CN107273968A - A kind of Multiobjective Scheduling method and device based on dynamic fuzzy Chaos-Particle Swarm Optimization - Google Patents
A kind of Multiobjective Scheduling method and device based on dynamic fuzzy Chaos-Particle Swarm Optimization Download PDFInfo
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Abstract
The invention discloses a kind of Multiobjective Scheduling method that Chaos-Particle Swarm Optimization is obscured based on dynamic chaos, including initialization population, chaos processing is carried out to the initial population, and calculate the fitness of each particle, when iterations is not up to termination iterations, when more than iterations set in advance, the object function is determined by fuzzy membership function, particle position is updated after the value for filtering out Pbest and Gbest；Said process is carried out redefining object function after chaos processing until reaching final iterations.It can thus be appreciated that, each object function is converted into satisfaction by Multiobjective Scheduling method provided in an embodiment of the present invention by fuzzy membership function, artificial subjective setting weight coefficient is avoided, the better numerical value stability of result is obtained, improves the dispatching efficiency of multi-objective particle swarm.The embodiment of the present invention additionally provides a kind of Multiobjective Scheduling device that Chaos-Particle Swarm Optimization is obscured based on dynamic chaos, can equally reach above-mentioned technique effect.
Description
Technical field
The present invention relates to Multiobjective Scheduling field, more specifically to a kind of based on dynamic fuzzy Chaos-Particle Swarm Optimization
Multiobjective Scheduling method and device.
Background technology
Particle swarm optimization algorithm is a kind of evolutionary computation technique, and nineteen ninety-five is carried by doctor Eberhart and doctor kennedy
Go out, come from the behavioral study preyed on to flock of birds.The algorithm is inspired by the regularity of flying bird cluster activity, and then is utilized
The simplified model that swarm intelligence is set up.Particle cluster algorithm utilizes colony on the basis of to animal cluster activity behavior observation
In the shared motion that makes whole colony of the individual to information evolution from disorder to order is produced in problem solving space
Journey, so as to obtain optimal solution.
In recent years, some scholars have carried out many improvement, promoted by PSO algorithms on the basis of standard particle group's algorithm
To constrained optimization problem, crowded grid and external archival boosting algorithm global optimizing ability are introduced, so as to improve result precision.Such as
Group space is updated using multi-objective particle swarm algorithm in cultural population multi-objective optimization algorithm.With stylish situational knowledge,
Standardization knowledge and historical knowledge definition make belief space be more suitable for multiple-objection optimization, the chaos grain of such as adaptive inertia weight
Swarm optimization, is initialized using the chaos sequence of cube mapping to particle position, while the adaptive inertia power in searching process
Weight, improves convergence rate.With various dimensions, multiple target, the complex nonlinear optimization problem of multi-constraint condition generation, existing skill
Dispatching method in art can not ensure the stability and scheduling of resource efficiency of result.
Therefore, the dispatching efficiency for how ensureing to improve multi-objective particle swarm on the premise of the stability of result is this area skill
The problem of art personnel need to solve.
The content of the invention
It is an object of the invention to provide it is a kind of based on dynamic chaos obscure Chaos-Particle Swarm Optimization Multiobjective Scheduling method and
Device, to improve the dispatching efficiency of multi-objective particle swarm, and ensures the stability of result.
To achieve the above object, the embodiments of the invention provide following technical scheme：
A kind of Multiobjective Scheduling method based on dynamic fuzzy Chaos-Particle Swarm Optimization, including：
S101：Each parameter of particle cluster algorithm is initialized, initial population is produced in the case where meeting each constraints；
S102：Chaos processing is carried out to the initial population, and calculates the fitness of each particle；
S103：Judge whether iterations reaches termination iterations, if so, then terminating flow, if it is not, then entering
S104；
S104：Judge whether iterations exceedes setting iterations, if it is not, then determining target letter by weighting factor method
Number, and record the numerical value of the object function；If so, then determining that fuzzy membership function is joined according to the numerical value of the object function
Number, the object function is used as using the satisfaction that the fuzzy membership function is determined；
S105：Pbest and Gbest value are filtered out according to the object function, and according to the Pbest's and Gbest
Value updates particle position；
S106：Iterative process to S103 to S105 carries out chaos processing, and reenters S103.
Wherein, chaos processing is carried out to the initial population includes：
Mapped with Logistics and map the combination chaos sequence being combined to initial population progress with Chebyshev
Chaos processing.
Wherein, using the fuzzy membership function determine satisfaction as the object function, including：
Using the numerical value of the object function as maximum or minimum value, by the fuzzy membership function by the target
Function is converted to corresponding satisfaction；
It is the object function to determine the maximum in the satisfaction.
Wherein, the S106 includes：
Chaos processing is carried out to iterative process with Logistics mappings.
