A method and system for transmit power optimization of an improved simulated annealing algorithm

Abstract

The application discloses a transmitting power optimization method and system of an improved simulated annealing algorithm, and the method comprises the following steps: modeling self-emission noise of an amplifier of an ultra-wideband wavelength division multiplexing system, obtaining linear noise of the ultra-wideband wavelength division multiplexing system, and obtaining nonlinear noise of the ultra-wideband wavelength division multiplexing system according to an ISRS GN model; determining the relationship between the throughput of the ultra-wideband wavelength division multiplexing system and the channel transmitting power through a Shannon formula and the linear noise and the nonlinear noise; and optimizing the transmitting power of the ultra-wideband wavelength division multiplexing system through the improved simulated annealing algorithm based on the relationship between the throughput of the ultra-wideband wavelength division multiplexing system and the channel transmitting power. The application can improve the convergence speed of the algorithm while searching for the optimal transmitting power distribution.

Description

TECHNICAL FIELD

[0001] The present application relates to the field of communication technology, in particular to a transmission power optimization method and system based on improved simulated annealing algorithm. BACKGROUND

[0002] With the development of communication technology, in recent years, there have been many applications, such as the Internet of Things, ultra-high-definition video streaming and cloud computing, which require higher transmission capacity. In order to meet this challenge, in addition to using space division multiplexing technology to improve capacity in optical fiber communication systems, another key research direction is to use ultra-wideband wavelength division multiplexing technology. The main factor limiting the capacity of ultra-wideband wavelength division multiplexing communication systems is the nonlinear effect occurring in optical fibers, mainly manifested as four-wave mixing between frequency components and stimulated Raman scattering between channels. Stimulated Raman scattering between channels amplifies the energy of low-frequency photons by consuming the energy of high-frequency photons, resulting in significant differences in the performance of each wavelength division multiplexing channel. As the total input power or modulation bandwidth increases, these differences become more pronounced. The classical uniform transmission power distribution (assuming that the total input power is evenly distributed among all channels) cannot achieve the best system performance, so finding the appropriate transmission power distribution is crucial to improving system throughput.

[0003] The commonly used transmission power optimization scheme is divided into heuristic algorithms and convex approximation algorithms. Heuristic algorithms provide better transmission power distribution but slower convergence speed, and convex approximation algorithms provide faster convergence speed but poorer transmission power distribution. SUMMARY

[0004] To solve the above technical problems existing in the prior art, the present application provides a transmission power optimization method and system based on improved simulated annealing algorithm, which can improve the convergence speed of the algorithm while finding the best transmission power distribution.

[0005] In one aspect, to achieve the above-mentioned purpose, the present application provides a transmission power optimization method based on improved simulated annealing algorithm, comprising:

[0006] Modeling the amplifier spontaneous emission noise of the ultra-wideband wavelength division multiplexing system to obtain the linear noise of the ultra-wideband wavelength division multiplexing system, and obtaining the nonlinear noise of the ultra-wideband wavelength division multiplexing system according to the ISRS GN model;

[0007] Determining the relationship between the throughput of the ultra-wideband wavelength division multiplexing system and the channel transmission power by Shannon formula and the linear noise and the nonlinear noise;

[0008] Based on the relationship between the throughput of the ultra-wideband wavelength division multiplexing system and the channel transmission power, the transmission power of the ultra-wideband wavelength division multiplexing system is optimized by an improved simulated annealing algorithm, wherein the improved simulated annealing algorithm is to reconstruct the perturbation function of the traditional simulated annealing algorithm, and introduce an iteration number variable, so that the variance of the perturbation function decreases with the increase of the iteration number.

[0009] Preferably, the amplifier spontaneous emission noise of the ultra-wideband wavelength division multiplexing system is modeled, comprising:

[0010]

[0011] Wherein, Ns is the number of fiber spans, G EDFA is the gain of the fiber amplifier EDFA, k is the channel number, NF is the noise factor of the fiber amplifier EDFA, h is the Planck constant, f k is the center frequency of the kth channel, Δf is the channel spacing, is the amplifier spontaneous emission noise.

