An improved scarab optimization algorithm for connector impedance optimization

Abstract

The application discloses an improved scarab optimization algorithm for connector impedance optimization, comprising the following steps: step S1, determining the size parameter allowable range of the connector; step S2, designing a target function; step S3, initializing a population and generating individuals; step S4, calculating the impedance of the population individuals; step S5, judging whether the impedance requirement is met; step S6, generating an initialized population; step S7, population iteration; and step S8, outputting a solution, iteration times and running time. Through the method in the application, the Circle map chaotic mapping method is adopted to improve the quality of population initialization, so that the suitable solution can be converged more quickly. A large amount of calculation time and resources can be saved. The method in the application is designed to have global search capability, can overcome the local optimal solution problem and is helpful to find the global optimal solution in the connector impedance design space.

Description

Technical Field

[0001] This invention belongs to the field of elastic element product design technology, specifically an improved dung beetle optimization algorithm for connector impedance optimization. Background Technology

[0002] Connector impedance design is a crucial aspect of electronic circuit design, primarily used to ensure the quality and performance of signal transmission. Connector impedance design aims to meet the signal integrity requirements of connectors, preventing signal reflection, attenuation, and timing deviations that can occur during signal transmission between components such as circuit boards, cables, and connectors if impedance mismatch exists. In connector impedance design, the target impedance requirement can be achieved through different connector dimensions. Traditional impedance sizing design is highly empirical, computationally intensive, and offers numerous connector size combinations to achieve the same impedance. Designers need to perform extensive calculations and simulations with various parameter combinations to determine a suitable set of parameters. Finally, based on one or more initially designed suitable parameter sets, adjustments and improvements are made considering material costs to obtain the set of parameters that meets the load-bearing requirements while minimizing material costs. This set of parameters serves as the final connector impedance design parameter.

[0003] The key technical shortcomings in existing connector impedance design mainly lie in dimensional design. Dimensional design is the core of connector impedance matching, but it suffers from the following problems: Connector dimensional design relies too heavily on experience and lacks a systematic approach, often requiring multiple trials and adjustments in complex high-frequency applications, consuming significant time and resources. Traditional dimensional design is often constrained by manual adjustments, making it difficult to achieve high levels of automation and intelligence, resulting in low design efficiency. Dimensional design is typically a multi-parameter optimization problem, but existing algorithms struggle to handle high-dimensional optimization spaces, easily getting trapped in local optima and limiting the maximization of connector performance. Differences in dimensional parameter selection exist between different connector types and manufacturers, lacking universal standards, leading to inconsistencies in design results and interoperability issues. Connector dimensional design struggles to achieve broad impedance matching across different frequency ranges, especially in broadband or multi-band applications. Existing dimensional design methods ignore the complex interrelationship between material properties and dimensions, making it difficult to fully optimize connector performance.

[0004] Existing connector impedance design techniques suffer from shortcomings, primarily due to reliance on experience, lack of automation, limitations in multi-parameter optimization, insufficient standardization, and neglect of material properties. These issues restrict the efficiency and performance optimization potential of connector impedance design, necessitating smarter, more automated, and more efficient methods to overcome these challenges. Summary of the Invention

[0005] The purpose of this invention is to provide an improved dung beetle optimization algorithm for connector impedance optimization, in order to solve the problems in the prior art mentioned in the background art, such as the reliance on experience, lack of automation, limitation by multi-parameter optimization, insufficient standardization, and neglect of material properties in the dimensional design of connector impedance design.

[0006] To solve the above-mentioned technical problems, the technical solution adopted by the present invention is as follows:

[0007] An improved dung beetle optimization algorithm for connector impedance optimization includes the following steps:

[0008] Step S1: Determine the allowable range of connector size parameters; the connector size parameters include the connector diameter d and the connector length L;

[0009] Step S2, design the objective function of the dung beetle optimization algorithm; the objective function is expressed by the following formula:

[0010]

[0011] In the formula, M is the manufacturing cost of the connector per unit volume, d is the diameter of the connector, L is the length of the connector, and f(x) represents the optimization objective function of the dung beetle optimization algorithm.

[0012] Step S3: Initialize the dung beetle population using a circle map to generate individual dung beetles; the specific steps for initializing the population using a circle map are as follows:

[0013]

[0014] In the formula, parameters a and b are parameters to be set, and x n and x n+1 This represents the current individual and the next individual; mod1 means taking the remainder when modulo 1.

[0015] Step S4: Calculate the impedance of an individual dung beetle;

[0016] Step S5: Compare the impedance calculated in step S4 with the set threshold to determine whether the impedance requirement is met; if it is met, continue; if it is not met, return to step S3 and reinitialize.

