Band-pass filter design method, system, and storage medium

Abstract

The application provides a band-pass filter design method, system and storage medium, the method comprising: randomly generating a plurality of film system parameter matrices, and taking the plurality of film system parameter matrices as an initial population, wherein the film system parameter matrix is constructed by a material type and a thickness corresponding to a plurality of film layers of a band-pass filter; and iteratively optimizing the initial population based on an unbalanced multi-modal multi-objective optimization algorithm of two-stage evolution, wherein a band-pass curve corresponding to the film system parameter matrix is obtained, a band-pass performance corresponding to the band-pass curve is obtained according to the band-pass curve, the population is iteratively optimized according to the band-pass performance, until a preset iteration number is satisfied, and a plurality of film system parameter matrix optimal solutions are output; one of the plurality of film system parameter matrix optimal solutions is selected at random, the material type and the thickness corresponding to the plurality of film layers are determined, and the band-pass filter is designed. The application improves the design efficiency of the band-pass filter.

Description

TECHNICAL FIELD

[0001] The application belongs to the technical field of optical optimization, and particularly relates to a band-pass filter design method and system and a storage medium. BACKGROUND

[0002] Band-pass filters are widely used in various optical scenes, and in the working wavelength band, the characteristics thereof are that only light of a specified wavelength band is allowed to be transmitted, and light of other wavelength bands is cut off.

[0003] The preparation of a band-pass filter can be achieved by using different materials such as various dielectric and metal materials with different film thicknesses for multi-layer coating on a transparent substrate. The performance indicators of the band-pass filter, such as the central wavelength, the half-width, the transmittance and the optical density, are determined by the materials and thicknesses of the layers in the preparation process. When the film system parameters are determined, that is, the materials and thicknesses of the layers are determined, the band-pass curve corresponding to the film system parameters can be calculated according to the finite-difference time-domain (FDTD) method. Therefore, when a band-pass filter is prepared by using multi-layer coating, the film system parameters need to be designed according to the four band-pass indicators that are expected to be met, that is, the central wavelength, the half-width, the transmittance and the optical density, so that the band-pass curve corresponding to the film system parameters meets the required band-pass indicators.

[0004] It can be seen that the design of the film system parameters is essentially a multi-objective optimization problem, and since the mapping relationship between the film system parameters and the band-pass curve is highly complex, the problem has a multi-modal nature and is a multi-modal multi-objective optimization problem. At the same time, since there may be specific requirements for the materials used for specific layers in practice, the multi-modal problem has an imbalance. Therefore, the design of the film system parameters is a complex imbalanced multi-modal multi-objective optimization problem.

[0005] In the implementation process, the design of the film system parameters belongs to an NP problem, that is, the exact solution of the problem cannot be found by using existing mathematical methods, and the commonly used solution is to find an acceptable optimal solution by using an intelligent optimization method. However, there is still a large room for improvement in the performance of the existing optimization methods. SUMMARY

[0006] The application solves the technical problem that it is difficult to find an optimal solution for the multi-objective optimization of the existing film system parameters.

[0007] To solve the above technical problems, in a first aspect, the application provides a band-pass filter design method, comprising the following steps:

[0008] S101, a plurality of film system parameter matrices are randomly generated, and the plurality of film system parameter matrices are used as an initial population, wherein the film system parameter matrix is constructed by the material types and thicknesses corresponding to a plurality of film layers of a band-pass filter;

[0009] S102, iteratively optimize the initial population based on the two-stage evolutionary unbalanced multi-modal multi-objective optimization algorithm, wherein a band-pass curve corresponding to the membrane system parameter matrix is obtained, and a band-pass performance corresponding to the band-pass curve is obtained, the population is iteratively optimized according to the band-pass performance, until a preset iteration number is satisfied, and a plurality of membrane system parameter matrix optimal solutions are output;

[0010] S103, randomly selecting one from the plurality of membrane system parameter matrix optimal solutions, determining the material type and thickness corresponding to the plurality of film layers, and designing the band-pass filter.

[0011] Further, in step S102, the two-stage evolutionary unbalanced multi-modal multi-objective optimization algorithm includes the following steps:

[0012] S201, initialize the iteration number to 0;

[0013] S202, when the iteration number is less than the product of the preset stage boundary point and the preset iteration number, enter the first iteration stage and execute step S203; otherwise, enter the second iteration stage and execute step S206:

[0014] S203, generate a mating pool according to the different individual corresponding to the Pareto dominance level in the current population, and generate a child population by simulating binary crossover and polynomial mutation, wherein the Pareto dominance level is obtained by comparing the band-pass performance corresponding to the current individual to the band-pass performance corresponding to other individuals according to the Pareto dominance relationship;

[0015] S204, obtain the fitness of each individual in the first type set composed of the parent and child populations of the current iteration stage, and process the convergence of the individuals in the first type set according to a preset convergence strategy, wherein the fitness represents the degree to which an individual may be retained in the population iteration process, and the convergence represents the degree to which an individual approaches an optimal solution;

[0016] S205, select individuals from the first type set to form a next generation population according to the fitness, increment the iteration number by 1, and return to step S202;

[0017] S206, cluster the current population by a clustering algorithm to obtain a plurality of sub-populations, each of which corresponds to an optimal solution region;

