Wireless sensor network node configuration optimization method based on VGPSO algorithm

Abstract

The invention provides a wireless sensor network node configuration optimization method based on a VGPSO algorithm, and relates to the field of wireless sensor networks, and the method comprises the steps: defining the wireless sensor network node configuration optimization as a problem M; the particle swarm optimization is improved through a random Halton sequence, a multi-factor cosine dynamic parameter adjustment strategy, a multi-population multi-strategy evolution mode and Voronoi diagram guidance; and solving the problem M through an improved particle swarm algorithm, and outputting the global optimal position of each sensor node in the wireless sensor network. And a multi-factor cosine dynamic parameter adjustment strategy is introduced to balance global and local optimization searching capabilities and enhance the diversity of the particle population. A Voronoi polygon is used to guide an evolution direction, and a coverage hole direction is searched by calculating a Voronoi centroid so as to optimize coverage vulnerabilities in a population evolution process.

Description

Technical Field

[0001] The present application relates to the field of wireless sensor networks, and in particular to a method for optimizing node configuration in wireless sensor networks based on a VGPSO algorithm. Background Art

[0002] In the 5G network era, the rapid development of Internet of Things technology has promoted the widespread application of wireless sensor networks (WSN) in many industrial fields such as power equipment monitoring and ship communications. WSN is composed of many sensor nodes, which generally have computing, storage and wireless communication capabilities. The performance of WSN is usually measured by the coverage of sensor nodes. In order to avoid the waste of sensor resources caused by random deployment and reduce the construction cost of the network, it is very important to optimize the configuration of sensor nodes in wireless sensor networks. By optimizing the configuration, sensor nodes can be more evenly distributed and covered in a wider range, thereby effectively utilizing spatial resources and improving the efficiency of environmental perception and information acquisition. This not only helps to improve the performance and reliability of the network, but also reduces the cost of network construction. Summary of the invention

[0003] The purpose of the present invention is to provide a wireless sensor network node configuration optimization method based on the VGPSO algorithm in order to solve the problem of low coverage of existing sensor node configuration methods.

[0004] The above-mentioned purpose of the present application is achieved through the following technical solutions: S1: Define wireless sensor network node configuration optimization as a problem ; S2: Improved particle swarm algorithm through random Halton sequence, multi-factor cosine dynamic parameter adjustment strategy, multi-population and multi-strategy evolution method and Voronoi diagram guidance; S3: The improved particle swarm algorithm is used to solve the problem Solve and output the global optimal position of each sensor node in the wireless sensor network.

[0005] Optionally, step S3 includes: S31: Setup Issues The basic parameters of and the parameters of improved particle swarm algorithm; S32: Determine the optimal number of sensor nodes in the wireless sensor network; S33: Initialize the population using a random Halton sequence; S34: Based on the cosine function, a control factor is introduced to design a multi-factor cosine dynamic parameter adjustment strategy; through the multi-factor cosine dynamic parameter adjustment strategy, the inertia weight and the global and individual learning factors are dynamically adjusted; S35: adopting a multi-population multi-strategy evolutionary method, dividing the initial population into three sub-populations according to the preset ratio according to the fitness function value of the individuals in the population; adopting a multi-population multi-strategy evolutionary method, designing a speed update strategy for each sub-population and updating the sub-population; S36: performing reflection out-of-bounds processing and position update on the updated sub-population; S37: Calculate the bootstrap probability, Centroid and guide step length; S38: Update the position of the sensor node and the current individual fitness value through the guidance probability and guidance step size; compare the current individual fitness value of the population with the optimal fitness value of the historical evolution algebra, update the optimal solution of the position of the sensor node until the maximum evolution algebra is reached, and output the global optimal position of each sensor node.

