Wave-absorbing material parameter determination method and device, computer device, and storage medium
By combining fuzzy mathematics and the hybrid optimization algorithm IGA-SM, the problem of balancing thickness, absorption characteristics and bandwidth of absorbing materials is solved, providing a comprehensive optimal solution for multilayer absorbing materials and realizing the optimized design of materials.
Patent Information
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- CRRC TANGSHAN CO LTD
- Filing Date
- 2026-01-12
- Publication Date
- 2026-05-29
AI Technical Summary
Existing microwave absorbing materials have shortcomings in balancing thickness, absorption characteristics, and bandwidth, which makes the materials prone to cracking or falling off, making it difficult to achieve ideal microwave absorption performance.
The sub-objective functions of each material parameter category are solved using fuzzy mathematics. A comprehensive fuzzy objective function is constructed using linear membership functions and optimized using the hybrid optimization algorithm IGA-SM to obtain the comprehensive optimal solution, thereby balancing thickness, wave absorption characteristics, and bandwidth.
The multi-layer structure optimization of the absorbing material was achieved, which improved the objectivity and rationality of the calculation results and output a comprehensive optimal solution that can simultaneously satisfy thickness, absorption properties and bandwidth, thus avoiding the subjectivity of weight coefficient selection.
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Figure CN122113572A_ABST
Abstract
Description
Technical Field
[0001] This application relates to the field of materials technology, and more specifically, to a method, apparatus, computer equipment, and storage medium for determining the parameters of microwave absorbing materials. Background Technology
[0002] Microwave-absorbing materials should possess excellent absorption performance, which is mainly reflected in two aspects: the absorption intensity of electromagnetic waves (represented by reflection loss RL) and the absorption frequency band. Based on the current level of research in microwave-absorbing materials, a certain thickness is often required to achieve ideal absorption performance. However, excessive thickness can easily lead to cracking or detachment. Therefore, the design of microwave-absorbing materials needs to balance absorption performance indicators with thickness and to achieve lightweight construction. From the perspective of absorption intensity, within a given frequency range, the greater the absorption and loss of electromagnetic waves by the microwave-absorbing material, the better, while the reflection coefficient should be as small as possible. From the perspective of the absorption frequency band, the wider the band below a certain threshold, the better. It is known that excessive material thickness will lead to cracking and detachment; therefore, the material thickness should be as small as possible.
[0003] Therefore, there is an urgent need for a microwave absorbing material that can balance thickness, microwave absorption properties, and bandwidth. Summary of the Invention
[0004] This application provides a method, apparatus, computer device, and storage medium for determining the parameters of microwave absorbing materials.
[0005] A first aspect of this application provides a method for determining the parameters of a microwave absorbing material, applied to a microwave absorbing material to be optimized. The method for determining the parameters of the microwave absorbing material includes: The sub-objective functions for each material parameter category in the microwave absorbing material to be optimized are solved to obtain the single-objective optimal solution for each sub-objective function; For each material parameter category, the corresponding linear membership function is determined based on the single-objective optimal solution; The comprehensive fuzzy objective function of the absorbing material to be optimized is constructed based on the intersection of all the linear membership functions. Taking the categories of material parameters in the microwave absorbing material to be optimized as the solution objective, the comprehensive fuzzy objective function is optimized and solved based on the hybrid optimization algorithm to obtain the comprehensive optimal solution; Each optimized value in the comprehensive optimal solution is determined as the optimization target value for the corresponding material parameter category.
[0006] In one optional embodiment of this application, the material parameter categories include at least one of: reflection coefficient, absorption bandwidth, and total material thickness; correspondingly, The sub-objective functions include at least one of the following: reflection coefficient sub-objective function, absorption bandwidth sub-objective function, and total material thickness sub-objective function.
[0007] In one optional embodiment of this application, solving the sub-objective functions for each material parameter category in the microwave absorbing material to be optimized, and obtaining the single-objective optimal solution for each sub-objective function, includes: Based on the material parameter categories, initial population size, and genetic probability of the absorbing material to be optimized, an initial population is randomly generated using coding. Based on the initial population, construct the sub-objective functions for each material parameter category; The IGA-SM algorithm is used to optimize and solve each of the sub-objective functions to obtain the single-objective optimal solution of each sub-objective function.
[0008] In an optional embodiment of this application, determining the corresponding linear membership function based on the single-objective optimal solution for each material parameter category includes: For each of the material parameter categories corresponding to the sub-objective functions, the single-objective optimal solution and the single-objective initial value of the sub-objective function are respectively used as the upper limit and lower limit values; For each of the sub-objective functions, the linear membership function value of the corresponding linear membership function is calculated based on the upper limit value and the lower limit value.