[0012] Preferably, the ISRS GN model is to extend the nonlinear noise GN model to the ultra-wideband wavelength division multiplexing system and consider the stimulated Raman scattering between channels, wherein the expression of the nonlinear coefficient of the ISRS GN model is:

[0013]

[0014] Wherein, P k,n is the incident power of the kth channel and the nth span, P k is the incident power of the kth channel and the first span, η SPM,n is the influence of self-phase modulation of the nth span, η XPM,n is the influence of cross-phase modulation of the nth span, k is the channel number, Ns is the number of fiber spans, ε is the coherence factor, η k is the nonlinear coefficient.

[0015] Preferably, the relationship between the throughput of the ultra-wideband wavelength division multiplexing system and the channel transmission power is determined as:

[0016]

[0017] Wherein, P k is the in-fiber optical power of the kth channel, N is the total number of channels, η k is the nonlinear coefficient, is the amplifier spontaneous emission noise, and C is the system throughput.

[0018] Preferably, the transmission power of the ultra-wideband wavelength division multiplexing system is optimized by the improved simulated annealing algorithm, comprising:

[0019] S1, numbering the channels in the ultra-wideband wavelength division multiplexing system, and taking the transmission power of each channel as the independent variable x;

[0020] S2, generating a set of random independent variables x' within the preset upper and lower limits as the initial solution;

[0021] S3, obtaining a system throughput function Fitness(x) uniquely related to the transmission power of each channel through a linear noise, a nonlinear noise calculation model, and taking the system throughput function Fitness(x) as the object to be optimized. Replace the fixed variance perturbation function in the traditional simulated annealing algorithm with a segmented Gaussian function, update the independent variable x to obtain x1 through the segmented Gaussian function, and obtain a new fitness function value Fitness(x1);

[0022] S4, according to the Metropolis criterion, accept x1 as the current solution with a preset probability. If the new fitness function value Fitness(x1) is greater than the previous fitness function value, update the optimal solution to x1 and the optimal value to Fitness(x1);

[0023] S5, after a specified number of iterations, output the optimal solution of the transmission power distribution and the optimal value of the channel capacity.

[0024] On the other hand, the embodiment also provides a transmission power optimization system for improving the simulated annealing algorithm, comprising:

[0025] A linear noise generation module is configured to model the amplifier spontaneous emission noise of the ultra-wideband wavelength division multiplexing system to obtain the linear noise of the ultra-wideband wavelength division multiplexing system.

[0026] A nonlinear noise generation module is configured to obtain the nonlinear noise of the ultra-wideband wavelength division multiplexing system according to the ISRS GN model.

[0027] A system capacity determination module is configured to determine the relationship between the throughput of the ultra-wideband wavelength division multiplexing system and the channel transmission power through the Shannon formula and the linear noise and the nonlinear noise.

[0028] A power optimization module is configured to optimize the transmission power of the ultra-wideband wavelength division multiplexing system based on the relationship between the throughput of the ultra-wideband wavelength division multiplexing system and the channel transmission power through the improved simulated annealing algorithm.

[0029] Preferably, the nonlinear noise generation module includes an ISRS GN model building unit, which is used to extend the nonlinear noise GN model to the ultra-wideband wavelength division multiplexing system and consider stimulated Raman scattering between channels to build the ISRS GN model.

[0030] Preferably, the power optimization module includes a perturbation function reconstruction unit, which is used to reconstruct the perturbation function of the simulated annealing algorithm, introduces the iteration number variable, and makes the variance of the perturbation function decrease as the iteration number increases.

[0031] A computer device includes a memory, a processor, and a computer program stored in the memory, wherein the processor executes the computer program to implement the steps of the optimization method.

[0032] A computer-readable storage medium having a computer program / instructions stored thereon, which, when executed by a processor, implement the steps of the optimization method.

[0033] Compared with the prior art, the present invention has the following advantages and technical effects:

[0034] In ultra-wideband wavelength division multiplexing (UWDM) systems, stimulated Raman scattering between channels is considered to accurately model the nonlinear noise generated during signal transmission. Based on this, an improved simulated annealing algorithm is introduced for transmitter power optimization. Compared to commonly used heuristic algorithms, this method improves convergence speed without sacrificing accuracy. Therefore, the method of this invention has significant advantages in evaluating and optimizing the performance of UWDM systems. Attached Figure Description

[0035] The accompanying drawings, which form part of this application, are used to provide a further understanding of this application. The illustrative embodiments and descriptions of this application are used to explain this application and do not constitute an undue limitation of this application. In the drawings:

[0036] Figure 1 This is a flowchart of an improved simulated annealing algorithm for optimizing transmit power according to an embodiment of the present invention;

