CN108108532A - With the method for particle cluster algorithm optimization power electronic circuit - Google Patents

With the method for particle cluster algorithm optimization power electronic circuit Download PDF

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CN108108532A
CN108108532A CN201711275293.4A CN201711275293A CN108108532A CN 108108532 A CN108108532 A CN 108108532A CN 201711275293 A CN201711275293 A CN 201711275293A CN 108108532 A CN108108532 A CN 108108532A
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张军
陈伟能
詹志辉
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South China University of Technology SCUT
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Abstract

The invention discloses a kind of methods with particle cluster algorithm optimization power electronic circuit, based on the purpose for reducing computing load, optimization process is divided into two parts by the present invention with decoupling technology, is the power transmission optimization of power electronic circuit and feedback network optimization respectively.Mutation operator is introduced in particle cluster algorithm simultaneously, to increase the diversity of group, improves the optimization efficiency of algorithm.It is tested by taking the optimization design of a voltage-releasing voltage stabilizer as an example, it was demonstrated that the method for the present invention is effective.

Description

Method for optimizing power electronic circuit by applying particle swarm optimization
Technical Field
The invention relates to the technical field of power electronics and intelligent computing, in particular to a method for optimizing a power electronic circuit by applying a particle swarm algorithm.
Background
Power electronic circuits are circuits that provide power to a load, and the power electronic circuits are circuits that effectively control the delivery of electrical energy by regulating the supply voltage or current to enable a user's load to achieve the desired power. Power electronic circuits have a wide range of applications, particularly in mobile electronic devices, televisions, computers and the like. With the development of semiconductor technology and electronic packaging technology, the demand for the automated design of power electronic circuits is higher and higher.
The automatic design and optimization method of the power electronic circuit is mainly divided into a deterministic algorithm and a random algorithm. Deterministic algorithms, such as gradient methods and hill climbing methods, are prone to fall into local optima, resulting in suboptimal component parameter values. Also, some deterministic algorithm performance is too dependent on the choice of initial search points. The optimization of power electronic circuits is a highly non-linear problem, and therefore deterministic algorithms tend to perform poorly. In contrast, the stochastic algorithm can search a solution space extensively, has low dependency on initialization search points and high robustness, and is therefore more suitable for optimizing and designing power electronic circuits than deterministic methods.
In recent years, an evolutionary algorithm subordinate to a random algorithm has attracted attention of many researchers. The evolutionary algorithm is characterized by requiring only information of the objective function and no special knowledge about the specific problem. The method is not restricted by search space restrictive hypothesis, does not require hypothesis such as continuity, conductibility and the like, can find a global optimal solution with high probability from discrete, multi-extremal and noisy high-dimensional problems, and is a global optimization method with high robustness and wide applicability. Therefore, the evolutionary algorithm is well suited for the design and optimization of power electronic circuits.
The particle swarm algorithm is a heuristic random algorithm, belongs to a branch of an evolutionary algorithm, and works by simulating the predation behaviors of bird swarms and fish swarms in nature. The particle swarm algorithm is clear in definition and easy to realize, and has been widely applied since the introduction. In particular, in the field of analog circuit design, particle swarm optimization has been used for radio frequency circuit design, on-chip spiral inductor design, microwave filter design, and the like. Compared with other evolutionary algorithms, the particle swarm algorithm has the advantages of high convergence speed, high solution precision, stable quality and the like, and is very suitable for solving the optimization problem of power electronic circuit design.
Disclosure of Invention
The present invention is directed to solve the above-mentioned drawbacks of the prior art, and provides a method for optimizing a power electronic circuit by using a particle swarm optimization.
The purpose of the invention can be achieved by adopting the following technical scheme:
the invention discloses a method for optimizing a power electronic circuit by applying a particle swarm algorithm, which applies the particle swarm algorithm to the optimization design of the power electronic circuit. In order to reduce the operation load, the particle swarm optimization algorithm does not perform the optimization of the whole circuit like the traditional method, but divides the power electronic circuit into two decoupled parts, namely a power transmission network and a feedback network, and then optimizes the two parts respectively. The encoding of the particles adopts real number encoding, and the value of each dimension corresponds to the parameter of a component to be optimized. The method for optimizing the power electronic circuit by applying the particle swarm algorithm comprises the following specific steps:
and S1, setting algorithm parameters for optimizing the power transmission part, and randomly initializing the first generation particle swarm of the power transmission part within the range of the upper limit and the lower limit of the value of a given device.
