Filter comprehensive multi-solution obtaining method based on multi-objective auxiliary optimization, terminal and readable storage medium

Abstract

The invention relates to a filter comprehensive multi-solution obtaining method based on multi-target auxiliary optimization, a terminal and a readable storage medium, and the method comprises the steps: defining a topological transformation matrix of a filter according to a coupling matrix of a conventional topology and a coupling matrix of a target topology of the filter; defining a multi-objective function according to the coupling matrix of the target topology and the topological transformation matrix; and solving the decision vector of the topological transformation matrix according to the multi-objective function to obtain a plurality of candidate topological transformation matrixes, and outputting a coupling matrix of the target topology and the plurality of candidate topological transformation matrixes. According to the method, multiple candidate solutions are reserved in the optimization process, the situation that only the optimal solution is concerned like single-target optimization is avoided, the values of all target functions and the positions of the target functions on the solution boundaries are comprehensively considered, more potential solutions are explored, multiple solutions meeting the targets can be generated at the same time in the same operation process, and design flexibility is improved.

Description

Technical Field

[0001] The present invention relates to the field of data processing technology, and in particular to a filter comprehensive multi-solution acquisition method based on multi-objective auxiliary optimization, a terminal and a readable storage medium. Background Art

[0002] In microwave and RF systems, filters and multiplexers that combine multiple filters are critical to communication systems. This device allows specific frequencies to pass while blocking other frequencies. With the rapid development of fifth-generation (5G) communication systems and low-Earth orbit satellite Internet, the demand for efficient use of spectrum resources has intensified, making the synthesis and design of high-performance filters and multiplexers increasingly challenging. Due to the complex design requirements of high-order filters and multiplexers, it is necessary to use the solution that best suits the physical layout and manufacturing process.

[0003] In the prior art, the analysis and synthesis methods of conventional filter topologies are already mature. Based on the classic filter coupling matrix rotation method, the corresponding implementation methods of conventional structures, such as the folded topology and arrow topology, can be easily obtained. However, for an arbitrary topology, it is difficult to determine the corresponding rotation order. In addition, for high-order multiplexers, such as full-resonator multiplexers, only optimization-based synthesis methods can be used to obtain the coupling matrix. Traditionally, high-order filter synthesis can also be performed through numerical analysis methods. Although this exhaustive method can search out all filter synthesis solutions, it is extremely time-consuming and difficult to use in actual industrial production.

[0004] To this end, the prior art has proposed various optimization algorithms for filter synthesis, but these methods can only achieve single-solution filter synthesis. Even if the solution is successfully obtained through the optimization method, only one solution can be searched, ignoring a large number of other solutions, and this solution may not be the optimal solution for the corresponding filter design. Therefore, the existing optimization scheme is applied to the method of filter synthesis, which has the following problems: first, it may fall into a local optimal solution; second, even if the global optimal solution is achieved, it may be difficult to use in actual industrial production, which ultimately leads to the solution obtained by the existing optimization method not necessarily meeting the needs of technicians and lacking design flexibility. Summary of the invention

[0005] The purpose of the present invention is to provide a filter synthesis multi-solution acquisition method based on multi-objective assisted optimization, a terminal and a readable storage medium, aiming to solve the problem that the filter synthesis method in the prior art only obtains a single solution and lacks design flexibility.

[0006] The technical solution adopted by the present invention to solve the technical problem is as follows: The present invention provides a filter comprehensive multi-solution acquisition method based on multi-objective auxiliary optimization, and the filter comprehensive multi-solution acquisition method based on multi-objective auxiliary optimization comprises: A topology transformation matrix of the filter is defined according to a coupling matrix of a conventional topology of the filter and a coupling matrix of a target topology; Defining a multi-objective function according to the coupling matrix of the target topology and the topology transformation matrix; The decision vector of the topology transformation matrix is ​​solved according to the multi-objective function to obtain a plurality of candidate topology transformation matrices, and a coupling matrix of the target topology and the plurality of candidate topology transformation matrices are output.

