A radio frequency receiving chain link topology optimization method based on an empirical intervention genetic algorithm

Abstract

The application discloses a radio frequency receiving link topology optimization method based on an experience intervention genetic algorithm, relates to the field of radio frequency link simulation and link optimization, and realizes mapping of a radio frequency receiving link topology structure and genetic algorithm gene coding through gene coding design, and designs selection, crossover and variation rules of the genetic algorithm based on gene coding of the link topology, so as to improve the random search range of the algorithm. The application innovatively fuses the experience of a radio frequency link design expert and the genetic algorithm, and the genetic algorithm based on experience intervention greatly reduces the solution search space and eliminates unreasonable solutions by relying on the experience of the expert, and realizes heuristic rapid search by using the genetic algorithm, so that the efficiency and robustness of radio frequency receiving link topology optimization are effectively improved.

Description

Technical Field

[0001] This invention relates to the field of radio frequency link simulation and link optimization, specifically to a radio frequency receiver link topology optimization method based on an empirical intervention genetic algorithm. Background Technology

[0002] The statements in this section are provided only as background information in relation to this disclosure and may not constitute prior art.

[0003] With the rapid development of electronic information technology, the electromagnetic environment faced by frequency-using equipment is becoming increasingly complex, making the radio frequency front-end receiving channel easily blocked by certain high-power interference signals, which seriously affects the overall system performance. Therefore, when facing interference in the environment, the receiving link of the radio frequency front-end often uses multiple notch filters to suppress interference, and adjusts the topological order of notch filters, low-noise amplifiers, attenuators and other devices in the link to compensate for link noise and linearity, so that the link meets performance design constraints.

[0004] For radio frequency (RF) receiver links, different device topologies represent different link performance. When the power, frequency, and quantity of interference signals in the electromagnetic environment change, the optimal link topology should be selected from among many available options.

[0005] However, as the scale of links increases, the number of possible link topology combinations grows exponentially, posing a significant challenge to quickly determining the optimal link topology that meets performance design constraints. Faced with a massive number of configuration combinations, traditional methods relying on manual experience and simple exhaustive search are not only extremely time-consuming but also struggle to support adaptive optimization for different application scenarios. Furthermore, the difficulty in constructing a unified analytical expression for link topology regarding RF link performance metrics renders traditional gradient-based optimization algorithms inapplicable. Summary of the Invention

[0006] The purpose of this invention is to address the shortcomings of traditional methods in RF receiver link topology optimization by providing an RF receiver link topology optimization method based on an empirically intervened genetic algorithm. First, a mapping is established between the RF receiver link topology and the genetic algorithm's gene encoding through coding design. Then, selection, crossover, and mutation rules for the genetic algorithm are designed based on this gene encoding to improve the algorithm's random search efficiency. Finally, expert experience in RF receiver link design is integrated with the genetic algorithm. This experience-intervention-based genetic algorithm significantly reduces the search space for solutions and eliminates results that do not conform to the empirical design, thereby improving the algorithm's robustness and efficiency, and providing an effective method for RF receiver link topology optimization.

[0007] The technical solution of the present invention is as follows:

[0008] A radio frequency receiver link topology optimization method based on empirical intervention genetic algorithm, comprising:

[0009] Step S1: Map the RF receiving link topology to the genetic code of the genetic algorithm through coding design;

[0010] Step S2: Based on the gene encoding, perform genetic algorithm selection, crossover rule design, and mutation rule design;

[0011] Step S3: Design a constrained multi-objective optimization fitness function based on the performance indicators and design constraints of the RF receiver link;

[0012] Step S4: Integrate the expert experience in RF receiver link design with the genetic algorithm selected in step S2;

[0013] Step S5: Iteratively optimize the link topology using the computational process of the fused genetic algorithm.

[0014] Further, step S1 includes:

[0015] Step S11: Number all n devices on the link sequentially from 1 to n;

[0016] Step S12: Use the decimal array of length n as the gene code. The numbers in the array can be arbitrarily sorted to form a one-to-one mapping with different link topologies. To avoid duplicate devices in the link, it is stipulated that the same number cannot appear in the gene code.

[0017] Furthermore, the selection of the genetic algorithm includes:

[0018] A roulette wheel selection method is used to perform probabilistic selection operations on the population in each generation.

[0019] Furthermore, the design of the cross rules includes:

[0020] Two points are randomly selected in the gene coding as the start and end points of the crossover;

[0021] By reordering the numbers contained in the cross segments of parent generation 1 according to their order of appearance in parent generation 2, a new offspring is obtained.

