swarm optimization algorithm in combination with the wind-solar power generation power in different days.
3. In order to consume clean energy as much as possible, the invention utilizes the standby battery in the battery replacement station to carry out power peak clipping and valley filling service for the micro-grid.
4. The invention provides a theoretical foundation for the infrastructure construction scheme of the electric vehicle charging and battery replacing station of the power system, and also provides a theoretical basis for the subsequent work such as battery replacing scheduling of the battery replacing station and the like.
5. According to the invention, the QPSO algorithm is applied in the battery swapping service model, so that complete convergence of full probability can be achieved, and the obtained optimization result can meet the battery swapping requirement of the electric vehicle at any time.
Drawings
FIG. 1 is a diagram of a stand-alone microgrid architecture;
FIG. 2 is a schematic diagram of a power swapping service of a power swapping station;
FIG. 3 is a QPSO iteration curve of different innovation parameter extraction methods;
FIG. 4 is a QPSO algorithm operation optimization model of the power swapping station;
FIG. 5 shows the actual operation of the power station under different weather types;
fig. 6 shows the actual operation condition of the power conversion station under a special working condition.
Detailed Description
The technical solution of the present invention is further described below with reference to the accompanying drawings, but the present invention is not limited thereto, and modifications or equivalent substitutions may be made to the technical solution of the present invention without departing from the spirit and scope of the technical solution of the present invention.
The invention provides a QPSO optimization algorithm-based power change station operation optimization model analysis method, which comprises the following steps of:
the method comprises the following steps: and establishing a power swapping station service model.
The standby battery in the battery replacement station has the following four states: full, charging, waiting to charge, discharging state. When the battery changing station provides a battery changing service for the electric automobile, the battery with full capacity is used for changing the battery with the exhausted capacity of the electric automobile, so that the total amount of the standby batteries in the battery changing station is kept unchanged, and the total amount of the batteries in four states at any time is constant. Because the centralized charging of the standby batteries in the charging station is asynchronous with the charging of the electric vehicles on the highway, the wind-light storage capacity can be reduced by optimizing the charging strategy, and wind and light abandonment is avoided.
Step two: defining an optimization objective function of a battery swapping service model: the wind-solar load deviation rate, the purchase and operation cost of wind-solar energy storage, the service life of an energy storage battery and the proportion of new energy power generation in the charging electric energy of the electric automobile.
Before optimizing the operation strategy by the QPSO algorithm, an objective function to be optimized in the operation strategy of the power swapping station is determined.
Step three: and obtaining a power swapping station dynamic operation strategy under a power swapping station service model based on a QPSO optimization algorithm according to the objective function.
The method comprises the following substeps:
step three, first: setting the particle dimension of a QPSO optimization algorithm of a highway battery replacement service model;
step two: calculating an attractor;
step three: updating the particle position, and selecting an innovative parameter of a proper power station changing service model;
step three and four: and obtaining a dynamic operation strategy of the power swapping station based on a QPSO algorithm.
At present, most of electric automobile charging and battery replacing facilities are connected with a large power grid, the invention considers a completely clean wind-solar storage micro-power grid to supply energy to a battery replacing station, and considers a reserve battery in the battery replacing station to provide a service for adjusting supply and demand balance for the micro-power grid through discharging. Therefore, in this case, unlike the optimization problem of the large power grid power change station, the battery in the power change station has four states, and the temporal state conversion of the battery states in the power change station is coupled. Therefore, aiming at the emerging power station replacement problem, the optimization model becomes a complex problem of multi-constraint and strong coupling among particles. The standard particle swarm algorithm cannot solve the problems, and the situation of non-convergence or incomplete convergence occurs. The QPSO algorithm has fewer control parameters than the PSO algorithm, and particles of a quantum system in the QPSO algorithm have no established line before measurement and appear at any position of a feasible domain with a certain probability distribution, so that the global searching capability and the convergence performance of the algorithm are enhanced.
The technical scheme of the invention is further explained by combining the specific embodiment as follows:
the method comprises the following steps: establishing a power change station service model;
fig. 1 is a diagram illustrating a structure of an independent microgrid established in the present embodiment. The power supply comprises a wind driven generator, a photovoltaic cell panel and a diesel generator. The diesel generator is used as a standby battery and is used for coping with extreme weather in remote areas and improving the reliability of the independent power generation system. And when the power generated by the new energy can meet the operation requirement of the power conversion station, the diesel generator is not considered to be used. The load is an electric automobile battery replacement station which plays a role of 'dual management', on one hand, battery replacement service is provided for electric automobiles in a remote expressway research road section, and on the other hand, a standby battery in the battery replacement station provides a power tracking function for an independent micro-grid.