[0007] Furthermore, solving the decision vector of the topology transformation matrix according to the multi-objective function to obtain multiple candidate topology transformation matrices specifically includes: Dividing the decision vector of the topological transformation matrix according to the multi-objective function, and dividing each element of the decision vector into a plurality of subcomponents; Iteratively optimizing each subcomponent to obtain a plurality of decision vectors; A plurality of candidate topology transformation matrices are obtained according to each of the decision vectors, and a coupling matrix of the target topology is output.

[0008] Furthermore, dividing the decision vector of the topological transformation matrix according to the multi-objective auxiliary optimization function and dividing each element of the decision vector into a plurality of subcomponents specifically includes: Identify the correlation between the elements according to the multi-objective function; Group related elements into the same subcomponent.

[0009] Further, the identifying the correlation between the elements according to the multi-objective function specifically includes: Calculate the synergy difference between the two elements, the first element difference, and the second element difference according to the multi-objective function; Determining whether the synergy difference, the first element difference, and the second element difference satisfy an association condition; If the collaborative difference, the first element difference and the second element difference meet the association condition, it is determined that the two elements are associated; if the collaborative difference, the first element difference and the second element difference do not meet the association condition, it is determined that the two elements are not associated.

[0010] Furthermore, the iterative optimization of each subcomponent to obtain a plurality of decision vectors specifically includes: Generating an initial population according to the solvable space of the decision vector; Iterate and perform differential evolution on each subcomponent in turn until the termination condition is met; A Pareto optimal solution set of the initial population is obtained to obtain a plurality of the decision vectors.

[0011] Furthermore, the differential evolution is iterated on each subcomponent in turn until the termination condition is met, and each differential evolution includes: For each sub-individual, a random optimal individual is randomly selected from the Pareto optimal solution set, and two different sub-individuals are randomly selected from the population; Perform mutation according to the random optimal individual and the different sub-individuals to obtain a mutation variable; Randomly select a crossover variable from the mutation variable and the variable of the corresponding subcomponent; Replace the crossover variables with the variables of the corresponding subcomponents to obtain the crossover individuals of the sub-individuals; Select the crossover individuals and sub-individuals to update the sub-individuals; Update the Pareto optimal solution set of the population according to each selected sub-individual.

[0012] Furthermore, the iterative optimization of each subcomponent to obtain a plurality of decision vectors further includes: Perform local search optimization on each decision vector.

[0013] Furthermore, the local search optimization is specifically optimized with the goal of minimizing the cumulative value of each objective function in the multi-objective function.

[0014] In addition, to achieve the above-mentioned purpose, the present invention also provides a terminal, which includes: a memory, a processor, and a filter comprehensive multi-solution acquisition program based on multi-objective assisted optimization stored in the memory and run on the processor, and when the filter comprehensive multi-solution acquisition program based on multi-objective assisted optimization is executed by the processor, the terminal is controlled to implement the steps of the filter comprehensive multi-solution acquisition method based on multi-objective as described above.

[0015] In addition, to achieve the above-mentioned purpose, the present invention also provides a readable storage medium, which stores a filter comprehensive multi-solution acquisition program based on multi-objective assisted optimization. When the filter comprehensive multi-solution acquisition program based on multi-objective assisted optimization is executed by a processor, the steps of the filter comprehensive multi-solution acquisition method based on multi-objective assisted optimization as described above are implemented.

[0016] The present invention adopts the above technical solution to achieve the following effects: The present invention retains multiple candidate solutions during the optimization process of filter synthesis solution, avoiding focusing only on the optimal solution like single-objective optimization. It not only does not rely on a single objective value, but also comprehensively considers the values ​​of all objective functions and their positions on the solution boundary, thereby encouraging the algorithm to explore more potential solutions in the solution space of filter synthesis, and can simultaneously generate multiple solutions that meet the objectives in the same operation process, thereby improving the flexibility of filter design. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] Figure 1 It is a flowchart of the steps of a filter comprehensive multi-solution acquisition method based on multi-objective auxiliary optimization in a preferred embodiment of the present invention; Figure 2 It is a schematic diagram of a framework of a filter comprehensive multi-solution acquisition method based on multi-objective auxiliary optimization in a preferred embodiment of the present invention; Figure 3 is a comparative schematic diagram of the topological structure and response of the filter in a preferred embodiment of the present invention; Figure 4 Schematic diagram of topological structure transformation of the filter in a preferred embodiment of the present invention; Figure 5 This is a schematic diagram of the values ​​of the multiple solution problem in the preferred embodiment of the present invention; Figure 6 is a schematic diagram of the topological structure of a multiplexer in a preferred embodiment of the present invention; Figure 7 is a schematic diagram of the response of a multiplexer in a preferred embodiment of the present invention; Figure 8 Schematic diagram of the convergence position of the multi-objective function in a preferred embodiment of the present invention; Fig. 9 It is a schematic diagram comparing topological responses before and after transformation in a preferred embodiment of the present invention.

