CN102722613A - Method for optimizing electronic component parameters in antenna broadband matching network by adopting genetic-simulated annealing combination - Google Patents

Abstract

The invention discloses a method for optimizing electronic component parameters in an antenna broadband matching network by adopting the genetic-simulated annealing combination. According to the method, on the basis of a genetic algorithm, secondary optimization is carried out by a simulated annealing algorithm, so that the defect of poor fine tuning capacity of the genetic algorithm is overcome; and meanwhile, an optimum individual obtained by adopting the genetic algorithm to optimize is used as an initial value of a variable to be optimized by the simulated annealing algorithm, so that the independence of the simulated annealing algorithm on the initial value is avoided. Moreover, aiming at the optimization problem of the antenna matching network, the combination method adopts a multi-target parallel selection method for giving consideration to the requirements of two important technical indexes of the antenna standing wave ratio and the conversion efficiency, introduces the self-adaptive adjustment of crossover and mutation operators and is beneficial for improving the calculating speed and efficiency of the algorithm. Meanwhile, an optimal solution retention strategy is introduced so as to prevent the optimum individual from losing.

Description

Adopt heredity-simulated annealing to make up to electronic component Parameter Optimization method in the antenna broadband matching network

Technical field

The present invention relates to the parameter optimization method of a kind of antenna broadband matching network in electromagnetism field, more particularly say, be meant that a kind of employing heredity-simulated annealing makes up electronic component Parameter Optimization method in the antenna broadband matching network.

Background technology

In the electronic equipment, because the limiting factor of many aspects such as antenna performance requirement and mounting condition, independent Antenna Design often is difficult to satisfy simultaneously designing requirement broadband, miniaturization under many circumstances in modern times.For the fixed antenna of version, the Design of Broadband matching network is a kind of effective technology means of further improving antenna miniaturization and broadband performance.

At present the method for designing of broadband matching network mainly contains three kinds of analytical method, numerical method and intelligent optimization methods.Analytical method is the basis with single matching network design theory, but that analytical method exists at the aspects such as Analytical Expression of the confirming of gain function optimal form, source and load is obviously not enough, is difficult to satisfy the requirement of actual engineering design.Numerical method is representative with real audio data method and parametric technique method.Numerical method is compared analytical method and is had remarkable advantages, but also exists some intrinsic shortcomings, such as be difficult to obtain globally optimal solution, to twice computation optimization poor reliability in very sensitive, the real audio data method of the selection of initial value etc.Along with the development of global search technology, and genetic algorithm (National Defense Industry Press, publish in June, 1999; " principle of genetic algorithm and application "; Author Zhou Ming, Sun Shudong), simulated annealing (Science Press, in May, 2003, " nonumeric parallel algorithm-simulated annealing "; Author Kang Lishan, Xie Yun, You Shiyong, Luo Zuhua) wait the appearance of some intelligent algorithms, for the design of broadband matching network provides new technological means.Compare with numerical method with analytical method; Intelligent optimization method need not carry out analytic representation to load; But, seek the optimum solution of network element value through optimization technique according to some the discrete impedance values in the load frequency band, aspect practical applications, have more dirigibility and practicality.But intelligent optimization method is not perfection at aspects such as speed of convergence, fine-tuning capability, counting yield, algorithm stabilities yet, and the selection of intelligent optimization method and application need concrete analysis for concrete design problem.

Summary of the invention

The objective of the invention is to propose a kind of employing heredity-simulated annealing makes up electronic component Parameter Optimization method in the antenna broadband matching network; This optimization method adopts heredity-simulated annealing combined method to design the parameter value of each lumped-parameter element in the antenna broadband matching network; Obtain the optimum solution in the genetic algorithm with genetic algorithm; Carry out the secondary optimizing through simulated annealing then; Overcome the shortcoming of genetic algorithm fine-tuning capability difference,, avoided the dependence of simulated annealing initial value simultaneously with the initial value of the optimum solution in the described genetic algorithm as simulated annealing variable to be optimized.In addition; Optimization problem to the antenna broadband matching network; This combined method has adopted multiple goal back-and-forth method arranged side by side; Be used to take into account the requirement of antenna standing wave ratio and two important techniques indexs of conversion efficiency, introduce the self-adaptation of intersection and mutation operator and regulate, help improving the computing velocity and the efficient of genetic algorithm.Introduce the optimum solution retention strategy, avoided the loss of optimum individual.

A kind of employing heredity of the present invention-simulated annealing makes up electronic component Parameter Optimization method in the antenna broadband matching network, and it includes the following step:

Step 1:, obtain variable X to be optimized={ XC based on the initialization of population of genetic algorithm _a, XL _b, XR _d, XT _e;

In step 1, with electric capacity in the broadband matching network equivalent electrical circuit, inductance and resistance adopt based on genetic algorithm population handle, obtain variable X to be optimized={ XC _a, XL _b, XR _d, XT _e;

Said variable X to be optimized={ XC _a, XL _b, XR _d, XT _eMiddle XC _aExpression electric capacity population, a representes the sign of electric capacity in the equivalent electrical circuit, is designated as XC like the first electric capacity population of first capacitor C 1 _C1In like manner can get, the second electric capacity population is designated as XC _C2, the 3rd electric capacity population is designated as XC _C3, the 4th electric capacity population is designated as XC _C4, the 5th electric capacity population is designated as XC _C5All electric capacity populations adopt the set form to be expressed as XC in the equivalent electrical circuit _a={ XC _C1, XC _C2, XC _C3, XC _C4, XC _C5;

XL _bExpression inductance population, b representes the sign of inductance in the equivalent electrical circuit, is designated as XL like the first inductance population of first inductance L 1 _L1In like manner can get, the second inductance population is designated as XL _L2, the 3rd inductance population is designated as XL _L3, the 4th inductance population is designated as XL _L4, the 5th inductance population is designated as XL _L5All inductance populations adopt the set form to be expressed as XL in the equivalent electrical circuit _b={ XL _L1, XL _L2, XL _L3, XL _L4, XL _L5;

XR _dExpression resistance population, d representes the sign of resistance in the equivalent electrical circuit, is designated as XR like the first resistance population of first resistance R 1 _R1In like manner can get, the second resistance population is designated as XR _R2, the 3rd resistance population is designated as XR _R3All resistance populations adopt the set form to be expressed as XR in the equivalent electrical circuit _d={ XR _R1, XR _R2, XR _R3;

XT _eThe indication transformer population, e representes the input/output voltage ratio of transformer in the equivalent electrical circuit;

Step 2: the chromosome based on genetic algorithm is handled, and obtains total population

In step 2, based on the chromosome in the genetic algorithm, to electric capacity population XC _aIn variable-value DC, generate m variate-value at random

0＜DC≤800pF;

The variate-value of expression sign a electric capacity population in the 1st chromosome,

The variate-value of expression sign a electric capacity population in the 2nd chromosome ...,

The variate-value of expression sign a electric capacity population in m chromosome also claimed the variate-value of sign a electric capacity population in any chromosome;

Based on the chromosome in the genetic algorithm, to inductance population XL _bIn variable-value DL, generate w variate-value at random

0＜DL≤0.1 μ H;

The variate-value of expression sign b inductance population in the 1st chromosome,

The variate-value of expression sign b inductance population in the 2nd chromosome ...,

The variate-value of expression sign b inductance population in w chromosome also claimed the variate-value of sign b inductance population in any chromosome;

Based on the chromosome in the genetic algorithm, to resistance population XR _dIn variable-value DR, generate v variate-value at random

0＜DR≤5k Ω;

The variate-value of expression sign d resistance population in the 1st chromosome,

The variate-value of expression sign d resistance population in the 2nd chromosome ..., The variate-value of expression sign d resistance population in v chromosome also claimed the variate-value of sign d resistance population in any chromosome;

Based on the chromosome in the genetic algorithm, to transformer population XT _eIn variable-value DT, generate n variate-value at random

0.1≤DT≤10;

The variate-value of expression sign e transformer population in the 1st chromosome,

The variate-value of expression sign e transformer population in the 2nd chromosome ...,

The variate-value of expression sign e transformer population in n chromosome also claimed the variate-value of sign e transformer population in any chromosome;

For variable X to be optimized={ XC _a, XL _b, XR _d, XT _eChromosome in genetic algorithm handles and to obtain total population

Step 3:, be that each function divides mating group in the objective function according to back-and-forth method arranged side by side with the multiple-objection optimization function;

In step 3, with total population

In chromosome function M according to target _Target={ f _Target, l _TargetNumber be divided into the first sub-group Q equably ₁With the second sub-group Q ₂, each sub-group is distributed objective function M _Target={ f _Target, l _TargetIn one be optimized;

Step 4: the optimized amount of obtaining sub-population with cross and variation;

In step 4, to the first sub-group Q ₁Carry out cross and variation, keep each, be i.e. the first optimized amount DQ for optimized amount ₁To the second sub-group Q ₂Carry out cross and variation, keep each, be i.e. the second optimized amount DQ for optimized amount ₂Cross and variation is obtained each concrete steps for optimized amount:

Step 401: obtain the first sub-group Q ₁In any 2 chromosomes

As working as prochromosome Be also referred to as current first chromosome

Obtain the second sub-group Q ₂In any 2 chromosomes

As working as prochromosome

Be also referred to as current second chromosome

Step 402: two individuals in current first chromosome are carried out cross processing; Generate first chromosome of new first chromosome expression intersection back, back second chromosome of

expression intersection; Said cross processing is according to the first adaptability Policy model $P_{c1}=\left\{\begin{array}{cc}(f_{\min }-f_{\mathrm{avg}})/(f_{\min }-f),& f\≤f_{\mathrm{avg}}\\ 1.0,& f>f_{\mathrm{avg}}\end{array}\right.$ Carry out; P _C1Represent the first sub-group Q ₁Crossover probability (being also referred to as first crossover probability), f _MinRepresent the first sub-group Q ₁Middle optimized individual fitness value, f is expressed as the adaptive value that adapts in two individuals that will intersect, and f=min{f ₁, f ₂, f ₁Expression chromosome

Corresponding standing-wave ratio (SWR) optimization aim f _TargetValue, f ₂Expression chromosome

Corresponding standing-wave ratio (SWR) optimization aim f _TargetValue, f _AvgRepresent the first sub-group Q ₁Average fitness value;

Two individuals in current second chromosome

are carried out cross processing; Generate the 3rd chromosome in new second chromosome

expression intersection back, the 4th chromosome in

expression intersection back; Said cross processing is according to the second adaptability Policy model $P_c$ $2_{}=\left\{\begin{array}{cc}(l_{\max }-l)/(l_{\max }-l_{\mathrm{avg}}),& l\&GreaterEqual;l_{\mathrm{avg}}\\ 1.0,& l<l_{\mathrm{avg}}\end{array}\right.$ Carry out; P _C2Represent the second sub-group Q ₂Crossover probability, be also referred to as second crossover probability, l _MaxRepresent the second sub-group Q ₂Middle optimized individual fitness value, l is expressed as the adaptive value that adapts in two individuals that will intersect, and l=max{l ₁, l ₂, l ₁Expression chromosome