[0006] Optionally, step S31 includes: Defining the problem The fitness function ,question The decision vector , Dimensions ,as well as The length of the search region in the problem. The upper boundary vector and the lower boundary vector , and the width upper boundary vector and the lower boundary vector ; Set the sensor's sensing radius to , number of nodes , the discrete granularity is 1; Determine the relevant parameters of the particle swarm algorithm, including: evolutionary generation Maximum speed , minimum speed , initialize the population size , control factor ; The problem M The search area in is discretized into pixels, if the wireless sensor network covers pixels, the coverage for .

[0007] Optionally, step S32 includes: set up and They represent the locations of three sensor nodes in the wireless sensor network; and The triangle formed by three points in a two-dimensional plane is an equilateral triangle; let A represent the center point of the equilateral triangle; By line segment The radius , and As the center of the circle, construct three circles, point A is the intersection of the three circles, and point B is the center of the circle. and circle The intersection of Connect line segments and line segments , the intersection of the two line segments is point C; According to the properties of the circle , calculate the optimal number of sensor nodes , the formula is as follows:

[0008] in Represents a line segment Length; Represents a line segment and line segments Angle.

[0009] Optionally, step S33 includes: exist Within the scope, use Sequence initialization random generation Individuals, that is The solution vector of each individual is , where the odd-numbered bits range from ], the range of even-numbered digits is ; individual Together they constitute the parent population, and the formula for initializing the parent population is as follows:

[0010] Is The range of sequence generation values ​​is A randomly distributed variable, The formula for the process is as follows:

[0011] in and Represent the upper and lower boundaries of odd-numbered digits, respectively. and Respectively represent the upper and lower boundaries of even-numbered digits; Indicates the population An odd-dimensional variable of individuals, Indicates the population An even-dimensional variable of individuals, and constitutes an individual ; To generate the The prime number of the cardinality of the dimension, Represents an integer In the cardinality The next Number of digits; Is the cardinality The next No. Dimensional Sequence value, is an integer In the cardinality The index of the highest digit under ; is an integer In the cardinality The next Number of digits.

[0012] Optionally, step S34 includes: Introducing control factors , controlled by the factor and the current generation of the particle swarm algorithm calculate ; Use the cosine function to adjust the inertia weight , the formula is as follows:

[0013] Using Inertia Weight and , calculate the global and individual learning factors and , the formula is as follows:

[0014] in ; Indicates an intermediate parameter.

[0015] Optionally, step S35 includes: According to the fitness function value of the individuals in the population, the initial population is divided into three sub-populations according to the preset ratio. , according to the fitness level, they are: development sub-population, moderate sub-population and exploration sub-population; Speed ​​update strategies include: elite learning strategy, coordinated learning strategy and generalized learning strategy; Development subpopulation Use elite learning strategy, moderate subpopulation Using coordinated learning strategies to explore subpopulations Use generalized learning strategies; The speed update formula of the elite learning strategy is as follows:

[0016] The speed update formula of the coordinated learning strategy is as follows:

[0017] The speed update formula of the generalized learning strategy is as follows:

[0018] in is a random integer, For the The speed of the next generation of individuals, For the current The speed of an individual, and is an individual randomly selected from the parent population, It is one of the individuals in the population whose fitness function value ranks in the top preset percentage from small to large; is the current individual historical optimal solution; is a random number in the random value range of [-0.5,0.5], is the value range of each variable; and The random value range is [0,0.5].

[0019] Optionally, step S36 includes: If the speed If the speed exceeds the limit, the reflection method with a certain interval is used to reflect the variable value that exceeds the range back to the legal range. The formula of the reflection process is as follows:

[0020] in The function is used to find the remainder of non-integer numbers. Indicates the minimum speed; Indicates the maximum speed; Update the position of the sensor node: according to the speed Calculate the position of the next generation of population individuals , for position Perform reflection out-of-bounds processing.