[0018] Fig.10 A schematic diagram of an operating environment of a preferred embodiment of a terminal of the present invention. DETAILED DESCRIPTION

[0019] In order to make the purpose, technical solution and advantages of the present invention clearer and more specific, the present invention is further described in detail below with reference to the accompanying drawings and examples. It should be understood that the specific embodiments described herein are only used to explain the present invention and are not used to limit the present invention.

[0020] Embodiment 1 See also Figure 1 and Figure 2 Embodiment 1 of the present application is a filter comprehensive multi-solution acquisition method based on multi-objective assisted optimization, which comprises the steps of: S1. Define a topology transformation matrix of the filter according to a coupling matrix of a conventional topology of the filter and a coupling matrix of a target topology.

[0021] In this embodiment, the filter device includes a single filter and also includes a multiplexer composed of multiple filters. First, a single filter is taken as an example for description.

[0022] Please refer to Figure 3 As shown, in order to achieve Figure 3 The topology of (a) is realized Figure 3 The response curve in (b) requires adjustment of the coupling between the input and output of the filter, as well as the size of a resonator between the input and output, where the S parameter represents the loss or gain parameter.

[0023] Specifically, in the actual design of the filter, in order to achieve Figure 3 The response curve of (b) in the figure can be used to better express the correspondence between the structures. Figure 4 The topological diagram is used to represent the filter synthesis method based on the prior art, which can easily determine Figure 4 The mathematical model of (a) in FIG. 1 is shown in FIG. 2 , where S and L both represent filter ports. However, the actual physical structure corresponding to the mathematical model is difficult to implement in industrial production of filters. For example, port L needs to feed resonator 1 and resonator 8 at the same time, and resonator 8 also needs to be connected to resonators 1, 2, and 7 at the same time. In the actual design process, the fewer the connection paths, the easier it is to implement. Figure 4 Therefore, if we want to design Figure 4 The filter corresponding to the topology (b) in Figure 3 The topology of (a) is realized Figure 3 The response curve of (b) in the figure needs to find the corresponding coupling matrix. Figure 4 Starting from the conventional topology of (a), it is transformed into Figure 4 The target topology is shown in (b).

[0024] The coupling matrix of the conventional topology is used It is expressed by the filter synthesis method of the prior art, and the coupling matrix of the target topology is expressed by The conversion of the conventional topology to the target topology while keeping the filter response unchanged can be expressed as This process follows the principle of orthogonal transformation, and the mathematical representation of the transformation is as follows: ; Among them, the superscript Indicates transpose.

[0025] Therefore, find the appropriate topological transformation matrix This is very important for filter design. In this embodiment, the topology transformation matrix It is expressed as: ; Topological transformation matrix The unknown variables are all To represent, each unknown variable constitutes the decision vector ,in, Represents the decision vector The first element of Represents the decision vector The second element of Represents the decision vector No. elements, Represents the decision vector No. elements, Represents the decision vector No. elements, Represents the number of unknowns in a row of a matrix.

[0026] Therefore, in order to achieve effective topological transformation, the problem is transformed into finding a decision vector , to ensure that the coupling matrix of the transformed target topology Satisfy the specified requirements. The goal is to find a suitable vector , so that the coupling matrix of the target topology is obtained Align with the desired filter topology.

[0027] S2. Define a multi-objective function according to the coupling matrix of the target topology and the topology transformation matrix.

[0028] Traditionally, a single objective function is set and solved to obtain a solution. In this embodiment, a multi-objective function is set to assist in obtaining multiple solutions. In the synthesis of filters and multiplexers, there may be multiple solutions that meet the actual design standards of the filters. Therefore, different parameters can be selected to achieve the same target response. Then there is a question: what kind of parameters are the easiest to implement in industrial production? Designers need to find the best solution among multiple solutions.