The 24 hours a day are divided into 24 time periods, and if the number of the batteries in the four states of full charge, charging, waiting for charging and discharging in the battery replacement station at the time i is Ns (i), nc (i), nw (i) and Nd (i), respectively. The number of the batteries in four states at the next moment is obtained by a formula:
in the formula, Ns(i-1) the number of power battery packs in the full-power state in the (i-1) th time period; n is a radical ofc(i-1) the number of power battery packs in a charging state in the (i-1) th time period; n is a radical ofd(i-1) the number of power battery packs in a discharging state in the (i-1) th time period; n is a radical of hydrogenw(i-1) the number of the power battery packs which are changed from the electric automobile in the (i-1) th time period; n is a radical of hydrogenc_start(i-1) the number of power battery packs which start to be charged in the (i-1) th time period; n is a radical of hydrogenc_finish(i-1) charging for the i-1 st periodThe number of fully charged power battery packs; n is a radical ofd_start(i-1) the number of power battery packs which start to discharge in the (i-1) th time period;
Nd_finish(i-1) the number of power battery packs which are discharged in the (i-1) th time period; n is a radical of hydrogenEV_need(i-1) is the battery with the exhausted electric quantity unloaded by the electric automobile in the period of i-1.
A schematic diagram of the power swapping station power swapping service is shown in fig. 2. In order to ensure the availability of the battery replacement service, the number of available batteries of the battery replacement station in each time interval is greater than the number of battery replacement vehicles when an operation plan is made, and the formula is as follows:
Ns(i)≥NEV_need(i)。
step two: and defining an optimization objective function of the battery swapping service model.
Before QPSO algorithm optimization is carried out, an objective function is determined, and a power conversion station dynamic operation strategy under a power conversion service model is obtained through the QPSO optimization algorithm according to the objective function.
The objective functions are respectively:
(1) Wind-solar-load deviation ratio:
in the formula (I), the compound is shown in the specification,
the average power of all loads in a time period is represented by kw; pPV(i)、 PWT(i) Photovoltaic power and fan power generation power at the moment i respectively; n is the number of samples; p isL(i) The power of the load at time i is shown in kw.
(2) The purchase and operation cost of wind and light storage is as follows:
the objective function is that on the premise of meeting the requirement of the electric automobile power conversion on the highway, the benefit of the electric automobile power conversion station is maximized by matching the wind-solar storage capacity and finding a proper strategy of concentrated charging of batteries in the power conversion station.
C＝TCd-(Cin+Cop+CFC)；
In the formula, CdThe unit is the sum of the average single-day electric vehicle battery replacement income, and is ten thousand yuan; copThe unit is ten thousand yuan for the maintenance cost of the power station equipment; cinThe investment cost of the power station changing equipment is ten thousand yuan; cFCThe unit of fuel cost of the diesel generating set is ten thousand yuan; t is the number of profitable days of the power station.
(3) Service life of the energy storage battery:
curve fitting is performed on the corresponding relation between the Depth Of charge and Discharge (DOD) and the cycle number Of the lithium iron phosphate battery, and in the embodiment, a cubic function formula is obtained by fitting an nth order function as follows:
Nm＝-2083DOD 3+8750DOD 2-13170DOD+11200；
in the formula, NmIs the cycle number of the energy storage battery.
The decay rate of the energy storage life in the working period is as follows:
wherein n is the number of charge-discharge cycles of the electric vehicle.
The life (years) of the stored energy is calculated:
in the formula, gammaEVThe driving energy consumption attenuation rate (%) of the single-day electric automobile is shown; gamma raydThe discharge attenuation rate (%) in the single-day power conversion station is shown; n is a radical ofEV、NbThe total amount of the electric automobiles on the highway and the total amount of the power batteries in the power change station are respectively.