Wherein, also include before the S104：
Update inertia weight；Wherein, the inertia weight of iterative process described in early stage is more than the used of iterative process described in the later stage
Property weight.
To achieve the above object, the embodiment of the present invention additionally provides a kind of multiple target based on dynamic fuzzy Chaos-Particle Swarm Optimization
Dispatching device, including：
Initialization module, for initializing each parameter of particle cluster algorithm, initial population is produced in the case where meeting each constraints；
Computing module, for carrying out chaos processing to the initial population, and calculates the fitness of each particle；
Judge module, for judging whether iterations reaches termination iterations, if so, then terminating flow, if it is not, then
Judge whether iterations exceedes setting iterations；
First determining module, for when iterations is not less than setting iterations, mesh to be determined by weighting factor method
Scalar functions, and record the numerical value of the object function；
Second determining module, for when iterations exceedes setting iterations, according to the numerical value of the object function
Fuzzy membership function parameter is determined, the object function is used as using the satisfaction that the fuzzy membership function is determined；
Screening module, the value for filtering out Pbest and Gbest according to the object function, and according to the Pbest and
Gbest value updates particle position；
Processing module, carries out chaos processing, and reenter S103 for the iterative process to S103 to S105.
Wherein, the computing module is specially and is mapped to map the combination chaos being combined with Chebyshev with Logistics
Initial population described in sequence pair carries out chaos processing, and calculates the module of the fitness of each particle.
Wherein, second determining module includes：
Converting unit, for when iterations exceedes setting iterations, using the numerical value of the object function as most
Big or minimum value, corresponding satisfaction is converted to by the fuzzy membership function by the object function；
Determining unit, for determining that the maximum in the satisfaction is the object function.
Wherein, the processing module is specially that the module that chaos processing is carried out to iterative process is mapped with Logistics.
Wherein, the judge module also includes：
Weight unit is updated, for when iterations is not up to termination iterations, updating inertia weight；Wherein, it is preceding
The inertia weight of iterative process described in phase is more than the inertia weight of iterative process described in the later stage.
Pass through above scheme, the multiple target provided in an embodiment of the present invention that Chaos-Particle Swarm Optimization is obscured based on dynamic chaos
Dispatching method, including initialization each parameter of particle cluster algorithm, initial population is produced in the case where meeting each constraints；To described initial
Population carries out chaos processing, and calculates the fitness of each particle；When iterations is not up to termination iterations, target is determined
Function, and particle position is updated after filtering out Pbest and Gbest value according to the object function；Said process is mixed
Object function is redefined after ignorant processing until reaching final iterations.Multiobjective Scheduling method provided in an embodiment of the present invention
When more than iterations set in advance, the final goal function is determined by weighting factor method, more than presetting repeatedly
During generation number, each object function is satisfied with by fuzzy membership function using each target function value as maximum or minimum value
Degree conversion determines the final goal function.It follows that Multiobjective Scheduling method provided in an embodiment of the present invention is by being subordinate to
Function is spent, each object function is converted into satisfaction, Noninferior Solution Set is drawn by minimax theory, it is to avoid artificial subjective setting
Weight coefficient, obtains the better numerical value stability of result, improves the dispatching efficiency of multi-objective particle swarm, realizes the effective of resource
Configuration.The embodiment of the present invention additionally provides a kind of Multiobjective Scheduling device that Chaos-Particle Swarm Optimization is obscured based on dynamic chaos, equally
Above-mentioned technique effect can be reached.
Brief description of the drawings
In order to illustrate more clearly about the embodiment of the present invention or technical scheme of the prior art, below will be to embodiment or existing
There is the accompanying drawing used required in technology description to be briefly described, it should be apparent that, drawings in the following description are only this
Some embodiments of invention, for those of ordinary skill in the art, on the premise of not paying creative work, can be with
Other accompanying drawings are obtained according to these accompanying drawings.
Fig. 1 is a kind of flow chart of Multiobjective Scheduling method disclosed in the embodiment of the present invention；
Fig. 2 is the flow chart of another Multiobjective Scheduling method disclosed in the embodiment of the present invention；
Fig. 3 is a kind of flow chart of Multiobjective Scheduling method concrete application disclosed in the embodiment of the present invention；
Fig. 4 is a kind of load chart of Multiobjective Scheduling method concrete application disclosed in the embodiment of the present invention；
Fig. 5 is a kind of honourable power of Multiobjective Scheduling method concrete application disclosed in the embodiment of the present invention；
Fig. 6 contrasts for a kind of three kinds of algorithm optimizing of Multiobjective Scheduling method concrete application disclosed in the embodiment of the present invention
Figure；
Fig. 7 is a kind of the grid-connected of Multiobjective Scheduling method concrete application, battery, electronic vapour disclosed in the embodiment of the present invention
Car optimal power conceptual scheme；
Fig. 8 is a kind of structure chart of Multiobjective Scheduling device disclosed in the embodiment of the present invention.