[0022] By updating the intersection segment of parent generation 2 in the same way, another child generation is obtained.

[0023] Furthermore, the design of the mutation rules includes:

[0024] Two points on the gene coding are randomly selected as mutation points, and then the numbers at the two mutation points are swapped to obtain the mutated gene.

[0025] Furthermore, the multi-objective optimization fitness function includes:

[0026] f=(∝1×G normal +∝2×NF normal +∝3×OP1dB normal )×δ

[0027] in:

[0028] ∝1, ∝2, and ∝3 represent gain, noise, and output P, ​​respectively. -1 The weight of the total optimization objective;

[0029] G normal This is the normalized gain;

[0030] NF normal The normalized noise figure;

[0031] OP1dB normal This is the normalized link output;

[0032] δ is the penalty function.

[0033] Furthermore, the G normal Calculated using the following formula:

[0034]

[0035] The NF normal Calculated using the following formula:

[0036]

[0037] The OP1dB normal Calculated using the following formula:

[0038]

[0039] The penalty function δ includes:

[0040]

[0041] in:

[0042] G represents the gain;

[0043] G e The desired link gain;

[0044] NF is the noise figure;

[0045] OP1dB max and OP1dB min These represent the maximum and minimum output P that the link can achieve. -1 ;

[0046] NF_constraint is the noise figure NF constraint.

[0047] Furthermore, the expert experience includes:

[0048] Experience 1: When considering the notch filtering order of interference signals, notch high-power interference signals first, and notch low-power interference signals later.

[0049] Experience 2: When optimizing the topology, pay attention not only to the output P of the entire link. -1 Furthermore, the input P at each notch filter on the link should be guaranteed. -1 Satisfy specific constraints;

[0050] Experience 3: The further forward the notch filter is in the link, the lower its input P. -1 As high as possible.

[0051] Further, step S4 includes:

[0052] Step S41: Based on the notch filter order in Experience 1, determine the relative order of notch filters in gene coding, and then complete gene crossover and mutation while ensuring that the relative order of notch filters remains unchanged, thereby greatly reducing the search space of the solution.

[0053] Step S42: Integrate the multi-objective optimization fitness function with experiences 2 and 3. By setting a penalty function, the experiences can be incorporated into the multi-objective optimization fitness function through constraints to obtain the modified fitness function.

[0054] Furthermore, the modified fitness function includes:

[0055] f = f × δ² × δ³

[0056] in:

[0057]

[0058]

[0059] Compared with existing technologies, the advantages of this invention are:

[0060] An empirically-interventional genetic algorithm-based method for optimizing RF receiver link topology is presented. This algorithm maps the RF receiver link topology to the genetic algorithm's gene encoding through gene coding design. Based on the gene encoding of the link topology, selection, crossover, and mutation rules for the genetic algorithm are designed to improve the algorithm's random search range. This invention innovatively integrates expert experience in RF link design with a genetic algorithm. The empirically-interventional genetic algorithm significantly reduces the search space and eliminates unreasonable solutions by relying on expert experience, while simultaneously enabling heuristic and rapid search, thereby effectively improving the efficiency and robustness of RF receiver link topology optimization. Attached Figure Description

[0061] Figure 1 This is a flowchart of a radio frequency receiver link topology optimization method based on an empirical intervention genetic algorithm;

[0062] Figure 2 Schematic diagram of the radio frequency receiving link topology of this invention;

[0063] Figure 3 A schematic diagram of gene encoding for the genetic algorithm of this invention;

[0064] Figure 4 Schematic diagram of gene crossover rule design in this invention: unreasonable result (left), reasonable result (right);

[0065] Figure 5 A schematic diagram of the gene mutation rule design of this invention;

[0066] Figure 6 A schematic diagram illustrating how the invention utilizes expert experience in radio frequency link design to intervene in the genetic algorithm;

[0067] Figure 7 The link topology obtained by optimizing the algorithm of this invention is used in the embodiments;

[0068] Figure 8 The fitness convergence curve of topology optimization using the algorithm of the present invention is shown in the embodiment. Detailed Implementation

[0069] It should be noted that relational terms such as "first" and "second" are used merely to distinguish one entity or operation from another, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Furthermore, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or apparatus. Without further limitations, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or apparatus that includes said element.

[0070] The features and performance of the present invention will be further described in detail below with reference to embodiments.