[0021] Optionally, step S37 includes: The formula for calculating the bootstrap probability is as follows:

[0022] in , used to control the steepness of the logistic function, represents the threshold that controls the maximum increase in the bootstrap probability, For continuous algebra without improvement; is the minimum bootstrap probability, is the maximum bootstrap probability; Indicates The bootstrap probability of the generation; According to the population position of the current generation, split Axis direction coordinate list and Y axis direction coordinate list, get the center list ; Calculate based on the circle center list Centroid , the formula is as follows:

[0023] in is the center of the circle list The X-axis coordinate of the coordinate, is the center of the circle list The Y-axis coordinate of the coordinate; Indicates the current The number of vertices of the polygon; Traverse the list of circle centers obtained from the population layout position, In the list, find the circle with the current center. The distance is minimum and smaller than the perception radius The guiding centroid ); The formula for calculating the bootstrap step length is as follows:

[0024] in The current center and Centroid distance; The center and The angle difference of the centroid, and is the guide step length and guide angle, is the guide vector; is a randomization function, cos and sin are trigonometric functions; is the function for calculating the angle.

[0025] Optionally, step S38 includes: The formula for updating the position of the sensor node is as follows:

[0026] in, is the guide probability; is the randomization function.

[0027] The beneficial effects of the technical solution provided by this application are: 1. When solving the problem of optimizing node configuration in wireless sensor networks, the initialization population obtained by initializing the random Halton sequence greatly speeds up the search process in the early stage of the algorithm.

[0028] 2. Multi-factor cosine dynamic parameter adjustment strategy: Based on the existing dynamic adjustment of inertia weight and learning factor parameters according to the cosine function, in order to enhance the diversity of particle population, a new control factor is introduced to balance the global and local optimization search capabilities: 3. Multi-population and multi-strategy evolutionary method: According to the fitness function value of the individuals in the population, the individuals are divided into development sub-populations, moderate sub-populations and exploration sub-populations. The development sub-population is committed to improving the utilization capacity of the population; the moderate sub-population randomly explores or utilizes to balance exploration and utilization; the exploration sub-population is committed to improving the exploration capacity of the population. The mutation strategy of the differential evolution algorithm is introduced in the speed update process to design the speed update strategy of each sub-population.

[0029] 4. Voronoi polygons guide the evolution direction: Optimize the coverage holes that appear during the population evolution process, use Voronoi polygons to find the direction of the coverage holes, calculate the Voronoi centroid, and guide the further evolution direction of some individuals through the guidance process to improve the local optimization ability of the algorithm. BRIEF DESCRIPTION OF THE DRAWINGS

[0030] The present application will be further described below with reference to the accompanying drawings and embodiments, in which: Figure 1 is a schematic diagram of sensor coverage in an embodiment of the present application; Figure 2 is a schematic diagram of the boot area in an embodiment of the present application; Figure 3 is a randomly initialized node coverage map in an embodiment of the present application; Figure 4 It is a node coverage graph initialized based on Halton sequence in an embodiment of the present application; Figure 5 It is a VGPSO coverage simulation diagram in an embodiment of the present application; Figure 6 It is a curve diagram of coverage variation of various algorithms in the embodiments of the present application; Figure 7 It is a flow chart in an embodiment of the present application. DETAILED DESCRIPTION

[0031] In order to have a clearer understanding of the technical features, purposes and effects of the present application, the specific implementation methods of the present application are now described in detail with reference to the accompanying drawings.

[0032] The embodiments of the present application provide a method for optimizing node configuration in a wireless sensor network based on a VGPSO algorithm.

[0033] Please refer to Figure 7 , Figure 7 It is a flowchart of a wireless sensor network node configuration optimization method based on the VGPSO algorithm in an embodiment of the present application, including: S1: Define wireless sensor network node configuration optimization as a problem ; S2: Improved particle swarm algorithm through random Halton sequence, multi-factor cosine dynamic parameter adjustment strategy, multi-population and multi-strategy evolution method and Voronoi diagram guidance; S3: The improved particle swarm algorithm is used to solve the problem Solve and output the global optimal position of each sensor node in the wireless sensor network.