[0009] In one optional embodiment of this application, the step of optimizing and solving the comprehensive fuzzy objective function based on a hybrid optimization algorithm, using the category of each material parameter in the absorbing material to be optimized as the solution objective, to obtain the comprehensive optimal solution, includes: Taking the category of each material parameter in the absorbing material to be optimized as the solution objective of the comprehensive fuzzy objective function, the fuzzy fitness of all individuals in the current group is calculated for each group of material parameter categories. If the fitness of the current individual is greater than or equal to the preset fuzzy fitness threshold or the current iteration number is greater than or equal to the preset iteration number, then the iteration ends and the result is output. If the fitness of the current individual is less than the preset fuzzy fitness threshold and the current iteration number is less than the preset iteration number, then the next generation population is generated based on the IGA-SM algorithm, and the new fuzzy fitness of all individuals in the next generation population is recalculated until the new fuzzy fitness is greater than or equal to the preset fuzzy fitness threshold or the current iteration number is greater than or equal to the preset iteration number, then the iteration ends and the result is output. Output the comprehensive optimal solution based on the output results.
[0010] In an optional embodiment of this application, before recalculating the new fuzzy fitness of all individuals in the next generation population, the method further includes: The current population is subjected to tournament selection and elimination selection to obtain a new population after processing. Perform simplex operations on individuals in the new population after the treatment to obtain the next generation population after the treatment.
[0011] In an optional embodiment of this application, the step of performing tournament selection and elimination selection on the current population to obtain a new population after processing includes: For all individuals in the current population, perform tournament selection, crossover, and mutation to obtain the first population after the treatment. Calculate the nonlinear transformation factor for all individuals in the first population after the treatment; If the nonlinear conversion factor is greater than a preset random value, then all individuals in the processed first population are eliminated to obtain a new processed population containing a preset number of individuals.
[0012] A second aspect of this application provides a device for determining the parameters of a microwave absorbing material, applied to a microwave absorbing material to be optimized, the device comprising: The first solution module is used to solve the sub-objective functions of each material parameter category in the microwave absorbing material to be optimized, and to obtain the single-objective optimal solution of each sub-objective function; The first determining module is used to determine the corresponding linear membership function for each material parameter category based on the single-objective optimal solution. A construction module is used to construct a comprehensive fuzzy objective function for the microwave absorbing material to be optimized based on the intersection of all the linear membership functions. The second solution module is used to optimize and solve the comprehensive fuzzy objective function based on a hybrid optimization algorithm, taking the category of each material parameter in the microwave absorbing material to be optimized as the solution objective, to obtain the comprehensive optimal solution; The second determining module is used to determine each optimized value in the comprehensive optimal solution as the optimization target value of the corresponding material parameter category.
[0013] A third aspect of this application provides a computer device, including: a memory and a processor, wherein the memory stores a computer program, and the processor executes the computer program to implement the steps of any of the above methods.
[0014] A fourth aspect of this application provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the steps of the method described in any of the preceding claims.
[0015] This application employs fuzzy mathematics to solve the problem. First, it calculates the optimal single-objective solution obtained from optimizing each sub-objective function, and then calculates the linear membership function accordingly. Subsequently, it forms a comprehensive fuzzy objective function by finding the intersection of all linear membership functions, and finally obtains the comprehensive optimal solution. Fuzzification avoids the need for selecting weight coefficients, making the calculation results more objective and reasonable. Furthermore, it integrates the optimal single-objective solutions for various material parameter categories, outputting a comprehensive optimal solution that combines all material parameter categories. This provides a multilayer absorbing material that can balance various material parameter categories such as thickness, absorption characteristics, and bandwidth. Attached Figure Description
[0016] The accompanying drawings, which are included to provide a further understanding of this application and form part of this application, illustrate exemplary embodiments and are used to explain this application, but do not constitute an undue limitation of this application. In the drawings: Figure 1 This is a model diagram of a multilayer absorbing material in a method for determining absorbing material parameters provided in one embodiment of this application; Figure 2 A flowchart illustrating a method for determining microwave absorbing material parameters according to an embodiment of this application; Figure 3 An interactive diagram illustrating a method for determining microwave absorbing material parameters according to an embodiment of this application; Figure 4 A flowchart illustrating a method for determining microwave absorbing material parameters according to an embodiment of this application; Figure 5 A flowchart illustrating a method for determining microwave absorbing material parameters according to an embodiment of this application; Figure 6 A graph of the membership function in the method for determining the parameters of absorbing materials provided in one embodiment of this application; Figure 7 A flowchart illustrating a method for determining microwave absorbing material parameters according to an embodiment of this application; Figure 8 A flowchart illustrating a method for determining microwave absorbing material parameters according to an embodiment of this application; Figure 9 A flowchart illustrating a method for determining microwave absorbing material parameters according to an embodiment of this application; Figure 10 This is a schematic diagram of the structure of a device for determining the parameters of absorbing materials provided in one embodiment of this application; Figure 11 This is a schematic diagram of a computer device structure provided in one embodiment of this application. Detailed Implementation
[0017] In the process of realizing this application, the inventors discovered that there is an urgent need for a wave-absorbing material that can take into account thickness, wave absorption properties and bandwidth.
