A design method and device of wave-absorbing composite material based on genetic algorithm

Abstract

The application provides a design method and device of a wave-absorbing composite material based on a genetic algorithm, and the method comprises the following steps: based on a plurality of different types of wave-absorbing materials, random coding is performed to obtain a plurality of genetic individuals, and an initial population is formed; the reflectivity of each genetic individual in the population is calculated; the fitness of each genetic individual is evaluated; according to the number of bits corresponding to the data variable in the coding and the current population evolution generation number, the mutation rate of each genetic individual is calculated; the genetic individual with the minimum fitness is retained, and the other genetic individuals are crossed and mutated by using the mutation rate to obtain a new generation population, and the calculation of the reflectivity of each genetic individual in the population is performed on the new generation population, until the termination condition of the genetic algorithm is met, so that the genetic individuals in the new generation population obtained form the wave-absorbing composite material. According to the scheme, the mutation rate can be gradually increased to accelerate the optimization speed under the premise of ensuring the early rapid convergence, and the rapid convergence and rapid optimization of the genetic algorithm are realized.

Description

Technical Field

[0001] This invention relates to the field of genetic algorithm technology, and in particular to a design method and apparatus for composite materials based on genetic algorithms. Background Technology

[0002] Reflectivity is the percentage of radiant energy reflected by an object relative to its total radiant energy. In practical applications, due to varying requirements, composite materials need to achieve different target reflectivities in different wavelength bands. By designing multilayer absorbing materials, it is possible to design absorbing composite materials with different reflectivities in different wavelength bands while maintaining the same thickness or even making them thinner.

[0003] In existing technologies, iterative optimization computational models based on genetic algorithms (GA) have been used to calculate the performance of microwave absorbing composite materials. However, these models still suffer from drawbacks such as difficulty in convergence or slow optimization in later stages. Therefore, it is necessary to study a design method for microwave absorbing composite materials based on genetic algorithms. Summary of the Invention

[0004] This invention provides a design method and apparatus for microwave absorbing materials based on a genetic algorithm. This design method can gradually increase the mutation rate to accelerate the optimization speed while ensuring rapid convergence in the early stage, thereby solving the problems of difficult convergence and slow optimization in the later stage of the genetic algorithm in the prior art.

[0005] In a first aspect, an embodiment of the present invention provides a design method for microwave absorbing composite materials based on a genetic algorithm, comprising:

[0006] Based on several different types of microwave absorbing materials that can be used to form microwave absorbing composite materials, several genetic individuals are randomly encoded, and these genetic individuals form an initial population.

[0007] Calculate the reflectance of each genetic individual in the population;

[0008] The fitness of each genetic individual is assessed based on its reflectivity and the target reflectivity required to form the absorbing composite material.

[0009] The mutation rate of each genetic individual is calculated based on the number of bits of the data variable corresponding to the encoding and the current generation number of the population.

[0010] The genetic individual with the lowest fitness is retained, and the other genetic individuals are crossovered and mutated using the mutation rate to obtain a new generation of population. The reflectivity of each genetic individual in the new generation of population is calculated until the termination condition of the genetic algorithm is met. The absorbing composite material is formed based on the genetic individuals in the new generation of population.

[0011] Preferably, the type is a combination of material type, material thickness, and number of layers in the microwave absorbing composite material.

[0012] Preferably, after generating the initial population, the method further includes: decoding the genetic individuals in the initial population to obtain the material type and thickness corresponding to each layer of material, and determining its corresponding dielectric constant and magnetic permeability, so as to use the dielectric constant and magnetic permeability to perform the calculation of the reflectivity of each genetic individual in the population.