[0071] Example 1

[0072] Please see Figure 1 A radio frequency receiver link topology optimization method based on empirical intervention genetic algorithm, specifically including the following steps:

[0073] Step S1: Map the RF receiving link topology to the genetic code of the genetic algorithm through coding design;

[0074] Step S2: Based on the gene encoding, perform genetic algorithm selection, crossover rule design, and mutation rule design;

[0075] Step S3: Design a constrained multi-objective optimization fitness function based on the performance indicators and design constraints of the RF receiver link;

[0076] Step S4: Integrate the expert experience in RF receiver link design with the genetic algorithm selected in step S2;

[0077] Step S5: Iteratively optimize the link topology using the computational process of the fused genetic algorithm.

[0078] In this embodiment, specifically, step S1 includes:

[0079] Step S11: Number all n devices on the link sequentially from 1 to n;

[0080] Step S12: Use the decimal array of length n as the gene code. The numbers in the array can be arbitrarily sorted to form a one-to-one mapping with different link topologies. To avoid duplicate devices in the link, it is stipulated that the same number cannot appear in the gene code.

[0081] It should be noted that the RF receiving link topology is as follows: Figure 2 As shown, it is composed of several notch filters, amplifiers and attenuators cascaded together. By optimizing the cascading order of the components on the link, different performance design constraints and interference suppression requirements can be met.

[0082] Based on the characteristics of cascaded radio frequency receiver links, this invention designs a method such as... Figure 3 The genetic algorithm gene encoding shown is as follows: First, all n devices on the link are numbered sequentially from 1 to n. Then, a decimal array of length n is used as the gene encoding. The numbers in the array can be arbitrarily sorted, thus forming a one-to-one mapping with different link topologies. Furthermore, in order to avoid duplicate devices in the link, it is stipulated that the same number cannot appear in the gene encoding.

[0083] In this embodiment, specifically, the selection of the genetic algorithm includes:

[0084] The roulette wheel selection algorithm is used to perform probabilistic selection operations on the population in each generation. It should be noted that the genetic algorithm is a method that searches for the optimal solution by simulating the natural evolution process, transforming the optimization process of the problem into a process similar to gene selection, crossover, and mutation in biological evolution. Therefore, in the genetic algorithm, the higher the fitness of a certain gene, the higher the probability that gene will be selected. Genes with high fitness represent high-performance link topologies. In order to ensure that genes with higher fitness can be inherited with a higher probability, this invention uses the roulette wheel selection algorithm to perform probabilistic selection operations on the population in each generation.

[0085] In this embodiment, specifically, the design of the cross rule includes:

[0086] Two points are randomly selected in the gene coding as the start and end points of the crossover;

[0087] By reordering the numbers contained in the cross segments of parent generation 1 according to their order of appearance in parent generation 2, a new offspring is obtained.

[0088] By updating the intersection segment of parent generation 2 in the same way, another child generation is obtained;

[0089] It should be noted that gene crossover in genetic algorithms generates two new offspring by exchanging partial gene segments between two parent generations. Each offspring inherits some characteristics from both parents. However, for gene encoding in link topology, directly exchanging gene segments between two parents may result in unreasonable offspring (e.g., ...). Figure 4 The left image shows the crossover process of two genes with a length of 8. At this point, duplicate numbers will appear in the gene coding, indicating a duplicate component in the link topology, which clearly contradicts the description in step S1. Therefore, the gene crossover rule in this invention is:

[0090] First, two points are randomly selected on the gene as the start and end points of the crossover. Then, the numbers contained in the crossover segment of parent generation 1 are reordered according to their order of appearance in parent generation 2 to obtain a new offspring. Finally, the crossover segment of parent generation 2 is updated in the same way to obtain another offspring.

[0091] In this embodiment, specifically, the design of the mutation rules includes:

[0092] Two points on the gene coding are randomly selected as mutation points, and then the numbers at the two mutation points are swapped to obtain the mutated gene;

[0093] It should be noted that both crossover and mutation operations are used to expand the random search range of the genetic algorithm and avoid getting trapped in local optima. The mutation rules in this invention are as follows: Figure 5 As shown ( Figure 5 Taking a gene mutation process of length 8 as an example, firstly, two points on the link topology gene are randomly selected as mutation points, and then the numbers at the two mutation points are swapped to obtain the mutated gene.