[0017] Furthermore, in step S4, the impedance of each individual is calculated, requiring an input impedance. Assuming the current input impedance requirement is 0.01Ω, the impedance of each individual is calculated in S4 and compared with the input impedance requirement. If the calculated impedance is less than the impedance requirement, the algorithm continues; otherwise, it returns to S3 for reinitialization.

[0018] Step S6: Generate an initial dung beetle population;

[0019] Step S7: The dung beetle population begins to iterate;

[0020] Step S8: Output the connector's size parameters, the number of iterations of the dung beetle optimization algorithm, and the running time;

[0021] Step S9: Determine whether the current dung beetle optimization algorithm has the highest running efficiency; if so, end the process; otherwise, optimize the dung beetle optimization algorithm by using the sparrow search algorithm.

[0022] The algorithm reaches its highest efficiency when the dung beetle optimization algorithm enters a steady state, meaning that the solution output by the dung beetle optimization algorithm no longer changes.

[0023] Based on the above technical solution, optimizing the Circle map parameters using the Sparrow Search algorithm includes the following steps:

[0024] Furthermore, since the quality of population initialization affects the efficiency of the algorithm itself, and the population initialization described in this invention is performed using the Circle map chaotic mapping method, it means that the parameters b and a of the Circle map chaotic mapping method will affect the quality of initialization and the operation of the entire algorithm. Therefore, the Sparrow Search algorithm is used to automatically optimize these two parameters to obtain better initial population quality and ultimately improve the efficiency of the algorithm.

[0025] Step A1: Run the dung beetle optimization algorithm to obtain the output connector size parameters, the number of iterations of the dung beetle optimization algorithm, and the running time of the dung beetle optimization algorithm; use the obtained connector size parameters, the number of iterations of the dung beetle optimization algorithm, and the running time of the dung beetle optimization algorithm as metrics to input into the sparrow search algorithm to optimize the Circle map parameters;

[0026] Step A2: Initialize the population according to different Circle map parameters, that is, initialize the population using different parameters a and b;

[0027] Furthermore, only two parameters of the Circle map need optimization (i.e., parameter a and parameter b). Set the upper and lower limits of the parameters to be between 0 and 1, and randomly select different combinations of parameters to generate different initial individuals.

[0028] Step A3, Sparrow Search Algorithm Iteration;

[0029] Furthermore, the number of iterations is limited to 100. Once the limit of 100 iterations is reached, the algorithm terminates.

[0030] Step A4: Output parameters a and b of the Circle map parameters, and return parameters a and b to step S3 for recalculation.

[0031] According to the above technical solution, in step S4, the calculation of the individual impedance of the population is specifically as follows:

[0032]

[0033] Where R is the resistance of the cylindrical part; ρ is the resistivity of the material; L is the length of the cylindrical part; and d is the diameter of the cylindrical part.

[0034] According to the above technical solution, in step S9, the specific conditions for determining whether the current algorithm has the highest running efficiency are: whether the quality of the solution after the algorithm reaches a steady state is the highest, whether the number of iterations is the fewest, and whether the iteration time is the shortest.

[0035] According to the above technical solution, the specific determination of whether the quality of the solution after the algorithm reaches steady state is the highest is as follows: the optimal solution obtained by each algorithm iteration is a set of diameter and length parameters. The volume is calculated by using the diameter and length at the iteration point. The solution with the smallest connector volume among all solutions is the highest quality solution.

[0036] According to the above technical solution, the determination of whether the number of iterations is the minimum is as follows: the number of iterations is directly output by the algorithm, and the solution with the fewest iterations among all solutions is found.

[0037] According to the above technical solution, the determination of whether the iteration time is the shortest is as follows: the iteration time is statistically analyzed by the algorithm, and the solution with the shortest iteration time is found.

[0038] Compared with the prior art, the present invention has the following beneficial effects:

[0039] The improved dung beetle optimization algorithm, as described in this invention, demonstrates high efficiency in connector impedance optimization. It improves population initialization quality by employing a circle map chaotic mapping method, leading to faster convergence to a suitable solution. This saves significant computation time and resources. The method is designed with global search capabilities, overcoming local optima and facilitating the finding of the global optimum in the connector impedance design space. This helps ensure maximum connector performance. The improved dung beetle optimization algorithm is adaptive, capable of handling connector design problems of varying types and complexities. This means it can be used for diverse applications and requirements without large-scale algorithm adjustments. The algorithm exhibits diversity in population initialization and regeneration. This ensures the algorithm starts searching from different initial points, improving global search efficiency and reducing the risk of getting trapped in local optima. When the algorithm's performance is not optimal, it automatically inputs the solution, iteration count, and runtime into the sparrow search algorithm parameter optimizer for further self-optimization. This enhances the algorithm's robustness and performance.