[0013] Preferably, the reflectance of each genetic individual in the calculated population is obtained by using the reflectance formula of the absorbing material;

[0014] The reflectivity formula of the absorbing material is:

[0015]

[0016] Where R is reflectivity, z i(n) Let z0 be the input impedance of the n-layer absorbing material, z0 be the vacuum impedance, and γ be the input impedance. n Let d be the complex propagation factor of the nth layer material. n Let μ be the thickness of the nth layer of material. n Let ε be the relative complex permeability of the nth layer material. n The relative permittivity, c is the speed of light in vacuum, j is the imaginary number, f is the frequency, and n is the number of layers of the absorbing material, n≥1.

[0017] Preferably, the assessment of the fitness of each genetic individual based on the reflectivity of each individual and the target reflectivity required to form the absorbing composite material includes:

[0018] First, the reflectance of each genetic individual at the target frequency is calculated using the following formula;

[0019] When the desired reflectance at a given frequency point equals the target reflectance, the reflectance at the target frequency point for each genetic individual is calculated using the following formula:

[0020] B = w|R (fi) -T|

[0021] When the desired reflectance at a given frequency is less than the target reflectance, the reflectance of each genetic individual at the target frequency is calculated using the following formula:

[0022]

[0023] When the desired reflectance at a corresponding frequency point is greater than the required target reflectance, the reflectance of each genetic individual at the target frequency point is calculated using the following formula:

[0024]

[0025] The fitness of each genetic individual is assessed using the following formula:

[0026]

[0027] Where F is the fitness value, R (fi) Let be the reflectance of the i-th individual, T be the target reflectance, and w be the weight of the corresponding frequency point.

[0028] Preferably, the step of calculating the mutation rate of each genetic individual based on the number of bits of the data variable corresponding to the encoding and the current generation number of the population includes:

[0029] The variability rate is determined using the following formula:

[0030]

[0031] Among them, P m The variation rate of each bit of a binary data variable must be greater than 0 and less than or equal to 1. d denoted as the number of bits for each data variable in the binary encoding of the absorbing material, k is the generation number of the population, and i is a user-defined coefficient that must be greater than 0.

[0032] Secondly, embodiments of the present invention also provide a design device for microwave absorbing composite materials based on a genetic algorithm, comprising:

[0033] The encoding module is used to randomly encode several genetic individuals based on multiple different types of microwave absorbing materials that can be used to form microwave absorbing composite materials. These genetic individuals form an initial population.

[0034] The first calculation module calculates the reflectance of each genetic individual in the population;

[0035] The evaluation module is used to assess the fitness of each genetic individual based on the reflectivity of each individual and the target reflectivity required to form the microwave absorbing composite material.

[0036] The second calculation module is used to calculate the mutation rate of each genetic individual based on the number of bits of the data variable corresponding to the encoding and the current generation number of the population.

[0037] The genetic algorithm module is used to retain the genetic individual with the lowest fitness and use the mutation rate to perform crossover and mutation on other genetic individuals to obtain a new generation of population. For this new generation of population, the module jumps to execute the calculation of the reflectivity of each genetic individual in the population until the termination condition of the genetic algorithm is met, and forms a microwave absorbing composite material based on the genetic individuals in the currently obtained new generation of population.

[0038] Thirdly, embodiments of the present invention also provide a computing device, including a memory and a processor, wherein the memory stores a computer program, and when the processor executes the computer program, it implements the method described in any embodiment of this specification.

[0039] Fourthly, embodiments of the present invention also provide a computer-readable storage medium having a computer program stored thereon, which, when executed in a computer, causes the computer to perform the methods described in any embodiment of this specification.

[0040] Compared with the prior art, the present invention has at least the following beneficial effects:

[0041] This invention combines the idea of ​​genetic algorithm optimization design with the design of microwave absorbing materials. First, the reflectivity of each genetic individual in the initial population is calculated. Based on the reflectivity of each genetic individual and the target reflectivity required by the microwave absorbing composite material, the fitness of each genetic individual is evaluated. Then, based on the number of bits of the corresponding data variable and the current generation of the population, the mutation rate of each genetic individual is calculated. The genetic individual with the lowest fitness is then subjected to genetic operation according to the above mutation rate. This process is repeated in multiple generations, and the genetic individual with the lowest fitness is continuously retained until the termination condition of the genetic algorithm is met. The optimal result is then output as the design result, thus obtaining the optimized microwave absorbing composite material.