Corresponding power optimization target l _TargetValue, l ₂Expression chromosome

Corresponding power optimization target l _TargetValue, l _AvgRepresent the second sub-group Q ₂Average fitness value;

Step 403: compare f ₁With f ₃And f ₂With f ₄, if f ₁>=f ₃And f ₂>=f ₄The time, use AQ _IntersectReplace AQ _CurrentIf f ₁＜f ₃Or f ₂＜f ₄The time, AQ then _CurrentConstant; f ₃First chromosome after expression intersects

Corresponding standing-wave ratio (SWR) optimization aim f _TargetValue, f ₄Back second chromosome of expression intersection

Corresponding standing-wave ratio (SWR) optimization aim f _TargetValue;

Compare l ₁With l ₃And l ₂With l ₄, if l ₁≤l ₃And l ₂≤l ₄The time, use BQ _IntersectReplace BQ _CurrentIf l ₁＞l ₃Or l ₂＞l ₄The time, BQ then _CurrentConstant; l ₃The 3rd chromosome after expression intersects

Corresponding power optimization target l _TargetValue, l ₄Tetrasome after expression intersects Corresponding power optimization target l _TargetValue;

Step 404: to the processing that makes a variation respectively of two individuals in current first chromosome

; Generate the chromosome after variation first chromosome

expression

makes a variation, the chromosome after

expression

variation; Described variation is handled according to the 3rd adaptability Policy model $P_{m1}=\left\{\begin{array}{cc}0.5(f_{\min }-f_{\mathrm{avg}})/(f_{\min }-f^{\&prime;}),& f^{\&prime;}\&le;f_{\mathrm{avg}}\\ 0.5,& f^{\&prime;}>f_{\mathrm{avg}}\end{array}\right.$ Carry out; P _M1Represent the first sub-group Q ₁Variation probability (be also referred to as first variation probability), f _MinRepresent the first sub-group Q ₁Middle optimized individual fitness value, f _AvgRepresent the first sub-group Q ₁Average fitness value, f ' is for needing the individual fitness value of variation, and

f ₁Expression chromosome

Corresponding standing-wave ratio (SWR) optimization aim f _TargetValue, f ₂Expression chromosome

Corresponding standing-wave ratio (SWR) optimization aim f _TargetValue;

To the processing that makes a variation respectively of two individuals in current second chromosome

; Generate the chromosome after variation second chromosome

expression

makes a variation, the chromosome after

expression

variation; Said variation is handled according to the 4th adaptability Policy model $P_m$ $2_{}=\left\{\begin{array}{cc}0.5(l_{\max }-l^{\&prime;})/(l_{\max }-l_{\mathrm{avg}}),& l^{\&prime;}\&GreaterEqual;l_{\mathrm{avg}}\\ 0.5,& l^{\&prime;}<l_{\mathrm{avg}}\end{array}\right.$ Carry out; P _M2Represent the second sub-group Q ₂The variation probability, be also referred to as second the variation probability, l _MaxRepresent the second sub-group Q ₂Middle optimized individual fitness value, l _AvgRepresent the second sub-group Q ₂Average fitness value, l ' for to make a variation individual fitness value and l ₁Expression chromosome

Corresponding power optimization target l _TargetValue, l ₂Expression chromosome

Corresponding power optimization target l _TargetValue;

Step 405: compare f ₁With f ₅, if f ₁＞f ₅The time, use

Replace

If f ₁≤f ₅The time, then

Constant; f ₅Expression

Chromosome after the variation

Corresponding standing-wave ratio (SWR) optimization aim f _TargetValue;

Compare f ₂With f ₆, if f ₂＞f ₆The time, use

Replace

If f ₂≤f ₆The time, then

Constant; f ₆Expression

Chromosome after the variation

Corresponding standing-wave ratio (SWR) optimization aim f _TargetValue;

Compare l ₁With l ₅, if l ₁＜l ₅The time, use

Replace

If l ₁>=l ₅The time, then

Constant; l ₅Expression

Chromosome after the variation

Corresponding power optimization target l _TargetValue;

Compare l ₂With l ₆, if l ₂＜l ₆The time, use

Replace

If l ₂>=l ₆The time, then

Constant; l ₆Expression

Chromosome after the variation

Corresponding power optimization target l _TargetValue;

Repeating step 401 is to step 405, up to the first sub-group Q ₁With the second sub-group Q ₂The whole cross and variation of middle chromosome are accomplished, and obtain current generation first sub-group Q ₁Optimum optimized amount, i.e. the first optimized amount DQ ₁, the second sub-group Q ₂Optimum optimized amount, i.e. the second optimized amount DQ ₂

Step 5: according to objective function M _Target={ f _Target, l _TargetThe total population Q of traversal optimization _Always' in all chromosome obtain in the genetic algorithm when the former generation optimum individual;

In step 5, merge the first optimized amount DQ ₁With the second optimized amount DQ ₂Form new population, promptly optimize total population Q _Always', according to objective function M _Target={ f _Target, l _TargetThe total population Q of traversal optimization _Always' in all chromosome obtain in the genetic algorithm when the former generation optimum individual

And Compose and give I _Hbest, so that optimum individual of future generation compares with the optimum individual of working as former generation, in both, select more excellent individuality, and compose and give I _Hbest

Judge whether to reach the end condition of genetic algorithm,, then return step 4,, then draw the optimum individual I in the genetic algorithm if satisfy hereditary end condition if do not satisfy hereditary end condition _Hbest, and remain, get into step 6; The end condition of said genetic algorithm is meant whether iteration step number K is 0, if iteration step number K is not 0, then returns step 3, if iteration step number K is 0, then draws the optimum individual I in the genetic algorithm _Hbest, and remain, get into step 6; For with variable X to be optimized={ XC _a, XL _b, XR _d, XT _eExpression-form corresponding, described electronic component parametric optimal solution I _HbestThe set expression-form does $I_{\mathrm{hbest}}=\left[\begin{array}{ccccc}{C}_{C1}^{\mathrm{hbest}},& {\mathrm{<figure-callout\; id="2"\; label="C\; C"\; filenames="HDA00001713824700012.png"\; state="\{\{state\}\}">C</figure-callout>}}_C^2,& C_{C3}^{\mathrm{hbest}},& {\mathrm{<figure-callout\; id="4"\; label="C\; C"\; filenames="HDA00001713824700012.png"\; state="\{\{state\}\}">C</figure-callout>}}_C^4,& C_{C5}^{\mathrm{hbest}}\\ L_{L1}^{\mathrm{hbest}},& {\mathrm{<figure-callout\; id="2"\; label="L\; L"\; filenames="HDA00001713824700012.png"\; state="\{\{state\}\}">L</figure-callout>}}_L^2,& L_{L3}^{\mathrm{hbest}},& {\mathrm{<figure-callout\; id="4"\; label="L\; L"\; filenames="HDA00001713824700012.png"\; state="\{\{state\}\}">L</figure-callout>}}_L^4,& L_{L5}^{\mathrm{hbest}}\\ R_{R1}^{\mathrm{hbest}},& {\mathrm{<figure-callout\; id="2"\; label="R\; R"\; filenames="HDA00001713824700012.png"\; state="\{\{state\}\}">R</figure-callout>}}_R^2,& R_{R3}^{\mathrm{hbest}}& & \\ T_e^{\mathrm{hbest}}& & & & \end{array}\right];$

Step 6: to working as former generation optimum individual I _HbestThe assignment of initially annealing obtains initial individual I _InitiallyRight $I_{\mathrm{hbest}}=\left[\begin{array}{ccccc}{C}_{C1}^{\mathrm{hbest}},& {\mathrm{<figure-callout\; id="2"\; label="C\; C"\; filenames="HDA00001713824700012.png"\; state="\{\{state\}\}">C</figure-callout>}}_C^2,& C_{C3}^{\mathrm{hbest}},& {\mathrm{<figure-callout\; id="4"\; label="C\; C"\; filenames="HDA00001713824700012.png"\; state="\{\{state\}\}">C</figure-callout>}}_C^4,& C_{C5}^{\mathrm{hbest}}\\ L_{L1}^{\mathrm{hbest}},& {\mathrm{<figure-callout\; id="2"\; label="L\; L"\; filenames="HDA00001713824700012.png"\; state="\{\{state\}\}">L</figure-callout>}}_L^2,& L_{L3}^{\mathrm{hbest}},& {\mathrm{<figure-callout\; id="4"\; label="L\; L"\; filenames="HDA00001713824700012.png"\; state="\{\{state\}\}">L</figure-callout>}}_L^4,& L_{L5}^{\mathrm{hbest}}\\ R_{R1}^{\mathrm{hbest}},& {\mathrm{<figure-callout\; id="2"\; label="R\; R"\; filenames="HDA00001713824700012.png"\; state="\{\{state\}\}">R</figure-callout>}}_R^2,& R_{R3}^{\mathrm{hbest}}& & \\ T_e^{\mathrm{hbest}}& & & & \end{array}\right]$ The assignment of initially annealing, then have simulated annealing initial individuality is

Step 7: to working as former generation optimum individual I _HbestSound out assignment, obtain souning out individual I _{Sound out}

Initial individuality

to simulated annealing is soundd out assignment, and new exploration value individual

is then arranged

Step 8: according to objective function M _Target={ f _Target, l _Target, initial individual I _InitiallyWith the individual I of exploration _{Sound out}Carry out simulated annealing optimization, obtain the electronic component Parameter Optimization and separate;

Step 801: calculate and sound out individuality

Corresponding objective function M _Target={ f _Target, l _TargetValue be respectively ff ₁And ll ₁, ff ₁Individual I is soundd out in expression _{Sound out}Corresponding standing-wave ratio (SWR) optimization aim f _TargetValue, ll ₁Individual I is soundd out in expression _{Sound out}Corresponding power optimization target l _TargetValue;

Step 802: calculate initial individual Corresponding target function value M _Target={ f _Target, l _TargetValue be respectively ff ₂And ll ₂, ff ₂Represent initial individual I _InitiallyCorresponding standing-wave ratio (SWR) optimization aim f _TargetValue, ll ₂Represent initial individual I _InitiallyCorresponding power optimization target l _TargetValue;

Step 803: judge and sound out individual I _{Sound out}Target function value M _Target={ f _Target, l _TargetValue ff ₁And ll ₁Whether be superior to initial individual I _InitiallyCorresponding target function value M _Target={ f _Target, l _TargetValue ff ₂And ll ₂, if ff ₁≤ff ₂And ll ₁>=ll ₂, then get into step 804, otherwise return step 801;

