CN109670210A - A kind of method of optimization for power electronic circuit based on parallel distributed particle swarm algorithm - Google Patents
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Abstract
The invention discloses a kind of method of optimization for power electronic circuit based on parallel distributed particle swarm algorithm, specifically: n process of setting carries out feedback network optimization after first progress power transmission optimization again.The present invention realizes the parallelization of algorithm under distributed environment, has been obviously shortened the time calculated in this way.So that particle swarm algorithm was preferably applied in the problem of extensive, complex adaptability value and strong real-time.
Description
Technical field
The present invention relates to field of power electronics more particularly to a kind of power electronics based on parallel distributed particle swarm algorithm
Circuit optimization method.
Background technique
Power electronic circuit can transmit by adjusting supply electric current or voltage to efficiently control electric energy, adapt to user
Load, be now widely used in various everyday devices, such as mobile device, computer, television set and uninterruptible power supply.And
With the progress of semiconductor technology and Electronic Encapsulating Technology, the demand generated to power electronic circuit automation is higher and higher.
In the prior art, there are many methods for the optimization of power electronic circuit, classical algorithm includes such as gradient method
With climbing method etc., but these methods tend to fall into the trap of local minimum, cause optimum results undesirable.Meanwhile by
Corresponding nominal value member can not be found in actual production in the optimum option value that traditional design optimization method is calculated
Part, it is thus impossible to realize real Automated Design and optimization.Therefore, a kind of evolution algorithm of randomization comes into being, for example loses
Propagation algorithm, ant group algorithm etc..The application of genetic algorithm is also fewer, and majority is in conceptual phase.And particle swarm algorithm is to evolve
One branch of algorithm, particle swarm algorithm is simple and practical since its definition is clear, and answering for wide hair has just been obtained since proposition
With, such as each field such as dynamic allocation, medical graphical registration, machine learning and training, data mining and classification and signal control.
Compared with other evolution algorithms, particle swarm algorithm has many advantages, such as fast convergence rate, and the quality of solution is stablized, therefore is very suitable for
Such optimization problem is designed in power electronic circuit.But calculating of the adaptive value in circuit optimization problem is very time-consuming
, as the computing resource that the increase of problem scale needs also dramatically increases, the time calculated in this way also can be elongated therewith.It calculates
The increase of time causes particle swarm algorithm to encounter bottleneck in terms of optimization for power electronic circuit.So that some extensive, adaptive values
The problem of complicated and real-time, is not well solved.
With the development of computer technology, cloud computing is that we provide many computing resources.The development of computing resource makes
It obtains us and breaches the limitation of one process, can allow calculated value in different processes while calculate, here it is distributed parallels
The thought of calculating.Under parallel environment, the execution time of algorithm is substantially reduced.It is same that each particle is assigned to different processes
When calculate, the runing time of program can be reduced.Therefore, operation is carried out under distributed parallel environment for solving the big of complexity
Scale issue and the high problem of requirement of real-time have uses prospect well.
Summary of the invention
It is an object of the invention to overcome the deficiencies of the prior art and provide a kind of based on parallel distributed particle swarm algorithm
Method of optimization for power electronic circuit.
The purpose of the present invention can be achieved through the following technical solutions:
A kind of method of optimization for power electronic circuit based on parallel distributed particle swarm algorithm, specifically: setting n into
Journey carries out feedback network optimization after first carrying out power transmission optimization again；Wherein, No. 0 process is for initializing and optimizing；
The power transmission optimization, specifically includes:
The population that A1, No. 0 process initialization power transmission optimize, and the upper and lower of population is determined according to point element
Limit；
A2, the adaptive value for calculating each particle, and the adaptive value being calculated is sent back into No. 0 process；
A3, No. 0 process receive the adaptive value sent back to, update the history optimal location vector and all grains of each particle
Global optimum's position vector of son；
A4, the speed and position vector for updating each particle；
A5, the random number r between 0 to 1 is generated to every one-dimensional random of particle；When r is less than mutation probability PmWhen, it is just random
Change the value of corresponding element；
A6, when meet power transmission optimization termination condition, then execute feedback network optimization, otherwise return to step A1.