[0018] S207, in each of the optimal solution regions, generate a mating pool according to the different individual corresponding to the Pareto dominance level, and generate a child population by simulating binary crossover and polynomial mutation;

[0019] S208, merging each of the sub-population with its corresponding offspring population to form a second type of set, respectively counting the global non-dominated individuals and dominated individuals in different second type of sets according to the Pareto dominance level, obtaining a global non-dominated individual set and a dominated individual set;

[0020] S209, if the size of the global non-dominated individual set is smaller than the size of the initial population, moving the current non-dominated individuals in the dominated individual set into the non-dominated individual set until the size of the non-dominated individual set is the same as the initial population;

[0021] S210, judging whether the number of the global non-dominated individuals in the sub-population is less than a preset convergence threshold value:

[0022] If yes, selecting individuals from the non-dominated individual set based on population diversity to form a next generation population;

[0023] If no, selecting individuals from the non-dominated individual set based on the convergence and the fitness to form a next generation population;

[0024] S211, increasing the iteration number by 1, if the iteration number meets the preset iteration number, outputting the optimal individual in each of the optimal solution region to obtain a plurality of optimal solution of the membrane system parameter matrix; otherwise, returning to step S206.

[0025] Further, the population of the current iteration stage is defined as wherein the individual is In step S204, the preset convergence strategy processes the convergence of the individual in the population The process satisfies the following relationship:

[0026] ;

[0027] wherein, represents the convergence of the individual in the population , and the minimum value is 0, represents the number of times that the individual is dominated by other individuals in the population , represents the average number of times that each individual in the population is dominated by other individuals, and respectively represent the current iteration number and the preset iteration number, represents the preset stage boundary point, which is a parameter between 0 and 1, and:

[0028] ;

[0029] ;

[0030] for determining whether an individual dominates an individual , when an individual Pareto dominates an individual .

[0031] Further, in step S210, the preset convergence threshold is specifically:

[0032] A product of a preset sub-population convergence state judgment threshold τ and an average value of the number of the global non-dominated individuals in each of the sub-populations.

[0033] In a second aspect, the present application further provides a band-pass filter design system, comprising:

[0034] An initial generation module, configured to randomly generate a plurality of film system parameter matrices, and take the plurality of film system parameter matrices as an initial population, wherein the film system parameter matrix is constructed by a material type and a thickness corresponding to a plurality of film layers of a band-pass filter;

[0035] An optimization module, configured to perform iterative optimization on the initial population based on an unbalanced multi-modal multi-objective optimization algorithm of two-stage evolution, wherein a band-pass curve corresponding to the film system parameter matrix is obtained, and a band-pass performance corresponding to the band-pass curve is obtained according to the band-pass curve, and iterative optimization of the population is performed according to the band-pass performance until a preset iteration number is met, and a plurality of film system parameter matrix optimal solutions are output;

[0036] A design module, configured to randomly select one from the plurality of film system parameter matrix optimal solutions, determine a material type and a thickness corresponding to a plurality of film layers thereof, and design the band-pass filter.

[0037] Further, the unbalanced multi-modal multi-objective optimization algorithm of two-stage evolution performed by the optimization module comprises the following steps:

[0038] S201, initialize an iteration number as 0;

[0039] S202, when the iteration number is less than a product of a preset stage boundary point and the preset iteration number, enter a first iteration stage and perform step S203; otherwise, enter a second iteration stage and perform step S206:

[0040] S203, generating a mating pool according to the Pareto dominance levels of different individuals in the current population, and generating a child population by simulated binary crossover and polynomial mutation, wherein the Pareto dominance levels are obtained by comparing the band-pass performance of the current individual with the band-pass performance of other individuals according to a Pareto dominance relationship;

[0041] S204, obtaining the fitness of each individual in a first type of set composed of the parent and child populations of the current iteration stage, and processing the convergence of individuals in the first type of set according to a preset convergence strategy, wherein the fitness represents the degree of possibility that an individual is retained in the population iteration process, and the convergence represents the degree of proximity of an individual to an optimal solution;

[0042] S205, selecting individuals from the first type of set to form a next generation population according to the fitness, increasing the iteration number by 1, and returning to step S202;

[0043] S206, clustering the current population by a clustering algorithm to obtain a plurality of sub-populations, each of which corresponds to an optimal solution region;

[0044] S207, in each of the optimal solution regions, generating a mating pool according to the Pareto dominance levels of different individuals, and generating a child population by simulated binary crossover and polynomial mutation;

[0045] S208, merging each of the sub-populations and the corresponding child population to form a second type of set, respectively counting the global non-dominated individuals and dominated individuals in different second type of sets in terms of the Pareto dominance levels, and obtaining a global non-dominated individual set and a dominated individual set;

[0046] S209, if the size of the global non-dominated individual set is smaller than the size of the initial population, moving the current non-dominated individual in the dominated individual set to the non-dominated individual set until the size of the non-dominated individual set is the same as that of the initial population;

[0047] S210, determining whether the number of global non-dominated individuals in the sub-population is less than a preset convergence threshold:

[0048] If yes, selecting individuals from the non-dominated individual set based on population diversity to form a next generation population;

[0049] If no, selecting individuals from the non-dominated individual set based on the convergence and the fitness to form a next generation population;

[0050] S211, increasing the iteration number by 1, and outputting the optimal individual in each of the optimal solution regions to obtain a plurality of optimal solutions of the membrane system parameter matrix if the iteration number meets the preset iteration number; otherwise, returning to step S206.