Embodiment
Below in conjunction with the accompanying drawing in the embodiment of the present invention, the technical scheme in the embodiment of the present invention is carried out clear, complete
Site preparation is described, it is clear that described embodiment is only a part of embodiment of the invention, rather than whole embodiments.It is based on
Embodiment in the present invention, it is every other that those of ordinary skill in the art are obtained under the premise of creative work is not made
Embodiment, belongs to the scope of protection of the invention.
The embodiment of the invention discloses a kind of Multiobjective Scheduling method and dress that Chaos-Particle Swarm Optimization is obscured based on dynamic chaos
Put, to improve the dispatching efficiency of multi-objective particle swarm, and ensure the stability of result..
Referring to Fig. 1, a kind of flow chart of Multiobjective Scheduling method provided in an embodiment of the present invention.As shown in figure 1, including：
S101：Each parameter of particle cluster algorithm is initialized, initial population is produced in the case where meeting each constraints；
S102：Chaos processing is carried out to the initial population, and calculates the fitness of each particle；
Because typical Logistic chaos systems are stronger to the dependence of initial value, can using Logistic mapping with
The combination chaos sequence that Chebyshev mappings are combined is incorporated into the initialization procedure of FCPSO algorithms, to improve particle distribution
Randomness and uniformity.
Combination chaos sequence mathematical description be：
S103：Judge whether iterations reaches termination iterations, if so, then terminating flow, if it is not, then entering
S104；
S104：Judge whether iterations exceedes setting iterations, if it is not, then determining target letter by weighting factor method
Number, and record the numerical value of the object function；If so, then determining that fuzzy membership function is joined according to the numerical value of the object function
Number, the object function is used as using the satisfaction that the fuzzy membership function is determined；
In specific implementation, first determine whether whether iterations exceedes setting iterations.It is understood that for the first time
During iteration, iterations now, object function is determined by weighting factor method necessarily without more than setting iterations, and
Need to record the ginseng when numerical value of the object function sets iterations so that iterations exceedes as fuzzy membership function
Number；When iterations exceedes setting iterations, the numerical value of each object function to record before uses mould as maximin
Paste membership function and satisfaction conversion is carried out to former object function, object function is used as using the maximum of the satisfaction after conversion.
Because algorithm optimizing is with the minimum target of fitness, and the operation expense of system and Environmental costs also are intended to get over
It is low better, therefore each object function can be converted into using the liter half mould trapezoidal membership function of following formula by degree of membership λ1、λ2,
According to minimax principle, max { λ are taken1,λ2As object function, pareto noninferior solutions are drawn, can prove that pareto is non-bad
Solve as set of feasible solution.As max { λ1,λ2When reaching sufficiently low numerical value, then pareto noninferior solutions also tend to optimal.
Rise half trapezoidal membership function：
In formula：λ is degree of membership；X is function to be transformed, and object function F is taken herein1、F2；Xmax、XminFor function to be transformed
Maximum, minimum value, object function F is taken herein1、F2Maximum, minimum value.
The maximin of known function to be transformed is needed because membership function is converted, therefore iteration early stage, to weight
Y-factor method Y determines object function, whereby the maximin of process record function to be transformed.Object function is in algorithm searching process
In dynamically convert.In iteration early stage, object function is determined with weighting factor method, in the iteration middle and later periods, and object function is conversion
Membership function maximum afterwards, i.e. max { λ1,λ2}.Dynamic fuzzy mesh object function F is conducive to avoiding algorithm from being absorbed in part most
It is excellent, reach more preferable effect of optimization.
Dynamic fuzzy objective function F：
In formula：F1、F2It is conversion coefficient for weight coefficient, Z, determines in iterative process, the critical point of switch target function, its
Middle Z is taken between [0,1].
S105：Pbest and Gbest value are filtered out according to the object function, and according to the Pbest's and Gbest
Value updates particle position；
S106：Iterative process to S103 to S105 carries out chaos processing, and reenters S103.
In order to increase algorithm iteration process population diversity, Logistic mappings are introduced in particle iterative process herein and are increased
The ergodic of computation system, so as to avoid algorithm from sinking into local optimum.
Typical Logistic mapping equations：
xn+1=f (μ, xn)=μ xn(1-xn)
In above-mentioned formula：x0、y0For the initial value of particle, n, μ are control parameter, set μ=4, n=4, now system is in
Complete Chaos state.
On the basis of above-described embodiment, preferably, judging that it is secondary that iterations is not up to termination iteration
After number, in addition to：Update inertia weight；Wherein, the inertia weight of iterative process described in early stage is more than iterative process described in the later stage
Inertia weight.
It is understood that inertia weight is particle position update in represent the information proportion of historical data, larger is used
Property weight can increase particle in global search capability, less inertia weight is then conducive to local optimizing.