[0037] Figure 2 This is a flowchart of the improved simulated annealing algorithm according to an embodiment of the present invention;

[0038] Figure 3 This is a comparison chart of the optimization results of an improved simulated annealing algorithm for emitter power optimization according to an embodiment of the present invention with those of genetic algorithm and particle swarm optimization algorithm;

[0039] Figure 4This is a comparison chart showing the optimization speed of an improved simulated annealing algorithm for emitter power optimization according to an embodiment of the present invention, compared with that of genetic algorithm, particle swarm optimization algorithm, and traditional simulated annealing algorithm. Detailed Implementation

[0040] It should be noted that, unless otherwise specified, the embodiments and features described in this application can be combined with each other. This application will now be described in detail with reference to the accompanying drawings and embodiments.

[0041] It should be noted that the steps shown in the flowchart in the accompanying drawings can be executed in a computer system such as a set of computer-executable instructions, and although a logical order is shown in the flowchart, in some cases the steps shown or described may be executed in a different order than that shown here.

[0042] The technical terms used in this embodiment are explained below:

[0043] 1. The traditional Simulated Annealing Algorithm (SAA) is a probabilistic optimization algorithm inspired by the principle of solid annealing. It is a stochastic optimization algorithm based on a Monte Carlo iterative solution strategy, grounded in the similarity between the annealing process of solid materials in physics and general combinatorial optimization problems. Starting from a relatively high initial temperature, the SAA algorithm, with the temperature parameter continuously decreasing, uses probabilistic jump characteristics to randomly search for the global optimum of the objective function in the solution space. That is, it probabilistically escapes local optima and eventually converges to the global optimum.

[0044] The traditional simulated annealing algorithm process includes:

[0045] Initial state: The algorithm starts from a random solution or a certain initial solution;

[0046] Temperature parameter: Set an initial temperature and gradually decrease it during the iteration process;

[0047] Neighborhood search: In each iteration, the algorithm randomly selects a new solution within the neighborhood of the current solution;

[0048] Acceptance criteria: If a new solution is better than the current solution, it is always accepted; if a new solution is worse than the current solution, it may still be accepted according to a probability function related to a temperature parameter. This probability function is usually related to the cost difference between the old and new solutions and the current temperature. As the temperature decreases, the probability of accepting a worse solution gradually decreases.

[0049] Cooling scheme: Define a cooling rate, i.e. how the temperature parameter decreases over time;

[0050] Termination conditions: The algorithm terminates when a predetermined number of iterations is reached, the temperature drops below a certain threshold, or no better solution is found within a certain time.

[0051] 2. The principle of ultra-wideband wavelength division multiplexing (WDM) transmission system is based on optical wavelength multiplexing technology, which allows multiple optical signals of different wavelengths to be transmitted simultaneously in a single optical fiber, thereby significantly increasing the transmission capacity of the optical fiber.

[0052] The working principle of an ultra-wideband wavelength division multiplexing (UWDM) transmission system includes:

[0053] Multiplexing: At the transmitting end, a multiplexer (Mux) combines multiple signals from different light sources into a single signal. Each light source emits a unique wavelength, spaced apart to avoid cross-interference. The multiplexer precisely aligns and combines these different wavelengths using a series of optical elements, such as prisms, filters, or waveguides. The combined signal is then transmitted through a single optical fiber.

[0054] Transmission: Composite signals are transmitted over long distances via optical fibers. The optical fiber serves as the transmission medium, carrying the composite optical signals for transmission.

[0055] Demultiplexing: At the receiver, a demultiplexer separates the composite optical signal back into the original multiple signals. A demultiplexer contains similar optical elements to a multiplexer, but operates on the opposite principle. It identifies and separates each individual wavelength from the combined signal and then directs them to their respective photodetectors. In this way, each wavelength of signal can be received and processed independently.

[0056] The system consists of:

[0057] Light source: generates light signals of a specific wavelength. In a WDM system, multiple light sources of different wavelengths are required to generate the desired light signals.

[0058] Modulator: Converts electrical signals into optical signals and modulates the optical signals to carry the required information.

[0059] Wavelength division multiplexer (WDM): At the transmitting end, multiple optical signals of different wavelengths are combined into a single composite optical signal, which is then sent into an optical fiber for transmission.

[0060] Optical fiber: as a transmission medium, it carries composite optical signals for long-distance transmission.