[0018] To address the aforementioned issues, this application provides a method, apparatus, computer device, and storage medium for determining the parameters of microwave absorbing materials.
[0019] The solutions in this application embodiment can be implemented using various computer languages, such as the object-oriented programming language Java and the interpreted scripting language JavaScript.
[0020] To make the technical solutions and advantages of the embodiments of this application clearer, the exemplary embodiments of this application will be described in further detail below with reference to the accompanying drawings. Obviously, the described embodiments are only a part of the embodiments of this application, and not an exhaustive list of all embodiments. It should be noted that, unless otherwise specified, the embodiments and features in the embodiments of this application can be combined with each other.
[0021] The method for determining the parameters of absorbing materials provided in this application is applied to an absorbing material to be optimized. This absorbing material is a multilayer material, where each layer is not entirely identical. This application embodiment uses... Figure 1 This solution will be explained using the example of five layers of absorbing material. Figure 1 The multilayer material is 200mm in length and width. The first layer is epoxy resin, 10mm thick. The second to fifth layers contain four different CF (carbon fiber) contents: 0.0125wt%, 0.025wt%, 0.075wt%, and 0.4wt%, respectively, for a total of five materials. Data for these materials are shown in Tables 1 and 2. Table 1 shows the real part of the dielectric constant for epoxy foam composites with different carbon fiber contents, and Table 2 shows the dielectric loss tangent for epoxy foam composites with different carbon fiber contents. Table 1
[0022] Table 2
[0023] Please see Figure 2 and Figure 3 The method for determining the parameters of the absorbing material provided in this application includes the following steps 201-205: Step 201: Solve the sub-objective functions for each material parameter category in the microwave absorbing material to be optimized, and obtain the single-objective optimal solution for each sub-objective function; The categories of material parameters include, but are not limited to, reflection coefficient, absorption bandwidth, and thickness. Specific material parameter categories can be flexibly set according to actual conditions, and this application does not impose specific limitations. Each sub-objective function corresponds one-to-one with a material parameter category; one material parameter category corresponds to one sub-objective function. .
[0024] Step 202: For each material parameter category, determine the corresponding linear membership function based on the single-objective optimal solution; The linear membership function is used to characterize the degree of satisfaction of the decision-maker with the values of each material parameter category. The value range is usually [0,1], where 1 represents complete satisfaction (optimal), and 0 represents complete unacceptability. The single-objective optimal solution refers to the optimal solution for each material parameter category. In this application, the form of the constructed linear membership function is not specifically limited and can be flexibly adjusted or set according to the actual situation. It is only necessary to construct the linear membership function corresponding to each material parameter category based on each single-objective optimal solution.
[0025] Step 203: Construct a comprehensive fuzzy objective function for the absorbing material to be optimized based on the intersection of all the linear membership functions; In fuzzy set theory, the operator representing "simultaneously satisfying all conditions" is called intersection. In this embodiment, the intersection operator can be "min" and "product". For a microwave absorbing material with n material parameter categories, each parameter category has a corresponding linear membership function, which represents the satisfaction level under that parameter value. Based on the linear membership functions of each material parameter category determined above, all linear membership functions are aggregated by constructing intersections to form a comprehensive fuzzy objective function used in this embodiment to comprehensively evaluate the performance of each material category of the microwave absorbing material. Through fuzzy logic, the satisfaction levels of multiple, and even potentially conflicting, performance indicators (such as microwave absorption performance, mechanical properties, weight, cost, etc.) are integrated into a single "overall satisfaction" indicator, i.e., the comprehensive fuzzy objective function.
[0026] Step 204: Taking the categories of material parameters in the absorbing material to be optimized as the solution objective, optimize and solve the comprehensive fuzzy objective function based on the hybrid optimization algorithm to obtain the comprehensive optimal solution; Using the comprehensive fuzzy objective function constructed in the above steps as the evaluation criterion, the hybrid optimization algorithm IGA-SM is used to search for the optimal solution with the "highest overall satisfaction" in multiple material parameter category spaces, thus obtaining the comprehensive optimal solution.
[0027] Step 205: Determine each optimized value in the comprehensive optimal solution as the optimization target value for the corresponding material parameter category.
[0028] This application employs fuzzy mathematics to solve the problem. First, it calculates the optimal single-objective solution obtained from optimizing each sub-objective function, and then calculates the linear membership function accordingly. Subsequently, it forms a comprehensive fuzzy objective function by finding the intersection of all linear membership functions, and finally obtains the comprehensive optimal solution. Fuzzification avoids the need for selecting weight coefficients, making the calculation results more objective and reasonable. Furthermore, it integrates the optimal single-objective solutions for various material parameter categories, outputting a comprehensive optimal solution that combines all material parameter categories. This provides a multilayer absorbing material that can balance various material parameter categories such as thickness, absorption characteristics, and bandwidth.