Weight coefficient formula is as follows：
Wherein ω is inertia weight；ωminFor inertia weight minimum value, 0.4 is taken；ωmaxFor inertia weight maximum, take
0.9；Iter is current iteration number of times；Maxiter is maximum iteration.
Multiobjective Scheduling method provided in an embodiment of the present invention is when more than iterations set in advance, by weighting system
Number method determines the final goal function, during more than presetting iterations, and maximum or minimum is used as using each target function value
Value carries out satisfaction conversion to each object function by fuzzy membership function and determines the final goal function.It follows that
Each object function is converted into satisfaction, passed through by Multiobjective Scheduling method provided in an embodiment of the present invention by membership function
Minimax theory draws Noninferior Solution Set, it is to avoid artificial subjective setting weight coefficient, obtains the better numerical value stability of result, improves
The dispatching efficiency of multi-objective particle swarm, realizes effective configuration of resource.The embodiment of the present invention additionally provides a kind of based on dynamic
The Multiobjective Scheduling device of state Chaos and Fuzzy Chaos-Particle Swarm Optimization, can equally reach above-mentioned technique effect.
The embodiment of the invention discloses a kind of specific Multiobjective Scheduling method, relative to a upper embodiment, the present embodiment
Further instruction and optimization have been made to technical scheme.Specifically：
Referring to Fig. 2, the flow chart of another Multiobjective Scheduling method provided in an embodiment of the present invention.As shown in Fig. 2 bag
Include：
S201：Each parameter of particle cluster algorithm is initialized, initial population is produced in the case where meeting each constraints；
S202：Mapped with Logistics and map the combination chaos sequence being combined to the initial population with Chebyshev
Chaos processing is carried out, and calculates the fitness of each particle；
S203：Judge whether iterations reaches termination iterations, if so, then terminating flow, if it is not, then, entering
S204；
S204：Inertia weight is updated, judges whether iterations exceedes setting iterations, if it is not, being by weighting then
Number method determines object function, and records the numerical value of the object function, into S205；If so, then entering S241；
S241：Using the numerical value of the object function as maximum or minimum value, by the fuzzy membership function by institute
State object function and be converted to corresponding satisfaction；
S242：Determine that the maximum in the satisfaction is the object function, into S205；
S205：Pbest and Gbest value are filtered out according to the object function, and according to the Pbest's and Gbest
Value updates particle position；
S206：Mapped with Logistics and chaos processing is carried out to S203 to S205 iterative process, and reentered
S203。
Multiobjective Scheduling method provided in an embodiment of the present invention utilizes variable inertia weight, and early stage increases inertia weight and is beneficial to
Global optimizing, the later stage reduces inertia weight and is beneficial to local optimal searching, is prevented effectively from optimizing and enters local optimum；Using chaology,
Carry out algorithm ergodic with introducing different mappings, increase in renewal process respectively in population initialization, increase particle populations are various
Property, algorithm operation efficiency is greatly improved, it is to avoid precocious, final result is also more excellent.
Multiobjective Scheduling method can be applied particularly in the micro-capacitance sensor models containing electric automobile disclosed in the embodiment of the present invention.
Specifically：Referring to Fig. 3, a kind of flow chart of Multiobjective Scheduling method concrete application provided in an embodiment of the present invention.Such as Fig. 3 institutes
Show, including：
S301：Determine wind-driven generator in micro-capacitance sensor, photovoltaic cell, miniature gas turbine, fuel cell, battery, electricity
The scheduling model of electrical automobile；
(1) wind-driven generator scheduling model
Wind power calculation formula is as follows：
In formula：PrIt is the rated power of wind-driven generator；vci、vcoIt is incision, cut-out wind speed；vr, v be rated wind speed and reality
Border wind speed.
(2) photovoltaic cell scheduling model
The power output of photovoltaic array can be calculated with following formula：
In formula：PpvFor power output of the photovoltaic array in intensity of illumination G (t)；Gstc、Tstc、PstcFor standard test environment
1000 (W/m2), intensity of illumination, photovoltaic array temperature and peak power output at 25 DEG C；K is temperature coefficient；When T (t) is t
Carve the surface temperature of photovoltaic array.
(3) miniature gas turbine scheduling model
Consumption characteristic when miniature gas turbine is run：
In formula：PMTFor the real-time power of miniature gas turbine (kW)；Δ t is miniature gas turbine run time ηeTo be miniature
Gas turbine power generation efficiency；HgFor natural gas Lower heat value (kWh/m3)；VfThe day consumed for miniature gas turbine in run time
Right tolerance (m3)。
(4) fuel cell scheduling model
The energy dissipation behavior of fuel cell：
In formula：N is Inlet Fuel molar flow (kmol/h)；HFCFor the low heat value (J/mol) of fuel；ηFCIt is SOFC systems
System generating efficiency；PFCFor SOFC generated energy (kW).