[0061] Wavelength demultiplexer (WDM): At the receiving end, it separates the composite optical signal into original optical signals of different wavelengths.

[0062] Demodulator: Converts optical signals back into electrical signals for further processing.

[0063] Optical amplifier: Used to compensate for the attenuation of optical signals during transmission, ensuring that the signal can be stably transmitted to the receiving end.

[0064] Monitoring and Management System: Responsible for real-time monitoring and management of the WDM system's operational status to ensure stable operation and efficient transmission.

[0065] Example 1

[0066] This invention proposes an improved method for optimizing transmit power in simulated annealing algorithms, such as... Figure 1 ,include:

[0067] The spontaneous emission noise of the amplifier in the ultra-wideband wavelength division multiplexing (UWDM) system is modeled to obtain the linear noise of the UWDM system, and the nonlinear noise of the UWDM system is obtained according to the ISRS GN model.

[0068] The relationship between the throughput of an ultra-wideband wavelength division multiplexing system and the channel transmit power is determined using Shannon's formula, the linear noise, and the nonlinear noise.

[0069] Based on the relationship between the throughput and channel transmit power of the ultra-wideband wavelength division multiplexing system, the transmit power of the ultra-wideband wavelength division multiplexing system is optimized using an improved simulated annealing algorithm.

[0070] In ultra-wideband wavelength division multiplexing (UWDM) systems, stimulated Raman scattering between channels is considered to accurately model the nonlinear noise generated during signal transmission. Based on this, an improved simulated annealing algorithm is introduced for transmitter power optimization. Compared to commonly used heuristic algorithms, this method improves convergence speed without sacrificing accuracy. The method described in this embodiment has significant advantages in evaluating and optimizing the performance of UWDM systems.

[0071] Furthermore, the spontaneous emission noise of the amplifiers in the ultra-wideband wavelength division multiplexing system is modeled, including:

[0072]

[0073] Where Ns is the number of fiber optic segments, G EDFA Let be the gain of the fiber optic amplifier EDFA, k be the channel number, NF be the noise figure of the fiber optic amplifier EDFA, h be Planck's constant, and f be the noise figure of the fiber optic amplifier EDFA. k Let be the center frequency of the k-th channel, and Δf be the channel spacing. This refers to the amplifier's spontaneous emission noise.

[0074] Furthermore, the ISRS GN model is obtained by extending the nonlinear noise GN model to the ultra-wideband wavelength division multiplexing system and considering stimulated Raman scattering between channels. The expression for the nonlinear coefficients of the ISRS GN model is as follows:

[0075]

[0076] Among them, P k,n Let P be the incident power of the k-th channel and the n-th segment. k Let η be the incident power of the k-th channel and the 1st segment. SPM,n For the effect of self-phase modulation of the nth segment, η XPM,n The effect of cross-phase modulation on the nth span, k is the channel number, Ns is the number of fiber spans, ε is the coherence factor, and η is the cross-phase modulation factor. k These are nonlinear coefficients.

[0077] Specifically, the ISRS GN model is an exact closed-form expression used to evaluate the nonlinear interference power in a coherent optical transmission system when there is stimulated Raman scattering between channels. In addition, the model also considers the dispersion slope and arbitrary transmit power distribution.

[0078] Furthermore, the relationship between the throughput of an ultra-wideband wavelength division multiplexing system and the channel transmit power is determined as follows:

[0079]

[0080] In the formula, P k Let η be the input optical power of the k-th channel, N be the total number of channels, and η be the input optical power of the k-th channel. k These are nonlinear coefficients. C represents the amplifier's spontaneous emission noise, and C represents the system throughput.

[0081] Furthermore, the transmit power of the ultra-wideband wavelength division multiplexing system is optimized using an improved simulated annealing algorithm, as shown in the flowchart below. Figure 2 As shown, it includes:

[0082] S1. Number the channels in the ultra-wideband wavelength division multiplexing system and use the transmit power of each channel as the independent variable x;

[0083] S2. Generate a set of random independent variables x' within preset upper and lower limits as the initial solution;

[0084] S3. Obtain the system throughput function Fitness(x) that is uniquely related to the transmit power of each channel through linear noise and nonlinear noise calculation models, and solve the system throughput function Fitness(x) as the object to be optimized. Replace the perturbation function with fixed variance in the traditional simulated annealing algorithm with a piecewise Gaussian function. Update the independent variable x through the piecewise Gaussian function to obtain x1, and obtain the new fitness function value Fitness(x1).

