A radome electromechanical integration optimization method based on sparrow search algorithm

Abstract

The application discloses a kind of based on sparrow search algorithm's radome electromechanical integration optimization method, comprising the following steps;Step (1): input radome structure parameter and electric parameter;Step (2): determine radome optimization target, optimization variable;Step (3): multiple target is converted into single target optimization;Step (4): with radome optimization target function as fitness function, determine fitness function, establish optimization model;Step (5): find the optimal target of radome optimization model;Step (6): initialize population radome thickness parameter in optimization algorithm;Step (7): calculate to obtain fitness value, and population is classified;Step (8): update population radome thickness;Step (9): judge whether to meet maximum iteration number;Step (10): output the optimal value of sighting difference and transmission loss.The sparrow search algorithm is combined with radome optimization model in the application, and the efficient optimization of radome structure and its electromechanical performance is realized.

Description

TECHNICAL FIELD

[0001] The application belongs to the technical field of radomes, and particularly relates to a radome electromechanical integration optimization method based on a sparrow search algorithm. BACKGROUND

[0002] With the development of modern communication systems, radomes play a crucial role in wireless communication equipment, as they not only protect antennas from external environmental influences, but also directly affect the electromagnetic performance and power efficiency of the system. The electromechanical integration optimization of radomes is particularly critical, as it involves multiple aspects such as structural design, material selection, electromagnetic compatibility, and energy efficiency optimization.

[0003] In the prior art, Hu Bing et al. proposed an improved conjugate gradient algorithm in the document "Optimized Conjugate Gradient Algorithm for Antenna-Radome System Analysis" to optimize the antenna-radome system, reducing the multiplication operation of matrices and vectors and thus shortening the operation time. However, the conjugate gradient algorithm is prone to local optimization in complex multi-peak function space, making it difficult to find a global optimal solution, thus making it difficult to effectively handle complex design spaces and multi-objective optimization problems.

[0004] Li Jie et al. proposed using the complex method to optimize the electrical performance of the radome in the document "Optimization Design of Radome Electrical Performance Based on Surface Model", but the complex method is limited by the selection of initial values and is difficult to solve high-dimensional problems, thus it is not suitable for finding a global optimum in a complex space.

[0005] In summary, traditional gradient optimization algorithms are difficult to effectively handle complex design spaces and multi-objective optimization problems. SUMMARY

[0006] In order to overcome the deficiencies of the prior art, the purpose of the present application is to provide a radome electromechanical integration optimization method based on a sparrow search algorithm, which combines the sparrow search algorithm with the radome optimization model to achieve efficient optimization of the radome structure and its electromechanical performance.

[0007] To achieve the above purpose, the technical solution adopted by the present application is:

[0008] A radome electromechanical integration optimization method based on a sparrow search algorithm, comprising the following steps:

[0009] Step (1): input the radome structural parameters and electrical parameters;

[0010] Step (2): take the boresight error (BSE) and transmission loss (TL) as the optimization objectives, and the radome thickness d1,…,d m as optimization variables;

[0011] Step (3): two targets of sighting error (BSE) (mrad) and transmission loss (TL) (dB) are changed into one target by weighted sum, and converted into single target optimization;

[0012] Step (4): the radome optimization target function is taken as the fitness function, and an optimization model is established;

[0013] Step (5): the sparrow search algorithm is combined with the optimization model to find the optimal target of the radome optimization model;

[0014] Step (6): the population radome thickness parameters in the algorithm are initialized;

[0015] Step (7): the position of each individual in the population, i.e. the radome thickness value of each individual, is substituted into the radome optimization model fitness function, the fitness value is calculated, and the population is classified;

[0016] Step (8): the population radome thickness is updated;

[0017] Step (9): whether the iteration stopping condition is met is judged;

[0018] Step (10): the optimal value of sighting error and transmission loss is output.

[0019] The step (1) is specifically:

[0020] The structure parameters of the input radome bottom diameter, cover height, material relative dielectric constant, loss tangent, outer surface coating thickness, coating relative dielectric constant, coating loss tangent, internal antenna diameter, height of antenna rotation center, and the electrical parameters of working frequency are inputted;

[0021] The step (3) is specifically:

[0022] Two targets of sighting error (BSE) (mrad) and transmission loss (TL) (dB) are changed into one target by weighted sum, and single target optimization is carried out to ensure that the true Pareto frontier is obtained, and the optimization target is converted as follows:

[0023]

[0024] d1,…,d m The thickness value of the radome, BSE is the sighting error, TL is the transmission loss, and a is the weight coefficient.