S2, calculating the adaptive value of each particle to evaluate the quality of the solution, wherein the adaptive value function is as follows:
wherein v isinAnd RLRespectively input voltage and load value, Vin,maxAnd Vin,minFor maximum and minimum values of the input voltage, RL,maxAnd RL,minAt maximum and minimum values of the load, δ vinAnd δ RLFor varying the input voltage and the step size, CP, of the load, respectivelyiThe ith particle swarm individual code is shown. F1For evaluating the stability of the output voltageError in constant state, F2Constraints for evaluating the operation of the circuit, F3For calculating the steady-state ripple voltage, F, on the output voltage4For evaluating intrinsic properties of the device, such as overall price, physical size, etc.
And S3, updating the individual history optimal pBest of each particle and the global optimal gBest of all the particles according to the adaptive value.
And S4, updating the velocity and position vector of each particle.
And S5, increasing the diversity of the population by using a mutation operator. The specific method is that a random number r randomly distributed between 0 and 1 is generated for each dimension of each particle. If r is less than the mutation probability PmThe value of the dimension, i.e., the corresponding device parameter value, is then randomly changed within the range of the upper and lower limits of the given device value.
S6, if the end condition of the power transmission section (such as maximum iteration number) is reached, then step S7 is executed, otherwise, the process returns to step S2.
And S7, setting algorithm parameters for optimizing the feedback network, and randomly initializing the first generation of particle swarm of the feedback network within the range of the upper limit and the lower limit of the value of a given device.
S8, calculating an adaptive value of each particle, wherein the adaptive value function is as follows:
wherein, CFiRepresents the code of the ith particle swarm individual. F5For evaluating steady state errors at the output voltage, F6For evaluating the maximum overshoot and undershoot, and the settling time of the output voltage during start-up, F7By evaluating the steady ripple voltage on the output voltage, F8The method is used for evaluating the dynamic performance of the circuit when the input voltage and the output resistance are disturbed.
And S9, updating the individual history optimal pBest of each particle and the global optimal gBest of all the particles according to the adaptive value.
And S10, updating the velocity and position vector of each particle.
S11, the same method as the power transmission part, the group diversity of the feedback network is increased by using the mutation operator.
And S12, if the end condition of the feedback network part is reached, ending the optimization, otherwise, returning to the step S8.
Compared with the prior art, the invention has the following advantages and effects:
the particle swarm algorithm has the advantages of simple concept, easy realization and high convergence speed, thereby being widely applied. However, in the optimization design of the power electronic circuit, due to the complexity of the problem, sometimes the particle swarm optimization converges to the local optimal solution, which is caused by insufficient population diversity. Therefore, mutation operators are introduced into the particle swarm algorithm, the population diversity in the evolution process is increased, and the performance of the particle swarm algorithm for optimizing the power electronic circuit is improved.
Drawings
FIG. 1 is a basic block diagram of a power electronic circuit;
FIG. 2 is a flow chart of a method of optimizing a power electronic circuit using a particle swarm algorithm as disclosed in the present invention;
fig. 3 is a basic configuration diagram of the step-down regulator.
Detailed Description
In order to make the objects, technical solutions and advantages of the embodiments of the present invention clearer, the technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are some, but not all, embodiments of the present invention. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
Examples
A flowchart of the method for optimizing a power electronic circuit using a particle swarm algorithm disclosed in this embodiment is shown in fig. 2.
