Crease layout parameter optimization method for deployable thin film microstrip reflective array antenna

Abstract

The invention discloses a folding mark layout parameter optimization method of a deployable thin film microstrip reflective array antenna, which comprises the following steps: determining an optimization target of the thin film microstrip reflective array antenna, the optimization target comprising an inter-plate distance PD and a folding-unfolding ratio, and giving a value range of folding mark structure characteristic parameters of the reflective array antenna; establishing a thin film microstrip reflective array antenna optimization model according to a Flasher model; solving and screening the optimization model by adopting a dung beetle optimization algorithm to obtain an optimal value of the characteristic parameter; and thus, an optimal crease layout scheme is obtained. According to the invention, a film is taken as a flexible hinge of an antenna dielectric substrate, and a gap needs to be considered when the plates are folded, that is, under the condition of a determined optimization model, the distribution area of each plate is limited to be smaller than that of the original plate, so that the patch antenna is enabled to avoid a crease area.

Description

Technical Field

[0001] The invention belongs to the technical field of space deployable antennas, and in particular relates to a method for optimizing fold layout parameters of a deployable thin-film microstrip reflective array antenna. Background Art

[0002] Deployable origami space arrays improve the ability of large deployment area to loading volume ratio. This can help devices such as antennas, solar energy and optical systems meet critical space constraints during launch while maximizing efficiency after deployment. Flasher, as a model of intermediate winding folding, is often used in space film folding structures. However, in practical applications, the thickness of these devices cannot be ignored, such as solar panels, antenna dielectric panels, and solar sails, which cause them to deform during storage and deployment, resulting in a decrease in the electrical performance indicators of the deployable antenna.

[0003] Taking into account the thickness of the antenna structure and the limited internal space of the launch vehicle, it is necessary to design a reliable multi-objective optimization algorithm process to optimize the fold layout and construct a fold distribution of a deployable thin-film microstrip reflectarray antenna to meet actual needs. Summary of the invention

[0004] The purpose of the present invention is to provide a method for optimizing the fold parameter layout of a deployable thin-film microstrip reflectarray antenna, based on the high fold-to-stretch ratio of the deployable thin-film microstrip reflectarray antenna taking into account the thickness of the antenna dielectric plate and the limited space of the launch vehicle, to further optimize the fold layout to meet actual needs.

[0005] The technical solution adopted by the present invention is a method for optimizing the layout of fold parameters of an expandable thin-film microstrip reflective array antenna, wherein the array antenna includes an antenna metal patch layer, a dielectric substrate, a polyimide film layer and a metal bottom plate support layer in sequence. The array unit of the complete microstrip reflective array antenna is divided into regions of different sizes for arranging patch units according to the plates of the Flasher model, and the fold distribution parameters are optimized;

[0006] Step 1: Determine the optimization target of the thin film microstrip reflector array antenna, wherein the optimization target includes the inter-plate distance PD and the fold-to-spread ratio, and the value range of the characteristic parameters of the fold structure of the given reflector array antenna;

[0007] Step 2: Establish the thin film microstrip reflectarray antenna optimization model based on the Flasher model;

[0008] Step 3: Use the dung beetle optimization algorithm to solve and screen the optimization model to obtain the optimal value of the characteristic parameters; thus obtaining the optimal crease layout solution.

[0009] This optimization problem has a large number of local optimal points and has the characteristics of multiple constraints and nonlinearity. The dung beetle optimization algorithm is a new type of swarm intelligence optimization algorithm, which has the characteristics of strong optimization ability and fast convergence speed. On this basis, the non-dominated sorting method is added to the algorithm. First, the Pareto front of the optimization problem is calculated, and then the minimum value of the total length of the fold is taken as the final result. Finally, the optimal solution of the fold layout parameters of the thin film microstrip reflectarray antenna is achieved.