(5) battery scheduling model
During charging：
During electric discharge：
In formula：CNFor battery nominal capacity；ηch、ηdisFor efficiency for charge-discharge；SOC is real-time state-of-charge, i.e. battery
Actual capacity and battery the ratio between nominal capacity；SOC0For the initial state-of-charge of battery.
(6) pure electric automobile (BEV) charging and recharging model
BEV charge models
The logarithm normal distribution function of daily travel：
In formula：μ=3.2, σ=0.88；L is daily travel, 0 ＜ L≤200.
Charge moment Normal Distribution, its probability density function under Shuffle Mode：
In formula：μ=17.6, σ=3.4；T0 is charging start time.
BEV discharging models
According to distance, speed and the relation between the time, it can obtain a length of during continuous discharge：
In formula：TcdisFor continuous discharge duration；CBEVFor battery of electric vehicle rated capacity；W is average for electric car distance travelled
Energy consumption；L is that electric car expects distance travelled；PcdFor electric car discharge power.
The probability density function that can obtain continuous discharge duration by daily travel probability distribution is：
In formula：Coefficient μ=3.2, σ=0.88.
S302：Foundation meets power-balance, micro- source power bound, controllable micro- source climbing, storage battery charge state limitation
Etc. constraints；
The optimization object function of the micro-capacitance sensor Multiobjective Optimal Operation model of electric automobile is：
Micro-capacitance sensor operation expense F1It is minimum：
F1=CF+CM+Cs+Cg
Environmental costs F2It is minimum：
F2=Em+Eg
In above-mentioned formula：CFFor miniature gas turbine and the fuel cost of fuel cell；CMFor maintenance cost, including each micro- source
And the maintenance cost of batteries of electric automobile；CsFor the start-up cost of miniature gas turbine, fuel cell；CgFor grid-connected interactive cost,
It is on the occasion of representing micro-capacitance sensor power purchase from bulk power grid, and negative value represents that micro-capacitance sensor sells electricity to bulk power grid.EmFor each micro- source production of micro-capacitance sensor
The conversion amount of raw each pollutant；EgIt is supplied to micro-capacitance sensor electricity to produce the conversion amount of each pollutant for bulk power grid.
Constraints is：
(1) power-balance
(2) each micro- source and bulk power grid interaction power limit
(3) storage battery charge state is limited
Battery battery status is usually described with state-of-charge, and state-of-charge refers to dump energy and full capacitance
Ratio.In order to extend the service life of battery, the state-of-charge of battery is limited.
socmin≤soc(t)≤socmax
In above-mentioned formula：PiFor i-th of micro- source power, the of i=1,2,3 ...；PgFor grid-connected power, on the occasion of representing microgrid from big electricity
Net absorbed power；PLoadFor the power of load absorption；PBEVFor pure electric automobile charge power, on the occasion of represent pure electric automobile from
Micro-capacitance sensor absorbed power, negative value represents pure electric automobile and provides power for micro-capacitance sensor.Pimin、PimaxOn power for i-th of micro- source
Lower limit；Pgmin、PgmaxThe bound of power is interacted with bulk power grid for micro-capacitance sensor.SOC (t) is the state-of-charge at certain moment；SOCmin、
SOCmaxRespectively the bound of state-of-charge, typically takes SOCmin=0.2, SOCmax=0.8.
(4) Climing constant in controllable micro- source
In formula：WithUpward, the downward creep speed in respectively i-th controllable micro- source；Δ t is the climbing time；Pi,tWith
Pi,t+1The power at respectively i-th controllable this moment of micro- source and lower moment.
S303：Map to enter micro-capacitance sensor models with the combination chaos sequence that Chebyshev mappings are combined with Logistics
The processing of row chaos, and calculate the fitness of each particle；
S304：Judge whether iterations reaches termination iterations, if so, then terminating flow, if it is not, then updating inertia
Weight, into S204；
S305：Judge whether iterations exceedes setting iterations, if it is not, then determining target letter by weighting factor method
Number, and the numerical value of the object function is recorded, into S306；If so, then entering S351；
S351：Using the numerical value of the object function as maximum or minimum value, by the fuzzy membership function by institute
State object function and be converted to corresponding satisfaction；
S352：Determine that the maximum in the satisfaction is the object function, into S306；
S306：Pbest and Gbest value are filtered out according to the object function, and according to the Pbest's and Gbest
Value updates particle position；
S307：Mapped with Logistics and chaos processing is carried out to S304 to S306 iterative process, and reentered
S304。
It is as shown in table 1 that micro-capacitance sensor sells tou power price from bulk power grid purchase；Each micro battery parameter is as shown in table 2；Micro-gas-turbine
Machine MT, fuel cell FC and grid-connected emission factor and processing cost are as shown in table 3；The load of micro-capacitance sensor is predicted according to historical data
Data and PV, WT's exerts oneself as shown in Figure 4, Figure 5.