[0051] Further, the population of the current iteration stage is defined as , wherein the individual is In step S204, the preset convergence strategy processes the convergence of the individual In the population The process satisfies the following relationship:

[0052] ;

[0053] Wherein, represents the convergence of the individual in the population , and the minimum value is 0, represents the number of times the individual is dominated by other individuals in the population , and represents the average number of times each individual in the population is dominated by other individuals, and respectively represent the current iteration number and the preset iteration number, represents the preset stage boundary point, which is a parameter with a value between 0 and 1, and:

[0054] ;

[0055] ;

[0056] For judging whether the individual dominates the individual , when it represents that the individual Pareto dominates the individual .

[0057] Further, in step S210, the preset convergence threshold is specifically:

[0058] The product of the preset sub-population convergence state judgment threshold τ and the average number of global non-dominated individuals in each of the sub-populations.

[0059] In a third aspect, the present application also provides a computer device, comprising a memory, a processor, and a band-pass filter design program stored on the memory and executable on the processor, wherein the processor implements the steps of the band-pass filter design method according to any one of the above embodiments when executing the band-pass filter design program.

[0060] In a fourth aspect, the present application further provides a storage medium, wherein the storage medium stores a band-pass filter design program, and the band-pass filter design program, when executed by a processor, implements the steps of the band-pass filter design method according to any one of the above embodiments.

[0061] The band-pass filter design method based on the two-stage evolution unbalanced multi-modal multi-objective optimization method has the advantages that the film system parameter matrix of the band-pass filter is taken as the optimization object, the problem that the difficult-to-search region is ignored or underdeveloped in the unbalanced multi-modal multi-objective optimization is effectively solved through the phased population convergence adjustment strategy and the environmental selection mode, the optimal solution of multiple regions is quickly obtained, and the band-pass filter design efficiency is improved. BRIEF DESCRIPTION OF DRAWINGS

[0062] The above and other aspects of the present application will become more apparent and more readily appreciated from the following detailed description of the application, taken in conjunction with the accompanying drawings in which:

[0063] Figure 1 is a step flow diagram of the band-pass filter design method provided by the present application, and the band-pass filter design method comprises the following steps:

[0064] Figure 2 is a structural diagram of the band-pass filter design system provided by the embodiment of the present application;

[0065] Figure 3 is a structural diagram of the computer device provided by the embodiment of the present application. DETAILED DESCRIPTION

[0066] In order to make the objectives, technical solutions and advantages of the present application clearer and more apparent, the present application will be further described in detail below in conjunction with the drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present application and do not limit the present application.

[0067] Embodiment One

[0068] Reference should be made to Figure 1 , Figure 1 is a step flow diagram of the band-pass filter design method provided by the present application, and the band-pass filter design method comprises the following steps:

[0069] S101, a plurality of film system parameter matrices are randomly generated, and the plurality of film system parameter matrices are taken as initial populations, wherein the film system parameter matrix is constructed by the material type and thickness corresponding to a plurality of film layers of the band-pass filter.

[0070] In the embodiment of the present application, the construction process of the film system parameter matrix is to take the film layer material type and thickness as the elements of the matrix, for example, define a film system parameter matrix as C:

[0071] ;

[0072] Wherein, each row of C corresponds to a film layer, represents the refractive index of the material used by the first film layer, represents the extinction coefficient of the material used by the first film layer, represents the thickness of the first film layer; represents the refractive index of the material used by the mth film layer, represents the extinction coefficient of the material used by the mth film layer, represents the thickness of the mth film layer.

[0073] The method of the embodiment of the present application is to solve the optimal solution that may exist for the combination of film layer materials and thicknesses, so the matrix can be planned and designed for different types of film layer materials, including but not limited to various metal materials such as aluminum (Al), silver (Ag), gold (Au), and germanium (Ge), various oxide materials such as titanium dioxide , silicon dioxide , aluminum oxide , zirconium oxide , yttrium sesquioxide , hafnium oxide , various fluoride materials such as magnesium fluoride , calcium fluoride , various sulfide / selenide materials such as zinc sulfide , zinc selenide , and various special functional materials such as silicon nitride , etc.

[0074] S102, iteratively optimize the initial population based on the two-stage evolutionary unbalanced multi-modal multi-objective optimization algorithm, wherein the bandpass curve corresponding to the film system parameter matrix is obtained, and the bandpass performance embodied by the bandpass curve is obtained according to the bandpass curve, and the population is iteratively optimized according to the bandpass performance, until the preset iteration number is met, and a plurality of film system parameter matrix optimal solutions are output.

[0075] The method for obtaining the bandpass curve can be based on the finite difference time domain method as described in the background art. The two-stage evolutionary unbalanced multi-modal multi-objective optimization algorithm is a multi-objective optimization solving method proposed in the embodiment of the present application.

[0076] Specifically, in step S102, the two-stage evolutionary-based imbalance multi-modal multi-objective optimization algorithm comprises the following steps.