The feedback network optimization, specifically includes:
The population that B1, No. 0 process initialization feedback network optimize, and the upper and lower of population is determined according to point element
Limit；
B2, the adaptive value for calculating each particle, and the adaptive value being calculated is sent back into No. 0 process；
B3, No. 0 process receive the adaptive value sent back to, update the history optimal location vector and all grains of each particle
Global optimum's position vector of son；
B4, the speed and position vector for updating each particle；
B5, the random number r between 0 to 1 is generated to every one-dimensional random of particle；When r is less than mutation probability PmWhen, it is just random
Change the value of corresponding element；
B6, when meet power transmission optimization termination condition, then terminate to optimize, otherwise return to step B1.
Specifically, the function of adaptive value is calculated in the power transmission optimization are as follows:
Wherein, CPnIndicate population individual UVR exposure corresponding with power transmission optimization, vinAnd RLRespectively indicate input electricity
Pressure and load value, Vin,maxAnd Vin,minRespectively indicate the maximum and minimum value of input voltage, RL,maxAnd RL,minRespectively indicate load
Maximum and minimum value, δ vinWith δ RLRespectively indicate the step-length for changing input voltage and load.OF1For assessing output voltage
Steady-state error, OF2For the constraint condition of assessment circuit work, OF3For calculating the stable state ripple on output voltage
Voltage, OF4For assessing the intrinsic property of element, such as average price, physics size etc..
Specifically, the function of adaptive value is calculated in the feedback network optimization are as follows:
Wherein, CFnIndicate population individual UVR exposure corresponding with feedback network optimization, vinAnd RLRespectively indicate input electricity
Pressure and load value, Vin,maxAnd Vin,minRespectively indicate the maximum and minimum value of input voltage, RL,maxAnd RL,minRespectively indicate load
Maximum and minimum value, δ vinWith δ RLRespectively indicate the step-length for changing input voltage and load.OF5For assessing in output voltage
Steady-state error, OF6For assessing maximum overshoot and undershoot, and during starting output voltage settling time,
OF7With the stabilization ripple voltage on assessment output voltage, OF8For assessment circuit when input voltage and output resistance disturb
Dynamic property.
Specifically, in power transmission optimization and feedback network optimization, adaptation is calculated using the method for parallel computation
Value.0 process is set as host process, remaining process is set as from process.Host process is for sending and receiving adaptive value and completing phase
The calculating answered.The adaptive value from host process and simultaneously parallel computation adaptive value are received from process, adaptive value after the completion of calculating
Send back host process.Sequence transmission method is taken in transmission, i.e. first parameter is sent to No. 1 process, and second parameter is sent to No. 2
Process, and so on cycle through, until all parameters are sent completely.In the case where total parameter indivisible number from process
This method can be still applicable in.
Further, for the parameter OF of calculating adaptive value function in power transmission optimization1, OF2, OF3, OF4, specifically
Are as follows:
For OF1, define a variance equation E2, to assess voWith vrefIn NsThe degree of closeness of a simulated point,
Calculation formula are as follows:
Wherein, voIndicate the voltage obtained by time-domain-simulation, vrefIndicate reference voltage.
If E2Value it is smaller, then steady-state error is small, OF1It can be larger.
OF1It indicates are as follows:
Wherein, K1Indicate OF1Attainable maximum value, K2To adjust OF1To E2Susceptibility.
For OF2, under steady-state conditions, some waveforms will receive the control of constraint condition.Assuming that λC,mIndicate control
Measure qmThe limit under m-th of constraint condition, then OF2It indicates are as follows:
Wherein, NCIndicate the number of constraint condition, K3,mIndicate the maximum value of m-th of constraint condition, and K4,mIndicate control
Amount q processedmSusceptibility.λCIndicate the maximum rated voltage of switch, q indicates actual voltage, as q > > λCWhen, OF2It will be very
Greatly.
For OF3, voOn ripple voltage must be in anticipated output vo,expNeighbouring ± Δ voWithin limit.In OF3Middle weighing apparatus
Measure CPnMethod be calculate in NSIn a simulated point, voBeyond vo,exp±ΔvoSimulated point number.OF3It indicates are as follows:
Wherein, K5Indicate OF3Attainable maximum value, K6For attenuation constant, A1It indicates beyond the simulated point for allowing sideband
Number.Work as A1When increase, OF3Reduce.