[0042] In this invention, the mutation rate of the selected individuals is determined based on the data variables in the absorbing composite material and the generation number of the population. The mutation rate changes with the number of bits of the data variables corresponding to the encoding of the absorbing composite material and the generation number of the population. This gradually increases the mutation rate to accelerate the optimization speed while ensuring the rapid convergence of the genetic algorithm in the early stage. This solves the problems of difficult convergence or slow optimization speed in the later stage of the genetic algorithm in the prior art. Attached Figure Description

[0043] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0044] Figure 1 This is a flowchart illustrating a design method for microwave absorbing composite materials based on a genetic algorithm, provided in an embodiment of the present invention.

[0045] Figures 2 to 3 These are the reflectivity curve and fitness curve calculated by a design method for a microwave absorbing composite material based on a genetic algorithm provided in an embodiment of the present invention.

[0046] Figures 4 to 5 These are the reflectance curve and fitness curve calculated using a genetic algorithm with a fixed low mutation rate in existing technology.

[0047] Figures 6 to 7 These are the reflectance curve and fitness curve calculated using a genetic algorithm with a fixed high mutation rate in existing technology.

[0048] Figure 8 This is a statistical percentage bar chart of the final fitness values ​​after 100 simulations each using the method of this invention and the method in the prior art;

[0049] Figure 9 This is a hardware architecture diagram of a computing device provided in an embodiment of the present invention;

[0050] Figure 10 This is a structural diagram of a microwave absorbing composite material device based on a genetic algorithm provided in an embodiment of the present invention. Detailed Implementation

[0051] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are some embodiments of the present invention, but not all embodiments. All other embodiments obtained by those skilled in the art based on the embodiments of the present invention without creative effort are within the scope of protection of the present invention.

[0052] Genetic algorithms are stochastic global search optimization algorithms developed by drawing on the selection, crossover, and mutation processes in biological evolution. In this embodiment of the invention, the idea of ​​genetic algorithm optimization design is combined with the design of microwave absorbing materials. First, the reflectivity of each genetic individual in the initial population is calculated. Based on the reflectivity of each genetic individual and the target reflectivity required by the microwave absorbing composite material, the fitness of each genetic individual is evaluated. Then, based on the number of bits of the corresponding data variable and the current generation of the population, the mutation rate of each genetic individual is calculated. After that, the genetic individual with the lowest fitness is subjected to genetic operation according to the above mutation rate. The search continues in multiple generations, and the genetic individual with the lowest fitness is continuously retained until the termination condition of the genetic algorithm is met. The optimal result is then output as the design result, thereby obtaining the optimized microwave absorbing composite material.

[0053] The specific implementation of the above concept is described below.

[0054] Please refer to Figure 1 This invention provides a design method for microwave absorbing composite materials based on genetic algorithms, the method comprising:

[0055] Step 100: Based on multiple different types of microwave absorbing materials that can be used to form microwave absorbing composite materials, several genetic individuals are randomly encoded, and these genetic individuals form an initial population;

[0056] Step 102: Calculate the reflectance of each genetic individual in the population;

[0057] Step 104: Assess the fitness of each genetic individual based on its reflectivity and the target reflectivity required to form the microwave absorbing composite material.

[0058] Step 106: Calculate the mutation rate of each genetic individual based on the number of bits of the data variable corresponding to the encoding and the current generation number of the population.

[0059] Step 108: Retain the genetic individual with the lowest fitness and use the mutation rate to perform crossover and mutation on the other genetic individuals to obtain a new generation of population. Then, for this new generation of population, jump to execute the calculation of the reflectivity of each genetic individual in the population until the termination condition of the genetic algorithm is met. Form a microwave absorbing composite material based on the genetic individuals in the currently obtained new generation of population.