Table 1
Table 2
Table 3
Three kinds of algorithms optimize solution, optimizing to the micro-capacitance sensor multi-objective optimization scheduling containing electric automobile respectively
Journey is as shown in Figure 6, it can be seen that Chaos-Particle Swarm Optimization (hereinafter referred to as CPSO) algorithm and the present invention improve Multiobjective Scheduling (with
Lower abbreviation FCPSO) algorithm introduces chaos processing due to initialization, makes its initial value effect more preferably, in iteration middle-end, standard particle group
(hereinafter referred to as PSO) algorithm optimizing is unstable, is absorbed in local optimum, CPSO algorithms gradually reduce cost with FCPSO algorithms, from most
Termination fruit sees that FCPSO makes its result effect preferably, micro-capacitance sensor operation expense and ring because its multi objective fuzzyization is handled
Border cost is minimum.
PSO algorithm, CPSO algorithms and FCPSO algorithms are respectively to the micro-capacitance sensor Multiobjective Optimal Operation containing electric automobile
Model is solved, and its cost and optimization are time-consuming as shown in table 4, as can be seen from Table 4, the operation maintenance of FCPSO arithmetic results into
This is minimum but Environmental costs highest, final the lowest cost.Because operation expense degree of membership degree is higher,
Therefore pay the utmost attention to operation expense to optimize, as a result it can be seen that totle drilling cost is lower, effect is more preferable.CPSO algorithms with
FCPSO algorithms all significant increases solution efficiency of PSO algorithm, shortens the solution time.FCPSO algorithms are calculated with respect to CPSO
Method improves 10% speed, further increases Practical Project practicality.
Table 4
Satisfaction meet the charged limitation of demand, grid-connected power constraint, battery, the unordered random discharge and recharge of electric automobile, can
Control micro- source to exert oneself under conditions of bound and creep speed, it is excellent that FCPSO optimized algorithms solve the multiple target of micro-capacitance sensor containing electric automobile
Change scheduling model, export optimal case.Micro-grid connection power, accumulator cell charging and discharging power, electric automobile is illustrated in figure 7 to fill
Discharge power optimal case；The controllable optimal scheme of exerting oneself in micro- source as shown in table 7.
Table 5
| Time | FC | MT1 | MT2 | MT3 | MT4 |
| 1 | 94.24 | 100 | 99.92 | 110.55 | 147.33 |
| 2 | 59.61 | 40 | 119.46 | 15.41 | 255.25 |
| 3 | 108.91 | 45.44 | 103.42 | 150.56 | 120.28 |
| 4 | 59.56 | 6.75 | 108.58 | 84.91 | 100.74 |
| 5 | 111.54 | 66.75 | 160.47 | 140.65 | 260.74 |
| 6 | 94.15 | 45.72 | 94.58 | 112.39 | 100.74 |
| 7 | 179.07 | 78.62 | 89.83 | 80.99 | 116.46 |
| 8 | 122.66 | 100 | 110.71 | 111.68 | 271.14 |
| 9 | 107.78 | 48.05 | 94.24 | 97.87 | 159.04 |
| 10 | 99.24 | 66.14 | 153.63 | 123.95 | 116.23 |
| 11 | 94.06 | 78.72 | 76.41 | 228.84 | 144.01 |
| 12 | 148.85 | 47.82 | 176.41 | 135.23 | 162.84 |
| 13 | 167.67 | 46.98 | 93.01 | 102.91 | 110.43 |
| 14 | 96.01 | 75.86 | 40 | 76.82 | 270.43 |
| 15 | 176.44 | 59.1 | 140 | 206.82 | 132.21 |
| 16 | 85.32 | 79.45 | 95.14 | 232.87 | 276.84 |
| 17 | 92.08 | 75.55 | 98.88 | 105.82 | 300 |
| 18 | 92.4 | 96.19 | 93.51 | 98.7 | 140 |
| 19 | 179.28 | 73.84 | 179.99 | 97.92 | 260.5 |
| 20 | 97.84 | 45.86 | 79.99 | 213.69 | 53.66 |
| 21 | 85.28 | 100 | 93.3 | 106.57 | 104.78 |
| 22 | 179.31 | 46.31 | 93.52 | 244.34 | 77.78 |
| 23 | 198.27 | 30 | 92.48 | 125.31 | 257.78 |
| 24 | 98.27 | 70 | 94.18 | 214.36 | 100.42 |
Multiobjective Scheduling device provided in an embodiment of the present invention is introduced below, a kind of multiple target described below is adjusted
Spending device can be with cross-referenced with a kind of above-described Multiobjective Scheduling method.