The particle swarm algorithm is an optimization algorithm based on swarm intelligence. Each individual (particle) in the population maintains two vectors, velocity vector V, during evolutionij＝[vi1,vi2,…,viD]And a position vector Xij＝[xi1,xi2,…,xiD](the stored in the position vector is the circuit device parameter value and the CP in the adaptive value functioniAnd CFiCorresponding), where i denotes the number of particles, D is the dimension of the solution problem, and in the optimized design of the power electronic circuit, denotes the number of devices to be optimized. The velocity vector of a particle determines its direction and velocity of motion, while the position vector represents the coordinates of the solution represented by the particle in solution space. The fitness function is used to evaluate the goodness of the particle position, i.e., the quality of the solution. The particles have information memory and communication capability, and specifically, each particle maintains a historical optimal position vector (pBest) of the particleiRepresentation), that is to say if a particle reaches a position with a better fitness value during the evolution process, the position is recorded into the historical optimum vector. In addition, the population maintains a global optimum location vector (denoted gBest), i.e., the optimum one of pBest for all particles, which acts to guide the particles toward the global optimum region. In each generation, the particle velocity and position update formula is as follows:
Vij (t+1)＝ωVij+c1rand1(pi-Xij(t))+c2rand2(pl-Xij(t))(1)
Xij (t+1)＝Xij(t)+Vij (t+1)
where ω is the inertial weight, c1And c2For acceleration factor, rand1And rand2Are two random numbers uniformly distributed from 0 to 1.
The basic block diagram of the power electronic circuit is shown in fig. 1, which includes two parts, a power delivery and a feedback network. The power transmission part comprises IPA resistance JPAn inductance and KPA capacitor; the feedback network part comprises IFA resistance JFAn inductance and KFA capacitor. Passive devices in both parts are represented by two vectors, respectively:
wherein,
in the optimization program, [ theta ] isPAnd ΘFAre optimized separately. The key to the optimization is to properly encode the optimization parameters and to design the optimization objective as an adaptive value function of the particle swarm algorithm.
The design will thetaPAnd ΘFThe real number strings CP and CF are respectively coded, and each bit represents the corresponding component parameter value by a real number as shown in the following formula.
The fitness functions of the two parts are defined as follows:
wherein phiPAnd phiFRespectively representing the adaptation value functions of the power transmission and feedback network parts. CP (CP)iAnd CFiRespectively represent a group with thetaPAnd ΘFEncoding of the corresponding population of individuals. Objective function F1And F5For evaluating steady state errors of the output voltage. F2For evaluating constraints on the operation of the circuit. F3And F7For calculating the steady state ripple voltage on the output voltage. F4For evaluating the intrinsic properties of the device, such as overall price, size, lifetime, etc. F6For evaluating the maximum overshoot and undershoot, and the settling time of the output voltage during start-up. F8The method is used for evaluating the dynamic performance of the circuit when the input voltage and the output resistance are disturbed.
Adaptation value function phi in power transmission sectionPIn (1), an objective function F1，F2，F3，F4Respectively designing as follows:
1)F1：
defining a variance accumulation equation E2For evaluating the output voltage voValue v 'after time domain simulation'oAnd referenceVoltage vrefIn NsDegree of approximation on simulated values
If E is2If the value of (A) is small, the steady state error is small, F1Will be larger. Formula F1Is defined as follows
Wherein, W1Is F1Maximum value, W, that can be reached2For adjusting F1To E2The sensitivity of (2).
2)F2：
In steady state conditions, some waveforms may be subject to constraints. Let λ beC,mIs the quantity qmLimit under the mth constraint, then F2Is defined as
Wherein N isCIs the number of constraints, W3,mIs the maximum value of the mth constraint, and W4,mThe sensitivity of the considered quantity is determined.
3)F3：
voThe ripple voltage on must be at the desired output vo,expNear ± Δ voWithin the limits. At F3Middle-measure particle swarm individual CPiIs calculated at NSIn one simulation point, voExceeding vo,exp±ΔvoThe number of simulation points. F3Is defined as follows
Wherein, W5Is F3Maximum value, W, that can be reached6Is the decay constant, Q1Is the number of emulation points beyond the allowable sidebands. It can be seen that when Q is1When increased, F3And decreases.
4)F4：
This objective function primarily takes into account some intrinsic device-related factors including physical size, device lifetime, overall price, etc. F4Can be expressed as
Wherein phiR，ΦLAnd phiCIs a function of measuring different types of devices separately. They are defined as follows
Wherein, W7,i，W8,jAnd W9,kIs phiR，ΦLAnd phiCRespectively, to a maximum value that can be reached. Ri,max，Lj,maxAnd Ck,maxAre each Ri，LjAnd CkIs measured.