[0060] In this invention, the mutation rate of the selected individuals is determined based on the data variables and the generation number of the population in the microwave absorbing composite material. This allows the mutation rate to change with the data variables and the generation number of the population in the microwave absorbing composite material. As a result, while ensuring the rapid convergence of the genetic algorithm in the early stage, the mutation rate is gradually increased to accelerate the optimization speed. This solves the problems of difficult convergence or slow optimization speed in the later stage of the genetic algorithm calculation process in the prior art.

[0061] The following description Figure 1 The execution method for each step is shown.

[0062] First, for step 100, based on multiple different types of absorbing materials that can be used to form absorbing composite materials, several genetic individuals are randomly encoded, and these genetic individuals form an initial population.

[0063] In this embodiment of the invention, firstly, different types and thicknesses of microwave absorbing materials are collected for optimization design, and the target parameters of the desired microwave absorbing composite material are set: 3 material layers, 0-5 mm thickness per layer, target reflectivity of -3 dB for 1-4 GHz, less than -10 dB for 6-13 GHz, and greater than -8 dB for 14-18 GHz, with a weight of 1 for each frequency band. The evolutionary termination generation for the genetic parameters is set to 100, and the crossover rate is 0.5. To balance data accuracy and computational speed, a reflectivity data step size of 0.05 GHz is selected. The collected material types, set material thicknesses, and number of layers are binary encoded, with a total binary code length of 'a' for each genetic individual. An initial population of 300 × 'a' is randomly generated based on this total binary code length.

[0064] Then, for step 102, the reflectance of each genetic individual in the population is calculated.

[0065] In this embodiment of the invention, before calculating the reflectivity of each genetic individual in the population, the binary code of each genetic individual in the population is first decoded to obtain the material type and thickness corresponding to each layer of material, and the dielectric constant and magnetic permeability of the material can be determined from this. Then, the reflectivity of each genetic individual in the population is calculated according to the reflectivity formula of the absorbing material.

[0066] The formula for the reflectivity of absorbing materials is:

[0067]

[0068]

[0069] Where R is reflectivity, z i(n) Let z0 be the input impedance of the n-layer absorbing material, z0 be the vacuum impedance, and γ be the input impedance. n Let d be the complex propagation factor of the nth layer material. n Let μ be the thickness of the nth layer of material. n Let ε be the relative complex permeability of the nth layer material. n Relative permittivity, c is the speed of light in vacuum, j is the imaginary number, f is the frequency, n is the number of layers of the absorbing material, n≥1, z i(0) =0.

[0070] Next, for step 104, the fitness of each genetic individual is evaluated based on the reflectivity of each genetic individual and the target reflectivity required to form the microwave absorbing composite material.

[0071] In this embodiment of the invention, fitness is introduced as a criterion for discriminating and selecting genetic individuals for genetic operations. In this invention, fitness is the sum of the differences between the reflectance at each frequency point and the desired target reflectance, multiplied by the weights of the corresponding frequency bands. The specific formula for calculating fitness is as follows:

[0072] First, the reflectance of each genetic individual at the target frequency is calculated using the following formula;

[0073] When the desired reflectance at a given frequency point equals the target reflectance, the reflectance at the target frequency point for each genetic individual is calculated using the following formula:

[0074] B = w|R (fi) -T|

[0075] When the desired reflectance at a given frequency is less than the target reflectance, the reflectance of each genetic individual at the target frequency is calculated using the following formula:

[0076]

[0077] When the desired reflectance at a given frequency point is greater than the target reflectance, the reflectance of each genetic individual at the target frequency point is calculated using the following formula:

[0078]

[0079] The fitness of each genetic individual is assessed using the following formula:

[0080]

[0081] Where F is the fitness value, R (fi) Let be the reflectance of the i-th individual, T be the target reflectance, and w be the weight of the corresponding frequency point.