[0029] Since individual populations are regarded as independent entities, the use of parallel computing and population partitioning can accelerate evolution. Specifically, in this embodiment, population partitioning adopts a density-based clustering method to identify high-density areas in the population and divide the solution space into several clusters (sub-populations). The high-density area is divided into a sub-population, and the low-density area is regarded as a noise point, which is usually not assigned to any sub-population. After clustering, each sub-population is optimized independently. Noise points can be allowed to be reclassified in multiple iterations to avoid falling into local optimality.

[0030] Please refer to Figure 5 , its multiple solutions are similar to the peaks of the solution space, and its comprehensive algorithm hopes to obtain multiple feasible solutions in one run. Then, the most suitable solution for processing is selected from these feasible solutions for the actual processing design of the filter.

[0031] Since the purpose of global search is to obtain as many solutions as possible, the objective function is set to be multi-objective to adapt to the multi-solution problem.

[0032] ; in, represents the feasible solution domain defined, represents the vector composed of the objective function, represents the first objective function, represents the second objective function, Indicates The objective function, Represents the number of objective functions.

[0033] The key to transforming a single-objective optimization problem into a multi-objective optimization problem is to decompose the objective function to form multiple interrelated optimization objectives. By transforming it into a multi-objective optimization, the mechanism of the multi-objective optimization algorithm is used to naturally maintain population diversity and avoid premature convergence to a single solution. This method not only enhances the exploratory nature of the search space, but also enables multiple different topological solutions to be retained in the solution, thereby providing more design options.

[0034] Specifically, in this embodiment, the multi-objective function of a single filter is: ; in, represents the objective value of the multi-objective function, express The index set of coupling values ​​that should be 0 in , is the identity matrix, Represents the matrix List, Represents the matrix OK, Right now No. Ledi The reconstruction process can be expressed as a system of nonlinear equations, solving Get vector .

[0035] That is, taking a single filter as an example, it can be decomposed into two objective functions, namely and .

[0036] In order to achieve the topological transformation of the filter, its two objective values ​​must both be 0, so that they can be completely equivalent in mathematical terms. Therefore, in order to achieve multiple solutions for solving the filter synthesis, this embodiment first separates the objective function and converts the original single objective function into two independent objective functions. For these two objective functions, Achieved topology transformation, The response function remains unchanged during the topological transformation, and it can be considered that the two goals influence each other but are not contradictory.

[0037] In this way, according to the decomposition of the objective function, it becomes two independent optimization objectives, each objective function corresponds to a different optimization path. This decomposition may generate multiple solutions during the optimization process. Compared with the single-objective optimization algorithm, it only corresponds to one optimization path, so it will eventually converge to one optimal solution. For multi-objective optimization, each objective will guide a different optimization path. Due to the interaction of multiple paths, multiple solutions that simultaneously meet each objective function will eventually be generated.

[0038] In addition, the method of the present invention can be used not only for a single filter, but also for the synthesis of other filter devices, such as the multi-solution search of a multiplexer. For details, please refer to Figure 6 , Figure 6 P1, P2, P3, P4 and P5 in the figure all represent the ports of the multiplexer. When implementing frequency division multiplexing, the multiplexer will use multiple filter channels to select different signal frequency bands and merge multiple signals through multiplexing technology. This is a common implementation of the multiplexer, especially multiple filters are used to select and isolate different frequency bands of signals. Each signal will occupy a specific frequency band. These filters ensure that each signal will not interfere with each other when merged.

[0039] like Figure 7 As shown, Figure 7 for Figure 6 The response curves that must be achieved for multiplexers of the type described in this paper can only be synthesized using optimization algorithms for non-conventional all-resonator multiplexers. Typically, the optimization model is built based on S-parameter specifications, which allows the coupling values ​​that meet the desired characteristics to be determined precisely.