[0029] In one optional embodiment of this application, the material parameter categories include at least one of: reflection coefficient, absorption bandwidth, and total material thickness; correspondingly, the sub-objective functions include at least one of: reflection coefficient sub-objective function, absorption bandwidth sub-objective function, and total material thickness sub-objective function.
[0030] This application uses the reflection coefficient, absorption bandwidth, and thickness of a multilayer absorbing material as the optimization objectives of the optimal solution model. It calculates the optimal solutions for each objective and uses the linear membership function in fuzzy mathematics for fuzzy processing to construct a multi-objective fuzzy mathematical model. The embodiments of this application avoid the selection of weight coefficients through fuzzification, making the calculation results more objective and reasonable. Furthermore, it can synthesize the single-objective optimal solutions for each material parameter category, outputting a comprehensive optimal solution that integrates the reflection coefficient, absorption bandwidth, and total material thickness. This provides a absorbing material that can balance the reflection coefficient, absorption bandwidth, and total material thickness.
[0031] Please see Figure 4 In an optional embodiment of this application, before step 201, which involves solving the sub-objective functions for each material parameter category in the absorbing material to be optimized to obtain the single-objective optimal solution for each sub-objective function, the method further includes the following steps 401-403: Step 401: Based on the material parameter categories, the set initial population size, and the set inheritance probability in the absorbing material to be optimized, an initial population is randomly generated using encoding. Set material data for each material parameter category, including initial population size POP_SIZE, number of iterations N_GENERATIONS, genetic probability, thickness D_BOUND, and frequency F_BOUND; When the loop starts, t=0, and the encoding generates an initial population pop[0]={X(i)|i=1,2,……,POP_SIZE}, where pop represents the population, POP_SIZE is the population size, and X(i) represents the i-th individual in the population; Step 402: Construct the sub-objective function for each material parameter category based on the initial population; Step 403: Optimize and solve each of the sub-objective functions based on the IGA-SM algorithm to obtain the single-objective optimal solution of each sub-objective function.
[0032] For example, three sub-objective functions are established: reflection coefficient, bandwidth, and total material thickness. The single-objective optimal solution for each sub-objective function is then obtained using the Hybrid Genetic Algorithm-SM (IGA-SM). And record the initial values of each objective function. This facilitates the determination of the linear membership function.
[0033] Please see Figure 5 In an optional embodiment of this application, step 202 above, determining the corresponding linear membership function for each material parameter category based on the single-objective optimal solution, includes the following steps 501-502: Step 501: For each of the material parameter categories corresponding to the sub-objective functions, the single-objective optimal solution and the single-objective initial value of the sub-objective function are respectively used as the upper limit and lower limit values; Step 502: For each of the sub-objective functions, calculate the linear membership function value of the corresponding linear membership function based on the upper limit value and the lower limit value.
[0034] For each material parameter category, the single-objective optimal solution of the sub-objective function is... As Acceptable upper limit The initial value of the single target As Acceptable lower limit Calculate the linear membership function value corresponding to each sub-objective function. , , The mathematical expression for the linear membership function is: (1) In formula (1): Denotes the i-th sub-objective function; for The linear membership function; express The upper limit of the range, express The lower limit of the range, the lower limit is taken from each sub-objective function. The single-objective optimal solution obtained during single-objective optimization The upper limit is taken as the initial value of the single objective during the initial optimization. .
[0035] Figure 6 The curve is a linear membership function, from Figure 6 It can be clearly determined that, in a single objective corresponding to multiple objectives, if the sub-objective function... Less than the lower limit set by the sub-objective function If the membership degree of the single objective is 1, then the membership degree of the sub-objective function is 1. Greater than the upper limit set by the sub-objective function If the membership degree of the single objective is 0, then the membership degree of the sub-objective function is 0. Given the upper limit set by this sub-objective function and lower limit value If the membership degree of a single target is between 0 and 1, then the membership degree of that single target is between 0 and 1, exhibiting a linear membership relationship.
[0036] By calculating the linear membership function values (i.e., membership degrees), for example, the linear membership function values of each objective function can be obtained: , ,as well as Then, substitute each sample into the equation to calculate each sub-objective function. The single-objective optimal solution is given. The membership function has multiple intersections, and the maximum value among the intersection values is returned.
[0037] Correspondingly, in an optional embodiment of this application, step 203 above, constructing the comprehensive fuzzy objective function of the absorbing material to be optimized based on the intersection of all the linear membership functions, can be achieved in the following way: Employing maximum fuzzy satisfaction, this is achieved by applying multiple linear membership functions. Taking the intersection yields This is taken as the objective function of the comprehensive fuzzy objective function. The specific calculation expression is: (2) In formula (2), This represents the comprehensive fuzzy objective function, where max[.] is used to calculate and return the maximum value of the intersection. Denotes the i-th sub-objective function; for The linear membership function.