[0082] In this embodiment of the invention, the smaller the fitness obtained, the closer it is to the set target reflectance value, that is, the better the result. Therefore, several genetic individuals with the smallest fitness can be selected for genetic operations.

[0083] Next, for step 106, the mutation rate of each genetic individual is calculated based on the number of bits of the data variable corresponding to the encoding and the current generation number of the population.

[0084] In this embodiment of the invention, by establishing a relationship between the mutation rate and the number of bits in the binary representation of the data variable and the number of generations of the population, the mutation rate changes with the computation, thereby achieving rapid convergence and rapid optimization. Specifically, the mutation rate in this invention is determined by the following formula:

[0085]

[0086] Among them, P m The variance rate of each bit of a binary data variable must be greater than 0 and less than or equal to 1. When the calculated P... m When P > 1, m =1,L d denoted as the number of bits for each data variable in the binary encoding of the absorbing material, k is the generation number of the population, and i is a user-defined coefficient that must be greater than 0. The smaller i is, the faster Pm changes.

[0087] In this embodiment of the invention, the variation rate of each data variable can be determined using the above formula:

[0088]

[0089] When L d When →+∞,

[0090] In existing genetic algorithms, the population typically uses a fixed low or high mutation rate during mutation. This fixed mutation rate makes the genetic algorithm prone to getting stuck in local optima and difficulty in convergence later on. Unlike the fixed low or high mutation rates commonly used in existing genetic algorithms, this invention establishes a relationship between the mutation rate and the number of bits in the binary representation of the data variable and the number of generations in the population. This allows the mutation rate to change with the number of generations. This ensures that the mutation rate is low in the early stages of population evolution, enabling the population to converge quickly, while also ensuring a high mutation rate in the later stages of evolution. Even after convergence, the population still maintains a fast evolutionary speed, thus ensuring the continuity of the search and increasing the probability of finding the global optimum.

[0091] Finally, for step 108, the genetic individual with the lowest fitness is retained, and the other genetic individuals are crossovered and mutated using the mutation rate to obtain a new generation of population. The calculation of the reflectivity of each genetic individual in the new generation of population is then performed until the termination condition of the genetic algorithm is met. The absorbing composite material is then formed based on the genetic individuals in the current new generation of population.

[0092] In this embodiment of the invention, during genetic operations, crossover is performed on genetic individuals in the population. Then, using the mutation rate of the current population's genetic individuals obtained above, mutation operations are performed on the genetic individuals in the population to obtain a new generation. This new generation is then used as a new initial population, and the reflectivity of each genetic individual in the population is recalculated until a preset genetic evolution generation of 100 is reached. The genetic individuals in the final population are then output as the result, thus obtaining the design result of the composite absorbing material. Starting from the second generation of the population, the individual with the lowest fitness from the previous generation is retained and combined with other offspring obtained through crossover and mutation from the previous generation to form a new population.

[0093] Figures 2 to 7 The typical reflectance and fitness evolution curves are calculated using three methods: a variable mutation rate based on the number of bits of the corresponding encoded data variable and the current generation of the population, as provided in this invention; a fixed low mutation rate; and a fixed high mutation rate in existing technologies. Figure 8 The statistical results of calculating the final fitness value 100 times for each of the three calculation methods are presented. Combined with... Figures 2 to 8 The results show that the genetic algorithm in this invention can achieve fast convergence and fast optimization. The reflectivity curve of the absorbing composite material calculated by the genetic algorithm in this invention best meets the target requirements, and the final value of the fitness curve is the smallest (that is, it represents the best result). Furthermore, the positions of each inflection point indicate that fast convergence and fast optimization can be achieved.

[0094] In summary, the composite material reflectance calculation method based on accelerated iterative genetic algorithm proposed in this invention establishes a relationship between the mutation rate in the genetic algorithm and the number of bits and iterations of the data variables in binary. By comparing the results obtained with those obtained by traditional genetic algorithms, the calculation method proposed in this invention can achieve the best calculation results under the same parameters and within the same time, which helps to improve the calculation speed of material reflectance.