Step 804: calculate and sound out individual I _{Sound out}Whether satisfy the receiver function relation $\left[\begin{array}{c}{P}_{\mathrm{VSWR}}=\exp \left(\frac{{\mathrm{ff}}_1-{\mathrm{ff}}_2}{T_{\mathrm{now}}}\right)>r\&Element;\left[\mathrm{0,1}\right]\\ P_G=\exp \left(\frac{{\mathrm{ll}}_2-{\mathrm{ll}}_1}{T_{\mathrm{now}}}\right)>r\&Element;\left[\mathrm{0,1}\right]\end{array}\right.,$ If satisfy and then will sound out individual I _{Sound out}Substitute initial individual I _Initially, get into step 805; If do not satisfy and then return step 801; P _VSWRThe corresponding probability of acceptance of expression standing-wave ratio (SWR), P _GThe corresponding probability of acceptance of expression conversion gain, T _NowExpression Current Temperatures, r are at the interior random number that generates with the probabilistic of uniformly distributed function of [0,1] scope, i.e. r=RAN (0,1)

Step 805: in annealing algorithm, carry out temperature and reduce, and judge Current Temperatures T with cooling rate a _NowWhether less than by temperature T _End, if T _NowGreater than T _End, then return step 801; If T _NowSmaller or equal to T _End, the initial individual I after then will optimizing _InitiallyElectronic component parameter optimization as after heredity-simulated annealing processing is separated I _Best, $I_{\mathrm{hbest}}=\left[\begin{array}{ccccc}{C}_{C1}^{\mathrm{hbest}},& {\mathrm{<figure-callout\; id="2"\; label="C\; C"\; filenames="HDA00001713824700012.png"\; state="\{\{state\}\}">C</figure-callout>}}_C^2,& C_{C3}^{\mathrm{hbest}},& {\mathrm{<figure-callout\; id="4"\; label="C\; C"\; filenames="HDA00001713824700012.png"\; state="\{\{state\}\}">C</figure-callout>}}_C^4,& C_{C5}^{\mathrm{hbest}}\\ L_{L1}^{\mathrm{hbest}},& {\mathrm{<figure-callout\; id="2"\; label="L\; L"\; filenames="HDA00001713824700012.png"\; state="\{\{state\}\}">L</figure-callout>}}_L^2,& L_{L3}^{\mathrm{hbest}},& {\mathrm{<figure-callout\; id="4"\; label="L\; L"\; filenames="HDA00001713824700012.png"\; state="\{\{state\}\}">L</figure-callout>}}_L^4,& L_{L5}^{\mathrm{hbest}}\\ R_{R1}^{\mathrm{hbest}},& {\mathrm{<figure-callout\; id="2"\; label="R\; R"\; filenames="HDA00001713824700012.png"\; state="\{\{state\}\}">R</figure-callout>}}_R^2,& R_{R3}^{\mathrm{hbest}}& & \\ T_e^{\mathrm{hbest}}& & & & \end{array}\right].$

The advantage of electronic component parameter optimization method is in the antenna broadband matching network of the present invention:

1. back-and-forth method arranged side by side

The design of antenna broadband matching network is made every effort to when satisfying the standing-wave ratio (SWR) requirement, makes the conversion power gain of transmission maximum.Therefore, this is a typical two objective optimization problem.On the basis of standard genetic algorithm, introduce multiple goal back-and-forth method arranged side by side, can take into account the requirement of standing-wave ratio (SWR) and two performance index of conversion power gain.The basic thought of back-and-forth method is side by side; Earlier with the whole individualities in total population according to target the number of function be divided into some sub-groups equably; Each sub-group is distributed a function in the objective function; Each function in the objective function is selected computing independently in corresponding sub-group, select the high individuality of some fitness separately and form a new sub-group, and then the sub-group that all these are newly-generated is merged into new population; So constantly carry out " cut apart-and column selection-merging " operate, finally can obtain the optimum solution of optimization problem in the multiple objective function.This method and the conventional linear summation of the fitness function difference of finding the solution multi-objective optimization question are that this its weights of way that all individual mixing are got up do not need artificial selected, but depend on the current generation.

2. evolution criterion

In the computation process of genetic algorithm, according to the concrete condition of individuality, the size of adaptively modifying crossover probability and variation probability helps improving the computing velocity and the efficient of algorithm.Adopt adaptability heredity strategy can make the genetic evolution process have ability of searching optimum preferably, avoid sinking into Local Extremum with bigger probability.

3. optimal result retention strategy

Algorithm among the present invention is preserved all best result that ran in the search procedure, when algorithmic procedure finishes, gained is finally separated and the process optimum that keeps is separated comparison, and got than the superior as end product.In the process of evolving, can keep optimum solution not lost all the time like this.

4. the secondary optimizing of simulated annealing

After adopting genetic algorithm under the situation of calculating the less number of times of less population, iteration, to obtain the optimum solution in the genetic algorithm,, carry out the secondary optimizing with the initial value of this optimum solution as simulated annealing.This helps improving the shortcoming and the precocious phenomenon of genetic algorithm fine-tuning capability difference, and with the Optimization result of the genetic algorithm initial value as simulated annealing, has also avoided the dependence of simulated annealing to initial value.The combination of the two has improved algorithm stability and on the whole to the dependence of initial value.

Description of drawings

Fig. 1 is the structural representation of traditional antenna broadband matching network.

Fig. 2 is the circuit theory diagrams of antenna broadband matching network to be optimized.

Fig. 3 is blending heredity of the present invention-simulated annealing process flow diagram.

Fig. 4 is the circuit theory diagrams of the antenna broadband matching network after optimizing.

Embodiment

To combine accompanying drawing and embodiment that the present invention is done further detailed description below.

In the present invention, the antenna broadband matching network is arranged on (referring to shown in Figure 1) between antenna and the concentric cable, and the antenna broadband matching network constitutes (referring to shown in Figure 2) by inductance, electric capacity, resistance and impedance transformer at least.

In the present invention, antenna is meant can serve the VHF/UHF frequency range, and with airborne conformal small size antenna.Said VHF, Very high frequency.Translation is: very high frequency(VHF).Said VHF is meant that frequency is the radiobeam of 30～3000MHz.Said UHF, Ultra High Frequency.Translation is: superfrequency.Said UHF is meant that frequency is the superfrequency radiobeam of 300～3000MHz.

In the present invention, the antenna broadband matching network is to be made up of 2 rank T shape L-C networks.In addition, introduced the complementary network of forming by impedance transformer and resistance respectively in the front-end and back-end of antenna broadband matching network.T shape L-C network has the effect of filtering and coupling, and complementary network is intended to certain correction is carried out in the impedance of antenna, makes it be easy to coupling.

Referring to shown in Figure 2, the present invention is a kind of airborne small size antenna broadband matching network of realizing impedance transformation characteristic that has, and includes the complementary network that two T shape L-C networks, resistance and transformer T0 form in this broadband matching network equivalent electrical circuit; T-network is made up of series connection L-C network and parallelly connected L-C network;

First T-network by first capacitor C 1 connect with first inductance L 1, second capacitor C 2 connects with the 3rd inductance L 3 with parallel connection of second inductance L 2 and the 3rd capacitor C 3 and constitutes;

Second T-network by the 3rd capacitor C 3 connect with the 3rd inductance L 3, the 4th capacitor C 4 connects with the 5th inductance L 5 with 4 parallel connections of the 4th inductance L and the 5th capacitor C 5 and constitutes; Two T-networks form repeatedly filtering; Resistor network in the complementary network is made up of first resistance R 1, second resistance R 2 and the 3rd resistance R 3, and complementary network is the impedance for the conversion antenna;

The link of antenna is connected with 1 end of transformer T0; The 2 end ground connection of transformer T0; 3 ends of transformer T0 are connected with 1 end (being the input end of cable) of concentric cable after first capacitor C 1, first inductance L 1, the 3rd capacitor C 3, the 3rd inductance L 3, the 5th capacitor C 5, the 5th inductance L 5, first resistance R 1 in order; 4 ends of transformer T0 are connected with 2 ends of concentric cable (being the earth terminal of cable); 2 ends of concentric cable (being the earth terminal of cable) ground connection;

3 ends of transformer T0 insert 4 ends of transformer T0 in order after first capacitor C 1, first inductance L 1, second capacitor C 2;

3 ends of transformer T0 insert 4 ends of transformer T0 in order after first capacitor C 1, first inductance L 1, second inductance L 2;

3 ends of transformer T0 insert 4 ends of transformer T0 in order after first capacitor C 1, first inductance L 1, the 3rd capacitor C 3, the 3rd inductance L 3, the 4th capacitor C 4;

3 ends of transformer T0 insert 4 ends of transformer T0 in order after first capacitor C 1, first inductance L 1, the 3rd capacitor C 3, the 3rd inductance L 3, the 4th inductance L 4;

3 ends of transformer T0 insert 4 ends of transformer T0 in order after first capacitor C 1, first inductance L 1, the 3rd capacitor C 3, the 3rd inductance L 3, the 5th capacitor C 5, the 5th inductance L 5, second resistance R 2;

3 ends of transformer T0 insert 4 ends of transformer T0 in order after first capacitor C 1, first inductance L 1, the 3rd capacitor C 3, the 3rd inductance L 3, the 5th capacitor C 5, the 5th inductance L 5, first resistance R 1, the 3rd resistance R 3.In the present invention, between the secondary of transformer T0 and concentric cable incoming end, use the processing mode of multiple-stage filtering (constituting) that antenna impedance is mated, make the rational in infrastructure of broadband matching network equivalent electrical circuit by two T shape L-C networks.

Said have that transformer and resistor network can carry out conversion to the impedance of antenna and laod network in the airborne small size antenna broadband matching network that can realize impedance transformation characteristic; Two T shape L-C networks play the effect of filtering and coupling, make the resistance value of antenna through transformer, two T shape L-C networks, resistor network after near the characteristic impedance value of coaxial cable, finally reach predetermined coupling.

In the present invention, define the standing-wave ratio (SWR) of each Frequency point in the antenna matching network bandwidth from the angle in antenna matching network work broadband.The 1st Frequency point ω ₁Standing-wave ratio (SWR) be designated as VSWR (ω ₁), the 2nd Frequency point ω ₂Standing-wave ratio (SWR) be designated as VSWR (ω ₂), in like manner individual arbitrarily (i) Frequency point ω _iStanding-wave ratio (SWR) be designated as VSWR (ω _i), then the standing-wave ratio (SWR) sum of each Frequency point is designated as VSWR (ω in the bandwidth _With)=VSWR (ω ₁)+VSWR (ω ₂)+... + VSWR (ω _i).In order to carry out electronic component Parameter Optimization in the antenna matching network; With the standing-wave ratio (SWR) sum of each Frequency point in the bandwidth as standing-wave ratio (SWR) optimization aim

in the present technique field, standing-wave ratio (SWR) optimization aim

the more little then antenna matching network of value performance is good more.ω representes frequency, ω _iI the Frequency point of expression antenna in bandwidth of operation, VSWR (ω _i) the corresponding standing-wave ratio (SWR) of i Frequency point of expression, N representes the Frequency point number of antenna in bandwidth of operation.