[0094] In this embodiment, specifically, the fitness function is the criterion for evaluating the quality of a solution, and is usually composed of the optimization objective and constraints of the problem to be solved. This invention designs a constrained multi-objective optimization fitness function targeting the performance indicators and design constraints of the RF receiver link. The link performance indicators mainly include gain G, noise figure NF, and output P. -1 The optimization objective is to maximize the link output P while satisfying the design constraints on the noise figure. -1 Simultaneously, the gain should be as stable as possible when the link size changes. Based on the above rules, the multi-objective optimization fitness function designed in this invention is as follows:

[0095] f=(∝1×G normal +∝2×NF normal +∝3×OP1dB normal )×δ (1)

[0096] in:

[0097] ∝1, ∝2, and ∝3 represent gain, noise, and output P, ​​respectively. -1 The weight of each indicator in the overall optimization objective is used to achieve multi-objective optimization. This is done to avoid weight imbalance in multi-objective optimization caused by large differences in the magnitude of each indicator.

[0098] Gain, noise, and P -1 Normalize the indicators and express them as G. normal NF normal and OP1dB normalTheir definitions are as shown in equations (2), (3) and (4) respectively.

[0099]

[0100]

[0101]

[0102] in:

[0103] G represents the gain;

[0104] G e The desired link gain;

[0105] NF is the noise figure;

[0106] OP1dB max and OP1dB min These represent the maximum and minimum output P that the link can achieve. -1 ;

[0107] It should be noted that once a certain topology of the link is determined, the various indicators can be calculated using the RF link cascading formulas shown in equations (5), (6), and (7):

[0108] G = G1 + G2 + ... + G n (5)

[0109]

[0110]

[0111] For the noise figure design constraints of the RF receiver link, the link noise figure is limited to the constraint range by adding a penalty function δ:

[0112]

[0113] NF_constraint is the noise figure NF constraint.

[0114] In this embodiment, it should be further noted that directly using the genetic algorithm described in steps S1 to S3 to perform a global search of the feasible topology solution set of the RF receiving link is extremely inefficient and time-consuming. This invention innovatively integrates expert experience in RF link design with a genetic algorithm. The experience-based genetic algorithm can significantly reduce the solution search space and improve computational efficiency; furthermore, it can filter the optimization results, further eliminating unreasonable solutions and improving robustness.

[0115] So-called expert experience refers to the quantifiable design principles or difficult-to-quantify design thinking of the radio frequency receiving link accumulated and refined through repeated iterations and full verification in the long-term design process. This invention introduces expert experience so that the link topology optimized by the algorithm is closer to the link topology designed by experienced designers.

[0116] Regarding the radio frequency receiving link involved in this invention, some expert experience can be summarized as follows:

[0117] Experience 1: When considering the notch filtering order of interference signals, notch high-power interference signals first, and notch low-power interference signals later.

[0118] Experience 2: When optimizing the topology, pay attention not only to the output P of the entire link. -1 Furthermore, the input P at each notch filter on the link should be guaranteed. -1 Satisfy specific constraints;

[0119] Experience 3: The further forward the notch filter is in the link, the lower its input P. -1 As high as possible.

[0120] In this embodiment, specifically, the process of using the aforementioned expert experience to intervene in the genetic algorithm in step S4 is as follows: Figure 6 As shown, the specific method is described below:

[0121] Step S41: Based on the notch filter order in Experience 1, determine the relative order of notch filters in the gene encoding. Then, while ensuring the relative order of the notch filters remains unchanged, complete gene crossover and mutation, thereby significantly reducing the search space for solutions. For example, if the link has n devices, including m notch filters, the feasible solution space will be reduced from... Reduce to

[0122] Step S42: Integrate the multi-objective optimization fitness function with Empirical Equations 2 and 3 by setting the penalty function as shown in Equations (9) and (10) (where P -1_notchfilter Input P at each notch filter -1 (Constraints), experience can be incorporated into the fitness function through constraints, and the modified fitness function is shown in Equation (11);

[0123] f=f×δ2×δ3 (11)

[0124]

[0125]

[0126] It is worth noting that expert experience in RF link design includes, but is not limited to, the three types of expert experience listed above. As long as they meet the definition of expert experience in RF link design as described in this invention, and are similar to the methods described in this invention for improving the efficiency and robustness of genetic algorithms using expert experience, they all fall within the scope of protection of this invention.

[0127] In this embodiment, specifically, step S5 includes:

[0128] For the topology optimization problem of the radio frequency receiving link, after designing an empirically intervened genetic algorithm using the methods described in steps S1 to S4, iterative optimization can be performed according to the calculation process of the genetic algorithm to finally obtain the optimal link topology that meets the performance design constraints.