[0025] The step (4) is specifically: the radome optimization target function is taken as the fitness function, the radome thickness is taken as the optimization variable, and the radome optimization model is established, as shown below:

[0026]

[0027] s.t.dmin ≤d i ≤d max i = 1, …, m

[0028] wherein, is a fitness function, d1, …, d m is a radome thickness, a is a weight coefficient, d min and d max represent the upper and lower bounds of the value of the thickness d i .

[0029] The step (5) is specifically: taking the radome optimization model function as a target function and combining it with the code of the sparrow search algorithm, combining the optimization algorithm with the optimization model, and using the search mechanism of the sparrow search algorithm to find the radome thickness value corresponding to the optimal target of the radome optimization model.

[0030] The step (6) is specifically: first, according to the complexity of the function, initializing the sparrow population number and the algorithm iteration number of the sparrow search optimization algorithm, the variable dimension and the variable upper and lower bounds of the radome optimization model, and then initializing the population position according to the variable dimension and the variable upper and lower bounds, the population position being the radome thickness value.

[0031] The step (7) is specifically: substituting the position of each individual in the population, i.e. the radome thickness value of each individual, into the radome optimization model fitness function, calculating to obtain the fitness value, and then sorting the fitness value in ascending order, taking the top 20% of the sorted population as the discoverer, the last 80% of the population as the follower, and randomly selecting 20% from the population as the alarm, which all have the information of the radome thickness value and the radome optimization model fitness function value. Through population classification, individuals with better fitness values are found, so as to facilitate subsequent searching in the vicinity of the better fitness value individuals, so as to find the radome thickness value corresponding to the optimal target value with greater probability.

[0032] The discoverer position updating formula is:

[0033]

[0034] wherein, indicates the position information of the ith sparrow in the jth dimension at the tth iteration; a e [0, 1] indicates a random number; R2 e [0, 1] indicates an alarm value; ST e [0.5, 1] indicates a safety value; Q indicates a random number subject to a standard normal distribution; iter max indicates the maximum iteration number;

[0035] The follower position updating formula is:

[0036]

[0037] wherein, represents the jth dimension of the position of the discoverer individual with the best fitness at the t+1th iteration, represents the jth dimension of the position of the individual with the worst fitness at the tth iteration, A represents a 1xd matrix, wherein each element is randomly assigned as -1 or 1, A + T T -1 , n represents the population size, L represents a 1xd matrix with all elements being 1, and d represents the dimension;

[0038] The position updating formula of the vigilante is:

[0039]

[0040] wherein, represents the jth dimension of the global optimal position at the tth iteration; β is a step adjustment parameter, which is subject to a normal distribution with a mean of 0 and a variance of 1; K is a random number in [-1, 1]; and ε is a constant to avoid a denominator of 0. i represents the fitness value of the current individual, g and f w respectively represent the fitness values of the global best and worst individuals at the current iteration.

[0041] The step (8) is specifically:

[0042] After dividing the population into discoverers, followers and vigilantes, the position iteration formula of each is used for updating, that is, the radome thickness value is calculated according to the iteration formula to obtain a new radome thickness value of the population.

[0043] The step (9) is specifically: judging whether the maximum iteration number is met, if yes, going to step (10) to output the optimized target value, and if not, going to step (7).

[0044] The step (10) is specifically: after the optimization algorithm meets the maximum iteration number, ending the iteration calculation and outputting the optimal values of the boresight error and the transmission loss.

[0045] The present application has the following advantages:

[0046] 1. In the optimization of the radome, the present application can effectively avoid falling into a local optimal solution and ensure the optimization ability in a multivariate, nonlinear and multimodal optimization problem by using the position updating formula of the discoverers, followers and vigilantes according to the steps (7), (8) and (9) for iteration optimization.

[0047] ​​​2. The traditional optimization method often depends on initial value selection and local search, while the optimization method has self-adaptability and high efficiency, the fitness value is calculated according to step (7), and the population is divided into discoverers, followers and vigilants, and the individual position is updated according to the formula of the three, which can quickly converge to the global optimal solution in the complex design space, and effectively improve the optimization efficiency. At the same time, the optimization method of the application is not only limited to the design of the radome, but also can be popularized to other mechatronic optimization problems, such as antenna design and other fields, and has strong universality and popularization value. BRIEF DESCRIPTION OF DRAWINGS

[0048] Figure 1 A flowchart of a radome mechatronic optimization method based on sparrow search algorithm is provided for the embodiments of the application.