In the present invention, the angle maximum from the antenna conversion efficiency defines the antenna matching network bandwidth, and the conversion power gain of the 1st Frequency point in the said antenna matching network bandwidth is designated as G (ω ₁), the conversion power gain of the 2nd Frequency point is designated as G (ω ₂), in like manner the conversion power gain of (i) Frequency point is designated as G (ω arbitrarily _i), then the conversion power gain sum of each Frequency point is designated as G (ω in the bandwidth _With)=G (ω ₁)+G (ω ₂)+... + G (ω _i).In order to carry out electronic component Parameter Optimization in the antenna matching network; With the conversion power gain sum of each Frequency point in the bandwidth as power optimization target

order

in the present technique field, more greatly then the antenna matching network performance is good more for power optimization target

value.ω _iI the Frequency point of expression antenna in bandwidth of operation, G (ω _i) the corresponding conversion power gain of i Frequency point of expression, P _Out(ω _i) represent from the average power of matching network supply coaxial cable, P _In(ω _i) represent from the maximum average power of antenna acquisition.

In the present invention, in order to realize best antenna matching network performance, with the standing-wave ratio (SWR) optimization aim

With the power optimization target

As the objective function that genetic algorithm combines with simulated annealing, it is M that described objective function adopts the mathematical set expression-form _Target={ f _Target, l _Target.

Choose for the parameter value to each electronic component in the antenna broadband matching network equivalent electrical circuit carries out optimum value, the inventive method operates on the computing machine that Matlab (Matlab 2008 versions or Matlab 2010 versions) simulation software is installed.Said computing machine is a kind ofly can carry out the modernized intelligent electronic device of massive values computation and various information processings automatically, at high speed according to prior program stored.Minimalist configuration is CPU 2GHz, internal memory 2GB, hard disk 180GB; Operating system is windows 2000/2003/XP.The optimization method that the present invention has adopted genetic algorithm to combine with simulated annealing, the electronic component optimizing parameter values coupling in the concrete antenna broadband matching network includes the following step:

Step 1:, obtain variable X to be optimized={ XC based on the initialization of population of genetic algorithm _a, XL _b, XR _d, XT _e;

In step 1, with electric capacity in the broadband matching network equivalent electrical circuit, inductance and resistance adopt based on genetic algorithm population handle, obtain variable X to be optimized={ XC _a, XL _b, XR _d, XT _e.In the present invention, adopt based on genetic algorithm population to handle be for electronic component in the equivalent circuit carries out digitized definition, conveniently to carry out each Parameter Optimization setting in the genetic algorithm.Broadband matching network equivalent electric routing capacitance, inductance, transformer and resistance form equivalent circuit structure, and equivalent circuit structure is in order to satisfy the request for utilization with airborne conformal antenna preferably.

Said variable X to be optimized={ XC _a, XL _b, XR _d, XT _eMiddle XC _aExpression electric capacity population, a representes the sign of electric capacity in the equivalent electrical circuit, is designated as XC like the first electric capacity population of first capacitor C 1 _C1In like manner can get, the second electric capacity population is designated as XC _C2, the 3rd electric capacity population is designated as XC _C3, the 4th electric capacity population is designated as XC _C4, the 5th electric capacity population is designated as XC _C5All electric capacity populations adopt the set form to be expressed as XC in the equivalent electrical circuit _a={ XC _C1, XC _C2, XC _C3, XC _C4, XC _C5;

XL _bExpression inductance population, b representes the sign of inductance in the equivalent electrical circuit, is designated as XL like the first inductance population of first inductance L 1 _L1In like manner can get, the second inductance population is designated as XL _L2, the 3rd inductance population is designated as XL _L3, the 4th inductance population is designated as XL _L4, the 5th inductance population is designated as XL _L5All inductance populations adopt the set form to be expressed as XL in the equivalent electrical circuit _b={ XL _L1, XL _L2, XL _L3, XL _L4, XL _L5;

XR _dExpression resistance population, d representes the sign of resistance in the equivalent electrical circuit, is designated as XR like the first resistance population of first resistance R 1 _R1In like manner can get, the second resistance population is designated as XR _R2, the 3rd resistance population is designated as XR _R3All resistance populations adopt the set form to be expressed as XR in the equivalent electrical circuit _d={ XR _R1, XR _R2, XR _R3;

XT _eThe indication transformer population, e representes the input/output voltage ratio of transformer in the equivalent electrical circuit.And the input/output voltage of the transformer T0 that adopts in the invention is than being DT: 1.

In the present invention, variable X to be optimized={ XC _a, XL _b, XR _d, XT _eAlso can launch to be expressed as $X=\left\{\begin{array}{ccccc}{\mathrm{XC}}_{C1},& {\mathrm{<figure-callout\; id="2"\; label="XC\; C"\; filenames="HDA00001713824700012.png"\; state="\{\{state\}\}">XC</figure-callout>}}_C,& {\mathrm{XC}}_{C3},& {\mathrm{<figure-callout\; id="4"\; label="XC\; C"\; filenames="HDA00001713824700012.png"\; state="\{\{state\}\}">XC</figure-callout>}}_C,& {\mathrm{XC}}_{C5}\\ {\mathrm{XL}}_{L1},& {\mathrm{<figure-callout\; id="2"\; label="XL\; L"\; filenames="HDA00001713824700012.png"\; state="\{\{state\}\}">XL</figure-callout>}}_L,& {\mathrm{XL}}_{L3},& {\mathrm{<figure-callout\; id="4"\; label="XL\; L"\; filenames="HDA00001713824700012.png"\; state="\{\{state\}\}">XL</figure-callout>}}_L,& {\mathrm{XL}}_{L5}\\ {\mathrm{XR}}_{R1},& {\mathrm{<figure-callout\; id="2"\; label="XR\; R"\; filenames="HDA00001713824700012.png"\; state="\{\{state\}\}">XR</figure-callout>}}_R,& {\mathrm{XR}}_{R3}& & \\ {\mathrm{XT}}_e& & & & \end{array}\right\}.$

Step 2: the chromosome based on genetic algorithm is handled, and obtains total population

In step 2, based on the chromosome in the genetic algorithm, to electric capacity population XC _aIn variable-value DC, generate m variate-value at random

0＜DC≤800pF.The variate-value of

expression sign a electric capacity population in the 1st chromosome; The variate-value of expression sign a electric capacity population in the 2nd chromosome; The variate-value of

expression sign a electric capacity population in m chromosome also claimed the variate-value of sign a electric capacity population in any chromosome.

Based on the chromosome in the genetic algorithm, to inductance population XL _bIn variable-value DL, generate w variate-value at random

0＜DL≤0.1 μ H.The variate-value of

expression sign b inductance population in the 1st chromosome; The variate-value of

expression sign b inductance population in the 2nd chromosome; The variate-value of

expression sign b inductance population in w chromosome also claimed the variate-value of sign b inductance population in any chromosome.

Based on the chromosome in the genetic algorithm, to resistance population XR _dIn variable-value DR, generate v variate-value at random

0＜DR≤5k Ω.The variate-value of

expression sign d resistance population in the 1st chromosome; The variate-value of

expression sign d resistance population in the 2nd chromosome; The variate-value of

expression sign d resistance population in v chromosome also claimed the variate-value of sign d resistance population in any chromosome.

Based on the chromosome in the genetic algorithm, to transformer population XT _eIn variable-value DT, generate n variate-value at random

0.1≤DT≤10.The variate-value of

expression sign e transformer population in the 1st chromosome; The variate-value of

expression sign e transformer population in the 2nd chromosome; The variate-value of

expression sign e transformer population in n chromosome also claimed the variate-value of sign e transformer population in any chromosome.

In the present invention, the value of chromosomal variable m, w, v and n is 200.All numerical value in the set of variables are encoded respectively, and set of variables is converted into chromosome, and a coding is called the body one by one in the chromosome.For variable X to be optimized={ XC _a, XL _b, XR _d, XT _eChromosome in genetic algorithm handles and to obtain total population

Step 3:, be that each function divides mating group in the objective function according to back-and-forth method arranged side by side with the multiple-objection optimization function;

In step 3, with total population

In whole individualities (chromosome) function M according to target _Target={ f _Target, l _TargetNumber be divided into two sub-Q of colony equably ₁And Q ₂(sub-group Q ₁Be also referred to as first sub-group, sub-group Q ₂Be also referred to as second sub-group), each sub-group is distributed objective function M _Target={ f _Target, l _TargetIn one be optimized.In the present invention, if the first sub-group Q ₁Adopted objective function M _Target={ f _Target, l _TargetIn antenna standing wave ratio f _TargetBe optimized, then the second sub-group Q ₂Should adopt objective function M _Target={ f _Target, l _TargetIn conversion power gain l _TargetBe optimized; Otherwise, if the second sub-group Q ₂Adopted objective function M _Target={ f _Target, l _TargetIn antenna standing wave ratio f _TargetMark is optimized, then the first sub-group Q ₁Should adopt objective function M _Target={ f _Target, l _TargetIn conversion power gain l _TargetBe optimized.

Step 4: the optimized amount of obtaining sub-population with cross and variation;

In step 4, to the first sub-group Q ₁Carry out cross and variation, keep each for optimized amount DQ ₁(be also referred to as the first optimized amount DQ ₁); To the second sub-group Q ₂Carry out cross and variation, keep each for optimized amount DQ ₂(be also referred to as the second optimized amount DQ ₂); Cross and variation is obtained each concrete steps for optimized amount:

Step 401: obtain the first sub-group Q ₁In any 2 chromosomes

As working as prochromosome

Be also referred to as current first chromosome

Obtain the second sub-group Q ₂In any 2 chromosomes

As working as prochromosome

Be also referred to as current second chromosome

Step 402: two individuals in current first chromosome

are carried out cross processing; Generate first chromosome of new first chromosome

expression intersection back, back second chromosome of

expression intersection; Said cross processing is according to the first adaptability Policy model $P_{c1}=\left\{\begin{array}{cc}(f_{\min }-f_{\mathrm{avg}})/(f_{\min }-f),& f\&le;f_{\mathrm{avg}}\\ 1.0,& f>f_{\mathrm{avg}}\end{array}\right.$ Carry out; P _C1Represent the first sub-group Q ₁Crossover probability (being also referred to as first crossover probability), f _MinRepresent the first sub-group Q ₁Middle optimized individual fitness value, f is expressed as the adaptive value that adapts in two individuals that will intersect, and f=min{f ₁, f ₂, f ₁Expression chromosome

Corresponding standing-wave ratio (SWR) optimization aim f _TargetValue, f ₂Expression chromosome

Corresponding standing-wave ratio (SWR) optimization aim f _TargetValue, f _AvgRepresent the first sub-group Q ₁Average fitness value;