[0077] S201, initialize the iteration number as 0;

[0078] S202, when the iteration number is less than the product of the preset stage boundary point and the preset iteration number, enter the first iteration stage and execute step S203; otherwise, enter the second iteration stage and execute step S206:

[0079] S203, generate a mating pool according to the different individual corresponding to the Pareto dominance level in the current population, and generate a child population by simulating binary crossover and polynomial mutation, wherein the Pareto dominance level is obtained by comparing the band-pass performance corresponding to the current individual to the band-pass performance corresponding to other individuals according to the Pareto dominance relationship;

[0080] S204, obtain the fitness of each individual in the first type set composed of the parent and child populations of the current iteration stage, and process the convergence of the individuals in the first type set according to a preset convergence strategy, wherein the fitness represents the degree to which an individual can be retained in the population iteration process, and the convergence represents the degree to which an individual approaches an optimal solution;

[0081] S205, select individuals from the first type set to form a next generation population according to the fitness, increment the iteration number by 1, and return to step S202;

[0082] S206, cluster the current population by a clustering algorithm to obtain a plurality of sub-populations, and each sub-population corresponds to an optimal solution region;

[0083] S207, in each of the optimal solution regions, generate a mating pool according to the different individual corresponding to the Pareto dominance level, and generate a child population by simulating binary crossover and polynomial mutation;

[0084] S208, combine each of the sub-populations and the corresponding child population to form a second type set, respectively count the global non-dominated individuals and dominated individuals in different second type sets in terms of the Pareto dominance level, and obtain a global non-dominated individual set and a dominated individual set;

[0085] S209, if the size of the global non-dominated individual set is less than the size of the initial population, move the current non-dominated individual in the dominated individual set to the non-dominated individual set until the size of the non-dominated individual set is the same as the size of the initial population;

[0086] S210, determine whether the number of global non-dominated individuals in the sub-population is less than a preset convergence threshold:

[0087] If yes, selecting individuals from the non-dominated individual set based on population diversity to form the next generation population;

[0088] If no, selecting individuals from the non-dominated individual set based on the convergence and the fitness to form the next generation population;

[0089] S211, incrementing the iteration number by 1, if the iteration number meets the preset iteration number, outputting the optimal individual in each current optimal solution region to obtain a plurality of membrane system parameter matrix optimal solutions; otherwise, returning to step S206.

[0090] The unbalanced multi-modal multi-objective optimization algorithm based on two-stage evolution provided in the embodiments of the application is a multi-objective optimization method, which involves the concepts of Pareto dominance level and Pareto dominance relationship.

[0091] The Pareto dominance relationship is a core rule for judging the advantages and disadvantages of two individuals in multi-objective optimization. In simple terms, for two individuals p and q:

[0092] If the performance of q on all objectives is not worse than that of p (i.e., the objective values are equal or better), and the performance of q on at least one objective is significantly better than that of p, it is said that "q Pareto dominates p", denoted as .

[0093] On the contrary, if neither of them satisfies the above condition (i.e., each has advantages and disadvantages), they are in a "non-dominated" relationship.

[0094] The Pareto dominance level is a hierarchical division of the advantages and disadvantages of all individuals in the population based on the above dominance relationship, and the core is:

[0095] The first level: individuals in the population that are not dominated by any other individual (i.e., the first echelon of global optimum).

[0096] The second level: individuals that are only dominated by the first level individuals and are not dominated by other level individuals (the second echelon). By analogy, the smaller the level number, the better the overall performance of the individual in the population.

[0097] In the embodiments of the application, the dominance level is used to generate a mating pool (preferentially selecting individuals with high level as parents), which is an important basis for guiding the evolution of the population to a better direction.

[0098] In the embodiments of the application, the population of the current iteration stage is defined as , and the individual in it is , and in step S204, the preset convergence strategy processes the convergence of the individual in the population satisfies the following relationship:

[0099] ;

[0100] denotes the individual in the population , the minimum value of which is 0, denotes the individual dominated by other individuals in the population , denotes the average number of times each individual in the population is dominated by other individuals, and respectively denote the current iteration number and the preset iteration number, denotes the preset phase boundary point, which is a parameter with a value between 0 and 1, and:

[0101] ;

[0102] ;

[0103] for judging whether the individual dominates the individual , denotes that the individual Pareto dominates the individual .

[0104] Step S205 selects individuals from the first type of set to form a next generation population according to the fitness. For the individual in the population , , the greater the value of , the worse the current convergence degree of the individual , and in the case of the same other indicators such as diversity, the greater the possibility that the individual is deleted in the environmental selection process, thereby causing the possibility that the area where the individual is located is further explored in subsequent evolution to be smaller. However, the difficult-to-search area of multi-objective optimization is an area where the convergence degree of the individual is relatively poor, and if the convergence of the individual is directly measured by

[0105] , it is not conducive to sufficient exploration of the difficult-to-search area. Therefore, based on the convergence calculation manner in the above embodiment, the embodiment of the present application takes as the basis, subtracts several times the average number of dominations of the current population , and adopts a truncation mechanismBy limiting the lower limit of the convergence value, the individual convergence value in the easy-to-search area with good convergence degree can be reduced to a limited extent, and the individual convergence value in the difficult-to-search area can be reduced to a greater extent, thereby reducing the overall difference in the individual convergence values ​​in different areas and reducing the impact of convergence on the optimization process;