[0049] Figure 2 A comparison chart of the results of optimizing the radome mechatronic model by different methods provided for the embodiments of the application. DETAILED DESCRIPTION

[0050] The application will be further described in detail below with reference to the drawings.

[0051] Please refer to Figure 1 , Figure 1 A flowchart of a radome mechatronic optimization method based on sparrow search algorithm is provided for the embodiments of the application, which comprises:

[0052] (1) Input the structural parameters and electrical parameters of the radome.

[0053] Input the structural parameters such as the diameter of the bottom surface of the radome, the height of the radome body, the relative dielectric constant of the material, the loss tangent, the thickness of the outer surface coating, the relative dielectric constant of the coating, the loss tangent of the coating, the diameter of the internal antenna, the height of the antenna rotation center, and the electrical parameters such as the working frequency.

[0054] (2) Determine the optimization target and the optimization variable.

[0055] Take the boresight error (BSE) and the transmission loss (TL) as the optimization target, and the thicknesses d1,…,d m as the optimization variable.

[0056] (3) Convert the multi-objective into single-objective optimization.

[0057] The two targets of BSE (boresight error) (mrad) and TL (transmission loss) (dB) are converted into one target through weighted sum for single-objective optimization to ensure that the true Pareto front is obtained. The optimization target conversion is as follows:

[0058]

[0059] d1,…,d m BSE is the pointing error, TL is the transmission loss, and a is the weight coefficient.

[0060] (4) Determine the fitness function and establish the optimization model.

[0061] The optimization model of the radome is established with the radome optimization objective function as the fitness function and the radome thickness as the optimization variable, as shown below:

[0062]

[0063] s.t.d min ≤d i ≤d max i = 1,…,m

[0064] wherein, is the fitness function, d1,…,d m is the radome thickness, a is the weight coefficient, and d min and d max represent the upper and lower bounds of the thickness d i .

[0065] (5) Combine the optimization algorithm with the optimization model.

[0066] The radome optimization model function is taken as the objective function and combined with the code of the sparrow search algorithm. By combining the optimization algorithm with the optimization model, the search mechanism of the sparrow search algorithm is used to find the radome thickness value corresponding to the optimal target of the radome optimization model.

[0067] (6) Initialize the population radome thickness and other parameters.

[0068] First, the population size and the number of iterations of the sparrow search optimization algorithm, the variable dimension and the upper and lower bounds of the variable of the radome optimization model are initialized. Then, the population position is initialized according to the variable dimension and the upper and lower bounds of the variable, and the population position is the radome thickness value.

[0069] Initialization is to determine the values of variables such as population and iteration number, which are obtained according to experience and can be freely set.

[0070] In this experiment, 100 is taken according to the following experimental data.

[0071] (7) Calculate the fitness value and classify the population.

[0072] The position of each individual in the population, i.e. the radome thickness value of each individual, is substituted into the radome optimization model fitness function to calculate the fitness value, and then the fitness values are sorted in ascending order. The top 20% of the sorted population are used as discoverers, the last 80% of the population are used as followers, and 20% of the population are randomly selected as sentinels, all of which have information about the radome thickness value and the radome optimization model fitness function value. The individuals with better fitness values are found through population classification, so that the optimal target value corresponding to the radome thickness value can be found with greater probability in the vicinity of the individuals with better fitness values in the subsequent search.

[0073] The position update formula of the discoverer is:

[0074]

[0075] wherein, represents the position information of the ith sparrow in the jth dimension at the tth iteration; a e [0, 1] represents a random number; R2 e [0, 1] represents an alert value; ST e [0.5, 1] represents a safety value; Q represents a random number subject to a standard normal distribution; iter max represents the maximum number of iterations.

[0076] The position update formula of the follower is:

[0077]

[0078] wherein, represents the jth dimension of the position of the discoverer individual with the best fitness at the t+1th iteration, represents the jth dimension of the position of the individual with the worst fitness at the tth iteration, and A represents a 1 x d matrix, wherein each element is randomly assigned a value of -1 or 1, A + = A T (AA T ) -1 , n represents the population size, L represents a matrix of size 1 x d with elements all being 1, and d represents the dimension.