[0040] This algorithm is suitable for connector impedance optimization, but it can also be extended to other optimization problems, especially those requiring global search and adaptability. This makes it widely applicable in engineering, science, and technology. The objective function includes the connector manufacturing cost as an optimization objective, which helps reduce manufacturing costs and improve economic efficiency. Attached Figure Description

[0041] Figure 1 This is a schematic diagram of a connector that requires impedance optimization according to the present invention;

[0042] Figure 2 This is a flowchart of the dung beetle optimization algorithm of the present invention. Detailed Implementation

[0043] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0044] Example 1

[0045] like Figure 2 As shown, an improved dung beetle optimization algorithm for connector impedance optimization is characterized by the following steps:

[0046] Step S1: Determine the allowable range of connector size parameters; the connector size parameters include the connector diameter d and the connector length L;

[0047] Step S2, design the objective function of the dung beetle optimization algorithm; the objective function is expressed by the following formula:

[0048]

[0049] In the formula, M is the manufacturing cost of the connector per unit volume, d is the diameter of the connector, L is the length of the connector, and f(x) represents the optimization objective function of the dung beetle optimization algorithm.

[0050] Step S3: Initialize the dung beetle population using a circle map to generate individual dung beetles; the specific steps for initializing the population using a circle map are as follows:

[0051]

[0052] In the formula, parameters a and b are parameters to be set, and x n and x n+1 This represents the current individual and the next individual; mod1 means taking the remainder when modulo 1.

[0053] Step S4: Calculate the impedance of an individual dung beetle;

[0054] Step S5: Compare the impedance calculated in step S4 with the set threshold to determine whether the impedance requirement is met; if it is met, continue; if it is not met, return to step S3 and reinitialize.

[0055] Step S6: Generate an initial dung beetle population;

[0056] Step S7: The dung beetle population begins to iterate;

[0057] Step S8: Output the connector's size parameters, the number of iterations of the dung beetle optimization algorithm, and the running time;

[0058] Step S9: Determine whether the current dung beetle optimization algorithm has the highest running efficiency; if so, end the process; otherwise, optimize the dung beetle optimization algorithm by using the sparrow search algorithm.

[0059] The improved dung beetle optimization algorithm, as described in this invention, demonstrates high efficiency in connector impedance optimization. It improves the quality of population initialization by employing a Circle map chaotic mapping method, thereby converging to a suitable solution more quickly. This saves significant computation time and resources. The method in this invention is designed with global search capabilities, overcoming the problem of local optima and facilitating the finding of the globally optimal solution in the connector impedance design space. This helps ensure the maximization of connector performance. The improved dung beetle optimization algorithm is adaptive, capable of adapting to connector design problems of varying types and complexities. This means it can be used for a wide range of applications and requirements without requiring large-scale algorithm adjustments. The algorithm exhibits diversity in population initialization and regeneration. This helps ensure the algorithm starts searching from different initial points, improving the efficiency of the global search and reducing the risk of getting trapped in local optima.

[0060] Example 2

[0061] This embodiment is a further refinement of Embodiment 1.

[0062] Optimizing Circle map parameters using the Sparrow Search algorithm includes the following steps:

[0063] Step A1: Run the dung beetle optimization algorithm to obtain the output connector size parameters, the number of iterations of the dung beetle optimization algorithm, and the running time of the dung beetle optimization algorithm; use the obtained connector size parameters, the number of iterations of the dung beetle optimization algorithm, and the running time of the dung beetle optimization algorithm as metrics to input into the sparrow search algorithm to optimize the Circle map parameters;

[0064] Step A2: Initialize the population according to different Circle map parameters, that is, initialize the population using different parameters a and b;

[0065] Step A3, Sparrow Search Algorithm Iteration;

[0066] Step A4: Output parameters a and b of the Circle map parameters, and return parameters a and b to step S3 for recalculation.

[0067] In step S4, the calculation of individual population impedance is specifically as follows:

[0068]

[0069] Where R is the resistance of the cylindrical part; ρ is the resistivity of the material; L is the length of the cylindrical part; and d is the diameter of the cylindrical part. The impedance of the part can be calculated using the above formula. Each individual part is a set of data consisting of the diameter and length.