[0106] Furthermore, as the optimization process progresses, the convergence of individuals in each region will generally get better and better, and the number of times each individual is dominated by other individuals will become less and less. In order to avoid the influence of excessive force on the convergence value, which will cause the convergence to lose its necessary influence on the optimization process, adaptive weights are used in the calculation process of convergence. Gradually reduce the average number of dominant species in the current population that is reduced in each round of evolution in the first iteration Specifically, in the convergence calculation of the initial iteration, the maximum reduction for each individual is twice , and then based on the number of iterations The growth is gradually decreasing When entering the last iteration of the first stage, the reduction factor is reduced to one. In addition, In the expression The reason for lowering the individual convergence value for the benchmark is that This effectively characterizes the overall convergence state of the current population, providing a reasonable basis for the reduction range of convergence values ​​in each iteration. This design avoids excessive reductions that cause the convergence of most individuals to reach zero, thus losing the significance of individual convergence differences in the optimization process. It also avoids insufficient reductions that cause the convergence of most individuals to fall below zero, resulting in only the same degree of reduction, which fails to differentiate the individual convergence values ​​in different regions.

[0107] In general, in the first iterative stage, the number of individuals in the easy-to-search area of ​​the final population will still be relatively large, and the convergence will be relatively better, but the diversity will be relatively poor; the number of individuals in the difficult-to-search area will be relatively small, and the convergence will be relatively poor, but the diversity will be relatively good.

[0108] In the second iteration stage, a plurality of optimal solution regions are first distinguished by a clustering algorithm. Based on the number of current global non-dominated individuals contained in each sub-population corresponding to each optimal solution region, different environmental selection operators (S210) are used. If the number of current global non-dominated individuals contained in one or more sub-populations is less than a preset convergence threshold, it indicates that the convergence degree of the regions corresponding to these sub-populations is significantly poorer than that of other regions, and the development of these regions needs to be increased to achieve balanced development of each optimal solution. On the contrary, if the number of current global non-dominated individuals contained in all sub-populations is greater than the preset convergence threshold, it indicates that the convergence degree of the regions corresponding to each sub-population is within a relatively small range, and considering the inherent randomness of the evolutionary algorithm, the development of some regions does not need to be additionally focused on to achieve balanced development of each optimal solution.

[0109] Specifically, in step S210, the preset convergence threshold is specifically:

[0110] The product of a preset sub-population convergence state judgment threshold τ and the average number of global non-dominated individuals in each of the sub-populations.

[0111] In step S210, when selecting individuals from the set of non-dominated individuals obtained based on the dominance relationship to generate the next generation population, selection is only made from the perspective of diversity, without considering convergence, so as to preferentially retain individuals in a difficult-to-search region which has relatively good diversity and relatively poor convergence, that is, to increase the development of the difficult-to-search region, thereby facilitating balanced development of the optimal solution region.

[0112] When there is no region that needs to be additionally developed in the optimization process, a fitness-based environmental selection operator is used. In the implementation process, fitness is defined in a conventional manner as the sum of convergence and diversity, and convergence is directly represented by the number of times that the current individual is dominated by other individuals in the population. At this time, when selecting individuals from the set of non-dominated individuals to generate the next generation population, convergence and diversity are considered simultaneously, and balanced development of the optimal solution region is achieved.

[0113] S103, selecting one of the plurality of film system parameter matrix optimal solutions, determining the material type and thickness of the plurality of film layers corresponding to the selected film system parameter matrix optimal solution, and designing the band-pass filter.

[0114] It can be understood that, since a plurality of optimal solution regions are searched and optimized at the same time, the number of optimal solutions obtained finally is also the same as the number of regions obtained by clustering. For the result of multi-objective optimization, these optimal solutions are all available and meet the original set optimization target (band-pass performance). For the design process of the band-pass filter, only one of the plurality of film system parameter matrix optimal solutions needs to be selected, and the band-pass filter is designed and prepared according to the corresponding film layer, material and thickness.

[0115] The application has the beneficial effects that a two-stage evolution-based unbalanced multi-modal multi-objective optimization method band-pass filter design method is provided, the film system parameter matrix of the band-pass filter is taken as the optimization object, the problem that the difficult search area is ignored or insufficiently developed in the unbalanced multi-modal multi-objective optimization is effectively solved through the phased population convergence adjustment strategy and the environment selection mode, the optimal solution of multiple areas is quickly obtained, and the band-pass filter design efficiency is improved.

[0116] Embodiment two

[0117] The application also provides a band-pass filter design system 300, please refer to Figure 2 , Figure 2 The application provides a band-pass filter design system 300, please refer to

[0118] The initial generation module 301 is used for randomly generating a plurality of film system parameter matrices, and taking the plurality of film system parameter matrices as initial populations, wherein the film system parameter matrix is constructed by the material type and thickness corresponding to a plurality of film layers of the band-pass filter;

[0119] The optimization module 302 is used for iteratively optimizing the initial population based on the two-stage evolution-based unbalanced multi-modal multi-objective optimization algorithm, wherein the band-pass curve corresponding to the film system parameter matrix is obtained, the band-pass performance embodied by the band-pass curve is obtained according to the band-pass curve, the population is iteratively optimized according to the band-pass performance, until the preset iteration number is met, and a plurality of film system parameter matrix optimal solutions are output;

[0120] The design module 303 is used for selecting one of the plurality of film system parameter matrix optimal solutions, determining the material type and thickness corresponding to a plurality of film layers, and designing the band-pass filter.