For OF4, mainly consider that some internal factors relevant with element, these factors include in this objective function
Average price, physics size, component life etc..OF4It indicates are as follows:
Wherein, IpIndicate resistive element number；JpIndicate inductance element number；KpIndicate capacity cell number；ΦR, ΦLWith
ΦCIt is the purpose function for measuring different elements type, respectively indicates are as follows:
Wherein, Ri, LjAnd CkRespectively indicate resistance i, inductance j, capacitor k；K7,i, K8,jAnd K9,kRespectively indicate ΦR, ΦLWith
ΦCAttainable maximum value respectively.Ri,max, Lj,maxAnd Ck,maxRespectively indicate Ri, LjAnd CkMaximum value.
Further, for the parameter OF of calculating adaptive value function in feedback network optimization5, OF6, OF7, OF8, specifically
Are as follows:
For OF5, this objective function and OF1It is similar, it indicates are as follows:
For OF6And OF8, during starting or external disturbance, it will a transient response v occurd, wherein vd=vref-
v'o, v'oIndicate the instantaneous voltage obtained by time-domain-simulation；OF6And OF8To assess vd, including 1) maximum overshoot, 2) it is maximum
Undershoot, 3) during starting or disturbance, the settling time of response.OF6And OF8It respectively indicates are as follows:
OF6=OV (RL,vin,CFn)+UV(RL,vin,CFn)+ST(RL,vin,CFn)
Wherein, NTIt is the number inputted in performance test with load disturbance.
Further, in above-mentioned formula, OV, UV and ST are for minimizing maximum overshoot, maximum undershoot and vdIt builds
Objective function between immediately, respectively indicates are as follows:
Wherein, K10It is the maximum value that this objective function can achieve, Mp0It is maximum overshoot, MpIt is actual overshoot, K11
It is passband constant.
Wherein, K12It is the maximum value that this objective function can achieve, Mv0It is maximum undershoot, MvIt is actual undershoot, K13
It is passband constant.
Wherein, K14It is the maximum value that this objective function can achieve, Ts0It is a constant, TsWhen being actual establish
Between, K15For adjusting susceptibility.TsIt is defined as vdFall into the settling time in α ± σ % passband.Wherein, | vd(t)|≤0.01σ,t
≥Ts。
For OF7, OF7With OF in power train portion3Definition method it is identical, calculate voBeyond vo,exp±ΔvoIt is imitative
True point number.OF7It indicates are as follows:
Particle swarm algorithm requires each individual (particle) to maintain two vectors, i.e. velocity vector in optimization processAnd position vector(what is saved in position vector is circuit element value, with adaptation
CP in value functionnAnd CFnIt is corresponding), wherein i indicates the number of particle, and D is to solve for the dimension of problem, in power electronic circuit
Optimization in indicate component number to be optimized.The speed of particle determines its direction of motion and rate, and position then embodies
Position of the solution representated by particle in solution space.Each particle is also required respectively to maintain itself the optimal position of history simultaneously
It sets vector and (uses pBestiIndicate), that is to say, that during evolution, if particle reaches some adaptive value better position,
Then the position is recorded in history optimal vector.In addition, group also safeguards global optimum's position vector (with gBest table
Show), that is, one optimal in the pBest of all particles, guidance particle plays to the area, global optimum in this global optimum
The convergent effect in domain.
Specifically, in step A4 and B4, particle rapidity is as follows with location update formula:
Wherein, ω is inertia weight, c1And c2For accelerator coefficient,WithIt is two equally distributed random numbers from 0 to 1.Indicate the value of the velocity vector d dimension of particle i；Indicate the value of the history optimal vector d dimension of particle i；It indicates
The value of the position vector d dimension of particle i.
The present invention compared to the prior art, have it is below the utility model has the advantages that
1, the present invention introduces mutation operator in optimization process, increases the population diversity in optimization process, so that
Performance of the particle swarm algorithm for optimization for power electronic circuit is improved.
2, the present invention realizes the parallelization of algorithm under distributed environment, has been obviously shortened the time calculated in this way.So that
Particle swarm algorithm was preferably applied in the problem of extensive, complex adaptability value and strong real-time.
Detailed description of the invention
Fig. 1 is the structural schematic diagram of power electronic circuit in the embodiment of the present invention.
Fig. 2 is the tool of the method for optimization for power electronic circuit based on parallel distributed particle swarm algorithm in the embodiment of the present invention
Body flow chart.