[0010] The present invention is also characterized in that:

[0011] The characteristic parameters in step 1 include: the number of sides m of the central polygonal hub of the thin film microstrip reflector array antenna, the number of segments h divided by the main crease line by the spiral crease line, the number r of regions divided by the main crease line by the secondary crease line, and the proportional distance dr between the two closest vertices at the same radial position and the same ordinate, dr being proportional to the unfolded diameter D of the antenna;

[0012] The initial value range of the characteristic parameters is:

[0013]

[0014] Step 2: Optimize the model min f1, f2

[0015] f1=|PD-PD0|=f(h,dr)

[0016] f2=|ratio-ratio0|=g(m,h,r,dr)

[0017] Among them, PD0 is the target inter-plate distance, measuring the distance between the outermost plates; ratio0 is the target fold-expansion ratio;

[0018] In step 3, the optimization model is solved using the dung beetle optimization algorithm, that is, the four characteristic parameters are solved, as follows:

[0019] Step 1: Parameter Characterization

[0020] The characteristic parameter is represented by x, x1 represents the number of sides m of the central polygonal hub, x2 represents the number of segments h divided by the main crease line by the spiral crease line, x3 represents the number of regions r divided by the main crease line by the secondary crease line, and x4 represents the proportional distance dr between the two closest vertices at the same radial position and the same ordinate;

[0021] Step 2: Use the dung beetle optimization algorithm to update the position of feature parameters;

[0022] Step 3: Based on the above position update strategy, add the steps of sorting and selecting the population, sort and select the objective function values ​​corresponding to the updated population, use the non-dominated sorting method to arrange individuals of different levels, and calculate the crowding distance at the same level:

[0023]

[0024] In the formula, represents the jth objective function value corresponding to the i-th individual in the non-dominated frontier, and are the maximum and minimum values ​​of the j-th objective function respectively;

[0025] After calculating the crowding distance of each objective function, the crowding distances are sorted in descending order, and the Pareto optimal frontier is selected using the crowding distance. This optimal frontier is passed to the next generation population as elite individuals. If the number of individuals in the optimal frontier is less than the number of individuals in the next generation population, the individuals in the next level frontier are selected to supplement the next generation population.

[0026] The location update strategy includes the following four types, and any one of them can be selected when updating parameters;

[0027] 1) Dung beetle community: The position update strategy of this population is:

[0028] If the random number rand(0,1)>0.1

[0029] x i (t+1)=x i (t)+α×k×x i (t-1)+b×Δx,

[0030] Δx=|x i (t)-X w |

[0031] Otherwise the update method is

[0032] x i (t+1)=x i (t)+tan(θ)|x i (t)-x i (t-1)|

[0033] In the formula, t represents the current iteration number, x i (t) represents the position information of the i-th dung beetle at the t-th iteration, α is a coefficient, randomly assigned to -1 or 1, k represents the deflection coefficient, k∈(0,0.2], generally set to 0.1; b is a constant, b∈(0,1), generally set to 0.3; X wrepresents the global worst position, Δx is used to simulate the change of light intensity, θ∈[0,π] is a random number, representing the deflection angle, when θ is equal to 0, or π, the position of the dung beetle will not be updated;

[0034] 2) Egg-laying dung beetle colony: The position update strategy of this population is:

[0035] x i (t+1)=X * +b1×(x i (t)-Lb * )+b i ×(x i (t)-Ub * )

[0036] Lb * =max(X * ×(1-R),Lb),Ub * =min(X * ×(1+R),Ub)

[0037]

[0038] Among them, X * represents the local optimal position of the community, T max represents the maximum number of iterations, Ub and Lb represent x i The upper and lower limits of (t), b1 and b2 represent two independent random vectors;

[0039] 3) Small dung beetle colony: The position update strategy of this population is:

[0040] x i (t+1)=x i (t)+c1×(x i (t)-Lb b )+c2×(x i (t)-Ub b )

[0041] Lb b =max(X b ×(1-R),Lb),Ub b =min(X b ×(1+R),Ub)

[0042] Among them, X b is the local optimal position of the community, c1 is a random number following a normal distribution, and c2∈(0,1) is a random vector;

[0043] 4) Thief dung beetle colony: The position update strategy of this population is:

[0044] x i (t+1)=X b +S×g×(x i (t)-X * |+|x i (t)-X b |)

[0045] Where g represents a column vector following a normal distribution, and S represents a constant.