[0121] The band-pass filter design system 300 can realize the steps in the band-pass filter design method in the above embodiment, and can realize the same technical effects, and the description in the above embodiment is referred to, and details are not repeated here.

[0122] Embodiment three

[0123] The application also provides a computer device, please refer to Figure 3 , Figure 3 The application provides a computer device, please refer to

[0124] The processor 401 invokes the program stored in the memory 402 to execute the steps in the method provided by the embodiment of the application. Please refer to Figure 1 , specifically comprising the following steps:

[0125] S101, randomly generate a plurality of film system parameter matrices, and take the plurality of film system parameter matrices as initial populations, wherein the film system parameter matrix is constructed by the material type and thickness of a plurality of film layers of a band-pass filter;

[0126] S102, iteratively optimize the initial population based on a two-stage evolutionary unbalanced multi-modal multi-objective optimization algorithm, wherein a band-pass curve corresponding to the film system parameter matrix is obtained, and a band-pass performance corresponding to the band-pass curve is obtained according to the band-pass curve, the population is iteratively optimized according to the band-pass performance, until a preset iteration number is satisfied, and a plurality of film system parameter matrix optimal solutions are output;

[0127] S103, randomly select one from the plurality of film system parameter matrix optimal solutions, determine the material type and thickness of the plurality of film layers, and design the band-pass filter.

[0128] Specifically, in step S102, the two-stage evolutionary unbalanced multi-modal multi-objective optimization algorithm includes the following steps:

[0129] S201, initialize the iteration number to 0;

[0130] S202, when the iteration number is less than the product of the preset stage boundary point and the preset iteration number, enter the first iteration stage and execute step S203; otherwise, enter the second iteration stage and execute step S206:

[0131] S203, generate a mating pool according to the different individual corresponding to the Pareto dominance level in the current population, and generate a child population by simulating binary crossover and polynomial mutation, wherein the Pareto dominance level is obtained by comparing the band-pass performance corresponding to the current individual to the band-pass performance corresponding to other individuals according to the Pareto dominance relationship;

[0132] S204, obtain the fitness of each individual in the first set composed of the parent and child populations in the current iteration stage, and process the convergence of the individuals in the first set according to a preset convergence strategy, wherein the fitness represents the degree to which an individual can be retained in the population iteration process, and the convergence represents the degree to which an individual approaches an optimal solution;

[0133] S205, select individuals from the first set to form a next generation population according to the fitness, increment the iteration number by 1, and return to step S202;

[0134] S206, clustering the current population by a clustering algorithm to obtain a plurality of sub-populations, each of the sub-populations corresponding to an optimal solution region;

[0135] S207, in each of the optimal solution regions, generating a mating pool according to the Pareto dominance levels of different individuals, and generating a child population by simulated binary crossover and polynomial mutation;

[0136] S208, merging each of the sub-populations and the corresponding child population to form a second type of set, respectively counting the global non-dominated individuals and dominated individuals in different second type of sets according to the Pareto dominance levels, to obtain a global non-dominated individual set and a dominated individual set;

[0137] S209, if the size of the global non-dominated individual set is smaller than the size of the initial population, moving the current non-dominated individuals in the dominated individual set to the non-dominated individual set until the size of the non-dominated individual set is the same as the size of the initial population;

[0138] S210, determining whether the number of global non-dominated individuals in the sub-population is less than a preset convergence threshold:

[0139] If yes, selecting individuals from the non-dominated individual set based on population diversity to form a next generation population;

[0140] If no, selecting individuals from the non-dominated individual set based on the convergence and the fitness to form a next generation population;

[0141] S211, increasing the iteration number by 1, if the iteration number meets the preset iteration number, outputting the optimal individual in each of the optimal solution regions to obtain a plurality of optimal solutions of the membrane system parameter matrix; otherwise, returning to step S206.

[0142] wherein the population of the current iteration stage is defined as wherein the individual is in step S204, the preset convergence strategy is applied to the individual The process of processing the convergence of the individual in the population satisfies the following relationship:

[0143] ;

[0144] represents the convergence of the individual in the population , and the minimum value is 0, represents the number of times that the individual is dominated by other individuals in the population , and represents the population the average number of times each individual is dominated by other individuals, and respectively represent the current iteration number and the preset iteration number, represents the preset stage boundary point, which is a parameter with a value between 0 and 1, and:

[0145] ;

[0146] ;

[0147] for judging whether an individual dominates an individual , represents that an individual Pareto dominates an individual .

[0148] Further, in step S210, the preset convergence threshold is specifically:

[0149] a product of a preset sub-population convergence state judgment threshold τ and an average value of the number of the global non-dominated individuals in each of the sub-populations.

[0150] The computer device 400 provided by the embodiment of the present application can realize the steps in the band-pass filter design method in the above-described embodiment and achieve the same technical effects. Refer to the description in the above-described embodiment, which will not be repeated here.