Referring to Fig. 8, a kind of structure chart of Multiobjective Scheduling device provided in an embodiment of the present invention.As shown in figure 8, including：
Initialization module 801, for initializing each parameter of particle cluster algorithm, initial plant is produced in the case where meeting each constraints
Group；
Computing module 802, for carrying out chaos processing to the initial population, and calculates the fitness of each particle；
Judge module 803, for judging whether iterations reaches termination iterations, if so, then terminate flow, if
It is no, then judge whether iterations exceedes setting iterations；
First determining module 841, for when iterations is not less than setting iterations, being determined by weighting factor method
Object function, and record the numerical value of the object function；
Second determining module 842, for when iterations exceedes setting iterations, according to the number of the object function
Value determines fuzzy membership function parameter, and the object function is used as using the satisfaction that the fuzzy membership function is determined；
Screening module 805, the value for filtering out Pbest and Gbest according to the object function, and according to described
Pbest and Gbest value updates particle position；
Processing module 806, carries out chaos processing, and reenter S3 for the iterative process to S3 to S5.
Multiobjective Scheduling device provided in an embodiment of the present invention is when more than iterations set in advance, by weighting system
Number method determines the final goal function, during more than presetting iterations, and maximum or minimum is used as using each target function value
Value carries out satisfaction conversion to each object function by fuzzy membership function and determines the final goal function.It follows that
Each object function is converted into satisfaction, passed through by Multiobjective Scheduling device provided in an embodiment of the present invention by membership function
Minimax theory draws Noninferior Solution Set, it is to avoid artificial subjective setting weight coefficient, obtains the better numerical value stability of result, improves
The dispatching efficiency of multi-objective particle swarm, realizes effective configuration of resource.The embodiment of the present invention additionally provides a kind of based on dynamic
The Multiobjective Scheduling device of state Chaos and Fuzzy Chaos-Particle Swarm Optimization, can equally reach above-mentioned technique effect.
On the basis of above-described embodiment, preferably, the computing module is specially to be reflected with Logistics
Penetrate and map the combination chaos sequence being combined to initial population progress chaos processing with Chebyshev, and calculate each particle
Fitness module.
On the basis of above-described embodiment, preferably, second determining module includes：
Converting unit, for when iterations exceedes setting iterations, using the numerical value of the object function as most
Big or minimum value, corresponding satisfaction is converted to by the fuzzy membership function by the object function；
Determining unit, for determining that the maximum in the satisfaction is the object function.
On the basis of above-described embodiment, preferably, the processing module is specially to be reflected with Logistics
Penetrate the module that chaos processing is carried out to iterative process.
On the basis of above-described embodiment, preferably, the judge module also includes：
Weight unit is updated, for when iterations is not up to termination iterations, updating inertia weight；Wherein, it is preceding
The inertia weight of iterative process described in phase is more than the inertia weight of iterative process described in the later stage.
Multiobjective Scheduling device provided in an embodiment of the present invention utilizes variable inertia weight, and early stage increases inertia weight and is beneficial to
Global optimizing, the later stage reduces inertia weight and is beneficial to local optimal searching, is prevented effectively from optimizing and enters local optimum；Using chaology,
Carry out algorithm ergodic with introducing different mappings, increase in renewal process respectively in population initialization, increase particle populations are various
Property, algorithm operation efficiency is greatly improved, it is to avoid precocious, final result is also more excellent.
The embodiment of each in this specification is described by the way of progressive, and what each embodiment was stressed is and other
Between the difference of embodiment, each embodiment identical similar portion mutually referring to.
The foregoing description of the disclosed embodiments, enables professional and technical personnel in the field to realize or using the present invention.
A variety of modifications to these embodiments will be apparent for those skilled in the art, as defined herein
General Principle can be realized in other embodiments without departing from the spirit or scope of the present invention.Therefore, it is of the invention
The embodiments shown herein is not intended to be limited to, and is to fit to and principles disclosed herein and features of novelty phase one
The most wide scope caused.
Claims (10)
1. a kind of Multiobjective Scheduling method based on dynamic fuzzy Chaos-Particle Swarm Optimization, it is characterised in that including：
S101：Each parameter of particle cluster algorithm is initialized, initial population is produced in the case where meeting each constraints；
S102：Chaos processing is carried out to the initial population, and calculates the fitness of each particle；
S103：Judge whether iterations reaches termination iterations, if so, then terminating flow, if it is not, then entering S104；
S104：Judge whether iterations exceedes setting iterations, if it is not, object function is then determined by weighting factor method,
And record the numerical value of the object function；If so, fuzzy membership function parameter is then determined according to the numerical value of the object function,
The object function is used as using the satisfaction that the fuzzy membership function is determined；
S105：Filter out Pbest and Gbest value according to the object function, and according to the Pbest and Gbest value more
New particle position；
S106：Iterative process to S103 to S105 carries out chaos processing, and reenters S103.