[0040] In this embodiment, for a multiplexer integrated with multiple filters, the objective function is set according to the multiplexer response, such as Figure 7 As shown, in the frequency band Return loss Less than the first return loss standard , in the frequency band Return loss within Less than the second return loss standard , in the frequency band Return loss within Less than the third return loss standard , in the frequency band Return loss within is less than the fourth return loss standard , and so on, where and represents the frequency points collected within the passband or stopband from port 1 to port 2, represents the frequency points collected in the stopband from port 1 to port 3, represents the frequency points collected in the stopband from port 1 to port 4, so its multi-objective function can be set as: ; in, represents the objective value of the multi-objective function, represents the frequency points collected in the passband corresponding to port 1 to port P, Represents the frequency points collected in the stopband from port 1 to port P.

[0041] Please refer to Figure 8 After solving the multi-objective function, unlike the single-objective function that finally converges to one point, the optimal solution of the multi-objective function will finally form a line, that is, multiple solutions can be obtained.

[0042] S3. Solve the decision vector of the topology transformation matrix according to the multi-objective function to obtain multiple candidate topology transformation matrices, and output the coupling matrix of the target topology and the multiple candidate topology transformation matrices.

[0043] Specifically, in this embodiment, a multi-objective assisted collaborative evolution algorithm is used to evolve all variables, and this process will continue until a predefined stopping criterion is reached. When solving the problem, an initial value is first generated. The initial value generation in this embodiment adopts a randomized generation method to form an initial population by randomly generating a large number of candidate solutions within the specified variable range.

[0044] This method ensures the diversity of the population and helps improve the global search capability. In actual operation, a certain number of population individuals can be generated using uniformly distributed random sampling according to the upper and lower bounds of the problem variables. Each individual contains the initial values ​​of all variables. The randomly generated population provides a good starting point for the subsequent optimization process and helps the algorithm avoid falling into a local optimal solution.

[0045] Coevolutionary algorithms group variables based on their relationships, breaking down a complex, large-scale problem into smaller subcomponents. To achieve variable decomposition, differential grouping is an effective method that examines the interdependencies and interactions between decision variables, effectively grouping decision variables and then iteratively performing mutation, crossover, and selection operations until the preset termination conditions are met.

[0046] Specifically, cooperative collaboration is an effective method for solving large-scale optimization problems. This effectiveness is attributed to the decomposition of large components into multiple subcomponents. Once the decomposition of cooperative collaboration is achieved, the entire problem can be solved by individual optimization. Due to its modular nature, the cooperative collaboration method is a framework suitable for large-scale optimization. However, the main difficulty in applying collaboration is to decompose the decision variables into subcomponents. In this embodiment, it is specifically carried out by taking a differential grouping approach.

[0047] The cumulative value of the objective function is defined as: ; in, represents the cumulative value of the objective function, Indicates An objective function.

[0048] The decision vector Elements and the decision vector Elements Interactions between the two can be identified by comparing the differences calculated using the following equation: ; ; ; in, represents the synergy difference, represents the first element difference, represents the second element difference, The decision vector Elements The disturbance value of The decision vector Elements Given these differences, two variables are correlated if the following conditions hold: ; in It is a parameter that specifies the allowable error, that is, sets the error threshold.

[0049] To decompose all vectors into subcomponents, the method first identifies the associations of all decision variables. It detects the associations between each variable and all other variables in turn. Specifically, the first decision variable is first checked for its interaction with all other variables in the set. If an interaction between the first variable and any other variable is detected, the algorithm removes the interacting variable from the entire set of decision variables and then places that variable into a subcomponent. This process continues until all variables that interact with the first variable are identified, forming the first subcomponent. The same process is then repeated for the remaining decision variables. This cycle is iteratively performed until all decision variables are evaluated and assigned to appropriate subcomponents. After the partitioning, the decision variables are An element is divided into several subcomponents.

[0050] After the variables are divided, differential evolution can be performed according to the groups. The core of the differential evolution (DE) method is mutation, which involves adding the weighted difference vector between two individuals to a third individual. After mutation, these mutant individuals will undergo a discrete crossover and selection process with the previous generation of individuals. The mutation, crossover and selection operations are iteratively performed and repeated for iterative optimization until the pre-established termination conditions are met.