[0095] like Figure 9 , Figure 10 As shown, this invention provides a design device for microwave absorbing composite materials based on a genetic algorithm. The device can be implemented in software, hardware, or a combination of both. From a hardware perspective, as... Figure 9 The diagram shown is a hardware architecture diagram of a computing device housing a design device for microwave absorbing composite materials based on a genetic algorithm, as provided in an embodiment of the present invention. (Except for...) Figure 9 In addition to the processor, memory, network interface, and non-volatile memory shown, the computing device in the embodiment may also include other hardware, such as a forwarding chip responsible for processing packets. Taking software implementation as an example, such as... Figure 10As shown, a device in a logical sense is formed by the CPU of its computing device reading the corresponding computer program from non-volatile memory into memory and running it. This embodiment provides a design device for microwave absorbing composite materials based on a genetic algorithm, comprising:

[0096] Encoding module 301 is used to randomly encode several genetic individuals based on multiple different types of microwave absorbing materials that can be used to form microwave absorbing composite materials, and these genetic individuals form an initial population;

[0097] The first calculation module 302 calculates the reflectance of each genetic individual in the population;

[0098] The evaluation module is used to assess the fitness of each genetic individual based on the reflectivity of each individual and the target reflectivity required to form the microwave absorbing composite material.

[0099] The second calculation module 303 is used to calculate the mutation rate of each genetic individual based on the number of bits of the data variable corresponding to the encoding and the current generation of the population.

[0100] The genetic algorithm module 304 is used to retain the genetic individual with the lowest fitness and use the mutation rate to perform crossover and mutation on other genetic individuals to obtain a new generation of population. It then jumps to the calculation of the reflectivity of each genetic individual in the new generation of population until the termination condition of the genetic algorithm is met, and forms a wave-absorbing composite material based on the genetic individuals in the currently obtained new generation of population.

[0101] In one embodiment of the present invention, in the encoding module 301, the type is a combination of material type, material thickness and number of layers in the microwave absorbing composite material.

[0102] In one embodiment of the present invention, after generating the initial population, the encoding module 301 further includes:

[0103] The genetic individuals in the initial population are decoded to obtain the material type and thickness corresponding to each layer of material, and the corresponding dielectric constant and magnetic permeability are determined, so as to use the dielectric constant and magnetic permeability to perform the calculation of the reflectivity of each genetic individual in the population.

[0104] In one embodiment of the present invention, in the first calculation module 302, the reflectivity formula of the absorbing material is:

[0105]

[0106]

[0107] Where R is reflectivity, z i(n)Let z0 be the input impedance of the n-layer absorbing material, z0 be the vacuum impedance, and γ be the input impedance. n Let d be the complex propagation factor of the nth layer material. n Let μ be the thickness of the nth layer of material. n Let ε be the relative complex permeability of the nth layer material. n The relative permittivity, c is the speed of light in vacuum, j is the imaginary number, f is the frequency, and n is the number of layers of the absorbing material, n≥1.

[0108] In one embodiment of the present invention, the fitness of each genetic individual is evaluated in the second calculation module 303 using the following formula:

[0109] First, the reflectance of each genetic individual at the target frequency is calculated using the following formula;

[0110] When the desired reflectance at a given frequency point equals the target reflectance, the reflectance at the target frequency point for each genetic individual is calculated using the following formula:

[0111] B = w|R (fi) -T|

[0112] When the desired reflectance at a given frequency is less than the target reflectance, the reflectance of each genetic individual at the target frequency is calculated using the following formula:

[0113]

[0114] When the desired reflectance at a given frequency point is greater than the target reflectance, the reflectance of each genetic individual at the target frequency point is calculated using the following formula:

[0115]

[0116] The fitness of each genetic individual is assessed using the following formula:

[0117]

[0118] Where F is the fitness value, R (fi) Let be the reflectance of the i-th individual, T be the target reflectance, and w be the weight of the corresponding frequency point.