[0151] Embodiment Four

[0152] The embodiment of the present application further provides a storage medium, and the storage medium stores a band-pass filter design program. The band-pass filter design program is executed by a processor to realize each process and step in the band-pass filter design method provided by the embodiment of the present application and achieve the same technical effects. To avoid repetition, the same will not be repeated here.

[0153] Those skilled in the art can understand that all or part of the processes in the above-described embodiments can be completed by a computer program or instruction-related hardware. The program can be stored in a computer-readable storage medium. When the program is executed, the program can include the processes of the above-described embodiments. The storage medium can be a magnetic disc, an optical disc, a read-only memory (ROM) or a random access memory (RAM).

[0154] It should be noted that, in this document, the terms "comprises", "comprising", or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements does not include only those elements but can include other elements not expressly listed or inherent to such process, method, article, or apparatus. An element proceeded by "comprises... a" does not, without more constraints, exclude the existence of additional identical elements in the process, method, article, or apparatus that comprises the element.

[0155] Those skilled in the art can clearly understand the above-mentioned embodiment method can be realized by means of software and necessary general hardware platform, of course, also can be realized by hardware, but in many cases, the former is a better embodiment. Based on such understanding, the technical solutions of the present application can be embodied in the form of software product, and the computer software product is stored in a storage medium (such as ROM / RAM, magnetic disk, optical disk), including a plurality of instructions to make a terminal (may be mobile phone, computer, server, air conditioner, or network equipment, etc.) execute the method described in various embodiments of the present application.

[0156] The embodiments of the present application are described above in conjunction with the accompanying drawings, the disclosed is only the preferred embodiment of the present application, but the present application is not limited to the above specific embodiments, the above specific embodiments are only illustrative, but not restrictive, those skilled in the art can make many equivalent changes in form without departing from the purpose of the present application and the scope of the claims, all of which belong to the protection of the present application.

Claims

1. A method of designing a bandpass filter tile, the method comprising: The method comprises the following steps: S101, randomly generating a plurality of film system parameter matrices, and taking the plurality of film system parameter matrices as an initial population, wherein the film system parameter matrix is constructed by a material type and a thickness of a plurality of film layers of a band-pass filter; S102, iteratively optimizing the initial population based on an unbalanced multi-modal multi-objective optimization algorithm of two-stage evolution, wherein a band-pass curve corresponding to the film system parameter matrix is obtained, and a band-pass performance corresponding to the band-pass curve is obtained according to the band-pass curve; the population is iteratively optimized according to the band-pass performance until a preset iteration number is satisfied, and a plurality of film system parameter matrix optimal solutions are output; S103, selecting one of the plurality of film system parameter matrix optimal solutions, determining the material type and the thickness of the plurality of film layers, and designing the band-pass filter; In step S102, the unbalanced multi-modal multi-objective optimization algorithm of two-stage evolution comprises the following steps: S201, initializing the iteration number as 0; S202, when the iteration number is less than the product of a preset stage boundary point and the preset iteration number, entering a first iteration stage and executing step S203; otherwise, entering a second iteration stage and executing step S206: S203, generating a mating pool according to a different individual in the current population corresponding to a Pareto dominance level, and generating a child population by simulating binary crossover and polynomial mutation, wherein the Pareto dominance level is obtained by comparing the band-pass performance corresponding to the current individual to the band-pass performance corresponding to other individuals according to a Pareto dominance relationship; S204, obtaining the fitness of each individual in a first set composed of the parent population and the child population in the current iteration stage, and processing the convergence of the individuals in the first set according to a preset convergence strategy, wherein the fitness represents the degree to which an individual can be retained in the population iteration process, and the convergence represents the degree to which an individual approaches an optimal solution; S205, selecting individuals from the first set to form a next generation population according to the fitness, increasing the iteration number by 1, and returning to step S202; S206, clustering the current population by a clustering algorithm to obtain a plurality of sub-populations, and each sub-population corresponds to an optimal solution region; S207, in each of the optimal solution regions, generating a mating pool according to a different individual corresponding to a Pareto dominance level, and generating a child population by simulating binary crossover and polynomial mutation; S208, merging each of the sub-populations and the corresponding child population to form a second set, respectively counting the global non-dominated individuals and the dominated individuals in different second sets according to the Pareto dominance level, and obtaining a global non-dominated individual set and a dominated individual set; S209, if the size of the global non-dominated individual set is smaller than the size of the initial population, moving the current non-dominated individual in the dominated individual set to the non-dominated individual set until the size of the non-dominated individual set is the same as the size of the initial population; S210, determining whether the number of global non-dominated individuals in the sub-population is less than a preset convergence threshold: If so, individuals are selected from the non-dominated individual set based on population diversity to form the next generation population; If not, individuals are selected from the non-dominated individual set based on the convergence and the fitness to form the next generation population; S211, adding 1 to the number of iterations. If the number of iterations meets the preset number of iterations, outputting the optimal individual in each of the current optimal solution regions to obtain multiple optimal solutions of the film system parameter matrix; otherwise, returning to step S206.