2. Multiobjective Scheduling method according to claim 1, it is characterised in that chaos processing is carried out to the initial population
Including：
Mapped with Logistics and map the combination chaos sequence being combined to initial population progress chaos with Chebyshev
Processing.
3. Multiobjective Scheduling method according to claim 2, it is characterised in that determined with the fuzzy membership function
Satisfaction as the object function, including：
Using the numerical value of the object function as maximum or minimum value, by the fuzzy membership function by the object function
Be converted to corresponding satisfaction；
It is the object function to determine the maximum in the satisfaction.
4. Multiobjective Scheduling method according to claim 3, it is characterised in that the S106 includes：
Chaos processing is carried out to iterative process with Logistics mappings.
5. the Multiobjective Scheduling method according to claim any one of 1-4, it is characterised in that also wrapped before the S104
Include：
Update inertia weight；Wherein, the inertia weight of iterative process described in early stage is more than the inertia power of iterative process described in the later stage
Weight.
6. a kind of Multiobjective Scheduling device based on dynamic fuzzy Chaos-Particle Swarm Optimization, it is characterised in that including：
Initialization module, for initializing each parameter of particle cluster algorithm, initial population is produced in the case where meeting each constraints；
Computing module, for carrying out chaos processing to the initial population, and calculates the fitness of each particle；
Judge module, for judging whether iterations reaches termination iterations, if so, then terminating flow, if it is not, then judging
Whether iterations exceedes setting iterations；
First determining module, for when iterations is not less than setting iterations, target letter to be determined by weighting factor method
Number, and record the numerical value of the object function；
Second determining module, for when iterations exceedes setting iterations, being determined according to the numerical value of the object function
Fuzzy membership function parameter, the object function is used as using the satisfaction that the fuzzy membership function is determined；
Screening module, the value for filtering out Pbest and Gbest according to the object function, and according to the Pbest and
Gbest value updates particle position；
Processing module, carries out chaos processing, and reenter S103 for the iterative process to S103 to S105.
7. Multiobjective Scheduling device according to claim 6, it is characterised in that the computing module be specially with
Logistics mappings map the combination chaos sequence being combined with Chebyshev and chaos processing are carried out to the initial population, and
Calculate the module of the fitness of each particle.
8. Multiobjective Scheduling device according to claim 7, it is characterised in that second determining module includes：
Converting unit, for when iterations exceedes setting iterations, using the numerical value of the object function as maximum or
Minimum value, corresponding satisfaction is converted to by the fuzzy membership function by the object function；
Determining unit, for determining that the maximum in the satisfaction is the object function.
9. Multiobjective Scheduling device according to claim 8, it is characterised in that the processing module be specially with
Logistics mappings carry out the module of chaos processing to iterative process.
10. the Multiobjective Scheduling device according to claim any one of 6-9, it is characterised in that the judge module is also wrapped
Include：
Weight unit is updated, for when iterations is not up to termination iterations, updating inertia weight；Wherein, early stage institute
The inertia weight for stating iterative process is more than the inertia weight of iterative process described in the later stage.
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| CN108921280A (en) * | 2018-04-13 | 2018-11-30 | 广东电网有限责任公司 | A kind of high-voltage circuit-breaker status appraisal procedure based on improvement chaotic particle swarm optimization neural network |
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| CN108921280A (en) * | 2018-04-13 | 2018-11-30 | 广东电网有限责任公司 | A kind of high-voltage circuit-breaker status appraisal procedure based on improvement chaotic particle swarm optimization neural network |
| CN110244555A (en) * | 2019-06-21 | 2019-09-17 | 武汉四创自动控制技术有限责任公司 | A kind of fuzzy decision ship lock control method and system based on particle swarm algorithm optimization |
| CN111953023A (en) * | 2020-07-20 | 2020-11-17 | 上海电力大学 | Charging pile-containing regional energy network management method based on MACPSO algorithm |
| CN111953023B (en) * | 2020-07-20 | 2022-02-22 | 上海电力大学 | Charging pile-containing regional energy network management method based on MACPSO algorithm |
| CN112132453A (en) * | 2020-09-22 | 2020-12-25 | 国网能源研究院有限公司 | Method, system and device for evaluating optimal admission scale of renewable energy sources of regional power grid |
| CN113435722A (en) * | 2021-06-17 | 2021-09-24 | 上海海事大学 | Multi-device hybrid scheduling method for U-shaped automatic wharf and electronic device |
| CN113435722B (en) * | 2021-06-17 | 2024-01-23 | 上海海事大学 | Multi-device mixed scheduling method for U-shaped automatic wharf and electronic device |
| CN114707294A (en) * | 2022-01-28 | 2022-07-05 | 湘南学院 | Multi-target scheduling method for job shop with limited transportation capacity constraint |
| CN114707294B (en) * | 2022-01-28 | 2023-02-07 | 湘南学院 | Multi-target scheduling method for job shop with limited transportation capacity constraint |
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