Adaptation value function phi in the feedback network partFIn (1), an objective function F5，F6，F7，F8Are defined as follows:
1)F5：
and F1Consistent, is defined as
2)F6And F8：
During start-up or external disturbances, a transient response v will occurdWherein
vd＝vref-vo
F6And F8To evaluate vdIncluding 1) maximum overshoot, 2) maximum undershoot, 3) settling time of the response during start-up or perturbation. F6And F8Can be expressed as follows
F6＝OV(RL,vin,CFi)+UV(RL,vin,CFi)+ST(RL,vin,CFi)
Wherein N isTIs the number of inputs and load disturbances in the performance test. OV, UV and ST are for maximum overshoot, maximum undershoot and v respectivelydThe set-up time of (a) is minimized. They are defined as follows:
wherein W10Is the maximum value that this objective function can reach, Mp0Is the maximum overshoot, MpIs a real overshoot, W11Is the pass band constant.
Wherein W12It is this objective function thatMaximum value reached, Mv0Is the maximum undershoot, MvIs a real undershoot, W13Is the pass band constant.
Wherein W14Is the maximum value, T, that this objective function can reachs0Is a constant, TsIs the actual setup time, W15For adjusting the sensitivity. T issIs defined as vdThe settling time falling within α + -sigma% of the pass band, i.e. is
|vd(t)|≤0.01σ,t≥Ts
3)F7：
F7And in the power transmission part F3The same design method is used to calculate voExceeding vo,exp±ΔvoThe number of simulation points. F7Is defined as follows
The algorithm of the invention was simulated using an optimized design of a buck regulator as an example, whose schematic diagram is shown in fig. 3. Wherein the devices to be optimized for the power transmission part are L and C, and the devices to be optimized for the feedback network are R1，R2，RC3，R4，C2，C3And C and4the parameters of the other devices are known. The maximum iteration number of the particle swarm algorithm is 500, and the parameters are shown in the following table:
parameter(s) Value taking
Number of particles 30
Inertial weight w 1.2→0.6
Acceleration factor c1 2.0
Acceleration factor c2 2.0
Probability of variation Pm 0.02
For comparison with the inventive algorithm, the same circuit is optimally designed using a genetic algorithm. And respectively carrying out simulation test on the optimization results of the two algorithms. The result shows that the establishment time of the simulation output waveform of the particle swarm optimization is about 5ms, which is shorter than 20ms of the genetic algorithm, and when the voltage or the load changes, the circuit optimized by the particle swarm optimization has smaller disturbance, i.e. the anti-interference capability is stronger, and the experimental result proves that the particle swarm optimization is effective in the optimization design of the power electronic circuit.
The above embodiments are preferred embodiments of the present invention, but the present invention is not limited to the above embodiments, and any other changes, modifications, substitutions, combinations, and simplifications which do not depart from the spirit and principle of the present invention should be construed as equivalents thereof, and all such changes, modifications, substitutions, combinations, and simplifications are intended to be included in the scope of the present invention.

Claims (5)

1. A method for optimizing a power electronic circuit using a particle swarm algorithm, the method comprising:
s1, initializing algorithm parameters for optimizing the power transmission part, and randomly initializing a first-generation particle swarm of the power transmission part according to the upper limit and the lower limit of the given working parameter value of the electronic component;