Two individuals in current second chromosome

are carried out cross processing; Generate the 3rd chromosome in new second chromosome

expression intersection back, the 4th chromosome in

expression intersection back; Said cross processing is according to the second adaptability Policy model $P_c$ $2_{}=\left\{\begin{array}{cc}(l_{\max }-l)/(l_{\max }-l_{\mathrm{avg}}),& l\&GreaterEqual;l_{\mathrm{avg}}\\ 1.0,& l<l_{\mathrm{avg}}\end{array}\right.$ Carry out; P _C2Represent the second sub-group Q ₂Crossover probability, be also referred to as second crossover probability, l _MaxRepresent the second sub-group Q ₂Middle optimized individual fitness value, l is expressed as the adaptive value that adapts in two individuals that will intersect, and l=max{l ₁, l ₂, l ₁Expression chromosome

Corresponding power optimization target l _TargetValue, l ₂Expression chromosome

Corresponding power optimization target l _TargetValue, l _AvgRepresent the second sub-group Q ₂Average fitness value;

Step 403: compare f ₁With f ₃And f ₂With f ₄, if f ₁>=f ₃And f ₂>=f ₄The time, use AQ _IntersectReplace AQ _CurrentIf f ₁＜f ₃Or f ₂＜f ₄The time, AQ then _CurrentConstant; f ₃First chromosome after expression intersects

Corresponding standing-wave ratio (SWR) optimization aim f _TargetValue, f ₄Back second chromosome of expression intersection

Corresponding standing-wave ratio (SWR) optimization aim f _TargetValue;

Compare l ₁With l ₃And l ₂With l ₄, if l ₁≤l ₃And l ₂≤l ₄The time, use BQ _IntersectReplace BQ _CurrentIf l ₁＞l ₃Or l ₂＞l ₄The time, BQ then _CurrentConstant; l ₃The 3rd chromosome after expression intersects

Corresponding power optimization target l _TargetValue, l ₄Tetrasome after expression intersects

Corresponding power optimization target l _TargetValue;

Step 404: to the processing that makes a variation respectively of two individuals in current first chromosome

; Generate the chromosome after variation first chromosome

expression makes a variation, the chromosome after

expression

variation; Described variation is handled according to the 3rd adaptability Policy model $P_{m1}=\left\{\begin{array}{cc}0.5(f_{\min }-f_{\mathrm{avg}})/(f_{\min }-f^{\&prime;}),& f^{\&prime;}\&le;f_{\mathrm{avg}}\\ 0.5,& f^{\&prime;}>f_{\mathrm{avg}}\end{array}\right.$ Carry out; P _M1Represent the first sub-group Q ₁Variation probability (be also referred to as first variation probability), f _MinRepresent the first sub-group Q ₁Middle optimized individual fitness value, f _AvgRepresent the first sub-group Q ₁Average fitness value, f ' is for needing the individual fitness value of variation, and f ₁Expression chromosome Corresponding standing-wave ratio (SWR) optimization aim f _TargetValue, f ₂Expression chromosome

Corresponding standing-wave ratio (SWR) optimization aim f _TargetValue;

To the processing that makes a variation respectively of two individuals in current second chromosome

; Generate the chromosome after variation second chromosome

expression

makes a variation, the chromosome after

expression

variation; Said variation is handled according to the 4th adaptability Policy model $P_m$ $2_{}=\left\{\begin{array}{cc}0.5(l_{\max }-l^{\&prime;})/(l_{\max }-l_{\mathrm{avg}}),& l^{\&prime;}\&GreaterEqual;l_{\mathrm{avg}}\\ 0.5,& l^{\&prime;}<l_{\mathrm{avg}}\end{array}\right.$ Carry out; P _M2Represent the second sub-group Q ₂The variation probability, be also referred to as second the variation probability, l _MaxRepresent the second sub-group Q ₂Middle optimized individual fitness value, l _AvgRepresent the second sub-group Q ₂Average fitness value, l ' for to make a variation individual fitness value and

l ₁Expression chromosome

Corresponding power optimization target l _TargetValue, l ₂Expression chromosome

Corresponding power optimization target l _TargetValue;

Step 405: compare f ₁With f ₅, if f ₁＞f ₅The time, use

Replace

If f ₁≤f ₅The time, then

Constant; f ₅Expression

Chromosome after the variation

Corresponding standing-wave ratio (SWR) optimization aim f _TargetValue;

Compare f ₂With f ₆, if f ₂＞f ₆The time, use

Replace

If f ₂≤f ₆The time, then

Constant; f ₆Expression Chromosome after the variation

Corresponding standing-wave ratio (SWR) optimization aim f _TargetValue;

Compare l ₁With l ₅, if l ₁＜l ₅The time, use

Replace If l ₁>=l ₅The time, then

Constant; l ₅Expression

Chromosome after the variation

Corresponding power optimization target l _TargetValue;

Compare l ₂With l ₆, if l ₂＜l ₆The time, use Replace

If l ₂>=l ₆The time, then

Constant; l ₆Expression Chromosome after the variation

Corresponding power optimization target l _TargetValue;

Repeating step 401 is to step 405, up to the first sub-group Q ₁With the second sub-group Q ₂The whole cross and variation of middle chromosome are accomplished, and obtain current generation first sub-group Q ₁Optimum optimized amount, i.e. the first optimized amount DQ ₁, the second sub-group Q ₂Optimum optimized amount, i.e. the second optimized amount DQ ₂

Step 5: according to objective function M _Target={ f _Target, l _TargetThe total population Q of traversal optimization _Always' in all chromosome obtain in the genetic algorithm when the former generation optimum individual;

In step 5, merge the first optimized amount DQ ₁With the second optimized amount DQ ₂Form new population, promptly optimize total population Q _Always', according to objective function M _Target={ f _Target, l _TargetThe total population Q of traversal optimization _Always' in all chromosome obtain in the genetic algorithm when the former generation optimum individual

And

Compose and give I _Hbest, so that optimum individual of future generation compares with the optimum individual of working as former generation, in both, select more excellent individuality, and compose and give I _Hbest

Judge whether to reach the end condition of genetic algorithm,, then return step 4,, then draw the optimum individual I in the genetic algorithm if satisfy hereditary end condition if do not satisfy hereditary end condition _Hbest, and remain, get into step 6; The end condition of said genetic algorithm is meant whether iteration step number K is 0, if iteration step number K is not 0, then returns step 3, if iteration step number K is 0, then draws the optimum individual I in the genetic algorithm _Hbest, and remain, get into step 6; For with variable X to be optimized={ XC _a, XL _b, XR _d, XT _eExpression-form corresponding, described electronic component parametric optimal solution I _HbestThe set expression-form does $I_{\mathrm{hbest}}=\left[\begin{array}{ccccc}{C}_{C1}^{\mathrm{hbest}},& {\mathrm{<figure-callout\; id="2"\; label="C\; C"\; filenames="HDA00001713824700012.png"\; state="\{\{state\}\}">C</figure-callout>}}_C^2,& C_{C3}^{\mathrm{hbest}},& {\mathrm{<figure-callout\; id="4"\; label="C\; C"\; filenames="HDA00001713824700012.png"\; state="\{\{state\}\}">C</figure-callout>}}_C^4,& C_{C5}^{\mathrm{hbest}}\\ L_{L1}^{\mathrm{hbest}},& {\mathrm{<figure-callout\; id="2"\; label="L\; L"\; filenames="HDA00001713824700012.png"\; state="\{\{state\}\}">L</figure-callout>}}_L^2,& L_{L3}^{\mathrm{hbest}},& {\mathrm{<figure-callout\; id="4"\; label="L\; L"\; filenames="HDA00001713824700012.png"\; state="\{\{state\}\}">L</figure-callout>}}_L^4,& L_{L5}^{\mathrm{hbest}}\\ R_{R1}^{\mathrm{hbest}},& {\mathrm{<figure-callout\; id="2"\; label="R\; R"\; filenames="HDA00001713824700012.png"\; state="\{\{state\}\}">R</figure-callout>}}_R^2,& R_{R3}^{\mathrm{hbest}}& & \\ T_e^{\mathrm{hbest}}& & & & \end{array}\right].$

Represent the first electric capacity population XC _C1Optimum capacitance value after genetic algorithm;

Represent the second electric capacity population XC _C2Optimum capacitance value after genetic algorithm;

Represent the 3rd electric capacity population XC _C3Optimum capacitance value after genetic algorithm;

Represent the 4th electric capacity population XC _C4Optimum capacitance value after genetic algorithm;

Represent the 5th electric capacity population XC _C5Optimum capacitance value after genetic algorithm;

Represent the first inductance population XL _L1Optimum inductance value after genetic algorithm;

Represent the second inductance population XL _L2Optimum inductance value after genetic algorithm;

Represent the 3rd inductance population XL _L3Optimum inductance value after genetic algorithm;

Represent the 4th inductance population XL _L4Optimum inductance value after genetic algorithm;

Represent the 5th inductance population XL _L5Optimum inductance value after genetic algorithm;

Represent the first resistance population XR _R1Optimum electric class value after genetic algorithm;

Represent the second resistance population XR _R2Optimum electric class value after genetic algorithm;

Represent the 3rd resistance population XR _R3Optimum electric class value after genetic algorithm;

Indication transformer population XT _eThe input/output voltage ratio of the optimum transformer after genetic algorithm.

Step 6: to working as former generation optimum individual I _HbestThe assignment of initially annealing obtains initial individual I _Initially

In the present invention, right $I_{\mathrm{hbest}}=\left[\begin{array}{ccccc}{C}_{C1}^{\mathrm{hbest}},& {\mathrm{<figure-callout\; id="2"\; label="C\; C"\; filenames="HDA00001713824700012.png"\; state="\{\{state\}\}">C</figure-callout>}}_C^2,& C_{C3}^{\mathrm{hbest}},& {\mathrm{<figure-callout\; id="4"\; label="C\; C"\; filenames="HDA00001713824700012.png"\; state="\{\{state\}\}">C</figure-callout>}}_C^4,& C_{C5}^{\mathrm{hbest}}\\ L_{L1}^{\mathrm{hbest}},& {\mathrm{<figure-callout\; id="2"\; label="L\; L"\; filenames="HDA00001713824700012.png"\; state="\{\{state\}\}">L</figure-callout>}}_L^2,& L_{L3}^{\mathrm{hbest}},& {\mathrm{<figure-callout\; id="4"\; label="L\; L"\; filenames="HDA00001713824700012.png"\; state="\{\{state\}\}">L</figure-callout>}}_L^4,& L_{L5}^{\mathrm{hbest}}\\ R_{R1}^{\mathrm{hbest}},& {\mathrm{<figure-callout\; id="2"\; label="R\; R"\; filenames="HDA00001713824700012.png"\; state="\{\{state\}\}">R</figure-callout>}}_R^2,& R_{R3}^{\mathrm{hbest}}& & \\ T_e^{\mathrm{hbest}}& & & & \end{array}\right]$ The assignment of initially annealing, then have simulated annealing initial individuality is

Represent the first electric capacity population XC _C1The initial value that in simulated annealing, is provided with, and