(4) The new energy power generation accounts for the proportion of the charging electric energy of the electric automobile:
the Charging electric Energy of The power battery is from a photovoltaic generator set, a wind generating set and a diesel generator, the proportion of New Energy power generation to stored Energy Charging electric Energy (The Percentage of New Energy in Total EV Charging Energy, PNTC) represents The proportion of clean Energy used for Charging The power battery to The whole Charging electric Energy, and The formula is as follows:
in the formula, NWT、NPVThe number of the wind driven generator and the number of the photovoltaic cell panels are respectively; n is a radical ofcThe number of the batteries being charged in the battery replacement station is changed; pcCharging power of the battery replacement station is represented in kw.
Step three: and obtaining a power swapping station dynamic operation strategy under a power swapping station service model based on a QPSO optimization algorithm according to the objective function.
Step three, firstly: and setting the particle dimension of a QPSO optimization algorithm of the expressway battery swapping service model according to the objective function.
In this embodiment, according to the swapping service model established in step one, the swapping service model is set in a 27-dimensional search space, and each population consists of 100 particles, i.e., X = { X = { (X) }1,...xi,...x100}. 27. Dimension particle set as x = (N)PV,NWT,Nb,Nc_start(1),Nc_start(2)，...，Nc_start(24))T. The 1-3D particles are the number of power batteries in a photovoltaic cell panel, a wind driven generator and a battery replacement station, and the 4-27D particles are the number of batteries which are charged by the battery replacement station every hour. By selecting 27-dimensional particles, the charging variables of the single-day charging station can be determined.
Step three: and calculating the attractors.
In the quantum-behaved particle swarm optimization, the state of the particle is determined by the wave function in Schrodinger equation
Each particle is described as passing through an attractor pi＝[pi,1pi,2...pi,n]To converge to a certain area, the attractor can be calculated by the following formula:
in the formula, pbest_iIs the ith particle history best position in the current iteration; gbestIs the current globally optimal particle; p is a radical ofiIs an attractor, for the update of the ith particle position.
Step three: and updating the particle position, and selecting an innovative parameter of a proper power station changing service model.
The particle position update formula is:
in the formula, xiIs the position of the ith particle; α is an innovation parameter;
mu is a random number among (0, 1) that follows uniform distribution. The probability of the formula taking + or-is 0.5, respectively.
The value of alpha is determined according to the situation, can be fixed and unchanged, and can dynamically change according to a certain mode, and several innovative parameter value modes are selected:
in the formula, TmaxIs the maximum number of iterations; itrtn is the current iteration number.
The multi-objective function of the invention is processed into a single objective function by a linear weighting method, so that the smaller the value of the fitness value in the range of not less than 1 in the multi-objective optimization, the better the optimization effect. And (3) simulating the influence of different innovative function value formulas of the QPSO optimization algorithm on the operation optimization result of the power conversion station by using a sunny example. The QPSO iteration curve for the different innovative parameter selection methods is shown in fig. 3.
TABLE 1 simulation experiment result of QPSO algorithm by different innovation parameter extraction method
According to FIG. 3, when the innovation parameter follows α1The formula of (d) converges in 3862 iterations; when innovation parameters are according to alpha2The formula of (2) is taken, and converges in 201 iterations; when innovation parameter is according to alpha3The formula of (c) converges on 1158 iterations. As shown in the combined graph, the QPSO algorithm still has a premature trend. The premature convergence is not related to the model of the algorithm, but is related to the value and the control mode of the innovation parameter alpha. On the whole trend, the innovation parameter is small, the iteration speed is high, and the optimal value of the global optimal particle fitness value cannot be ensured; the innovation parameter is large, the time for convergence is long, and the optimization effect is good. Therefore, by combining with an actual optimization model, selecting a proper method to control innovative parameters and enabling the particles to be close to or far away from the p point is an indispensable link in the QPSO algorithm. Selecting alpha from the innovative parameter by comparison of fitness values in the table1The optimization effect is best by selecting the mode.
Step three and four: and obtaining a dynamic operation strategy of the power swapping station based on a QPSO algorithm.