s2, calculating an adaptive value of each particle, wherein the adaptive value function is as follows:
<mfenced open = "" close = ""> <mtable> <mtr> <mtd> <mrow> <msub> <mi>&amp;Phi;</mi> <mi>P</mi> </msub> <mrow> <mo>(</mo> <msub> <mi>CP</mi> <mi>i</mi> </msub> <mo>)</mo> </mrow> <mo>=</mo> <munderover> <mo>&amp;Sigma;</mo> <mrow> <msub> <mi>R</mi> <mi>L</mi> </msub> <mo>=</mo> <msub> <mi>R</mi> <mrow> <mi>L</mi> <mo>,</mo> <mi>min</mi> </mrow> </msub> <mo>,</mo> <msub> <mi>&amp;delta;R</mi> <mi>L</mi> </msub> </mrow> <msub> <mi>R</mi> <mrow> <mi>L</mi> <mo>,</mo> <mi>max</mi> </mrow> </msub> </munderover> <munderover> <mo>&amp;Sigma;</mo> <mrow> <msub> <mi>v</mi> <mrow> <mi>i</mi> <mi>n</mi> </mrow> </msub> <mo>=</mo> <msub> <mi>V</mi> <mrow> <mi>i</mi> <mi>n</mi> <mo>,</mo> <mi>min</mi> </mrow> </msub> <mo>,</mo> <msub> <mi>&amp;delta;v</mi> <mrow> <mi>i</mi> <mi>n</mi> </mrow> </msub> </mrow> <msub> <mi>V</mi> <mrow> <mi>i</mi> <mi>n</mi> <mo>,</mo> <mi>max</mi> </mrow> </msub> </munderover> <mo>&amp;lsqb;</mo> <msub> <mi>F</mi> <mn>1</mn> </msub> <mrow> <mo>(</mo> <msub> <mi>R</mi> <mi>L</mi> </msub> <mo>,</mo> <msub> <mi>v</mi> <mrow> <mi>i</mi> <mi>n</mi> </mrow> </msub> <mo>,</mo> <msub> <mi>CP</mi> <mi>i</mi> </msub> <mo>)</mo> </mrow> <mo>+</mo> <msub> <mi>F</mi> <mn>2</mn> </msub> <mrow> <mo>(</mo> <msub> <mi>R</mi> <mi>L</mi> </msub> <mo>,</mo> <msub> <mi>v</mi> <mrow> <mi>i</mi> <mi>n</mi> </mrow> </msub> <mo>,</mo> <msub> <mi>CP</mi> <mi>i</mi> </msub> <mo>)</mo> </mrow> </mrow> </mtd> </mtr> <mtr> <mtd> <mrow> <mo>+</mo> <msub> <mi>F</mi> <mn>3</mn> </msub> <mrow> <mo>(</mo> <msub> <mi>R</mi> <mi>L</mi> </msub> <mo>,</mo> <msub> <mi>v</mi> <mrow> <mi>i</mi> <mi>n</mi> </mrow> </msub> <mo>,</mo> <msub> <mi>CP</mi> <mi>i</mi> </msub> <mo>)</mo> </mrow> <mo>+</mo> <msub> <mi>F</mi> <mn>4</mn> </msub> <mrow> <mo>(</mo> <msub> <mi>R</mi> <mi>L</mi> </msub> <mo>,</mo> <msub> <mi>v</mi> <mrow> <mi>i</mi> <mi>n</mi> </mrow> </msub> <mo>,</mo> <msub> <mi>CP</mi> <mi>i</mi> </msub> <mo>)</mo> </mrow> <mo>&amp;rsqb;</mo> </mrow> </mtd> </mtr> </mtable> </mfenced>
wherein, CPiDenotes the i-th particle, v, in the particle populationinAnd RLRespectively input voltage and load value, Vin,maxAnd Vin,minFor maximum and minimum values of the input voltage, RL,maxAnd RL,minAt maximum and minimum values of the load, δ vinAnd δ RLFor varying the input voltage and the step size of the load, respectively, F1For evaluating the steady state error of the output voltage, F2Constraints for evaluating the operation of the circuit, F3For calculating the steady-state ripple voltage, F, on the output voltage4For evaluating intrinsic properties of the device;
s3, updating the individual history optimal pBest of each particle and the global optimal gBest of all the particles according to the adaptive value;
s4, updating the velocity and position vector of each particle;
s5, applying mutation operators to increase diversity of the population;
s6, if the end condition of the power transmission section is reached, executing step S7, otherwise returning to step S2;
s7, initializing algorithm parameters for optimizing the feedback network, and initializing a first generation particle swarm of the feedback network according to upper and lower limits of a given device value;
s8, calculating an adaptive value of each particle, wherein the adaptive value function is as follows:
<mfenced open = "" close = ""> <mtable> <mtr> <mtd> <mrow> <msub> <mi>&amp;Phi;</mi> <mi>F</mi> </msub> <mrow> <mo>(</mo> <msub> <mi>CF</mi> <mi>i</mi> </msub> <mo>)</mo> </mrow> <mo>=</mo> <munderover> <mo>&amp;Sigma;</mo> <mrow> <msub> <mi>R</mi> <mi>L</mi> </msub> <mo>=</mo> <msub> <mi>R</mi> <mrow> <mi>L</mi> <mo>,</mo> <mi>min</mi> </mrow> </msub> <mo>,</mo> <msub> <mi>&amp;delta;R</mi> <mi>L</mi> </msub> </mrow> <msub> <mi>R</mi> <mrow> <mi>L</mi> <mo>,</mo> <mi>max</mi> </mrow> </msub> </munderover> <munderover> <mo>&amp;Sigma;</mo> <mrow> <msub> <mi>v</mi> <mrow> <mi>i</mi> <mi>n</mi> </mrow> </msub> <mo>=</mo> <msub> <mi>V</mi> <mrow> <mi>i</mi> <mi>n</mi> <mo>,</mo> <mi>min</mi> </mrow> </msub> <mo>,</mo> <msub> <mi>&amp;delta;v</mi> <mrow> <mi>i</mi> <mi>n</mi> </mrow> </msub> </mrow> <msub> <mi>V</mi> <mrow> <mi>i</mi> <mi>n</mi> <mo>,</mo> <mi>max</mi> </mrow> </msub> </munderover> <mo>&amp;lsqb;</mo> <msub> <mi>F</mi> <mn>5</mn> </msub> <mrow> <mo>(</mo> <msub> <mi>R</mi> <mi>L</mi> </msub> <mo>,</mo> <msub> <mi>v</mi> <mrow> <mi>i</mi> <mi>n</mi> </mrow> </msub> <mo>,</mo> <msub> <mi>CF</mi> <mi>i</mi> </msub> <mo>)</mo> </mrow> <mo>+</mo> <msub> <mi>F</mi> <mn>6</mn> </msub> <mrow> <mo>(</mo> <msub> <mi>R</mi> <mi>L</mi> </msub> <mo>,</mo> <msub> <mi>v</mi> <mrow> <mi>i</mi> <mi>n</mi> </mrow> </msub> <mo>,</mo> <msub> <mi>CF</mi> <mi>i</mi> </msub> <mo>)</mo> </mrow> </mrow> </mtd> </mtr> <mtr> <mtd> <mrow> <mo>+</mo> <msub> <mi>F</mi> <mn>7</mn> </msub> <mrow> <mo>(</mo> <msub> <mi>R</mi> <mi>L</mi> </msub> <mo>,</mo> <msub> <mi>v</mi> <mrow> <mi>i</mi> <mi>n</mi> </mrow> </msub> <mo>,</mo> <msub> <mi>CF</mi> <mi>i</mi> </msub> <mo>)</mo> </mrow> <mo>+</mo> <msub> <mi>F</mi> <mn>8</mn> </msub> <mrow> <mo>(</mo> <msub> <mi>R</mi> <mi>L</mi> </msub> <mo>,</mo> <msub> <mi>v</mi> <mrow> <mi>i</mi> <mi>n</mi> </mrow> </msub> <mo>,</mo> <msub> <mi>CF</mi> <mi>i</mi> </msub> <mo>)</mo> </mrow> <mo>&amp;rsqb;</mo> </mrow> </mtd> </mtr> </mtable> </mfenced>
wherein, F5For evaluating steady state errors at the output voltage, F6For evaluating the maximum overshoot and undershoot, and the settling time of the output voltage during start-up, F7For evaluating the steady ripple voltage on the output voltage, F8The circuit is used for evaluating the dynamic performance of the circuit when the input voltage and the output resistance are disturbed;
s9, updating the individual optimal pBest of each particle and the global optimal gBest of all the particles according to the adaptive value;
s10, updating the velocity and position vector of each particle;
s11, applying mutation operators to increase the group diversity of the feedback network;
and S12, if the ending condition of the power transmission part is reached, ending the optimization procedure, otherwise, returning to the step S8.
2. The method for optimizing power electronic circuits using particle swarm optimization as claimed in claim 1, wherein the step S11 of using mutation operator to increase the diversity of particle swarm comprises generating a random distribution between 0 and 1 for each dimension of each particleThe number r of machine, if r is less than the mutation probability PmThe value of the dimension, i.e., the corresponding device parameter value, is randomly changed.
3. The method of claim 1, wherein the method of optimizing the power electronic circuit using the particle swarm optimization first divides the power electronic circuit into two decoupled parts, namely the power transmission and feedback networks, and then optimizes the two parts.
4. The method for optimizing power electronic circuits by using particle swarm optimization according to claim 1, wherein the encoding of the particles is real number encoding, and the value of each dimension corresponds to a parameter of a component to be optimized.
5. The method of optimizing power electronic circuits using particle swarm optimization as claimed in claim 1, wherein the intrinsic properties of the device include overall price and physical size.
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