[0051] Specifically, in this embodiment, during iteration, differential evolution is performed after grouping. For example, during the first iteration, the first subcomponent is evolved, and during the second iteration, the second subcomponent is evolved. Each time the evolution is performed, the mutation step is performed first: ; in, and Indicates that the individuals randomly selected from the contemporary population are The variables of the subcomponents, The subcomponents that are differentially evolved for this round of optimization, is the random optimal individual in the contemporary population in the The variables of the subcomponents, is the scaling factor, For the The mutation variable after the sub-individual (i.e., the target individual) mutates. It is worth noting that since a multi-objective function is used in this embodiment, there will be multiple optimal solutions. Therefore, an optimal solution is selected from the multiple optimal solutions as a random optimal individual for mutation.

[0052] Cross the mutated individual with the target individual to generate a new solution: ; in, is a random function, Generates a random value between 0 and 1. is the crossover rate. For the The crossover individuals The cross variables of subcomponents, For the The sub-individual The variables of the subcomponents will Replace Sub-individual No. The variables of the subcomponents are obtained Crossover .

[0053] Then through the objective function Make your selection: ; Depending on the value of the objective function, it is determined whether the crossover individual or the original individual will be retained as the next generation.

[0054] in, The condition is based on the calculation of the Pareto frontier method. If the condition is met, it means The performance on all objective functions is better than , if it is not better than on an objective function, then the condition is not met.

[0055] After each evolutionary iteration, the Pareto optimal solution is derived based on the Pareto frontier (a Pareto optimal solution is a solution that has no other solution that performs better on all objectives), and these steps are repeated until the pre-established termination condition is met. At this point, the Pareto optimal solution is processed as a potential global optimal solution for the next step. The Pareto optimal frontier (PF) is defined as the set of objective value vectors in the Pareto optimal set (PS).

[0056] After completing the global search and collecting multiple Pareto optimal solutions in the Pareto optimal set, local searches are performed on each optimal solution to further optimize the collected multiple solutions.

[0057] Specifically, the Levenberg-Marquardt algorithm is an iterative optimization method (hereinafter referred to as the LM algorithm) for solving nonlinear least squares problems. The LM algorithm combines the advantages of the Gauss-Newton method (fast convergence near the minimum) and the robustness of the steepest descent method (the ability to handle non-convex objective functions and poor initial guesses). After the global search, each global potential solution (the Pareto optimal solution in the previous step) is used as the initial guess of the LM algorithm, and then the LM algorithm performs a local search around this point to find a more accurate solution.

[0058] At this time, the cumulative value of the objective function is used The lowest target is searched. In the filter, the cumulative value of the objective function at this time is: ; In other words, the objective at this time is modified to the sum of all objective functions. This change ensures that all specified constraints are satisfied. After that, all non-repeated solutions are combined to represent multiple solutions that satisfy all objectives.

[0059] At this point, all the solved decision vectors Substitute into the topological transformation matrix In this way, we obtain multiple different topological transformation matrices .

[0060] ; The topological transformation matrix Can be Transform to , that is, the synthesis of the target topology is achieved. Fig. 9 As shown, the responses corresponding to the two topologies are exactly the same.

[0061] ; Compared with the synthesis of filters, the synthesis of multiplexers is only different in the objective function, and the other steps are exactly the same. Therefore, for multiplexers, the multi-objective assisted co-evolution differential evolution algorithm can also identify multiple solutions. When a solution that meets the design specifications is found, the coupling matrix will accurately reflect the required characteristics, thus becoming a valuable tool to guide the actual physical structure design of the multiplexer.

[0062] The co-evolution differential evolution algorithm decomposes complex large-scale problems into manageable subcomponents, and each subcomponent is optimized independently, effectively avoiding the problem of dimensionality curse and improving the optimization efficiency of the algorithm in high-dimensional problems. The integrated parallel computing method enables multiple solutions to be optimized simultaneously, greatly accelerating the overall convergence speed of the algorithm, especially in complex, high-order filter and multiplexer design, which can significantly shorten the calculation time.

[0063] After obtaining each candidate topology transformation matrix, each candidate topology transformation matrix is ​​output as a result.

[0064] The present invention avoids focusing only on the optimal solution like single-objective optimization by retaining multiple candidate solutions during the optimization process. It not only does not rely on a single objective value, but also comprehensively considers the values ​​of all objective functions and their positions on the solution boundary, thereby encouraging the algorithm to explore more potential solutions in the solution space. It can simultaneously generate multiple solutions that meet the objectives in the same operation process, thereby improving design flexibility.