[0046] On the basis of the above, a sorting method based on the non-dominated principle is added to select the Pareto frontier Pareto in the way of Pareto optimal solution;

[0047] Considering that the number of folds will affect the size of the effective area ratio, reducing the number of folds as much as possible while satisfying the objective function can achieve the requirement of maximizing the utilization rate.

[0048] The third step is implemented as follows:

[0049] Step 3.1: Initialize the population P, generate different individuals according to random numbers, and calculate the objective function values ​​corresponding to different individuals;

[0050] Step 3.2: Divide the population into 4 groups according to a certain ratio. Each group updates the new feature parameters and objective function values ​​according to the position update strategy in the second step, i.e., the new population Q.

[0051] Step 3.3: Combine the original population P and the new population Q, sort the objective function values ​​according to the non-dominated principle, and determine the Pareto frontier and non-dominated frontiers of different levels;

[0052] Step 3.4: Calculate the crowding distances corresponding to different individuals, and combine the level and crowding distance of non-dominated sorting to screen out the next generation population;

[0053] Step 3.5: Determine whether the current number of iterations is less than the maximum number of iterations. If so, return to step 3.3. Otherwise, output the current Pareto frontier Pareto.

[0054] Step 3.6: After the cycle ends, a series of solutions that meet the optimization goal are obtained. Considering that the use area of ​​the antenna should be maximized, the number of folds of the polyimide film needs to be reduced. The optimal fold layout solution is selected in the Pareto front according to the principle of minimizing the total number of folds.

[0055] Step 3.7: Output a region that retains the Flasher plate taking into account the flexible hinge gap to meet the requirements of antenna dielectric board installation.

[0056] Step 3.7 is as follows:

[0057] After selecting the optimal crease layout scheme, considering that the film acts as a flexible hinge connecting the dielectric plates, the gap between the dielectric plates needs to be considered when folding. If the dielectric plate is folded around the crease, the minimum gap of the crease is twice the plate thickness t.

[0058] The beneficial effect of the present invention is that the technical effect of the fold layout parameter optimization method of the deployable thin film microstrip reflectarray antenna of the present invention is:

[0059] 1. The present invention takes the Flasher model taking thickness into consideration as the basic model, optimizes its parameters to obtain an optimized model, and inherits the characteristics of the Flasher deployable mechanism with single degree of freedom and large folding and unfolding ratio.

[0060] 2. The present invention optimizes the layout parameters to obtain an optimization model that takes the board thickness into consideration, which can meet the requirements of the folding aspect ratio, inter-board spacing and effective area ratio of the thin film microstrip reflectarray antenna.

[0061] 3. The present invention takes into account that the film serves as a flexible hinge of the antenna dielectric substrate, and the gaps between the panels need to be considered when folding. That is, under a determined Flasher model, the cloth area of ​​each panel is restricted to be smaller than the original panel, so that the patch antenna avoids the crease area. BRIEF DESCRIPTION OF THE DRAWINGS

[0062] Figure 1 A schematic diagram of the distance between plates, which is one of the optimization targets of the present invention;

[0063] Figure 2 A schematic diagram of the crease gap reserved for the present invention, wherein the black area in the figure represents the antenna dielectric substrate;

[0064] Figure 3 A schematic diagram of the antenna fabric area output by the present invention taking into account the crease gap;

[0065] Figure 4 for Figure 3 Schematic diagram of some areas, the dotted part represents the Flasher fold, and the solid part represents the actual allowed fabric area of ​​the antenna considering the gap limitation during the film folding process;

[0066] Figure 5 A flow chart of the fold layout parameter optimization method for a deployable thin-film microstrip reflectarray antenna;

[0067] Figure 6 This is the individual distribution diagram of the Pareto front generated after the iteration of Example 3. DETAILED DESCRIPTION

[0068] In order to make the purpose, technical solutions and advantages of the present invention more clear, the present invention will be further described in detail below in conjunction with the accompanying drawings and specific embodiments.