[0038] Taking the above three sub-objective functions as examples, formula (2) can be transformed into the following formula (3): (3) In formula (3), , ,as well as The linear membership function values of the sub-objective functions for reflection coefficient, absorption bandwidth, and total material thickness are respectively obtained.
[0039] The comprehensive fuzzy objective function The objective function is then used to simultaneously consider the optimal solution that minimizes reflection loss, maximizes bandwidth, and minimizes total material thickness, and then the solution is obtained.
[0040] As can be seen from equation (3), when using fuzzy mathematics to process multi-objective functions, the method first selects the sub-objective function corresponding to the minimum value among the linear membership function values of the three sub-optimal objectives as the optimization objective of the multi-objective optimal solution. Then, the hybrid single-objective genetic algorithm (IGA-SM) is used to optimize this objective, searching for a value that further reduces the objective function value. This operation is repeated until an optimal solution is found that allows all three objective function values to reach a satisfactory level simultaneously.
[0041] The method for determining the parameters of absorbing materials provided in this application addresses the problems of low solution accuracy and slow convergence speed in the process of optimizing the performance of absorbing materials using genetic algorithms. It helps to improve the solution accuracy and convergence speed of the optimization algorithm. At the same time, since the solution of the multi-objective model is closely related to the solution of the single-objective model, this application helps to balance the three sub-objective functions of RL, bandwidth and total material thickness, and obtain a relatively superior solution, making each objective function relatively satisfactory.
[0042] The hybrid optimization algorithm is used to solve the comprehensive fuzzy objective function of multiple objectives. The smaller the value of the comprehensive fuzzy objective function, the higher the fuzzy satisfaction of the model, which means that the comprehensive optimization solution of the multi-objective fuzzy mathematical model is better, so that each sub-objective function approaches its own optimal value at the same time. In this way, the optimal solution searched makes the values of the three sub-objective functions reach a satisfactory level at the same time.
[0043] Please see Figure 7 In an optional embodiment of this application, step 204 above, which involves optimizing and solving the comprehensive fuzzy objective function based on a hybrid optimization algorithm using the category of each material parameter in the absorbing material to be optimized as the solution objective, to obtain the comprehensive optimal solution, includes the following steps 701-704: Step 701: Taking the category of each material parameter in the absorbing material to be optimized as the solution objective of the comprehensive fuzzy objective function, calculate the fuzzy fitness of all individuals in the current group for each material parameter category group; Calculate the fitness value (fitness(X(i))) of all individuals X(i) in the current population pop[t]. The fitness value is used to characterize the “goodness” of a candidate solution (individual) relative to the problem to be solved. The higher the fitness of an individual, the higher the probability of it being selected to produce offspring, thereby passing on its “genes” (characteristics of the solution) to the next generation.
[0044] Step 702: If the fitness of the current individual is greater than or equal to the preset fuzzy fitness threshold or the current iteration number is greater than or equal to the preset iteration number, then end the iteration and output the result. Step 703: If the fitness of the current individual is less than the preset fuzzy fitness threshold and the current iteration number is less than the preset iteration number, then the next generation population is generated based on the IGA-SM algorithm, and the new fuzzy fitness of all individuals in the next generation population is recalculated until the new fuzzy fitness is greater than or equal to the preset fuzzy fitness threshold or the current iteration number is greater than or equal to the preset iteration number, then the iteration ends and the result is output. Step 704: Output the comprehensive optimal solution based on the output results.
[0045] The embodiments of this application strike a balance between computational cost and solution quality. For example, the calculation is completed when the fitness, which is used to characterize overall satisfaction, reaches 0.85 or above, or when the current iteration number reaches a preset number of iterations. This prevents infinite loops while meeting the requirements, thereby controlling the computation time and significantly reducing the number of actual simulations required while ensuring that a satisfactory solution is found.
[0046] Please see Figure 8 In an optional embodiment of this application, before recalculating the new fuzzy fitness of all individuals in the next generation population in step 504, the above-mentioned method for determining the parameters of the absorbing material further includes the following steps 801-802: Step 801: Perform tournament selection and elimination selection on the current population to obtain a new population after processing; Tournament selection is used to select superior individuals from the current population as parents, while elimination selection is used to further eliminate low-quality individuals and improve the quality of the population, that is, to select individuals with higher fitness and eliminate the individuals after elimination selection.
[0047] Step 802: Perform simplex operation on the individuals in the new population after processing to obtain the next generation population after processing.
[0048] Simplex operations are a local search technique derived from the Nelder-Mead simplex method, used for local exploration around an individual. For a D-dimensional optimization problem, the simplex is a geometric shape consisting of (D+1) points; operations include reflection, expansion, contraction, and shrinkage.
[0049] The embodiments of this application provide appropriate selection pressure through tournament selection to avoid premature convergence, eliminate selection to ensure the overall quality of the population, and simplex operation to enhance local search capabilities. By combining global search (selection) and local search (simplex), the optimization effect is better.