[0085] S4. According to the Metropolis criterion, x1 is accepted as the current solution with a preset probability. If the new fitness function value Fitness(x1) is greater than the previous fitness function value, the optimal solution is updated to x1 and the optimal value is Fitness(x1).

[0086] S5. After iterating through the loop a specified number of times, output the optimal solution for the transmit power distribution and the optimal value for the channel capacity.

[0087] Optimization results are as follows Figure 3 As shown, the horizontal axis represents the center wavelength of each channel, and the vertical axis represents the transmit power of each channel. The commonly used genetic algorithms and particle swarm optimization algorithms are compared. The ASA algorithm is an improved simulated annealing algorithm, the GA algorithm is a genetic algorithm, and the PSO algorithm is a particle swarm optimization algorithm. It can be seen that the improved simulated annealing algorithm maintains consistency with the current mainstream algorithms in terms of optimization results.

[0088] Optimize speed, such as Figure 4 As shown, the horizontal axis represents the running time of each algorithm for 3000 iterations, and the vertical axis represents the optimal channel capacity. It also compares commonly used genetic algorithms and particle swarm optimization algorithms, as well as the traditional simulated annealing algorithm. The ASA algorithm is an improved simulated annealing algorithm, the GA algorithm is a genetic algorithm, the PSO algorithm is a particle swarm optimization algorithm, and SA is the traditional simulated annealing algorithm. It can be seen that the improved simulated annealing algorithm can converge to the global optimum at a very fast speed, greatly reducing the algorithm's running time.

[0089] Example 2

[0090] This embodiment provides an improved transmit power optimization system based on a simulated annealing algorithm, including:

[0091] The linear noise generation module is used to model the spontaneous emission noise of amplifiers in an ultra-wideband wavelength division multiplexing (UWDM) system and obtain the linear noise of the UWDM system.

[0092] The nonlinear noise generation module is used to obtain the nonlinear noise of the ultra-wideband wavelength division multiplexing system based on the ISRS GN model.

[0093] The system capacity determination module is used to determine the relationship between the throughput of the ultra-wideband wavelength division multiplexing system and the channel transmit power using Shannon's formula, the linear noise, and the nonlinear noise.

[0094] The power optimization module is used to optimize the transmit power of the ultra-wideband wavelength division multiplexing system based on the relationship between the throughput and channel transmit power of the system, using an improved simulated annealing algorithm.

[0095] Furthermore, the nonlinear noise generation module includes an ISRS GN model building unit, which is used to extend the nonlinear noise GN model to the ultra-wideband wavelength division multiplexing system and consider stimulated Raman scattering between channels to build the ISRS GN model.

[0096] The power optimization module includes a perturbation function reconstruction unit, which is used to reconstruct the perturbation function of the simulated annealing algorithm by introducing an iteration number variable, so that the variance of the perturbation function decreases as the number of iterations increases.

[0097] This embodiment also provides a computer device, including a memory, a processor, and a computer program stored in the memory, wherein the processor executes the computer program to implement the steps of the transmit power optimization method of the improved simulated annealing algorithm.

[0098] This embodiment also provides a computer-readable storage medium storing a computer program / instructions thereon, which, when executed by a processor, implements the steps of the transmit power optimization method of the improved simulated annealing algorithm.

[0099] The above are merely preferred embodiments of this application, but the scope of protection of this application is not limited thereto. Any variations or substitutions that can be easily conceived by those skilled in the art within the scope of the technology disclosed in this application should be included within the scope of protection of this application. Therefore, the scope of protection of this application should be determined by the scope of the claims.