[0069] Example 1

[0070] The present invention discloses a method for optimizing fold layout parameters of a deployable thin-film microstrip reflective array antenna. The array antenna comprises an antenna metal patch layer, a dielectric substrate, a polyimide film layer and a metal bottom plate support in sequence. The array units of the complete microstrip reflective array antenna are divided into regions of different sizes for arranging patch units according to the plates of a Flasher model, and the fold distribution parameters are optimized.

[0071] This optimization problem has a large number of local optimal points and has the characteristics of multiple constraints and nonlinearity. The dung beetle optimization algorithm is a new type of swarm intelligence optimization algorithm, which has the characteristics of strong optimization ability and fast convergence speed. On this basis, the algorithm adds a non-dominated sorting method. First, the Pareto frontier of the optimization problem is calculated, and then the minimum value of the total length of the fold is taken as the final result. Finally, the optimal solution of the fold layout parameters of the thin film microstrip reflectarray antenna is achieved. The optimization objectives and optimization parameters are explained first.

[0072] Example 2

[0073] The fold parameter layout optimization method of the deployable thin film microstrip reflect array antenna of the present invention is as follows: Figure 5 The specific steps are as follows:

[0074] Step 1: Determine the optimization target of the thin film microstrip reflectarray antenna, wherein the optimization target includes the inter-plate distance PD (e.g. Figure 1 As shown) and the fold-to-spread ratio ratio, the value range of the characteristic parameters of the fold structure of the given reflectarray antenna;

[0075] The characteristic parameters in step 1 include: the number of sides m of the central polygonal hub of the thin film microstrip reflector array antenna, the number of segments h divided by the main crease line by the spiral crease line, the number r of regions divided by the main crease line by the secondary crease line, and the proportional distance dr between the two closest vertices at the same radial position and the same ordinate, dr being proportional to the unfolded diameter D of the antenna;

[0076] The initial value range of the characteristic parameters is:

[0077]

[0078] Step 2: Optimize the model min f1, f2

[0079] f1=|PD-PD0|=f(h,dr)

[0080] f2=|ratio-ratio0|=g(m,h,r,dr)

[0081] Among them, PD0 is the target inter-plate distance, measuring the distance between the outermost plates; ratio0 is the target fold-expansion ratio;

[0082] Step 2: Establish the thin film microstrip reflectarray antenna optimization model based on the Flasher model;

[0083] Step 3: Use the dung beetle optimization algorithm to solve and screen the optimization model to obtain the optimal value of the characteristic parameters, thereby obtaining the optimal crease layout solution;

[0084] The four characteristic parameters are solved as follows:

[0085] Step 1: Parameter Characterization

[0086] The characteristic parameter is represented by x, x1 represents the number of sides m of the central polygonal hub, x2 represents the number of segments h divided by the main crease line by the spiral crease line, x3 represents the number of regions r divided by the main crease line by the secondary crease line, and x4 represents the proportional distance dr between the two closest vertices at the same radial position and the same ordinate;

[0087] Step 2: Use the dung beetle optimization algorithm to update the position of feature parameters;

[0088] Step 3: Based on the above position update strategy, add the steps of sorting and selecting the population, sort and select the objective function values ​​corresponding to the updated population, use the non-dominated sorting method to arrange individuals of different levels, and calculate the crowding distance at the same level:

[0089]

[0090] In the formula, represents the jth objective function value corresponding to the i-th individual in the non-dominated frontier, f j max and are the maximum and minimum values ​​of the j-th objective function respectively;

[0091] After calculating the crowding distance of each objective function, the crowding distances are sorted in descending order, and the Pareto optimal frontier is selected using the crowding distance. This optimal frontier is passed to the next generation population as elite individuals. If the number of individuals in the optimal frontier is less than the number of individuals in the next generation population, the individuals in the next level frontier are selected to supplement the next generation population.