[0050] Please see Figure 9 In an optional embodiment of this application, step 801 above, which involves performing tournament selection and elimination selection on the current population to obtain a new population after processing, includes the following steps 901-903: Step 901: For all individuals in the current population, perform tournament selection, crossover, and mutation to obtain the first population after processing; The current population is subjected to tournament selection to obtain M(t). Then, crossover and mutation operations are performed on population M(t) with predetermined probabilities to obtain the processed first population pop[t+1]. Tournament selection is a very popular and efficient selection method in evolutionary algorithms. The principle of the tournament selection strategy is as follows: First, several individuals are randomly selected from the population. Then, the fitness of the individuals is compared, and the individual with the best fitness is selected to reproduce the next generation. This process is repeated iteratively until the number of selected individuals matches the size of the next generation population.
[0051] Step 902: Calculate the nonlinear transformation factor for all individuals in the first population after the treatment; Step 903: If the nonlinear conversion factor is greater than a preset random value, then all individuals in the processed first population are eliminated to obtain the processed new population containing a preset number of individuals.
[0052] Determine whether to perform the simplex operation: Calculate the nonlinear transformation factor Ps, generate a random number in the range of 0-1. If the value of the nonlinear transformation factor Ps is greater than the preset random value, then perform the simplex operation; otherwise, terminate the operation or regenerate a new population. Pop[t+1] is eliminated to select a certain number of individuals Q(t). Simplex operations are performed on these individuals Q(t) one by one to obtain the new population Q(t+1) after the above treatment. Then, population update operation is performed on the population pop[t+1].
[0053] In one optional embodiment of this application, the above-described Figure 1Taking the multilayer absorbing materials in Tables 1 and 2 as examples, the optimization frequency band is 0.75~12 GHz. The optimization objectives are: minimum reflection loss: RL≤-10 dB, single-layer design thickness [10-25] mm, total design thickness constraint: [50-125] mm, and bandwidth ≥5 GHz for RL≤-25 dB. The IGA-SM algorithm is used to perform single-objective structural optimization of the absorbing material. The algorithm parameters are set as follows: material type: T=5, population size: NP=180, maximum number of iterations: G=100, crossover probability: Pc=0.6, mutation probability: Pm=0.1. The optimization results obtained by optimizing each sub-objective function with reflection loss, bandwidth, and total thickness are shown in Table 3 below.
[0054] Table 3
[0055] The optimal values obtained from optimizing each of the above sub-objective functions are used as parameters of the linear membership function in the multi-objective fuzzy mathematical model. The multi-objective function is then converted into a fuzzy multi-objective function. The FI-GA-SM algorithm is used for iterative optimization, and the optimization results are shown in Table 4.
[0056] Table 4
[0057] It should be understood that although the steps in the flowchart are shown sequentially according to the arrows, these steps are not necessarily executed in the order indicated by the arrows. Unless explicitly stated herein, there is no strict order constraint on the execution of these steps, and they can be executed in other orders. Moreover, at least some steps in the diagram may include multiple sub-steps or multiple stages. These sub-steps or stages are not necessarily completed at the same time, but can be executed at different times. The execution order of these sub-steps or stages is not necessarily sequential, but can be performed alternately or in turn with other steps or at least some of the sub-steps or stages of other steps.
[0058] Please see Figure 10 One embodiment of this application provides a device 10001000 for determining the parameters of a microwave absorbing material, applied to a microwave absorbing material to be optimized. The device 1000 includes: The first solution module 1010 is used to solve the sub-objective functions of each material parameter category in the microwave absorbing material to be optimized, and to obtain the single-objective optimal solution of each sub-objective function; The first determining module 1020 is used to determine the corresponding linear membership function for each material parameter category based on the single-objective optimal solution. The construction module 1030 is used to construct a comprehensive fuzzy objective function for the absorbing material to be optimized based on the intersection of all the linear membership functions. The second solution module 1040 is used to optimize and solve the comprehensive fuzzy objective function based on a hybrid optimization algorithm, taking the category of each material parameter in the microwave absorbing material to be optimized as the solution objective, to obtain the comprehensive optimal solution; The second determining module 1050 is used to determine each optimized value in the comprehensive optimal solution as the optimization target value of the corresponding material parameter category.
[0059] In one optional embodiment of this application, the material parameter categories include at least one of: reflection coefficient, absorption bandwidth, and total material thickness; correspondingly, the sub-objective functions include at least one of: reflection coefficient sub-objective function, absorption bandwidth sub-objective function, and total material thickness sub-objective function.
[0060] In an optional embodiment of this application, the first solving module 1010 is specifically used to: encode and randomly generate an initial population according to the material parameter categories, the set initial population size, and the set genetic probability in the absorbing material to be optimized; construct the sub-objective functions for each material parameter category based on the initial population; optimize and solve each sub-objective function based on the IGA-SM algorithm to obtain the single-objective optimal solution of each sub-objective function.