Claims

1. A method for optimizing transmit power using an improved simulated annealing algorithm, characterized in that, include: The spontaneous emission noise of the amplifier in the ultra-wideband wavelength division multiplexing (UWDM) system is modeled to obtain the linear noise of the UWDM system, and the nonlinear noise of the UWDM system is obtained according to the ISRS GN model. The relationship between the throughput of an ultra-wideband wavelength division multiplexing system and the channel transmit power is determined using Shannon's formula, the linear noise, and the nonlinear noise. Based on the relationship between the throughput and channel transmit power of the ultra-wideband wavelength division multiplexing system, the transmit power of the ultra-wideband wavelength division multiplexing system is optimized by an improved simulated annealing algorithm. The improved simulated annealing algorithm reconstructs the perturbation function of the traditional simulated annealing algorithm and introduces the iteration number variable so that the variance of the perturbation function decreases as the number of iterations increases. Modeling the spontaneous emission noise of the amplifiers in the ultra-wideband wavelength division multiplexing system includes: Where Ns is the number of fiber optic segments, G EDFA Let be the gain of the fiber optic amplifier EDFA, k be the channel number, NF be the noise figure of the fiber optic amplifier EDFA, h be Planck's constant, and f be the noise figure of the fiber optic amplifier EDFA. k Let be the center frequency of the k-th channel, and Δf be the channel spacing. This refers to the amplifier's spontaneous emission noise. The ISRS GN model is obtained by extending the nonlinear noise GN model to the ultra-wideband wavelength division multiplexing system and considering stimulated Raman scattering between channels. The expression for the nonlinear coefficients of the ISRS GN model is as follows: Among them, P k,n Let P be the incident power of the k-th channel and the n-th segment. k Let η be the incident power of the k-th channel and the first segment. SPM,n For the effect of self-phase modulation of the nth segment, η XPM,n The effect of cross-phase modulation on the nth span, k is the channel number, Ns is the number of fiber spans, ε is the coherence factor, and η is the cross-phase modulation factor. k These are nonlinear coefficients; The relationship between the throughput and channel transmit power of the ultra-wideband wavelength division multiplexing system is determined as follows: In the formula, P k Let η be the input optical power of the k-th channel, N be the total number of channels, and η be the input optical power of the k-th channel. k These are nonlinear coefficients. C represents the amplifier's spontaneous emission noise, and C represents the system throughput. The transmit power of the ultra-wideband wavelength division multiplexing system is optimized using an improved simulated annealing algorithm, including: S1. Number the channels in the ultra-wideband wavelength division multiplexing system and use the transmit power of each channel as the independent variable x; S2. Generate a set of random independent variables x' within preset upper and lower limits as the initial solution; S3. Obtain the system throughput function Fitness(x) that is uniquely related to the transmit power of each channel through linear noise and nonlinear noise calculation models, and solve the system throughput function Fitness(x) as the object to be optimized. Replace the perturbation function with fixed variance in the traditional simulated annealing algorithm with a piecewise Gaussian function. Update the independent variable x through the piecewise Gaussian function to obtain x1, and obtain the new fitness function value Fitness(x1). S4. According to the Metropolis criterion, x1 is accepted as the current solution with a preset probability. If the new fitness function value Fitness(x1) is greater than the previous fitness function value, the optimal solution is updated to x1 and the optimal value is Fitness(x1). S5. After iterating through the loop a specified number of times, output the optimal solution for the transmit power distribution and the optimal value for the channel capacity.

2. A transmit power optimization system based on an improved simulated annealing algorithm, characterized in that, include: The linear noise generation module is used to model the spontaneous emission noise of amplifiers in an ultra-wideband wavelength division multiplexing (UWDM) system and obtain the linear noise of the UWDM system. The nonlinear noise generation module is used to obtain the nonlinear noise of the ultra-wideband wavelength division multiplexing system based on the ISRS GN model. The system capacity determination module is used to determine the relationship between the throughput of the ultra-wideband wavelength division multiplexing system and the channel transmit power using Shannon's formula, the linear noise, and the nonlinear noise. The power optimization module is used to optimize the transmit power of the ultra-wideband wavelength division multiplexing system based on the relationship between the throughput and channel transmit power of the system, using an improved simulated annealing algorithm.

3. The system according to claim 2, characterized in that, The nonlinear noise generation module includes an ISRS GN model building unit, which is used to extend the nonlinear noise GN model to the ultra-wideband wavelength division multiplexing system and consider stimulated Raman scattering between channels to build the ISRS GN model.

4. The system according to claim 2, characterized in that, The power optimization module includes a perturbation function reconstruction unit, which is used to reconstruct the perturbation function of the simulated annealing algorithm by introducing an iteration number variable, so that the variance of the perturbation function decreases as the number of iterations increases.

5. A computer device, comprising a memory, a processor, and a computer program stored in the memory, characterized in that, The processor executes the computer program to implement the steps of the optimization method of claim 1.

6. A computer-readable storage medium having a computer program / instructions stored thereon, characterized in that, When the computer program / instruction is executed by the processor, it implements the steps of the optimization method of claim 1.

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