[0092] The location update strategy includes the following four types, and any one of them can be selected when updating parameters;

[0093] 1) Dung beetle community: The position update strategy of this population is:

[0094] If the random number rand(0,1)>0.1

[0095] x i (t+1)=x i (t)+α×k×x i (t-1)+b×Δx,

[0096] Δx=|x i (t)-X w |

[0097] Otherwise the update method is

[0098] x i (t+1)=x i (t)+tan(θ)|x i (t)-x i (t-1)|

[0099] In the formula, t represents the current iteration number, x i (t) represents the position information of the i-th dung beetle at the t-th iteration, α is a coefficient, randomly assigned to -1 or 1, k represents the deflection coefficient, k∈(0,0.2], generally set to 0.1; b is a constant, b∈(0,1), generally set to 0.3; X w represents the global worst position, Δx is used to simulate the change of light intensity, θ∈[0,π] is a random number, representing the deflection angle, when θ is equal to 0, or π, the position of the dung beetle will not be updated;

[0100] 2) Egg-laying dung beetle colony: The position update strategy of this population is:

[0101] x i (t+1)=X * +b1×(x i (t)-Lb * )+b i ×(x i (t)-Ub * )

[0102] Lb * =max(X * ×(1-R),Lb),Ub * =min(X * ×(1+R),Ub)

[0103]

[0104] Among them, X * represents the local optimal position of the community, Tmax represents the maximum number of iterations, Ub and Lb represent x i The upper and lower limits of (t), b1 and b2 represent two independent random vectors;

[0105] 3) Small dung beetle colony: The position update strategy of this population is:

[0106] x i (t+1)=x i (t)+c1×(x i (t)-Lb b )+c2×(x i (t)-Ub b )

[0107] Lb b =max(X b ×(1-R),Lb),Ub b =min(X b ×(1+R),Ub)

[0108] Among them, X b is the local optimal position of the community, c1 is a random number following a normal distribution, and c2∈(0,1) is a random vector;

[0109] 4) Thief dung beetle colony: The position update strategy of this population is:

[0110] x i (t+1)=X b +S×g×(x i (t)-X * |+|x i (t)-X b |)

[0111] Where g represents a column vector following a normal distribution, and S represents a constant.

[0112] On the basis of the above, a sorting method based on the non-dominated principle is added to select the Pareto frontier Pareto in the way of Pareto optimal solution;

[0113] Considering that the number of folds will affect the size of the effective area ratio, reducing the number of folds as much as possible while satisfying the objective function can achieve the requirement of maximizing the utilization rate.

[0114] The third step is implemented as follows:

[0115] Step 3.1: Initialize the population P, generate different individuals according to random numbers, and calculate the objective function values ​​corresponding to different individuals;

[0116] Step 3.2: Divide the population into 4 groups according to a certain ratio. Each group updates the new feature parameters and objective function values ​​according to the position update strategy in the second step, i.e., the new population Q.

[0117] Step 3.3: Combine the original population P and the new population Q, sort the objective function values ​​according to the non-dominated principle, and determine the Pareto frontier and non-dominated frontiers of different levels;

[0118] Step 3.4: Calculate the crowding distances corresponding to different individuals, and combine the level and crowding distance of non-dominated sorting to screen out the next generation population;

[0119] Step 3.5: Determine whether the current number of iterations is less than the maximum number of iterations. If so, return to step 3.3. Otherwise, output the current Pareto frontier Pareto.

[0120] Step 3.6: Select the optimal fold layout scheme in the Pareto front according to the principle of minimizing the total number of folds;

[0121] Step 3.7: Output a region of the Flasher plate that retains the flexible hinge gap to meet the requirements of antenna dielectric plate installation; the details are as follows:

[0122] like Figure 2 As shown in the figure, after selecting the optimal crease layout scheme, considering that the film is used as a flexible hinge connecting the dielectric plates, the gap between the dielectric plates needs to be considered when folding. If the dielectric plates are folded around the crease, the minimum gap of the crease is twice the thickness of the plate t. Because the film is folded around the outer edges of the two dielectric plates at this time, the antenna fabric range needs to be limited to meet actual needs, such as Figure 3 As shown; Figure 4 This is an enlarged view of a portion of the area. The dotted line portion represents the Flasher fold, and the solid line portion represents the actual allowed fabric area of ​​the antenna taking into account the gap limitations during the film folding process.