[0070] In step S9, the specific conditions for determining whether the current algorithm has the highest running efficiency are: when the solution output by the algorithm no longer changes, the algorithm reaches a steady state; and after the algorithm reaches a steady state, it is determined whether the quality of its solution is the highest, the number of iterations is the fewest, and the iteration time is the shortest.

[0071] The specific determination of whether the quality of the solution is the highest after the algorithm reaches a steady state is as follows: the optimal solution obtained by each iteration of the algorithm is a set of diameter and length parameters. The volume is calculated by using the diameter and length at the iteration point. The solution with the smallest connector volume among all solutions is the highest quality solution.

[0072] The determination of whether the number of iterations is minimal is as follows: the number of iterations is directly output by the algorithm, and the solution with the fewest iterations among all solutions is found.

[0073] The determination of whether the iteration time is the shortest is as follows: the iteration time is statistically analyzed by the algorithm, and the solution with the shortest iteration time is found.

[0074] When the algorithm's performance is not optimal, it can automatically input the solution, number of iterations, and running time into the Sparrow Search algorithm parameter optimizer for further self-optimization. This enhances the algorithm's robustness and performance.

[0075] This algorithm is suitable for connector impedance optimization, but it can also be extended to other optimization problems, especially those requiring global search and adaptability. This makes it widely applicable in engineering, science, and technology. The objective function includes the connector manufacturing cost as an optimization objective, which helps reduce manufacturing costs and improve economic efficiency.

[0076] Example 3

[0077] The inventive concept of this invention is as follows:

[0078] For a connector structure, such as its center pin (used for signal transmission), due to structural considerations, it is not uniformly distributed and there will be some protrusions and depressions. These are impedance discontinuities. For high-speed signals, based on transmission line theory, the interconnection structure of the connector needs to be optimized to make the impedance consistent and controlled within a certain range.

[0079] A simplified diagram of a common connector requiring impedance optimization is shown below. Figure 1 As shown, the main design requirements are the connector size d and the distance L from the signal to the receiving point.

[0080] like Figure 2 The diagram shows a flowchart of an improved dung beetle optimization algorithm for connector impedance optimization. To better illustrate the workflow of this improved dung beetle optimization algorithm for connector impedance optimization, a detailed description is provided below.

[0081] Step 1: Improved Dung Beetle Optimization Algorithm for Connector Impedance Optimization. Its main optimization parameters are the connector dimensions (i.e., connector size d and the distance L from the signal to the receiving point), its constraint is the connector impedance requirement, and its optimization objective is the connector manufacturing cost. The objective function of the algorithm can be expressed by the following formula:

[0082]

[0083] Where M is the manufacturing cost per unit volume of connector, d is the diameter of connector, L is the length of connector, and f(x) represents the optimization objective function, which is used to determine the optimization objective of the algorithm.

[0084] Step 2: To improve the quality of population initialization (i.e., generating the first generation of the population, also known as initializing the population), the Circle map chaotic mapping method is used to perform chaotic mapping on the population initialization. The Circle map chaotic mapping formula is as follows:

[0085]

[0086] Where parameters a and b are parameters to be set, and x n and x n+1 This represents the current individual and the next individual. mod1 means taking the remainder after dividing by 1. Initially, a = 0.5 and b = 0.2, but these parameters are not necessarily the optimal parameter settings. Therefore, the Sparrow Search algorithm is used for optimization in subsequent steps.

[0087] Step 3: Generate the initial population; the formula in Step 2 is used to initialize the population and generate each individual. Constraint checks are then performed on each individual to determine if it meets the impedance requirements under the current parameter configuration. If not, individuals that meet the constraints are regenerated.

[0088] Step 4: Algorithm Iteration. The algorithm begins to run automatically in a loop, outputting the solution, number of iterations, and running time. The algorithm can automatically output the iteration time and number of iterations required to reach a steady state; the total running time is also output, and it is determined whether the current algorithm's running quality is the highest—that is, whether the solution quality is the highest, the number of iterations is the fewest, and the running time is the shortest. If not, the solution, number of iterations, and running time are input into the Sparrow Search algorithm parameter optimizer in Step 5. If yes, the algorithm terminates.

[0089] Step 5: Obtain the algorithm iterations and output the solution, iteration count, and running time. Initialize the population with different Circle map parameters and iterate the algorithm, outputting a set of Circle map parameters. Since the quality of population initialization affects the algorithm's efficiency, and the population initialization described in this invention is performed using the Circle map chaotic mapping method, the parameters 'a' and 'b' of the Circle map chaotic mapping method will affect the initialization quality and the overall algorithm's operation. Therefore, a sparrow search algorithm is used to automatically optimize these two parameters to obtain better initial population quality, ultimately improving the algorithm's running efficiency.