[0065] Embodiment 2 See also Fig.10 Based on the above method, the present invention further provides a terminal, which includes a processor 10, a memory 20 and a display 30. However, it should be understood that it is not required to implement all the components shown, and more or fewer components may be implemented instead.

[0066] In some embodiments, the memory 20 may be an internal storage unit of the terminal, such as a hard disk or memory of the terminal. In other embodiments, the memory 20 may also be an external storage device of the terminal, such as a plug-in hard disk, a smart memory card (Smart Media Card, SMC), a secure digital (SecureDigital, SD) card, a flash card (Flash Card), etc. equipped on the terminal. Further, the memory 20 may also include both an internal storage unit of the terminal and an external storage device. The memory 20 is used to store application software and various types of data installed on the terminal, such as the program code of the installation terminal. The memory 20 may also be used to temporarily store data that has been output or is to be output. In one embodiment, a filter comprehensive multi-solution acquisition program 40 based on multi-objective assisted optimization is stored on the memory 20, and the filter comprehensive multi-solution acquisition program 40 based on multi-objective assisted optimization can be executed by the processor 10, thereby realizing the terminal in the present application.

[0067] In some embodiments, the processor 10 may be a central processing unit (CPU), a microprocessor or other data processing chip, used to run the program code or process data stored in the memory 20, such as executing relevant programs of the filter comprehensive multi-solution acquisition method based on multi-objective assisted optimization.

[0068] In some embodiments, the display 30 may be an LED display, a liquid crystal display, a touch-sensitive liquid crystal display, an OLED (Organic Light Emitting Diode) touch device, etc. The display 30 is used to display information on the terminal and to display a visual user interface.

[0069] In one embodiment, when the processor 10 executes the filter comprehensive multiple solution acquisition program 40 based on multi-objective assisted optimization in the memory 20, the steps of the filter comprehensive multiple solution acquisition method based on multi-objective assisted optimization as described above are implemented.

[0070] Embodiment 3 The present embodiment provides a storage medium, which stores a filter comprehensive multi-solution acquisition program based on multi-objective assisted optimization. When the filter comprehensive multi-solution acquisition program based on multi-objective assisted optimization is executed by a processor, the steps of the filter comprehensive multi-solution acquisition method based on multi-objective assisted optimization as described above are implemented.

[0071] In summary, the present invention avoids focusing only on the optimal solution like single-objective optimization by retaining multiple candidate solutions during the evolution process. It not only does not rely on a single objective value, but also comprehensively considers the values ​​of all objective functions and their positions on the solution boundary, thereby encouraging the algorithm to explore more potential solutions in the solution space. It can simultaneously generate multiple solutions that meet the objectives in the same operation process, thereby improving design flexibility.

[0072] It should be noted that, in this article, the terms "include", "comprises" or any other variations thereof are intended to cover non-exclusive inclusion, so that a process, method, article or terminal including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, article or terminal. In the absence of further restrictions, an element defined by the sentence "comprises a ..." does not exclude the presence of other identical elements in the process, method, article or terminal including the element.

[0073] Of course, those skilled in the art can understand that all or part of the processes in the above-mentioned embodiments can be implemented by instructing related hardware (such as a processor, a controller, etc.) through a computer program, and the program can be stored in a computer-readable storage medium, and the program can include the processes of the above-mentioned method embodiments when executed. The storage medium can be a memory, a disk, an optical disk, etc.

[0074] It should be understood that the application of the present invention is not limited to the above examples. For ordinary technicians in this field, improvements or changes can be made based on the above description. All these improvements and changes should fall within the scope of protection of the claims attached to the present invention.

Claims

1. A filter comprehensive multi-solution acquisition method based on multi-objective assisted optimization, characterized in that: The filter comprehensive multi-solution acquisition method based on multi-objective auxiliary optimization includes: A topology transformation matrix of the filter is defined according to a coupling matrix of a conventional topology of the filter and a coupling matrix of a target topology; Defining a multi-objective function according to the coupling matrix of the target topology and the topology transformation matrix; The decision vector of the topology transformation matrix is ​​solved according to the multi-objective function to obtain a plurality of candidate topology transformation matrices, and a coupling matrix of the target topology and the plurality of candidate topology transformation matrices are output.