[0123] Example 3

[0124] Taking a 5m-diameter thin film microstrip reflectarray antenna as an example, the folding and spreading ratio is set to about 1:9, the thickness between the plates is 4mm, the population size is 50, and the maximum number of iterations is 60 generations; this embodiment uses Inter(R)Core(TM)i58300H CPU2.30GHz and 16.00GB memory, Windows10 operating environment, and uses MATLAB to simulate the method.

[0125] After inputting the optimization goal and generating the initial population P, the new feature parameters and objective function values ​​are updated according to the position update strategy, that is, the new population Q. Then, the original population P and the new population Q are combined, and the objective function values ​​are sorted according to the non-dominated principle and the crowding distance. The Pareto frontier is selected and sent to the next generation population.

[0126] After 50 iterations, the individuals and the corresponding objective function values ​​are plotted as Figure 6 , the horizontal and vertical coordinates are f1 and f2 respectively, and the individuals in the population all meet the results selected under the Pareto principle. According to the individuals in the Pareto frontier, the crease length of the crease graph corresponding to each individual is calculated respectively, and the individual with the smallest crease length is selected as the final solution; then, considering the gap between the dielectric plates when folding, a crease solution and cloth area that considers the gap between the flexible hinges is finally output.

Claims

1. A method for optimizing fold layout parameters of a deployable thin-film microstrip reflectarray antenna, characterized in that: The specific steps are as follows: Step 1: Determine the optimization target of the thin film microstrip reflector array antenna, wherein the optimization target includes the inter-plate distance PD and the fold-to-spread ratio, and the value range of the characteristic parameters of the fold structure of the given reflector array antenna; Step 2: Establish the thin film microstrip reflectarray antenna optimization model based on the Flasher model; Step 3: Use the dung beetle optimization algorithm to solve and screen the optimization model to obtain the optimal value of the characteristic parameters; thus obtaining the optimal crease layout solution.

2. According to the method for optimizing fold layout parameters of a deployable thin film microstrip reflectarray antenna according to claim 1, the characteristic parameters in step 1 include: The number of sides m of the central polygonal hub of the thin film microstrip reflectarray antenna, the number of segments h divided by the main crease line by the spiral crease line, the number r of regions divided by the main crease line by the secondary crease line, represented by the proportional distance dr between the two closest vertices at the same radial position and the same ordinate, dr is proportional to the unfolded diameter D of the antenna; The initial value range of the characteristic parameters is:

3. The method for optimizing fold layout parameters of a deployable thin film microstrip reflectarray antenna according to claim 2, characterized in that: Step 2: Optimize the model minf1,f2 f1=|PD-PD0|=f(h,dr) f2=|ratio-ratio0|=g(m,h,r,dr) Among them, PD0 is the target inter-board distance, measuring the distance between the outermost boards; ratio0 is the target fold-expansion ratio.

4. The method for optimizing fold layout parameters of a deployable thin film microstrip reflectarray antenna according to claim 3, characterized in that: In step 3, the optimization model is solved using the dung beetle optimization algorithm, that is, the four characteristic parameters are solved, as follows: Step 1: Parameter Characterization The characteristic parameter is represented by x, x1 represents the number of sides m of the central polygonal hub, x2 represents the number of segments h divided by the main crease line by the spiral crease line, x3 represents the number of regions r divided by the main crease line by the secondary crease line, and x4 represents the proportional distance dr between the two closest vertices at the same radial position and the same ordinate; Step 2: Use the dung beetle optimization algorithm to update the position of feature parameters; Step 3: Based on the above position update strategy, add the steps of sorting and selecting the population, sort and select the objective function values ​​corresponding to the updated population, use the non-dominated sorting method to arrange individuals of different levels, and calculate the crowding distance at the same level: In the formula, represents the jth objective function value corresponding to the i-th individual in the non-dominated frontier, and are the maximum and minimum values ​​of the j-th objective function respectively; After calculating the crowding distance of each objective function, the crowding distances are sorted in descending order, and the Pareto optimal frontier is selected using the crowding distance. This optimal frontier is passed to the next generation population as elite individuals. If the number of individuals in the optimal frontier is less than the number of individuals in the next generation population, the individuals in the next level frontier are selected to supplement the next generation population.