Represent the second electric capacity population XC _C2The initial value that in simulated annealing, is provided with, and

Represent the 3rd electric capacity population XC _C3The initial value that in simulated annealing, is provided with, and

Represent the 4th electric capacity population XC _C4The initial value that in simulated annealing, is provided with, and

Represent the 5th electric capacity population XC _C5The initial value that in simulated annealing, is provided with, and

Represent the first inductance population XL _L1The initial value that in simulated annealing, is provided with, and

Represent the second inductance population XL _L2The initial value that in simulated annealing, is provided with, and

Represent the 3rd inductance population XL _L3The initial value that in simulated annealing, is provided with, and

Represent the 4th inductance population XL _L4The initial value that in simulated annealing, is provided with, and

Represent the 5th inductance population XL _L5The initial value that in simulated annealing, is provided with, and

Represent the first resistance population XR _R1The initial value that in simulated annealing, is provided with, and

Represent the second resistance population XR _R2The initial value that in simulated annealing, is provided with, and

Represent the 3rd resistance population XR _R3The initial value that in simulated annealing, is provided with, and

Indication transformer population XT _eThe initial value that in simulated annealing, is provided with, and

Step 7: to working as former generation optimum individual I _HbestSound out assignment, obtain souning out individual I _{Sound out}

In the present invention; Initial individuality

to simulated annealing is soundd out assignment, and new exploration value individual

is then arranged

Be the first electric capacity population XC _C1In the variable-value scope is Δ x _C1In generate at random,

Be the second electric capacity population XC _C2In the variable-value scope is Δ x _C2In generate at random,

Be the 3rd electric capacity population XC _C3In the variable-value scope is Δ x _C3In generate at random,

Be the 4th electric capacity population XC _C4In the variable-value scope is Δ x _C4In generate at random,

Be the 5th electric capacity population XC _C5In the variable-value scope is Δ x _C5In generate at random,

Be the first inductance population XL _L1In the variable-value scope is Δ x _L1In generate at random,

Be the second inductance population XL _L2In the variable-value scope is Δ x _L2In generate at random,

Be the 3rd inductance population XL _L3In the variable-value scope is Δ x _L3In generate at random,

Be the 4th inductance population XL _L4In the variable-value scope is Δ x _L4In generate at random,

Be the 5th inductance population XL _L5In the variable-value scope is Δ x _L5In generate at random,

Be the first resistance population XR _R1In the variable-value scope is Δ x _R1In generate at random,

Be the second resistance population XR _R2In the variable-value scope is Δ x _R2In generate at random,

Be the 3rd resistance population XR _R3In the variable-value scope is Δ x _R3In generate at random,

Be transformer population XT _eIn the variable-value scope is Δ x _TeIn generate at random,

Step 8: according to objective function M _Target={ f _Target, l _Target, initial individual I _InitiallyWith the individual I of exploration _{Sound out}Carry out simulated annealing optimization, obtain the electronic component Parameter Optimization and separate;

Step 801: calculate and sound out individuality

Corresponding objective function M _Target={ f _Target, l _TargetValue be respectively ff ₁And ll ₁, ff ₁Individual I is soundd out in expression _{Sound out}Corresponding standing-wave ratio (SWR) optimization aim f _TargetValue, ll ₁Individual I is soundd out in expression _{Sound out}Corresponding power optimization target l _TargetValue;

Step 802: calculate initial individual

Corresponding target function value M _Target={ f _Target, l _TargetValue be respectively ff ₂And ll ₂, ff ₂Represent initial individual I _InitiallyCorresponding standing-wave ratio (SWR) optimization aim f _TargetValue, ll ₂Represent initial individual I _InitiallyCorresponding power optimization target l _TargetValue;

Step 803: judge and sound out individual I _{Sound out}Target function value M _Target={ f _Target, l _TargetValue ff ₁And ll ₁Whether be superior to initial individual I _InitiallyCorresponding target function value M _Target={ f _Target, l _TargetValue ff ₂And ll ₂, if ff ₁≤ff ₂And ll ₁>=ll ₂, then get into step 804, otherwise return step 801;

Step 804: calculate and sound out individual I _{Sound out}Whether satisfy the receiver function relation $\left[\begin{array}{c}{P}_{\mathrm{VSWR}}=\exp \left(\frac{{\mathrm{ff}}_1-{\mathrm{ff}}_2}{T_{\mathrm{now}}}\right)>r\&Element;\left[\mathrm{0,1}\right]\\ P_G=\exp \left(\frac{{\mathrm{ll}}_2-{\mathrm{ll}}_1}{T_{\mathrm{now}}}\right)>r\&Element;\left[\mathrm{0,1}\right]\end{array}\right.,$ If satisfy and then will sound out individual I _{Sound out}Substitute initial individual I _Initially, get into step 805; If do not satisfy and then return step 801; P _VSWRThe corresponding probability of acceptance of expression standing-wave ratio (SWR), P _GThe corresponding probability of acceptance of expression conversion gain, T _NowExpression Current Temperatures, r are at the interior random number that generates with the probabilistic of uniformly distributed function of [0,1] scope, i.e. r=RAN (0,1)

Step 805: in annealing algorithm, carry out temperature and reduce, and judge Current Temperatures T with cooling rate a _NowWhether less than by temperature T _End, if T _NowGreater than T _End, then return step 801; If T _NowSmaller or equal to T _End, the initial individual I after then will optimizing _InitiallyElectronic component parameter optimization as after heredity-simulated annealing processing is separated I _Best, $I_{\mathrm{hbest}}=\left[\begin{array}{ccccc}{C}_{C1}^{\mathrm{hbest}},& {\mathrm{<figure-callout\; id="2"\; label="C\; C"\; filenames="HDA00001713824700012.png"\; state="\{\{state\}\}">C</figure-callout>}}_C^2,& C_{C3}^{\mathrm{hbest}},& {\mathrm{<figure-callout\; id="4"\; label="C\; C"\; filenames="HDA00001713824700012.png"\; state="\{\{state\}\}">C</figure-callout>}}_C^4,& C_{C5}^{\mathrm{hbest}}\\ L_{L1}^{\mathrm{hbest}},& {\mathrm{<figure-callout\; id="2"\; label="L\; L"\; filenames="HDA00001713824700012.png"\; state="\{\{state\}\}">L</figure-callout>}}_L^2,& L_{L3}^{\mathrm{hbest}},& {\mathrm{<figure-callout\; id="4"\; label="L\; L"\; filenames="HDA00001713824700012.png"\; state="\{\{state\}\}">L</figure-callout>}}_L^4,& L_{L5}^{\mathrm{hbest}}\\ R_{R1}^{\mathrm{hbest}},& {\mathrm{<figure-callout\; id="2"\; label="R\; R"\; filenames="HDA00001713824700012.png"\; state="\{\{state\}\}">R</figure-callout>}}_R^2,& R_{R3}^{\mathrm{hbest}}& & \\ T_e^{\mathrm{hbest}}& & & & \end{array}\right].$

The realization of genetic algorithm and simulated annealing all needs a plurality of controlled variable of choose reasonable.Controlled variable of the present invention is selected as follows:

The genetic algorithm part: iteration step number K is 250.

Simulated annealing part: initial temperature T ₀Be 0.05 degree, by temperature T _EndBe 0.00001 degree, cooling rate a is 0.9.

Adopt matlab software that electronic component in the matching network (as shown in Figure 2) parameter is carried out heredity-simulated annealing combinational algorithm simulation process, each parameters optimization that obtains is as shown in table 1.

The matching network component value that table 1 heredity-optimization of simulated annealing combinational algorithm obtains

Element	Component value	Element	Component value
				T0(DT：1)	0.88	R1	7.2Ω
R2	955Ω	R3	900Ω
				C1	23pF	L1	1.8pH

C2	9pF	L2	18nH
				C3	7.8pF	L3	10nH
C4	0pF	L4	25nH
				C5	15pF	L5	1.1pH

Because it is very little to optimize the component value of L1, C4, L5 in the matching network that obtains, its contribution to matching network is little, can this part element be removed.The structural representation of the antenna broadband matching network after the simplification is as shown in Figure 4.In Fig. 4, the link of antenna is connected with 1 end of transformer T0 in this broadband matching network equivalent electrical circuit; The 2 end ground connection of transformer T0; 1 end of concentric cable is connected with an end of first resistance R 1, and 2 end ground connection of concentric cable are parallel with the 3rd resistance R 3 between 1 end of concentric cable and 2 ends;

3 ends of transformer T0 insert 4 ends of transformer T0 after first capacitor C 1, second capacitor C 2;

3 ends of transformer T0 insert 4 ends of transformer T0 after first capacitor C 1, second inductance L 2;

3 ends of transformer T0 insert 4 ends of transformer T0 after first capacitor C 1, the 3rd capacitor C 3, the 3rd inductance L 3, the 4th inductance L 4;

3 ends of transformer T0 insert 4 ends of transformer T0 after first capacitor C 1, the 3rd capacitor C 3, the 3rd inductance L 3, the 5th capacitor C 5, second resistance R 2;

3 ends of transformer T0 insert 4 ends of transformer T0 after first capacitor C 1, the 3rd capacitor C 3, the 3rd inductance L 3, the 5th capacitor C 5, first resistance R 1, the 3rd resistance R 3.

In the present invention, between the secondary of transformer T0 and concentric cable incoming end, use the processing mode of multiple-stage filtering (constituting) that antenna impedance is mated, make the rational in infrastructure of broadband matching network equivalent electrical circuit by two T shape L-C networks.