[0119] In one embodiment of the present invention, the mutation rate in the genetic algorithm module 304 is specifically determined by the following formula:

[0120]

[0121] Among them, P m The variation rate of each bit of a binary data variable must be greater than 0 and less than or equal to 1. ddenoted as the number of bits for each data variable in the binary encoding of the absorbing material, k is the generation number of the population, and i is a user-defined coefficient that must be greater than 0.

[0122] It is understood that the structures illustrated in the embodiments of the present invention do not constitute a specific limitation on a design device for a microwave absorbing composite material based on a genetic algorithm. In other embodiments of the present invention, a design device for a microwave absorbing composite material based on a genetic algorithm may include more or fewer components than illustrated, or combine some components, or split some components, or arrange different components. The illustrated components may be implemented in hardware, software, or a combination of software and hardware.

[0123] The information interaction and execution process between the modules in the above-mentioned device are based on the same concept as the method embodiment of the present invention, and the specific details can be found in the description of the method embodiment of the present invention, and will not be repeated here.

[0124] This invention also provides a computing device, including a memory and a processor. The memory stores a computer program, and when the processor executes the computer program, it implements a design method for a microwave absorbing composite material based on a genetic algorithm according to any embodiment of this invention.

[0125] This invention also provides a computer-readable storage medium storing a computer program. When executed by a processor, the computer program causes the processor to perform a design method for a microwave absorbing composite material based on a genetic algorithm according to any embodiment of this invention.

[0126] Specifically, a system or apparatus equipped with a storage medium may be provided, on which software program code implementing the functions of any of the embodiments described above is stored, and the computer (or CPU or MPU) of the system or apparatus may read and execute the program code stored in the storage medium.

[0127] In this case, the program code read from the storage medium can itself implement the function of any of the above embodiments, and therefore the program code and the storage medium storing the program code constitute part of the present invention.

[0128] Examples of storage media used to provide program code include floppy disks, hard disks, magneto-optical disks, optical disks (such as CD-ROM, CD-R, CD-RW, DVD-ROM, DVD-RAM, DVD-RW, DVD+RW), magnetic tapes, non-volatile memory cards, and ROMs. Alternatively, program code can be downloaded from a server computer via a communication network.

[0129] Furthermore, it should be clear that not only can the program code read by the computer be executed, but also the operating system or other components operating on the computer can be instructed based on the program code to perform some or all of the actual operations, thereby achieving the function of any of the embodiments described above.

[0130] Furthermore, it is understood that the program code read from the storage medium is written to the memory set in the expansion board inserted into the computer or to the memory set in the expansion module connected to the computer. Then, based on the instructions of the program code, the CPU or other components installed on the expansion board or expansion module execute some and all of the actual operations, thereby realizing the function of any of the above embodiments.

[0131] It should be noted that, in this document, relational terms such as "first" and "second" are used only to distinguish one entity or operation from another, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Furthermore, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or apparatus. Without further limitations, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or apparatus that includes said element.

[0132] Those skilled in the art will understand that all or part of the steps of the above method embodiments can be implemented by hardware related to program instructions. The aforementioned program can be stored in a computer-readable storage medium. When the program is executed, it performs the steps of the above method embodiments. The aforementioned storage medium includes various media that can store program code, such as ROM, RAM, magnetic disk, or optical disk.