Fig. 3 is the schematic diagram of buck converter in the embodiment of the present invention.
Specific embodiment
Present invention will now be described in further detail with reference to the embodiments and the accompanying drawings, but embodiments of the present invention are unlimited
In this.
Embodiment
Be as shown in Figure 1 the structural schematic diagram of power electronic circuit in the present embodiment, the circuit include power transmission and
Feedback network two parts.The passive element in two parts is indicated using two vectors:
Wherein, IPIndicate power transmission portion
The resistance number divided, JPIndicate the number of inductance, KPIndicate the number of capacitor；IFIndicate the number of feedback network portions resistance, JF
Indicate the number of inductance, KFIndicate the number of capacitor.During optimization, ΘPAnd ΘFIt optimizes respectively.
It is illustrated in figure 2 a kind of process of method of optimization for power electronic circuit based on parallel distributed particle swarm algorithm
Figure, specifically: n process of setting carries out feedback network optimization after first carrying out power transmission optimization again；Wherein, No. 0 process is used for
Initialization and optimization；
The power transmission optimization, specifically includes:
The population that A1, No. 0 process initialization power transmission optimize, and the upper and lower of population is determined according to point element
Limit；
A2, the adaptive value for calculating each particle, and the adaptive value being calculated is sent back into No. 0 process；
A3, No. 0 process receive the adaptive value sent back to, update the history optimal location vector and all grains of each particle
Global optimum's position vector of son；
A4, the speed and position vector for updating each particle；
A5, the random number r between 0 to 1 is generated to every one-dimensional random of particle；When r is less than mutation probability PmWhen, it is just random
Change the value of corresponding element；
A6, when meet power transmission optimization termination condition, then execute feedback network optimization, otherwise return to step A1.
The feedback network optimization, specifically includes:
The population that B1, No. 0 process initialization feedback network optimize, and the upper and lower of population is determined according to point element
Limit；
B2, the adaptive value for calculating each particle, and the adaptive value being calculated is sent back into No. 0 process；
B3, No. 0 process receive the adaptive value sent back to, update the history optimal location vector and all grains of each particle
Global optimum's position vector of son；
B4, the speed and position vector for updating each particle；
B5, the random number r between 0 to 1 is generated to every one-dimensional random of particle；When r is less than mutation probability PmWhen, it is just random
Change the value of corresponding element；
B6, when meet power transmission optimization termination condition, then terminate to optimize, otherwise return to step B1.
In the present embodiment, the present invention is tested using the example of buck converter optimization design, wherein reducing transformer
Schematic diagram it is as shown in Figure 3.Power train portion element to be optimized is L and C, and feedback network element to be optimized is R1, R2,
RC3, R4, C2, C3And C4.The particle number of particle swarm algorithm is selected as 30, maximum cycle 500, remaining parameter such as following table
It is shown:
| Parameter | Value |
| w | 0.9→0.4 |
| c1 | 2.0 |
| c2 | 2.0 |
| Pm | 0.02 |
In order to verify advantage of the Distributed Parallel Algorithm relative to serial algorithm, with particle swarm algorithm and distributed parallel
Particle swarm algorithm respectively optimizes circuit.Emulation testing is carried out respectively to the optimum results of two kinds of algorithms, from test
As a result it can learn that the settling time of the emulation wave output waveform of parallel algorithm is significantly less than the emulation wave output wave of serial algorithm
The settling time of shape.This prove Distributed Parallel Algorithm solves the problems, such as extensive, high complexity and requirement of real-time it is high in be
It is highly effective.
The above embodiment is a preferred embodiment of the present invention, but embodiments of the present invention are not by above-described embodiment
Limitation, other any changes, modifications, substitutions, combinations, simplifications made without departing from the spirit and principles of the present invention,
It should be equivalent substitute mode, be included within the scope of the present invention.