Claims (6)

1. one kind is adopted heredity-simulated annealing to make up electronic component Parameter Optimization method in the antenna broadband matching network, it is characterized in that including the following step:

Step 1:, obtain variable X to be optimized={ XC based on the initialization of population of genetic algorithm _a, XL _b, XR _d, XT _e;

In step 1, with electric capacity in the broadband matching network equivalent electrical circuit, inductance and resistance adopt based on genetic algorithm population handle, obtain variable X to be optimized={ XC _a, XL _b, XR _d, XT _e;

Said variable X to be optimized={ XC _a, XL _b, XR _d, XT _eMiddle XC _aExpression electric capacity population, a representes the sign of electric capacity in the equivalent electrical circuit, is designated as XC like the first electric capacity population of first capacitor C 1 _C1In like manner can get, the second electric capacity population is designated as XC _C2, the 3rd electric capacity population is designated as XC _C3, the 4th electric capacity population is designated as XC _C4, the 5th electric capacity population is designated as XCC _C5All electric capacity populations adopt the set form to be expressed as XC in the equivalent electrical circuit _a={ XC _C1, XC _C2, XC _C3, XC _C4, XC _C5;

XL _bExpression inductance population, b representes the sign of inductance in the equivalent electrical circuit, is designated as XL like the first inductance population of first inductance L 1 _L1In like manner can get, the second inductance population is designated as XL _L2, the 3rd inductance population is designated as XL _L3, the 4th inductance population is designated as XL _L4, the 5th inductance population is designated as XL _L5All inductance populations adopt the set form to be expressed as XL in the equivalent electrical circuit _b={ XL _L1, XL _L2, XL _L3, XL _L4, XL _L5;

XR _dExpression resistance population, d representes the sign of resistance in the equivalent electrical circuit, is designated as XR like the first resistance population of first resistance R 1 _R1In like manner can get, the second resistance population is designated as XR _R2, the 3rd resistance population is designated as XR _R3All resistance populations adopt the set form to be expressed as XR in the equivalent electrical circuit _d={ XR _R1, XR _R2, XR _R3;

XT _eThe indication transformer population, e representes the input/output voltage ratio of transformer in the equivalent electrical circuit;

Step 2: the chromosome based on genetic algorithm is handled, and obtains total population

In step 2, based on the chromosome in the genetic algorithm, to electric capacity population XC _aIn variable-value DC, generate m variate-value at random ${\mathrm{DXC}}_a^m=\{{\mathrm{XC}}_a^1,{\mathrm{XC}}_a^2,\&CenterDot;\&CenterDot;\&CenterDot;,{\mathrm{XC}}_a^m\},$ 0＜DC≤800pF; The variate-value of

expression sign a electric capacity population in the 1st chromosome; The variate-value of

expression sign a electric capacity population in the 2nd chromosome; The variate-value of

expression sign a electric capacity population in m chromosome also claimed the variate-value of sign a electric capacity population in any chromosome;

Based on the chromosome in the genetic algorithm, to inductance population XL _bIn variable-value DL, generate w variate-value at random

0＜DL≤0.1 μ H;

The variate-value of expression sign b inductance population in the 1st chromosome,

The variate-value of expression sign b inductance population in the 2nd chromosome ...,

The variate-value of expression sign b inductance population in w chromosome also claimed the variate-value of sign b inductance population in any chromosome;

Based on the chromosome in the genetic algorithm, to resistance population XR _dIn variable-value DR, generate v variate-value at random

0＜DR≤5k Ω;

The variate-value of expression sign d resistance population in the 1st chromosome,

The variate-value of expression sign d resistance population in the 2nd chromosome ..., The variate-value of expression sign d resistance population in v chromosome also claimed the variate-value of sign d resistance population in any chromosome;

Based on the chromosome in the genetic algorithm, to transformer population XT _eIn variable-value DT, generate n variate-value at random

0.1≤DT≤10;

The variate-value of expression sign e transformer population in the 1st chromosome,

The variate-value of expression sign e transformer population in the 2nd chromosome ..., The variate-value of expression sign e transformer population in n chromosome also claimed the variate-value of sign e transformer population in any chromosome;

For variable X to be optimized={ XC _a, XL _b, XR _d, XT _eChromosome in genetic algorithm handles and to obtain total population

Step 3:, be that each function divides mating group in the objective function according to back-and-forth method arranged side by side with the multiple-objection optimization function;

In step 3, with total population

In chromosome function M according to target _Target={ f _Target, l _TargetNumber be divided into the first sub-group Q equably ₁With the second sub-group Q ₂, each sub-group is distributed objective function M _Target={ f _Target, l _TargetIn one be optimized;

Step 4: the optimized amount of obtaining sub-population with cross and variation;

In step 4, to the first sub-group Q ₁Carry out cross and variation, keep each, be i.e. the first optimized amount DQ for optimized amount ₁To the second sub-group Q ₂Carry out cross and variation, keep each, be i.e. the second optimized amount DQ for optimized amount ₂Cross and variation is obtained each concrete steps for optimized amount:

Step 401: obtain the first sub-group Q ₁In any 2 chromosomes

As working as prochromosome

Be also referred to as current first chromosome

Obtain the second sub-group Q ₂In any 2 chromosomes

As working as prochromosome

Be also referred to as current second chromosome

Step 402: two individuals in current first chromosome

are carried out cross processing; Generate first chromosome of new first chromosome

expression intersection back, back second chromosome of

expression intersection; Said cross processing is according to the first adaptability Policy model $P_{c1}=\left\{\begin{array}{cc}(f_{\min }-f_{\mathrm{avg}})/(f_{\min }-f),& f\&le;f_{\mathrm{avg}}\\ 1.0,& f>f_{\mathrm{avg}}\end{array}\right.$ Carry out; P _C1Represent the first sub-group Q ₁Crossover probability (being also referred to as first crossover probability), f _MinRepresent the first sub-group Q ₁Middle optimized individual fitness value, f is expressed as the adaptive value that adapts in two individuals that will intersect, and f=min{f ₁, f ₂, f ₁Expression chromosome

Corresponding standing-wave ratio (SWR) optimization aim f _TargetValue, f ₂Expression chromosome

Corresponding standing-wave ratio (SWR) optimization aim f _TargetValue, f _AvgRepresent the first sub-group Q ₁Average fitness value;

Two individuals in current second chromosome

are carried out cross processing; Generate the 3rd chromosome in new second chromosome

expression intersection back, the 4th chromosome in

expression intersection back; Said cross processing is according to the second adaptability Policy model $P_{c2}=\left\{\begin{array}{cc}(l_{\max }-l)/(l_{\max }-l_{\mathrm{avg}}),& l\&GreaterEqual;l_{\mathrm{avg}}\\ 1.0,& l<l_{\mathrm{avg}}\end{array}\right.$ Carry out; P _C2Represent the second sub-group Q ₂Crossover probability, be also referred to as second crossover probability, l _MaxRepresent the second sub-group Q ₂Middle optimized individual fitness value, l is expressed as the adaptive value that adapts in two individuals that will intersect, and l=max{l ₁, l ₂, l ₁Expression chromosome

Corresponding power optimization target l _TargetValue, l ₂Expression chromosome

Corresponding power optimization target l _TargetValue, l _AvgRepresent the second sub-group Q ₂Average fitness value;

Step 403: compare f ₁With f ₃And f ₂With f ₄, if f ₁>=f ₃And f ₂>=f ₄The time, use AQ _IntersectReplace AQ _CurrentIf f ₁＜f ₃Or f ₂＜f ₄The time, AQ then _CurrentConstant; f ₃First chromosome after expression intersects

Corresponding standing-wave ratio (SWR) optimization aim f _TargetValue, f ₄Back second chromosome of expression intersection

Corresponding standing-wave ratio (SWR) optimization aim f _TargetValue;

Compare l ₁With l ₃And l ₂With l ₄, if l ₁≤l ₃And l ₂≤l ₄The time, use BQ _IntersectReplace BQ _CurrentIf l ₁＞l ₃Or l ₂＞l ₄The time, BQ then _CurrentConstant; l ₃The 3rd chromosome after expression intersects

Corresponding power optimization target l _TargetValue, l ₄Tetrasome after expression intersects

Corresponding power optimization target l _TargetValue;

Step 404: to current first chromosome

In the processing that makes a variation respectively of two individuals, generate variation first chromosome

Expression

Chromosome after the variation, Expression

Chromosome after the variation; Described variation is handled according to the 3rd adaptability Policy model

Carry out; P _M1Represent the first sub-group Q ₁Variation probability (be also referred to as first variation probability), f _MinRepresent optimized individual fitness value among the first sub-group Q1, f _AvgRepresent the first sub-group Q ₁Average fitness value, f ' is for needing the individual fitness value of variation, and f ₁Expression chromosome

Corresponding standing-wave ratio (SWR) optimization aim f _TargetValue, f ₂Expression chromosome

Corresponding standing-wave ratio (SWR) optimization aim f _TargetValue;

To the processing that makes a variation respectively of two individuals in current second chromosome

; Generate the chromosome after variation second chromosome

expression makes a variation, the chromosome after

expression

variation; Said variation is handled according to the 4th adaptability Policy model $P_{m2}=\left\{\begin{array}{cc}0.5(l_{\max }-l^{\&prime;})/(l_{\max }-l_{\mathrm{avg}}),& l^{\&prime;}\&GreaterEqual;l_{\mathrm{avg}}\\ 0.5,& l^{\&prime;}<l_{\mathrm{avg}}\end{array}\right.$ Carry out; P _M2Represent the second sub-group Q ₂The variation probability, be also referred to as second the variation probability, l _MaxRepresent the second sub-group Q ₂Middle optimized individual fitness value, l _AvgRepresent the second sub-group Q ₂Average fitness value, l ' for to make a variation individual fitness value and

l ₁Expression chromosome

Corresponding power optimization target l _TargetValue, l ₂Expression chromosome

Corresponding power optimization target l _TargetValue;

Step 405: compare f ₁With f ₅, if f ₁＞f ₅The time, use

Replace

If f ₁≤f ₅The time, then

Constant; f ₅Expression

Chromosome after the variation

Corresponding standing-wave ratio (SWR) optimization aim f _TargetValue;

Compare f ₂With f ₆, if f ₂＞f ₆The time, use Replace If f ₂≤f ₆The time, then

Constant; f ₆Expression

Chromosome after the variation

Corresponding standing-wave ratio (SWR) optimization aim f _TargetValue;

Compare l ₁With l ₅, if l ₁＜l ₅The time, use

Replace If l ₁>=l ₅The time, then

Constant; l ₅Expression

Chromosome after the variation

Corresponding power optimization target l _TargetValue;

Compare l ₂With l ₆, if l ₂＜l ₆The time, use

Replace

If l ₂>=l ₆The time, then

Constant; l ₆Expression

Chromosome after the variation Corresponding power optimization target l _TargetValue;

Repeating step 401 is to step 405, up to the first sub-group Q ₁With the second sub-group Q ₂The whole cross and variation of middle chromosome are accomplished, and obtain current generation first sub-group Q ₁Optimum optimized amount, i.e. the first optimized amount DQ ₁, the second sub-group Q ₂Optimum optimized amount, i.e. the second optimized amount DQ ₂

Step 5: according to objective function M _Target={ f _Target, l _TargetThe total population Q of traversal optimization _Always' in all chromosome obtain in the genetic algorithm when the former generation optimum individual;

In step 5, merge the first optimized amount DQ ₁With the second optimized amount DQ ₂Form new population, promptly optimize total population Q _Always', according to objective function M _Target={ f _Target, l _TargetThe total population Q of traversal optimization _Always' in all chromosome obtain in the genetic algorithm when the former generation optimum individual And

Compose and give I _Hbest, so that optimum individual of future generation compares with the optimum individual of working as former generation, in both, select more excellent individuality, and compose and give I _Hbest