[0079] The position update formula of the sentinel is:

[0080]

[0081] wherein, represents the jth dimension of the global optimal position at the tth iteration; β is a step adjustment parameter subject to a normal distribution with a mean of 0 and a variance of 1; K e [-1, 1] is a random number; and ε is a constant to avoid a denominator of 0. i represents the fitness value of the current individual, f g represents the fitness value of the individual with the best fitness, and f wrespectively represent the fitness value of the current iteration global best and worst individual.

[0082] (8) Update the population radome thickness.

[0083] After dividing the population into discoverers, followers and vigilantes, update according to the respective position iteration formula, that is, calculate the radome thickness value according to the iteration formula to obtain the new radome thickness value of the population.

[0084] (9) Determine whether the iteration stop condition is met.

[0085] Determine whether the maximum number of iterations set at initialization is met, if met, go to step (10) to output the optimal target value, if not met, go to step (7).

[0086] (10) Output the optimal value of the boresight error and the transmission loss.

[0087] When the optimization algorithm meets the maximum number of iterations, end the iteration calculation and output the optimal value of the boresight error and the transmission loss.

[0088] The advantages of the present application can be further illustrated by the following simulation experiment:

[0089] 1. Antenna radome simulation conditions:

[0090] The antenna radome is a tangent oval radome, and its parameters are shown in Table 1:

[0091] Table 1 Parameters of tangent oval radome

[0092]

[0093]

[0094] 2. Sparrow search algorithm simulation conditions:

[0095] The initialization parameter settings of the sparrow search algorithm are shown in Table 2:

[0096] Table 2 Initialization parameters of sparrow search algorithm

[0097]

[0098] The method of the present application is used for the mechanical and electrical integration optimization of the antenna radome.

[0099] 3. Simulation results:

[0100] The present application is a kind of based on sparrow search algorithm's radome mechanical and electrical integration optimization method. Please see Figure 2 , Figure 2 The comparison chart of the optimization method (SSA) of the present application and grey wolf algorithm (GWO), artificial bee colony algorithm (AHA).Figure 2 (a) shows the iteration diagrams for the three methods. Figure 2 (b) is a time comparison chart. From Figure 2 (a) It can be seen that the fitness values ​​of the Sparrow Search Algorithm (SSA) and the AHA algorithm are not significantly different, the objective function values ​​obtained are relatively small, and the convergence speed is faster. From Figure 2 (b) It can be seen that the SSA algorithm has the shortest iteration time, followed by AHA, and the GWO algorithm has the longest iteration time. Therefore, SSA is the most efficient. To better compare the optimization effects of the three methods, the optimization results are shown in Table 3.

[0101] Table 3. Radome Optimization Results

[0102]

[0103]

[0104] As can be seen from the table, SSA has a shorter iteration time and higher optimization efficiency. Furthermore, for the optimization target values ​​TL (dB) and BSE (mrad), AHA and SSA have a non-dominated relationship, with comparable optimal values. In contrast, GWO has a dominated relationship with the other two algorithms. Therefore, it can be seen that SSA has a better optimization effect on the electromechanical integration of radomes compared to the other two algorithms. This simulation example verifies the effectiveness of the method of this invention. Parts not described in detail in this implementation scheme are common and well-known methods in the industry and will not be described in detail here. The above examples are merely illustrative of this invention and do not constitute a limitation on the scope of protection of this invention. All designs that are the same as or similar to this invention fall within the scope of protection of this invention.

Claims

1. A method for electromechanical integration optimization of a radome based on a sparrow search algorithm, characterized in that, It comprises the following steps: Step (1): input the antenna cover structure parameters and electrical parameters; Step (2): With the antenna radome sight difference (BSE) and transmission loss (TL) as the optimization target, the antenna radome thickness d1,..., d m are optimization variables; Step (3): the boresight error (BSE) (mrad) and transmission loss (TL) (dB) two targets are changed into one target through weighted sum, and converted into single target optimization; Step (4): the antenna cover optimization objective function is taken as the fitness function, and the optimization model is established; Step (5): the sparrow search algorithm is combined with the optimization model to find the optimal target of the antenna cover optimization model; Step (6): initialize the population antenna cover thickness parameters in the algorithm; Step (7): the position of each individual in the population, i.e. the antenna cover thickness value of each individual, is substituted into the fitness function of the antenna cover optimization model, the fitness value is calculated, and the population is classified; Step (8): update the population antenna cover thickness; Step (9): determine whether the maximum number of iterations is met, if yes, go to step (10) to output the optimized target value, otherwise go to step (7); Step (10): output the optimal value of boresight error and transmission loss.