Claims (7)
1. a kind of method of optimization for power electronic circuit based on parallel distributed particle swarm algorithm, which is characterized in that specifically: it sets
N process is set, carries out feedback network optimization again after first carrying out power transmission optimization；Wherein, No. 0 process is for initializing and excellent
Change；
The power transmission optimization, specifically includes:
A1, the optimization of No. 0 process initialization power transmission population, and determine according to point element the bound of population；
A2, the adaptive value for calculating each particle, and the adaptive value being calculated is sent back into No. 0 process；
A3, No. 0 process receive the adaptive value sent back to, updates the history optimal location vector and all particles of each particle
Global optimum's position vector；
A4, the speed and position vector for updating each particle；
A5, the random number r between 0 to 1 is generated to every one-dimensional random of particle；When r is less than mutation probability PmWhen, it is just random to change
The value of corresponding element；
A6, when meet power transmission optimization termination condition, then execute feedback network optimization, otherwise return to step A1；
The feedback network optimization, specifically includes:
B1, the optimization of No. 0 process initialization feedback network population, and determine according to point element the bound of population；
B2, the adaptive value for calculating each particle, and the adaptive value being calculated is sent back into No. 0 process；
B3, No. 0 process receive the adaptive value sent back to, updates the history optimal location vector and all particles of each particle
Global optimum's position vector；
B4, the speed and position vector for updating each particle；
B5, the random number r between 0 to 1 is generated to every one-dimensional random of particle；When r is less than mutation probability PmWhen, it is just random to change
The value of corresponding element；
B6, when meet power transmission optimization termination condition, then terminate to optimize, otherwise return to step B1.
2. a kind of method of optimization for power electronic circuit based on parallel distributed particle swarm algorithm according to claim 1,
It is characterized in that, calculating the function of adaptive value in the power transmission optimization are as follows:
Wherein, CPnIndicate population individual UVR exposure corresponding with power transmission optimization, vinAnd RLRespectively indicate input voltage and
Load value, Vin,maxAnd Vin,minRespectively indicate the maximum and minimum value of input voltage, RL,maxAnd RL,minRespectively indicate load most
Big and minimum value, δ vinWith δ RLRespectively indicate the step-length for changing input voltage and load；OF1For assessing the stabilization of output voltage
State error, OF2For the constraint condition of assessment circuit work, OF3For calculating the stable state ripple electricity on output voltage
Pressure, OF4For assessing the intrinsic property of element.
3. a kind of method of optimization for power electronic circuit based on parallel distributed particle swarm algorithm according to claim 1,
It is characterized in that, calculating the function of adaptive value in the feedback network optimization are as follows:
Wherein, CFnIndicate population individual UVR exposure corresponding with feedback network optimization, vinAnd RLRespectively indicate input voltage and
Load value, Vin,maxAnd Vin,minRespectively indicate the maximum and minimum value of input voltage, RL,maxAnd RL,minRespectively indicate load most
Big and minimum value, δ vinWith δ RLRespectively indicate the step-length for changing input voltage and load；OF5For assessing in the steady of output voltage
Determine state error, OF6For assessing maximum overshoot and undershoot, and during starting output voltage settling time, OF7With
Assess the stabilization ripple voltage on output voltage, OF8For dynamic of the assessment circuit when input voltage and output resistance disturb
Performance.
4. a kind of method of optimization for power electronic circuit based on parallel distributed particle swarm algorithm according to claim 1,
It is characterized in that, calculating adaptive value using the method for parallel computation in power transmission optimization and feedback network optimization；0
Process is set as host process, remaining process is set as from process；Host process is for sending and receiving adaptive value and completing corresponding
It calculates；The adaptive value from host process and simultaneously parallel computation adaptive value are received from process, adaptive value is sent after the completion of calculating
Return host process；Sequence transmission method is taken in transmission, i.e. first parameter is sent to No. 1 process, second parameter be sent to No. 2 into
Journey, and so on cycle through, until all parameters are sent completely；In the case where total parameter indivisible number from process this
Kind method can be still applicable in.