[0129] It is worth noting that the optimal link topology may be the globally optimal link topology or the locally optimal link topology. It is the optimal link topology that the algorithm can find within a specific computation time requirement.

[0130] Example 2

[0131] This embodiment uses the suppression of multiple interference signals in a certain electromagnetic environment by a radio frequency receiving link as an example to verify the algorithm of the present invention.

[0132] Suppose that a certain electromagnetic spectrum contains 10 relatively strong interference signals, and the frequency distribution and power of the interference signals are shown in the table below.

[0133] Table 1. Frequency distribution and power of interference signals in a certain electromagnetic spectrum.

[0134]

[0135]

[0136] Adopting such Figure 2 The RF receiver link shown employs multiple notch filters to suppress interference, and utilizes low-noise amplifiers and attenuation to compensate for link gain, improve link linearity, and reduce link noise figure. The link's performance design constraints are as follows:

[0137] a) Gain: 18dB;

[0138] b) Noise figure: ≤12dB;

[0139] c) Output P -1 ≥-5dBm;

[0140] d) Input P at each notch filter in the link -1 ≥-15dBm.

[0141] The algorithm of this invention optimizes the radio frequency receiving link topology for the interference suppression scenario in the above-mentioned electromagnetic environment, including the following steps:

[0142] Step 1: Link Topology and Gene Encoding Mapping

[0143] The scenario described in the example includes 10 strong interference signals, requiring 10 notch filters corresponding to the frequency bands for interference suppression. In addition, it includes 8 low-noise amplifiers and 2 fixed attenuators. Therefore, there are a total of 20 devices in the link. These devices are numbered sequentially from 1 to 20. The link topology can be characterized by a gene code of length 20, with different numbering sequences in the gene mapping one-to-one with different link topologies.

[0144] Step 2: Design of Gene Manipulation Rules

[0145] Based on the link topology gene encoding described in step one, the gene selection, crossover, and mutation rules in this embodiment are described as follows:

[0146] (1) Select operation

[0147] The example uses a roulette wheel selection method to perform probabilistic selection on each generation of the population, thereby ensuring that genes with higher fitness are selected with a higher probability.

[0148] (2) Cross-rule design

[0149] The gene crossover method in this embodiment is as follows: First, two points are randomly selected on the gene as the start and end points of the crossover; then, the numbers contained in the crossover segment of parent generation 1 are reordered according to their order of appearance in parent generation 2 to obtain a new offspring; finally, the crossover segment of parent generation 2 is updated in the same way to obtain another offspring.

[0150] (3) Design of variation rules

[0151] The gene mutation method in this embodiment is as follows: First, two points on the link topology gene are randomly selected as mutation points, and then the numbers at the two mutation points are swapped to obtain the mutated gene.

[0152] Step 3: Fitness Function Design

[0153] The fitness function of the embodiment is shown in equation (1), which is a combination of link gain, noise figure, and output P. -1 A multi-objective optimization function.

[0154] For the scenario described in the embodiment, for a specific link topology, the gain, noise figure, and output P of the current topology are first calculated using equations (5), (6), and (7). -1Then, the normalized gain, noise figure, and output P are calculated using equations (2), (3), and (4). -1 And substitute them into equation (1) in turn; finally, use equation (8) to introduce the design constraint of the noise figure into the fitness function (1) in the form of a penalty function δ. In this example, NF constraint =12.

[0155] Step 4: Empirical intervention in the genetic algorithm

[0156] In this embodiment, expert experience in RF link design is leveraged to intervene in the genetic algorithm process, improving its efficiency and robustness:

[0157] (1) Based on the interference signal frequency and power information in Empirical Study 1 and Table 1, the relative order of notch filters in the link topology gene encoding is determined, and the relative order of notch filters is maintained during subsequent gene crossover and mutation processes, so that the search space of the solution expands from... Reduce to

[0158] (2) Based on experience 2 and 3, use equations (9) and (10) to apply the input P at each notch filter in the link in the embodiment. -1 The constraint is converted into a penalty function and substituted into equation (11) to obtain a new fitness function. In this example, P -1_notchfilter =-15.

[0159] Step 5: Link Topology Optimization

[0160] After designing an experience-based genetic algorithm using the steps described above, iterative optimization of the link topology is performed using the computational flow of the genetic algorithm. Simulation results are shown in Table 2. Simulation verification demonstrates that the algorithm can efficiently find link topologies that meet design constraints (such as...). Figure 7 As shown), the fitness value convergence process is as follows: Figure 8 As shown.