Judge whether to reach the end condition of genetic algorithm,, then return step 4,, then draw the optimum individual I in the genetic algorithm if satisfy hereditary end condition if do not satisfy hereditary end condition _Hbest, and remain, get into step 6; The end condition of said genetic algorithm is meant whether iteration step number K is O, if iteration step number K is not 0, then returns step 3, if iteration step number K is O, then draws the optimum individual I in the genetic algorithm _Hbest, and remain, get into step 6; For with variable X to be optimized={ XC _a, XL _b, XR _d, XT _eExpression-form corresponding, described electronic component parametric optimal solution I _HbestThe set expression-form does $I_{\mathrm{hbest}}=\left[\begin{array}{c}{C}_{C1}^{\mathrm{hbest}},C_{C2}^{\mathrm{hbest}},C_{C3}^{\mathrm{hbest}},C_{C4}^{\mathrm{hbest}},C_{C5}^{\mathrm{hbest}}\\ L_{L1}^{\mathrm{hbest}},L_{L2}^{\mathrm{hbest}},L_{L3}^{\mathrm{hbest}},L_{L4}^{\mathrm{hbest}},L_{L5}^{\mathrm{hbest}}\\ R_{R1}^{\mathrm{hbest}},R_{R2}^{\mathrm{hbest}},R_{R3}^{\mathrm{hbest}}\\ T_e^{\mathrm{hbest}}\end{array}\right];$ Wherein,

Represent the first electric capacity population XC _C1Optimum capacitance value after genetic algorithm;

Represent the second electric capacity population XC _C2Optimum capacitance value after genetic algorithm;

Represent the 3rd electric capacity population XC _C3Optimum capacitance value after genetic algorithm;

Represent the 4th electric capacity population XC _C4Optimum capacitance value after genetic algorithm;

Represent the 5th electric capacity population XC _C5Optimum capacitance value after genetic algorithm;

Represent the first inductance population XL _L1Optimum inductance value after genetic algorithm;

Represent the second inductance population XL _L2Optimum inductance value after genetic algorithm;

Represent the 3rd inductance population XL _L3Optimum inductance value after genetic algorithm;

Represent the 4th inductance population XL _L4Optimum inductance value after genetic algorithm;

Represent the 5th inductance population XL _L5Optimum inductance value after genetic algorithm;

Represent the first resistance population XR _R1Optimum electric class value after genetic algorithm;

Represent the second resistance population XR _R2Optimum electric class value after genetic algorithm;

Represent the 3rd resistance population XR _R3Optimum electric class value after genetic algorithm;

Indication transformer population XT.The input/output voltage ratio of the optimum transformer after genetic algorithm;

Step 6: to working as former generation optimum individual I _HbestThe assignment of initially annealing obtains initial individual I _InitiallyRight $I_{\mathrm{hbest}}=\left[\begin{array}{c}{C}_{C1}^{\mathrm{hbest}},C_{C2}^{\mathrm{hbest}},C_{C3}^{\mathrm{hbest}},C_{C4}^{\mathrm{hbest}},C_{C5}^{\mathrm{hbest}}\\ L_{L1}^{\mathrm{hbest}},L_{L2}^{\mathrm{hbest}},L_{L3}^{\mathrm{hbest}},L_{L4}^{\mathrm{hbest}},L_{L5}^{\mathrm{hbest}}\\ R_{R1}^{\mathrm{hbest}},R_{R2}^{\mathrm{hbest}},R_{R3}^{\mathrm{hbest}}\\ T_e^{\mathrm{hbest}}\end{array}\right]$ The assignment of initially annealing then has the initial individuality of simulated annealing to do

Wherein,

Represent the first electric capacity population XC _C1The initial value that in simulated annealing, is provided with, and

Represent the second electric capacity population XC _C2The initial value that in simulated annealing, is provided with, and

Represent the 3rd electric capacity population XC _C3The initial value that in simulated annealing, is provided with, and

Represent the 4th electric capacity population XC _C4The initial value that in simulated annealing, is provided with, and

Represent the 5th electric capacity population XC _C5The initial value that in simulated annealing, is provided with, and

Represent the first inductance population XL _L1The initial value that in simulated annealing, is provided with, and

Represent the second inductance population XL _L2The initial value that in simulated annealing, is provided with, and

Represent the 3rd inductance population XL _L3The initial value that in simulated annealing, is provided with, and

Represent the 4th inductance population XL _L4The initial value that in simulated annealing, is provided with, and Represent the 5th inductance population XL _L5The initial value that in simulated annealing, is provided with, and

Represent the first resistance population XR _R1The initial value that in simulated annealing, is provided with, and

Represent the second resistance population XR _R2The initial value that in simulated annealing, is provided with, and Represent the 3rd resistance population XR _R3The initial value that in simulated annealing, is provided with, and

Indication transformer population XT _eThe initial value that in simulated annealing, is provided with, and

Step 7: to working as former generation optimum individual I _HbestSound out assignment, obtain souning out individual I _{Sound out}

Initial individuality

to simulated annealing is soundd out assignment; New exploration value individual

is then arranged wherein

Be the first electric capacity population XC _C1In the variable-value scope is Δ x _C1In generate at random,

Be the second electric capacity population XC _C2In the variable-value scope is Δ x _C2In generate at random,

Be the 3rd electric capacity population XC _C3In the variable-value scope is Δ x _C3In generate at random,

Be the 4th electric capacity population XC _C4In the variable-value scope is Δ x _C4In generate at random,

Be the 5th electric capacity population XC _C5In the variable-value scope is Δ x _C5In generate at random,

Be the first inductance population XL _L1In the variable-value scope is Δ x _L1In generate at random,

Be the second inductance population XL _L2In the variable-value scope is Δ x _L2In generate at random,

Be the 3rd inductance population XL _L3In the variable-value scope is Δ x _L3In generate at random,

Be the 4th inductance population XL _L4In the variable-value scope is Δ x _L4In generate at random,

Be the 5th inductance population XL _L5In the variable-value scope is Δ x _L5In generate at random,

Be the first resistance population XR _R1In the variable-value scope is Δ x _R1In generate at random,

Be the second resistance population XR _R2In the variable-value scope is Δ x _R2In generate at random,

Be the 3rd resistance population XR _R3In the variable-value scope is Δ x _R3In generate at random,

Be transformer population XT _eIn the variable-value scope is Δ x _TeIn generate at random,

Step 8: according to objective function M _Target={ f _Target, l _Target, initial individual I _InitiallyWith the individual I of exploration _{Sound out}Carry out simulated annealing optimization, obtain the electronic component Parameter Optimization and separate;

Step 801: calculate and sound out individuality

Corresponding objective function M _Target={ f _Target, l _TargetValue be respectively ff ₁And ll ₁, ff ₁Individual I is soundd out in expression _{Sound out}Corresponding standing-wave ratio (SWR) optimization aim f _TargetValue, ll ₁Individual I is soundd out in expression _{Sound out}Corresponding power optimization target l _TargetValue;

Step 802: calculate initial individual

Corresponding target function value M _Target={ f _Target, l _TargetValue be respectively ff ₂And ll ₂, ff ₂Represent initial individual I _InitiallyCorresponding standing-wave ratio (SWR) optimization aim f _TargetValue, ll ₂Represent initial individual I _InitiallyCorresponding power optimization target l _TargetValue;

Step 803: judge and sound out individual I _{Sound out}Target function value M target={ f _Target, l _TargetValue ff ₁And ll ₂Whether be superior to initial individual I _InitiallyCorresponding target function value M _Target={ f _Target, l _TargetValue ff ₂And ll ₂, if ff ₁≤ff ₂And ll ₁>=ll ₂, then get into step 804, otherwise return step 801;

Step 804: calculate and sound out individual I _{Sound out}Whether satisfy the receiver function relation $\left[\begin{array}{c}{P}_{\mathrm{VSWR}}=\exp \left(\frac{{\mathrm{ff}}_1-{\mathrm{ff}}_2}{T_{\mathrm{now}}}\right)>r\&Element;\left[\mathrm{0,1}\right]\\ P_G=\exp \left(\frac{{\mathrm{ll}}_2-{\mathrm{ll}}_1}{T_{\mathrm{now}}}\right)>r\&Element;\left[\mathrm{0,1}\right]\end{array}\right.,$ If satisfy and then will sound out individual I _{Sound out}Substitute initial individual I _Initially, get into step 805; If do not satisfy and then return step 801; P _VSWRThe corresponding probability of acceptance of expression standing-wave ratio (SWR), P _GThe corresponding probability of acceptance of expression conversion gain, T _NowExpression Current Temperatures, r are at the interior random number that generates with the probabilistic of uniformly distributed function of [0,1] scope, i.e. r=RAN (0,1)

Step 805: in annealing algorithm, carry out temperature and reduce, and judge Current Temperatures T with cooling rate a _NowWhether less than by temperature T _End, if T _NowGreater than T _End, then return step 801; If T _NowSmaller or equal to T _End, the initial individual I after then will optimizing _InitiallyElectronic component parameter optimization as after heredity-simulated annealing processing is separated I _Best,

$I_{\mathrm{best}}=\left[\begin{array}{c}{C}_{C1}^{\mathrm{best}},C_{C2}^{\mathrm{best}},C_{C3}^{\mathrm{best}},C_{C4}^{\mathrm{best}},C_{C5}^{\mathrm{best}}\\ L_{L1}^{\mathrm{best}},L_{L2}^{\mathrm{best}},L_{L3}^{\mathrm{best}},L_{L4}^{\mathrm{best}},L_{L5}^{\mathrm{best}}\\ R_{R1}^{\mathrm{best}},R_{R2}^{\mathrm{best}},R_{R3}^{\mathrm{best}}\\ T_e^{\mathrm{best}}\end{array}\right].$

2. employing heredity according to claim 1-simulated annealing makes up electronic component Parameter Optimization method in the antenna broadband matching network, it is characterized in that: if the first sub-group Q ₁Adopted objective function M _Target={ f _Target, l _TargetIn antenna standing wave ratio f _TargetBe optimized, then the second sub-group Q ₂Should adopt objective function M _Target={ f _Target, l _TargetIn conversion power gain l _TargetBe optimized; Otherwise, if the second sub-group Q ₂Adopted objective function M _Target={ f _Target, l _TargetIn antenna standing wave ratio f _TargetBe optimized, then the first sub-group Q ₁Should adopt objective function M _Target={ f _Target, l _TargetIn conversion power gain l _TargetBe optimized.

3. employing heredity according to claim 1-simulated annealing makes up electronic component Parameter Optimization method in the antenna broadband matching network, it is characterized in that: the input/output voltage of the transformer TO of employing is than being DT:1.

4. employing heredity according to claim 1-simulated annealing makes up electronic component Parameter Optimization method in the antenna broadband matching network, and it is characterized in that: the value of chromosomal variable m, w, v and n is 200.

5. employing heredity according to claim 1-simulated annealing makes up electronic component Parameter Optimization method in the antenna broadband matching network, and it is characterized in that: the iteration step number K that selects for use in the said genetic algorithm is 250.

6. employing heredity according to claim 1-simulated annealing makes up electronic component Parameter Optimization method in the antenna broadband matching network, it is characterized in that: the initial temperature T0 that selects for use in the said simulated annealing is 0.05 degree, by temperature T _EndBe 0.00001 degree, cooling rate a is 0.9.

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