5. The method for optimizing fold layout parameters of a deployable thin film microstrip reflectarray antenna according to claim 4, characterized in that: The location update strategy includes the following four types, and any one of them can be selected when updating parameters; 1) Dung beetle community: The position update strategy of this population is: If the random number rand(0,1)>0.1 x i (t+1)=x i (t)+α×k×x i (t-1)+b×Δx, Δx=|x i (t)-X w | Otherwise the update method is x i (t+1)=x i (t)+tan(θ)|x i (t)-x i (t-1)| In the formula, t represents the current iteration number, x i (t) represents the position information of the i-th dung beetle at the t-th iteration, α is a coefficient, randomly assigned to -1 or 1, k represents the deflection coefficient, k∈(0,0.2]; b is a constant, b∈(0,1); X w represents the global worst position, Δx is used to simulate the change of light intensity, θ∈[0,π] is a random number, representing the deflection angle, when θ is equal to 0, or π, the position of the dung beetle will not be updated; 2) Egg-laying dung beetle colony: The position update strategy of this population is: x i (t+1)=X * +b1×(x i (t)-Lb * )+b i ×(x i (t)-Ub * ) Lb * =max(X * ×(1-R),Lb),Ub * =min(X * ×(1+R),Ub) Among them, X * represents the local optimal position of the community, T max represents the maximum number of iterations, Ub and Lb represent x i The upper and lower limits of (t), b1 and b2 represent two independent random vectors; 3) Small dung beetle colony: The position update strategy of this population is: x i (t+1)=x i (t)+c1×(x i (t)-Lb b )+c2×(x i (t)-Ub b ) Lb b =max(X b ×(1-R),Lb),Ub b =min(X b ×(1+R),Ub) Among them, X b is the local optimal position of the community, c1 is a random number following a normal distribution, and c2∈(0,1) is a random vector; 4) Thief dung beetle colony: The position update strategy of this population is: x i (t+1)=X b +S×g×(x i (t)-X * |+|x i (t)-X b |) Among them, g represents a column vector following normal distribution, and S represents a constant; On the basis of the above, a sorting method based on the non-dominated principle is added to select the Pareto frontier in the manner of Pareto optimal solution; Considering that the number of folds will affect the size of the effective area ratio, reducing the number of folds as much as possible while satisfying the objective function can achieve the requirement of maximizing the utilization rate.

6. The method for optimizing fold layout parameters of a deployable thin film microstrip reflectarray antenna according to claim 5, characterized in that: The third step is implemented as follows: Step 3.1: Initialize the population P, generate different individuals according to random numbers, and calculate the objective function values ​​corresponding to different individuals; Step 3.2: Divide the population into 4 groups according to a certain ratio. Each group updates the new feature parameters and objective function values ​​according to the position update strategy in the second step, i.e., the new population Q. Step 3.3: Combine the original population P and the new population Q, sort the objective function values ​​according to the non-dominated principle, and determine the Pareto frontier and non-dominated frontiers of different levels; Step 3.4: Calculate the crowding distances corresponding to different individuals, and combine the level and crowding distance of non-dominated sorting to screen out the next generation population; Step 3.5: Determine whether the current number of iterations is less than the maximum number of iterations. If so, return to step 3.

3. Otherwise, output the current Pareto frontier Pareto. Step 3.6: Select the optimal fold layout scheme in the Pareto front according to the principle of minimizing the total number of folds; Step 3.7: Output a region that retains the Flasher plate taking into account the flexible hinge gap to meet the requirements of antenna dielectric board installation.

7. The method for optimizing fold layout parameters of a deployable thin film microstrip reflectarray antenna according to claim 6, characterized in that: Step 3.7 is as follows: After selecting the optimal crease layout scheme, considering that the film acts as a flexible hinge connecting the dielectric plates, the gap between the dielectric plates needs to be considered when folding. If the dielectric plate is folded around the crease, the minimum gap of the crease is twice the plate thickness t.

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