2. The filter comprehensive multi-solution acquisition method based on multi-objective auxiliary optimization according to claim 1 is characterized in that: Solving the decision vector of the topology transformation matrix according to the multi-objective function to obtain multiple candidate topology transformation matrices specifically includes: Dividing the decision vector of the topological transformation matrix according to the multi-objective function, and dividing each element of the decision vector into a plurality of subcomponents; Iteratively optimizing each subcomponent to obtain a plurality of decision vectors; A plurality of candidate topology transformation matrices are obtained according to each of the decision vectors, and a coupling matrix of the target topology is output.

3. The filter comprehensive multi-solution acquisition method based on multi-objective auxiliary optimization according to claim 2 is characterized in that: The step of dividing the decision vector of the topological transformation matrix according to the multi-objective auxiliary optimization function and dividing each element of the decision vector into a plurality of subcomponents specifically includes: Identify the correlation between the elements according to the multi-objective function; Group related elements into the same subcomponent.

4. The filter comprehensive multi-solution acquisition method based on multi-objective auxiliary optimization according to claim 3 is characterized in that: The identifying the correlation between the elements according to the multi-objective function specifically includes: Calculate the synergy difference between the two elements, the first element difference, and the second element difference according to the multi-objective function; Determining whether the synergy difference, the first element difference, and the second element difference satisfy an association condition; If the collaborative difference, the first element difference and the second element difference meet the association condition, it is determined that the two elements are associated; if the collaborative difference, the first element difference and the second element difference do not meet the association condition, it is determined that the two elements are not associated.

5. The filter comprehensive multi-solution acquisition method based on multi-objective auxiliary optimization according to claim 2 is characterized in that: The iterative optimization of each subcomponent to obtain a plurality of decision vectors specifically includes: Generating an initial population according to the solvable space of the decision vector; Iterate and perform differential evolution on each subcomponent in turn until the termination condition is met; A Pareto optimal solution set of the initial population is obtained to obtain a plurality of the decision vectors.

6. The filter comprehensive multi-solution acquisition method based on multi-objective auxiliary optimization according to claim 5 is characterized in that: The differential evolution is iterated on each subcomponent in turn until the termination condition is met, and each differential evolution includes: For each sub-individual, a random optimal individual is randomly selected from the Pareto optimal solution set, and two different sub-individuals are randomly selected from the population; Perform mutation according to the random optimal individual and the different sub-individuals to obtain a mutation variable; Randomly select a crossover variable from the mutation variable and the variable of the corresponding subcomponent; Replace the crossover variables with the variables of the corresponding subcomponents to obtain the crossover individuals of the sub-individuals; Select the crossover individuals and sub-individuals to update the sub-individuals; Update the Pareto optimal solution set of the population according to each selected sub-individual.

7. The filter comprehensive multi-solution acquisition method based on multi-objective auxiliary optimization according to claim 2 is characterized in that: The iterative optimization of each subcomponent to obtain a plurality of decision vectors further includes: Perform local search optimization on each decision vector.

8. The filter comprehensive multi-solution acquisition method based on multi-objective auxiliary optimization according to claim 7 is characterized in that: The local search optimization is specifically optimized with the goal of minimizing the cumulative value of each objective function in the multi-objective function.

9. A terminal, characterized in that: The terminal includes: a memory, a processor, and a filter comprehensive multi-solution acquisition program based on multi-objective assisted optimization stored in the memory and executable on the processor. When the filter comprehensive multi-solution acquisition program based on multi-objective assisted optimization is executed by the processor, the terminal is controlled to implement the steps of the filter comprehensive multi-solution acquisition method based on multi-objective assisted optimization as described in any one of claims 1-8.

10. A readable storage medium, characterized in that: The readable storage medium stores a filter comprehensive multi-solution acquisition program based on multi-objective assisted optimization, and when the filter comprehensive multi-solution acquisition program based on multi-objective assisted optimization is executed by a processor, the steps of the filter comprehensive multi-solution acquisition method based on multi-objective assisted optimization as described in any one of claims 1-8 are implemented.

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