[0090] It should be noted that, in this document, relational terms such as "first" and "second" are used only 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 process, method, article, or apparatus.

[0091] Finally, it should be noted that the above descriptions are merely preferred embodiments of the present invention and are not intended to limit the present invention. Although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art can still modify the technical solutions described in the foregoing embodiments or make equivalent substitutions for some of the technical features. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the protection scope of the present invention.

Claims

1. An improved dung beetle optimization algorithm for connector impedance optimization, characterized in that: Includes the following steps: Step S1: Determine the allowable range of connector size parameters; the connector size parameters include the connector diameter d and the connector length L; Step S2, design the objective function of the dung beetle optimization algorithm; the objective function is expressed by the following formula: In the formula, M is the manufacturing cost of the connector per unit volume, d is the diameter of the connector, L is the length of the connector, and f(x) represents the optimization objective function of the dung beetle optimization algorithm. Step S3: Initialize the dung beetle population using a circle map, generating individual dung beetles. Each individual dung beetle corresponds to a set of connector size parameters, including diameter d and length L. The specific steps for initializing the population using a circle map are as follows: In the formula, parameter a and parameter b are parameters to be set. and This represents the current individual and the next individual; mod1 means taking the remainder when modulo 1. Step S4: Calculate the impedance of the connector corresponding to each individual dung beetle; Step S5: Compare the impedance calculated in step S4 with the set threshold to determine whether the impedance requirement is met; if it is met, continue; if it is not met, return to step S3 and reinitialize. Step S6: Generate an initial dung beetle population; Step S7: The dung beetle population begins to iterate; Step S8: Output the connector's size parameters, the number of iterations of the dung beetle optimization algorithm, and the running time; Step S9: Determine whether the current dung beetle optimization algorithm has the highest running efficiency; if so, end the process; otherwise, optimize the Circle map parameters using the sparrow search algorithm.

2. The improved dung beetle optimization algorithm for connector impedance optimization according to claim 1, characterized in that: Optimizing Circle map parameters using the Sparrow Search algorithm includes the following steps: Step A1: Run the dung beetle optimization algorithm to obtain the output connector size parameters, the number of iterations of the dung beetle optimization algorithm, and the running time of the dung beetle optimization algorithm; use the obtained connector size parameters, the number of iterations of the dung beetle optimization algorithm, and the running time of the dung beetle optimization algorithm as metrics to input into the sparrow search algorithm to optimize the Circle map parameters; Step A2: Initialize the population according to different Circle map parameters, that is, initialize the population using different parameters a and b; Step A3, Sparrow Search Algorithm Iteration; Step A4: Output parameters a and b of the Circle map parameters, and return parameters a and b to step S3 for recalculation.

3. The improved dung beetle optimization algorithm for connector impedance optimization according to claim 1, characterized in that: In step S4, the calculation of individual population impedance is specifically as follows: Where R is the resistance of the cylindrical part; ρ is the resistivity of the material; L is the length of the cylindrical part; and d is the diameter of the cylindrical part. The impedance of the part can be calculated using the above formula. Each individual part is a set of data consisting of the diameter and length.

4. The improved dung beetle optimization algorithm for connector impedance optimization according to claim 1, characterized in that: In step S9, the specific conditions for determining whether the current algorithm has the highest running efficiency are: when the solution output by the algorithm no longer changes, the algorithm reaches a steady state; and after the algorithm reaches a steady state, it is determined whether the quality of its solution is the highest, the number of iterations is the fewest, and the iteration time is the shortest.

5. An improved dung beetle optimization algorithm for connector impedance optimization according to claim 4, characterized in that: The specific determination of whether the quality of the solution is the highest after the algorithm reaches a steady state is as follows: the optimal solution obtained by each iteration of the algorithm is a set of diameter and length parameters. The volume is calculated by using the diameter and length at the iteration point. The solution with the smallest connector volume among all solutions is the highest quality solution.

6. An improved dung beetle optimization algorithm for connector impedance optimization according to claim 4, characterized in that: The determination of whether the number of iterations is minimal is as follows: the number of iterations is directly output by the algorithm, and the solution with the fewest iterations among all solutions is found.

7. An improved dung beetle optimization algorithm for connector impedance optimization according to claim 4, characterized in that: The determination of whether the iteration time is the shortest is as follows: the iteration time is statistically analyzed by the algorithm, and the solution with the shortest iteration time is found.

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