[0061] In an optional embodiment of this application, the first determining module 1020 is specifically used to: for each of the material parameter categories corresponding to the sub-objective functions, take the single-objective optimal solution and the single-objective initial value of the sub-objective function as the upper limit value and the lower limit value, respectively; and for each of the sub-objective functions, calculate the linear membership function value of the corresponding linear membership function based on the upper limit value and the lower limit value.
[0062] In an optional embodiment of this application, the second solving module 1040 is specifically used to: take the category of each material parameter in the absorbing material to be optimized as the solution objective of the comprehensive fuzzy objective function; calculate the fuzzy fitness of all individuals in the current population for each material parameter category; if the fitness of the current individual is greater than or equal to a preset fuzzy fitness threshold or the current iteration number is greater than or equal to a preset iteration number, then end the iteration and output the result; if the fitness of the current individual is less than the preset fuzzy fitness threshold and the current iteration number is less than the preset iteration number, then generate the next generation population based on the IGA-SM algorithm, and recalculate the new fuzzy fitness of all individuals in the next generation population until the new fuzzy fitness is greater than or equal to the preset fuzzy fitness threshold or the current iteration number is greater than or equal to the preset iteration number, then end the iteration and output the result; output the comprehensive optimal solution based on the output result.
[0063] In an optional embodiment of this application, the second solving module 1040 is further configured to perform tournament selection and elimination selection on the current population to obtain a new population after processing; and to perform simplex operation on individuals in the new population after processing to obtain the next generation population after processing.
[0064] In an optional embodiment of this application, the second solving module 1040 is further configured to: perform tournament selection, crossover and mutation processing on all individuals in the current population to obtain a processed first population; calculate the nonlinear transformation factor of all individuals in the processed first population; if the nonlinear transformation factor is greater than a preset random value, then perform elimination selection on all individuals in the processed first population to obtain the processed new population containing a preset number of individuals.
[0065] Specific limitations regarding the aforementioned device 1000 for determining microwave absorbing material parameters can be found in the above description of the method for determining microwave absorbing material parameters, and will not be repeated here. Each module in the aforementioned device 1000 for determining microwave absorbing material parameters can be implemented entirely or partially through software, hardware, or a combination thereof. These modules can be embedded in or independent of the processor in a computer device in hardware form, or stored in the memory of a computer device in software form, so that the processor can call and execute the operations corresponding to each module.
[0066] In one embodiment, a computer device is provided, the internal structure of which can be as follows: Figure 11 As shown. The computer device includes a processor, memory, network interface, and database connected via a system bus. The processor provides computing and control capabilities. The memory includes a non-volatile storage medium and internal memory. The non-volatile storage medium stores an operating system, computer programs, and a database. The internal memory provides an environment for the operation of the operating system and computer programs in the non-volatile storage medium. The database stores data. The network interface communicates with external terminals via a network connection. When the processor executes the computer program, it implements the above-described method for determining the parameters of a microwave absorbing material. It includes: a memory and a processor; the memory stores a computer program; and the processor executes the computer program to implement any step in the above-described method for determining the parameters of a microwave absorbing material.
[0067] In one embodiment, a computer-readable storage medium is provided having a computer program stored thereon, which, when executed by a processor, can perform any of the steps in the above method for determining the parameters of the absorbing material.
[0068] Those skilled in the art will understand that embodiments of this application can be provided as methods, systems, or computer program products. Therefore, this application can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, this application can take the form of a computer program product embodied on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.
[0069] This application is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of this application. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, generate instructions for implementing the flowchart... Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.
[0070] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to function in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1 One or more processes and / or boxes Figure 1 The function specified in one or more boxes.
[0071] These computer program instructions may also be loaded onto a computer or other programmable data processing equipment to cause a series of operational steps to be performed on the computer or other programmable equipment to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable equipment for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 The steps of the function specified in one or more boxes.
[0072] Although preferred embodiments of this application have been described, those skilled in the art, upon learning the basic inventive concept, can make other changes and modifications to these embodiments. Therefore, the appended claims are intended to be interpreted as including the preferred embodiments as well as all changes and modifications falling within the scope of this application.
[0073] Obviously, those skilled in the art can make various modifications and variations to this application without departing from the spirit and scope of this application. Therefore, if such modifications and variations fall within the scope of the claims of this application and their equivalents, this application also intends to include such modifications and variations.