2. The antenna radome electromechanical integration optimization method based on sparrow search algorithm according to claim 1, characterized in that, The step (1) is specifically: The structure parameters of the input antenna cover bottom diameter, cover body height, material relative dielectric constant, loss tangent, outer surface coating thickness, coating relative dielectric constant, coating loss tangent, internal antenna diameter, height of antenna rotation center; The electrical parameters of the working frequency.

3. The antenna radome electromechanical integration optimization method based on sparrow search algorithm according to claim 1, characterized in that, The step (3) is specifically: The boresight error (BSE) (mrad) and transmission loss (TL) (dB) two targets are changed into one target through weighted sum, and single target optimization is carried out, and the optimization target is converted as follows: d1,…,d m BSE is the boresight error, TL is the transmission loss, and a is a weight coefficient.

4. The antenna radome electromechanical integration optimization method based on sparrow search algorithm according to claim 1, characterized in that, The step (4) is specifically: the antenna cover optimization model is as follows: s.t.d min ≤d i ≤d max ,i = 1,..., m wherein, are the fitness functions, d1,..., d m is the radome thickness, a is a weight coefficient, d min and d max represent the upper and lower bounds of the thickness d i values, respectively.

5. The antenna radome electromechanical integration optimization method based on sparrow search algorithm according to claim 1, characterized in that, The step (5) is specifically: the antenna cover optimization model function is taken as the objective function and combined with the code of sparrow search algorithm, and the search mechanism of sparrow search algorithm is used to find the optimal target of the antenna cover optimization model corresponding to the antenna cover thickness value.

6. The antenna radome electromechanical integration optimization method based on sparrow search algorithm according to claim 5, characterized in that, The step (6) is specifically: first, according to the complexity of the function, the sparrow population size and the number of algorithm iterations of the sparrow search optimization algorithm are initialized, the variable dimension and variable upper and lower bounds of the antenna cover optimization model are set, then the population position is initialized according to the variable dimension and variable upper and lower bounds, and the population position is the antenna cover thickness value.

7. The method of claim 6, wherein the method is characterized by: The step (7) is specifically: the position of each individual in the population, i.e. the antenna cover thickness value of each individual, is substituted into the fitness function of the antenna cover optimization model, the fitness value is calculated, and the population is classified; The sorted population is divided into 20% of the population as discoverers, 80% of the population as followers, and 20% of the population as vigilantes; The discoverer position update formula is: wherein, represents the position information of the i-th sparrow in the j-th dimension at the t-th iteration; a e [0, 1] represents a random number; R2 e [0, 1] represents an alert value; ST e [0.5, 1] represents a safety value; Q represents a random number subject to a standard normal distribution; iter max represents the maximum number of iterations; The follower position update formula is: wherein, represents the jth dimension of the position of the best individual at the tth iteration, represents the jth dimension of the position of the worst individual at the tth iteration, A represents a 1 x d matrix, wherein each element is randomly assigned a value of -1 or 1, A + = A T (AA T ) -1 , n represents the population size, L represents a 1 x d matrix with elements all being 1, and d represents the dimension. The vigilante position update formula is: where, xj(t) represents the jth dimension of the global optimal position at the tth iteration; β is a step size adjustment parameter, which is subject to a normal distribution with mean 0 and variance 1; K ∈ [-1, 1] is a random number; ε is a constant to avoid denominator 0; f i f(x) represents the fitness value of the current individual, f g f(x) represents the fitness value of the current individual, f w f(x) represents the fitness value of the current individual, f 8. The antenna radome electromechanical integration optimization method based on sparrow search algorithm according to claim 7, characterized in that, The step (8) is specifically: After the population is divided into discoverers, followers and vigilantes, the position iteration formula of each is used for updating, i.e. the antenna cover thickness value is calculated according to the iteration formula to obtain the new antenna cover thickness value of the population.

9. The method of claim 1, wherein the method is characterized by: The step (10) is specifically: when the optimization algorithm meets the maximum number of iterations, the iteration calculation is ended and the optimal value of boresight error and transmission loss is output.

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