The QPSO algorithm operation optimization model of the power swapping station is shown in FIG. 4, and the specific optimization steps are as follows:
(1) Inputting power conversion demand prediction and wind-solar power generation power prediction in a power conversion service model to obtain a day-ahead power conversion plan of a power conversion station;
(2) Acquiring the actual running state of the battery replacement station, initializing particle swarm parameters, swarm positions and speed, executing the step (3), and performing rolling optimization;
(3) Updating the particle speed and the particle position, judging whether the selected particles conform to the battery replacement service model, if not, firstly setting a penalty function to restrict the fitness value, and then executing the step (4); if yes, directly executing the step (4);
(4) Evaluating the fitness value and updating the optimal position and velocity of the particle, if pbest≤GbestIf yes, executing the step (5); if p isbest＞GbestThen step (6) is performed, wherein: p is a radical of formulabest、 GbestRespectively obtaining a current population optimal value and a global optimal value;
(5) Updating the global optimal particle position and speed;
(6) If the itrtn is more than or equal to T, obtaining globally optimal particles; if irtn < TmaxThen step (3) is performed.
If the predicted data has no error, the power conversion station can be completely executed according to the day-ahead operation plan, and a good operation effect is achieved. However, the prediction of the new energy power generation and battery replacement requirements includes prediction errors, the accuracy of the prediction directly influences the execution effect of the operation plan, and the prediction errors cannot be avoided. And on the basis of the day-ahead operation plan, dynamically adjusting and optimizing the day-ahead operation plan by combining actual operation data and ultra-short-term power prediction data to obtain a dynamic operation control strategy of the power switching station.
And if the particles selected by the QPSO algorithm do not accord with inequality constraints (the battery replacement requirement of the electric automobile) or the state conversion logic error occurs when the battery replacement station is replaced, setting a penalty function to constrain the fitness value. And if the selected particles are feasible after verification in the battery replacement service model, directly calculating the fitness value of the current population, and performing the next iteration operation.
The simulation takes a certain section of expressway in northwest as the background, and the actual operation conditions of the power station under different weather types are obtained according to the quantum behavior particle swarm optimization algorithm as shown in figure 5. The results of the different weather type simulations are shown in table 2.
TABLE 2 simulation results for different weather types
In order to improve the continuous power supply capacity of the system, a special working condition (such as haze weather) is considered, namely the photovoltaic cell panel does not generate electricity. In this case, the power supply demand of the power station cannot be met only by the wind driven generator to generate power. Therefore, the backup power source (diesel generator) has to be used to generate power. In the optimization algorithm, the principle of reducing the power generation of the diesel generator to the maximum extent is still adhered to from the viewpoint of economic cost or environmental protection. The actual operation condition of the power change station under the special working condition is shown in fig. 6. The simulation results for the special conditions are shown in table 3.
TABLE 3 simulation results of special operating mode types
Through analysis of the operation conditions of the power conversion stations under different types of weather and special working conditions, the wind-solar-diesel storage capacity ratio, the investment cost and the power conversion station operation benefit of the power conversion stations are obtained, and are shown in tables 4 and 5.
TABLE 4 investment cost of power station
TABLE 5 simulation results and operational benefits of power swapping station
Claims (1)
1. A QPSO optimization algorithm-based power change station operation optimization model analysis method is characterized by comprising the following steps:
the method comprises the following steps: establishing a power conversion station service model, wherein the power conversion station service model meets the following conditions:
dividing 24 hours a day into 24 time periods, if the number of the batteries in the four states of full power, charging, waiting for charging and discharging in the battery replacement station at the time i is Ns (i), nc (i), nw (i) and Nd (i), obtaining the number of the batteries in the four states at the next time by a formula:
in the formula, Ns(i-1) the number of power batteries in the full-power state in the (i-1) th time period; n is a radical ofc(i-1) the number of power batteries in a charging state in the (i-1) th time period; n is a radical of hydrogend(i-1) the number of power batteries in a discharging state in the (i-1) th time period; n is a radical ofw(i-1) the number of the power batteries which are changed from the electric automobile in the (i-1) th time period; n is a radical ofc_start(i-1) the number of power batteries which are charged for the i-1 th time period; n is a radical ofc_finish(i-1) the number of the fully charged power batteries in the (i-1) th time period; n is a radical of hydrogend_start(i-1) the number of power batteries which start to discharge in the (i-1) th time period; n is a radical of hydrogend_finish(i-1) the number of power batteries which are discharged in the (i-1) th time interval; n is a radical ofEV_need(i-1) the number of the batteries with the exhausted electric quantity unloaded by the electric automobile in the period of i-1;