[0161] Table 2 Simulation results of the embodiments

[0162]

[0163] The embodiments described above merely illustrate specific implementation methods of this application, and while the descriptions are detailed and specific, they should not be construed as limiting the scope of protection of this application. It should be noted that those skilled in the art can make various modifications and improvements without departing from the concept of the technical solution of this application, and these modifications and improvements all fall within the scope of protection of this application.

[0164] This background section is provided to generally present the context of the invention. The work of the currently named inventors, the work to the extent described in this background section, and aspects of this section that did not constitute prior art at the time of application are neither expressly nor impliedly acknowledged as prior art to the invention.

Claims

1. A method for optimizing the topology of a radio frequency receiving link based on an empirical intervention genetic algorithm, characterized in that, include: Step S1: Map the RF receiving link topology to the genetic code of the genetic algorithm through coding design; Step S2: Based on the gene encoding, perform genetic algorithm selection, crossover rule design, and mutation rule design; Step S3: Design a constrained multi-objective optimization fitness function based on the performance indicators and design constraints of the RF receiver link; Step S4: Integrate the expert experience in RF receiver link design with the genetic algorithm selected in step S2; Step S5: Iteratively optimize the link topology using the computational flow of the fused genetic algorithm; The expert experience mentioned includes: Experience 1: When considering the notch filtering order of interference signals, notch high-power interference signals first, and notch low-power interference signals later. Experience 2: When optimizing the topology, we should not only focus on the output P-1 of the entire link, but also ensure that the input P-1 at each notch filter on the link meets specific constraints. Tip 3: The closer each notch filter is to the beginning of the link, the higher its input P-1 should be. Step S4 includes: Step S41: Based on the notch filter order in Experience 1, determine the relative order of notch filters in gene coding, and then complete gene crossover and mutation while ensuring that the relative order of notch filters remains unchanged, thereby greatly reducing the search space of the solution. Step S42: Integrate the multi-objective optimization fitness function with experiences 2 and 3. By setting a penalty function, the experiences can be incorporated into the multi-objective optimization fitness function through constraints to obtain the modified fitness function.

2. The radio frequency receiving link topology optimization method based on empirical intervention genetic algorithm according to claim 1, characterized in that, Step S1 includes: Step S11: Number all n devices on the link sequentially from 1 to n; Step S12: Use the decimal array of length n as the gene code. The numbers in the array can be arbitrarily sorted to form a one-to-one mapping with different link topologies. To avoid duplicate devices in the link, it is stipulated that the same number cannot appear in the gene code.

3. The radio frequency receiving link topology optimization method based on empirical intervention genetic algorithm according to claim 1, characterized in that, The selection of the genetic algorithm includes: A roulette wheel selection method is used to perform probabilistic selection operations on the population in each generation.

4. The radio frequency receiving link topology optimization method based on empirical intervention genetic algorithm according to claim 1, characterized in that, The design of the cross rules includes: Two points are randomly selected in the gene coding as the start and end points of the crossover; By reordering the numbers contained in the cross segments of parent generation 1 according to their order of appearance in parent generation 2, a new offspring is obtained. By updating the intersection segment of parent generation 2 in the same way, another child generation is obtained.

5. The radio frequency receiving link topology optimization method based on empirical intervention genetic algorithm according to claim 1, characterized in that, The mutation rule design includes: Two points on the gene coding are randomly selected as mutation points, and then the numbers at the two mutation points are swapped to obtain the mutated gene.

6. The radio frequency receiving link topology optimization method based on empirical intervention genetic algorithm according to claim 1, characterized in that, The multi-objective optimization fitness function includes: in: , , These are the gain, noise, and output P, ​​respectively. -1 The weight of the total optimization objective; This is the normalized gain; The normalized noise figure; The normalized link output P -1 ; Let be the penalty function.

7. The radio frequency receiving link topology optimization method based on empirical intervention genetic algorithm according to claim 6, characterized in that, The Calculated using the following formula: The Calculated using the following formula: The Calculated using the following formula: The penalty function ,include: in: For gain; The desired link gain; Noise figure; and These represent the maximum and minimum output P that the link can achieve. -1 ; The noise figure NF constraint condition.

8. The radio frequency receiving link topology optimization method based on empirical intervention genetic algorithm according to claim 7, characterized in that, The modified fitness function includes: in: ； 。

Images