2. The bandpass etalon design method of claim 1, wherein, The population of the current iteration stage is defined as The individuals in it are In step S204, the preset convergence strategy processes the convergence of the individual In the population The process of processing the convergence of the individual in the population satisfies the following relationship: ； wherein, denotes the individual in the population has a minimum value of 0, denotes the individual is dominated by other individuals in the population , the number of times denotes the individual in the population is dominated by other individuals, the average number of times denote the current iteration number and the preset iteration number, respectively, denotes the preset phase boundary point, a parameter with a numerical value between 0 and 1, and: ； ； for determining whether an individual is dominated by an individual , when an individual is Pareto dominated by an individual .

3. The method of designing a bandpass filter according to claim 1, wherein, In step S210, the preset convergence threshold is specifically: The product of the preset subpopulation convergence state judgment threshold τ and the average number of the global non-dominated individuals in each of the subpopulations is calculated.

4. A bandpass filter design system, comprising: include: an initial generation module, configured to randomly generate a plurality of film parameter matrices and use the plurality of film parameter matrices as an initial population, wherein the film parameter matrices are constructed by material types and thicknesses corresponding to a plurality of film layers of the bandpass filter; an optimization module for iteratively optimizing the initial population based on an unbalanced multi-modal multi-objective optimization algorithm using a two-stage evolutionary method, wherein a bandpass curve corresponding to the film parameter matrix is ​​obtained, and a bandpass performance corresponding to the bandpass curve is obtained based on the bandpass curve, and the population is iteratively optimized based on the bandpass performance until a preset number of iterations is met, and multiple optimal solutions of the film parameter matrices are output; A design module is used to select any one of the optimal solutions of the film parameter matrix, determine the material type and thickness corresponding to the multiple film layers, and design the bandpass filter; The unbalanced multi-modal multi-objective optimization algorithm based on two-stage evolution executed by the optimization module includes the following steps: S201, initialize the number of iterations to 0; S202: When the number of iterations is less than the product of the preset stage demarcation point and the preset number of iterations, the process enters the first iteration stage and executes step S203; otherwise, the process enters the second iteration stage and executes step S206: S203, generating a mating pool according to the Pareto dominance ranks corresponding to different individuals in the current population, and generating an offspring population by simulating binary crossover and polynomial mutation, wherein the Pareto dominance rank is obtained by comparing the bandpass performance corresponding to the current individual with the bandpass performance corresponding to other individuals according to the Pareto dominance relationship; S204. Obtain the fitness of each individual in the first set of parent and child populations in the current iteration phase, and process the convergence of the individuals in the first set according to a preset convergence strategy. The fitness indicates the degree to which an individual is likely to be retained during the population iteration process, and the convergence indicates the degree to which an individual is close to the optimal solution. S205, selecting individuals from the first set according to the fitness to form the next generation population, increasing the number of iterations by 1, and returning to step S202; S206, clustering the current population using a clustering algorithm to obtain multiple subpopulations, each of which corresponds to an optimal solution region; S207. In each of the optimal solution regions, generating a mating pool according to the Pareto dominance levels corresponding to different individuals, and generating an offspring population by simulating binary crossover and polynomial mutation; S208, merging each of the sub-population and its corresponding offspring population to form a second type of set, respectively counting the global non-dominated individuals and dominated individuals in different second type of sets according to the Pareto dominance level, obtaining a global non-dominated individual set and a dominated individual set; S209, if the size of the global non-dominated individual set is smaller than the size of the initial population, moving the current non-dominated individuals in the dominated individual set to the non-dominated individual set until the size of the non-dominated individual set is the same as the initial population; S210, judging whether the number of the global non-dominated individuals in the sub-population is less than a preset convergence threshold value: if yes, selecting individuals from the non-dominated individual set based on population diversity to form a next generation population; if no, selecting individuals from the non-dominated individual set based on the convergence and the fitness to form a next generation population; S211, increasing the iteration number by 1, if the iteration number meets the preset iteration number, outputting the optimal individual in each of the optimal solution region to obtain a plurality of membrane system parameter matrix optimal solutions; otherwise, returning to step S206.

5. The bandpass etalon design system of claim 4, wherein, The population of the current iteration stage is defined as The individuals in it are In step S204, the preset convergence strategy processes the convergence of the individual In the population The process satisfies the following relationship: ； wherein, denotes the individual in the population of convergence, with a minimum value of 0, denotes the individual dominated by other individuals in the population , the number of times denotes the individual in the population , the average number of times each individual is dominated by other individuals, denotes the current iteration number and the preset iteration number, respectively, denotes the preset phase boundary point, a parameter with a value between 0 and 1, and: ； ； for determining whether an individual is dominated by an individual , when representing an individual Pareto dominates an individual .

6. The bandpass etalon design system of claim 4, wherein, In step S210, the preset convergence threshold value is specifically: a product of a preset sub-population convergence state judgment threshold value τ and an average value of the number of the global non-dominated individuals in each of the sub-populations.

7. A computer device, comprising: Comprise: a memory, a processor and a program stored on the memory and executable on the processor, the processor executing the program to implement the steps in the band-pass filter design method according to any one of claims 1-3.

8. A storage medium, characterized by The storage medium has a program stored thereon, and the program is executed by the processor to implement the steps in the band-pass filter design method according to any one of claims 1-3.

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