in order to ensure the availability of the battery replacement service, the number of available batteries of the battery replacement station in each time period is required to be greater than the number of battery replacement vehicles when an operation plan is made, and the formula is as follows:
Ns(i)≥NEV_need(i)；
step two: defining an optimization objective function of a battery swapping service model: the wind-solar load deviation rate, the wind-solar storage purchase and operation cost, the service life of an energy storage battery and the proportion of new energy power generation in the charging electric energy of the electric automobile are as follows:
the wind-solar load deviation ratio calculation formula is as follows:
in the formula (I), the compound is shown in the specification,
average power of all loads over 24 hours; p isPV(i)、PWT(i) Photovoltaic power and fan power generation power at the moment i respectively; n is the number of samples; p isL(i) Is the power level of the load at time i;
the calculation formula of the purchasing and operating cost of the wind and light storage is as follows:
C＝TCd-(Cin+Cop+CFC)；
in the formula, CdIs the average single-day electric vehicle charging income sum; copMaintaining cost for the power station changing equipment; cinThe investment cost of the power station changing equipment is reduced; cFCIs the fuel cost of the diesel generating set; t is the number of profitable days of the power station;
the calculation formula of the service life of the energy storage battery is as follows:
in the formula, gammaEVThe driving energy consumption attenuation rate of the single-day electric automobile is obtained; gamma raydIs the discharge decay rate in the single-day power station; n is a radical of hydrogenEV、NbThe total amount of the electric automobiles on the highway and the total amount of the power batteries in the power changing station are respectively;
the calculation formula of the proportion of the new energy power generation to the electric vehicle charging energy is as follows:
in the formula, NWT、NPVThe number of the wind driven generator and the number of the photovoltaic cell panels are respectively; n is a radical of hydrogencThe number of the batteries being charged in the battery replacement station is calculated; pcIs the charging power of the battery replacement station;
step three: the method for obtaining the power swapping station dynamic operation strategy under the power swapping station service model based on the QPSO optimization algorithm according to the objective function comprises the following substeps:
step three, first: the method comprises the following steps of setting the particle dimension of a QPSO optimization algorithm of a highway battery swapping service model according to an objective function, wherein the method comprises the following specific steps:
according to the battery replacement service model established in the step one, each population is composed of 100 particles in a 27-dimensional search space, namely X = { X =1,...xi,...x100The 27-dimensional particle is set to x = (N)PV,NWT,Nb,Nc_start(1),Nc_start(2)，...，Nc_start(24))TThe 1-3D particles are the number of power batteries in a photovoltaic cell panel, a wind driven generator and a battery replacement station, and the 4-27D particles are the number of batteries which are charged by the battery replacement station every hour;
step two: calculating an attractor, wherein the attractor is calculated by the formula:
in the formula, pbest_iIs the ith particle history best position in the current iteration; g is a radical of formulabestIs the current global optimum particle; p is a radical of formulaiIs an attractor, for the update of the ith particle position;
step three: updating particle positions, and selecting innovative parameters of a proper power station changing service model, wherein the particle position updating formula is as follows:
in the formula, xiIs the position of the ith particle; α is an innovation parameter;
u is a random number obeying uniform distribution between (0, 1); the probability of the formula taking + or-is 0.5 respectively;
the value of alpha is as follows:
in the formula, TmaxIs the maximum number of iterations; itrtn is the current iteration number;
step three and four: obtaining a dynamic operation strategy of the power change station based on a QPSO algorithm, which comprises the following specific steps:
(1) Inputting power conversion demand prediction and wind-solar power generation power prediction in a power conversion service model to obtain a day-ahead power conversion plan of a power conversion station;
(2) Acquiring the actual running state of the power changing station, initializing particle swarm parameters, a swarm position and speed, executing the step (3) and performing rolling optimization;
(3) Updating the particle speed and the particle position, judging whether the selected particles conform to the battery replacement service model, if not, firstly setting a penalty function to restrict the fitness value, and then executing the step (4); if yes, directly executing the step (4);
(4) Evaluating the fitness value and updating the optimal position and velocity of the particle, if pbest≤GbestIf yes, executing the step (5); if p isbest＞GbestThen step (6) is performed, wherein: p is a radical of formulabest、GbestRespectively obtaining a current population optimal value and a global optimal value;
(5) Updating the global optimal particle position and speed;
if it is not
Obtaining globally optimal particles; if it is not
Step (3) is performed.
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