[0133] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, and not to limit them; although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features; and these modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. A design method for microwave absorbing composite materials based on genetic algorithms, characterized in that, include: Based on several different types of microwave absorbing materials that can be used to form microwave absorbing composite materials, several genetic individuals are randomly encoded, and these genetic individuals form an initial population. The reflectance of each genetic individual in the population is calculated using the reflectance formula of the absorbing material. The reflectivity formula of the absorbing material is: ， ， ， Where R is reflectivity, z i(n) Let z0 be the input impedance of the n-layer absorbing material, z0 be the vacuum impedance, and γ be the input impedance. n Let d be the complex propagation factor of the nth layer material. n Let μ be the thickness of the nth layer of material. n Let ε be the relative complex permeability of the nth layer material. n Relative permittivity, c is the speed of light in vacuum, j is the imaginary number, f is the frequency, n is the number of layers of the absorbing material, n≥1; The fitness of each genetic individual is assessed based on its reflectivity and the target reflectivity required to form the absorbing composite material. First, the reflectance of each genetic individual at the target frequency is calculated using the following formula; When the desired reflectance at a given frequency point equals the target reflectance, the reflectance at the target frequency point for each genetic individual is calculated using the following formula: When the desired reflectance at a given frequency is less than the target reflectance, the reflectance of each genetic individual at the target frequency is calculated using the following formula: When the desired reflectance at a given frequency point is greater than the target reflectance, the reflectance of each genetic individual at the target frequency point is calculated using the following formula: The fitness of each genetic individual is assessed using the following formula: Where F is the fitness value, R （fi） Let T be the reflectance of the i-th individual, T be the target reflectance, and w be the weight of the corresponding frequency point. The mutation rate of each genetic individual is calculated based on the number of bits of the data variable corresponding to the encoding and the current generation number of the population. The variability rate is determined using the following formula: Among them, P m The variation rate of each bit of a binary data variable must be greater than 0 and less than or equal to 1. d , where is the number of bits for each data variable in the binary encoding of the absorbing material, k is the number of generations in the population evolution, and i is a user-defined coefficient that must be greater than 0; The genetic individual with the lowest fitness is retained, and the other genetic individuals are crossovered and mutated using the mutation rate to obtain a new generation of population. The reflectivity of each genetic individual in the new generation of population is calculated until the termination condition of the genetic algorithm is met. The absorbing composite material is formed based on the genetic individuals in the new generation of population.

2. The design method for microwave absorbing composite materials based on genetic algorithm according to claim 1, characterized in that, The type is a combination of material type, material thickness, and the number of layers in the microwave absorbing composite material.

3. The design method for microwave absorbing composite materials based on genetic algorithm according to claim 2, characterized in that, After generating the initial population, the process further includes: The genetic individuals in the initial population are decoded to obtain the material type and thickness corresponding to each layer of material, and the corresponding dielectric constant and magnetic permeability are determined, so as to use the dielectric constant and magnetic permeability to perform the calculation of the reflectivity of each genetic individual in the population.

4. A design device for microwave absorbing composite materials based on a genetic algorithm, used to implement the method as described in any one of claims 1 to 3, characterized in that, include: The coding module is used to randomly encode several genetic individuals based on multiple different types of microwave absorbing materials that can be used to form microwave absorbing composite materials, and these genetic individuals form an initial population. The first calculation module calculates the reflectance of each genetic individual in the population; The evaluation module is used to assess the fitness of each genetic individual based on the reflectivity of each individual and the target reflectivity required to form the microwave absorbing composite material. The second calculation module is used to calculate the mutation rate of each genetic individual based on the number of bits of the data variable corresponding to the encoding and the current generation number of the population. The genetic algorithm module is used to retain the genetic individual with the lowest fitness and to perform crossover and mutation on other genetic individuals using the mutation rate to obtain a new generation of population. The module then jumps to calculate the reflectivity of each genetic individual in the new generation of population until the termination condition of the genetic algorithm is met, and forms a microwave absorbing composite material based on the genetic individuals in the current new generation of population.

5. A computing device comprising a memory and a processor, wherein the memory stores a computer program, and the processor, when executing the computer program, implements the method as described in any one of claims 1-3.

6. A computer-readable storage medium having a computer program stored thereon, which, when executed in a computer, causes the computer to perform the method of any one of claims 1-3.

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