5. a kind of method of optimization for power electronic circuit based on parallel distributed particle swarm algorithm according to claim 2,
It is characterized in that, for the parameter OF for calculating adaptive value function in power transmission optimization1, OF2, OF3, OF4, specifically:
For OF1, define a variance equation E2, to assess voWith vrefIn NsThe degree of closeness of a simulated point calculates public
Formula are as follows:
Wherein, voIndicate the voltage obtained by time-domain-simulation, vrefIndicate reference voltage；
If E2Value it is smaller, then steady-state error is small, OF1It can be larger；
OF1It indicates are as follows:
Wherein, K1It is OF1Attainable maximum value, K2To adjust OF1To E2Susceptibility；
For OF2, it is assumed that λC,mThe amount of being qmThe limit under m-th of constraint condition, then OF2It indicates are as follows:
Wherein, NCIt is the number of constraint condition, K3,mIt is the maximum value of m-th of constraint condition, and K4,mIndicate control amount qmIt is quick
Sensitivity；Work as λCWhen indicating the maximum rated voltage of switch, q is actual voltage, as q > > λC, OF2It will be very big；
For OF3, voOn ripple voltage must be in anticipated output vo,expNeighbouring ± Δ voWithin limit；In OF3Middle measurement dye
Colour solid CPnMethod be calculate in NSIn a simulated point, voBeyond vo,exp±ΔvoSimulated point number；OF3It indicates are as follows:
Wherein, K5It is OF3Attainable maximum value, K6It is attenuation constant, A1It is above the simulated point number for allowing sideband；Work as A1Increase
When adding, OF3Reduce；
For OF4, indicate are as follows:
Wherein, IpIndicate resistive element number；JpIndicate inductance element number；KpIndicate capacity cell number；ΦR, ΦLAnd ΦC
It is the purpose function for measuring different elements type, respectively indicates are as follows:
Wherein, Ri, LjAnd CkRespectively indicate resistance i, inductance j, capacitor k；K7,i, K8,jAnd K9,kRespectively indicate ΦR, ΦLAnd ΦCPoint
Not attainable maximum value；Ri,max, Lj,maxAnd Ck,maxRespectively indicate Ri, LjAnd CkMaximum value.
6. a kind of method of optimization for power electronic circuit based on parallel distributed particle swarm algorithm according to claim 3,
It is characterized in that, for the parameter OF for calculating adaptive value function in feedback network optimization5, OF6, OF7, OF8, specifically:
For OF5, this objective function and OF1It is similar, it indicates are as follows:
For OF6And OF8, during starting or external disturbance, it will a transient response v occurd, wherein vd=vref-v'o,
v'oIndicate the instantaneous voltage obtained by time-domain-simulation；OF6And OF8To assess vd, including 1) maximum overshoot, 2) it is maximum under
Punching, 3) during starting or disturbance, the settling time of response；OF6And OF8It respectively indicates are as follows:
OF6=OV (RL,vin,CFn)+UV(RL,vin,CFn)+ST(RL,vin,CFn)
Wherein, NTIt is the number inputted in performance test with load disturbance；
In above-mentioned formula, OV, UV and ST are for minimizing maximum overshoot, maximum undershoot and vdThe objective function of settling time,
It respectively indicates are as follows:
Wherein, K10It is the maximum value that this objective function can achieve, Mp0It is maximum overshoot, MpIt is actual overshoot, K11It is logical
Zonal Constant；
Wherein, K12It is the maximum value that this objective function can achieve, Mv0It is maximum undershoot, MvIt is actual undershoot, K13It is logical
Zonal Constant；
Wherein, K14It is the maximum value that this objective function can achieve, Ts0It is a constant, TsIt is actual settling time, K15
For adjusting susceptibility；TsIt is defined as vdFall into the settling time in α ± σ % passband；Wherein, | vd(t)|≤0.01σ,t≥Ts；
For OF7, OF7With OF in power train portion3Definition method it is identical, calculate voBeyond vo,exp±ΔvoSimulated point
Number；OF7It indicates are as follows:
Wherein, K5It is OF7Attainable maximum value, K6It is attenuation constant, A1It is above the simulated point number for allowing sideband；Work as A1Increase
When adding, OF7Reduce.
7. a kind of method of optimization for power electronic circuit based on parallel distributed particle swarm algorithm according to claim 1,
It is characterized in that, particle rapidity is as follows with location update formula in step A4 and B4:
Wherein, ω is inertia weight, c1And c2For accelerator coefficient, r1 dWithIt is two equally distributed random numbers from 0 to 1.Table
Show the value of the velocity vector d dimension of particle i；Indicate the value of the history optimal vector d dimension of particle i；Indicate particle
The value of the position vector d dimension of i.
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| CN112163389A (en) * | 2020-09-07 | 2021-01-01 | 华南理工大学 | Power electronic circuit optimization method based on self-adaptive distributed particle swarm optimization algorithm |
| CN112163387A (en) * | 2020-09-07 | 2021-01-01 | 华南理工大学 | Power electronic circuit optimization method based on brain storm algorithm and application thereof |
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