Claims
1. A method for determining the parameters of a microwave absorbing material, characterized in that, Applied to the microwave absorbing material to be optimized, the method for determining the parameters of the microwave absorbing material includes: The sub-objective functions for each material parameter category in the microwave absorbing material to be optimized are solved to obtain the single-objective optimal solution for each sub-objective function; For each material parameter category, the corresponding linear membership function is determined based on the single-objective optimal solution; The comprehensive fuzzy objective function of the absorbing material to be optimized is constructed based on the intersection of all the linear membership functions. Taking the categories of material parameters in the microwave absorbing material to be optimized as the solution objective, the comprehensive fuzzy objective function is optimized and solved based on the hybrid optimization algorithm to obtain the comprehensive optimal solution; Each optimized value in the comprehensive optimal solution is determined as the optimization target value for the corresponding material parameter category.
2. The method for determining the parameters of the absorbing material according to claim 1, characterized in that, The material parameter categories include at least one of: reflection coefficient, absorption bandwidth, and total material thickness; correspondingly, The sub-objective functions include at least one of the following: reflection coefficient sub-objective function, absorption bandwidth sub-objective function, and total material thickness sub-objective function.
3. The method for determining the parameters of the absorbing material according to claim 2, characterized in that, The process of solving the sub-objective functions for each material parameter category in the absorbing material to be optimized, and obtaining the single-objective optimal solution for each sub-objective function, includes: Based on the material parameter categories, initial population size, and genetic probability of the absorbing material to be optimized, an initial population is randomly generated using coding. Based on the initial population, construct the sub-objective functions for each material parameter category; The IGA-SM algorithm is used to optimize and solve each of the sub-objective functions to obtain the single-objective optimal solution of each sub-objective function.
4. The method for determining the parameters of the absorbing material according to claim 3, characterized in that, The determination of the corresponding linear membership function for each material parameter category based on the single-objective optimal solution includes: For each of the material parameter categories corresponding to the sub-objective functions, the single-objective optimal solution and the single-objective initial value of the sub-objective function are respectively used as the upper limit and lower limit values; For each of the sub-objective functions, the linear membership function value of the corresponding linear membership function is calculated based on the upper limit value and the lower limit value.
5. The method for determining the parameters of the absorbing material according to claim 4, characterized in that, The process involves using the categories of material parameters in the absorbing material to be optimized as the solution objective, optimizing and solving the comprehensive fuzzy objective function based on a hybrid optimization algorithm to obtain the comprehensive optimal solution, including: Taking the category of each material parameter in the absorbing material to be optimized as the solution objective of the comprehensive fuzzy objective function, the fuzzy fitness of all individuals in the current group is calculated for each group of material parameter categories. If the fitness of the current individual is greater than or equal to the preset fuzzy fitness threshold or the current iteration number is greater than or equal to the preset iteration number, then the iteration ends and the result is output. If the fitness of the current individual is less than the preset fuzzy fitness threshold and the current iteration number is less than the preset iteration number, then the next generation population is generated based on the IGA-SM algorithm, and the new fuzzy fitness of all individuals in the next generation population is recalculated until the new fuzzy fitness is greater than or equal to the preset fuzzy fitness threshold or the current iteration number is greater than or equal to the preset iteration number, then the iteration ends and the result is output. Output the comprehensive optimal solution based on the output results.
6. The method for determining the parameters of the absorbing material according to claim 3, characterized in that, Before recalculating the new fuzzy fitness of all individuals in the next generation population, the method further includes: The current population is subjected to tournament selection and elimination selection to obtain a new population after processing. Perform simplex operations on individuals in the new population after the treatment to obtain the next generation population after the treatment.
7. The method for determining the parameters of the absorbing material according to claim 6, characterized in that, The process of performing tournament selection and elimination selection on the current population to obtain a new population after processing includes: For all individuals in the current population, perform tournament selection, crossover, and mutation to obtain the first population after the treatment. Calculate the nonlinear transformation factor for all individuals in the first population after the treatment; If the nonlinear conversion factor is greater than a preset random value, then all individuals in the processed first population are eliminated to obtain a new processed population containing a preset number of individuals.
8. A device for determining the parameters of a microwave absorbing material, characterized in that, The device for determining the parameters of a microwave absorbing material, which is applied to a material to be optimized, includes: The first solution module is used to solve the sub-objective functions of each material parameter category in the microwave absorbing material to be optimized, and to obtain the single-objective optimal solution of each sub-objective function; The first determining module is used to determine the corresponding linear membership function for each material parameter category based on the single-objective optimal solution. A construction module is used to construct a comprehensive fuzzy objective function for the microwave absorbing material to be optimized based on the intersection of all the linear membership functions. The second solution module is used to optimize and solve the comprehensive fuzzy objective function based on a hybrid optimization algorithm, taking the category of each material parameter in the absorbing material to be optimized as the solution objective, to obtain the comprehensive optimal solution; The second determining module is used to determine each optimized value in the comprehensive optimal solution as the optimization target value of the corresponding material parameter category.
9. A computer device, comprising: A memory and a processor, the memory storing a computer program, characterized in that the processor, when executing the computer program, implements the steps of the method according to any one of claims 1 to 7.
10. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by a processor, it implements the steps of the method according to any one of claims 1 to 7.