[0034] Step S3 includes: S31: Setup Issues The basic parameters of and the parameters of improved particle swarm algorithm; Step S31 includes: Defining the problem The fitness function ,question The decision vector , Dimensions ,as well as The length of the search region in the problem. The upper boundary vector and the lower boundary vector , and the width upper boundary vector and the lower boundary vector ; Set the sensor's sensing radius to , number of nodes , the discrete granularity is 1; Determine the relevant parameters of the particle swarm algorithm, including: evolutionary generation Maximum speed , minimum speed , initialize the population size , control factor ; The problem M The search area in is discretized into pixels, if the wireless sensor network covers pixels, the coverage for .

[0035] S32: Determine the optimal number of sensor nodes in the wireless sensor network; Step S32 includes: set up and They represent the locations of three sensor nodes in the wireless sensor network; and The triangle formed by three points in a two-dimensional plane is an equilateral triangle; let A represent the center point of the equilateral triangle; By line segment The radius , and As the center of the circle, construct three circles, point A is the intersection of the three circles, and point B is the center of the circle. and circle The intersection of Connect line segments and line segments , the intersection of the two line segments is point C; According to the properties of the circle , calculate the optimal number of sensor nodes , the formula is as follows:

[0036] in Represents a line segment Length; Represents a line segment and line segments Angle.

[0037] Specifically, the triangle formed by the sensor nodes is an equilateral triangle, such as Figure 1 shown.

[0038] S33: Initialize the population using a random Halton sequence; Step S33 includes: exist Within the scope, use Sequence initialization random generation Individuals, that is The solution vector of each individual is , where the odd-numbered bits range from ], the range of even-numbered digits is ; individual Together they constitute the parent population, and the formula for initializing the parent population is as follows:

[0039] Is The range of sequence generation values ​​is A randomly distributed variable, The formula for the process is as follows:

[0040] in and Represent the upper and lower boundaries of odd-numbered digits, respectively. and Respectively represent the upper and lower boundaries of even-numbered digits; Indicates the population An odd-dimensional variable of individuals, Indicates the population An even-dimensional variable of individuals, and constitutes an individual ; To generate the The prime number of the cardinality of the dimension, Represents an integer In the cardinality The next Number of digits; Is the cardinality The next No. Dimensional Sequence value, is an integer In the cardinality The index of the highest digit under ; is an integer In the cardinality The next Number of digits.

[0041] S34: Based on the cosine function, a control factor is introduced to design a multi-factor cosine dynamic parameter adjustment strategy; through the multi-factor cosine dynamic parameter adjustment strategy, the inertia weight and the global and individual learning factors are dynamically adjusted; Step S34 includes: Introducing control factors , controlled by the factor and the current generation of the particle swarm algorithm calculate ; Use the cosine function to adjust the inertia weight , the formula is as follows:

[0042] Using Inertia Weight and , calculate the global and individual learning factors and , the formula is as follows:

[0043] in ; Indicates an intermediate parameter.

[0044] Specifically, through adaptive adjustment strategies, the exploration and exploitation capabilities of particles are dynamically balanced during the optimization process, thereby improving the overall performance of the algorithm.

[0045] S35: adopting a multi-population multi-strategy evolutionary method, dividing the initial population into three sub-populations according to the preset ratio according to the fitness function value of the individuals in the population; adopting a multi-population multi-strategy evolutionary method, designing a speed update strategy for each sub-population and updating the sub-population; Step S35 includes: According to the fitness function value of the individuals in the population, the initial population is divided into three sub-populations according to the preset ratio. , according to the fitness level, they are: development sub-population, moderate sub-population and exploration sub-population; Speed ​​update strategies include: elite learning strategy, coordinated learning strategy and generalized learning strategy; Development subpopulation Use elite learning strategy, moderate subpopulation Using coordinated learning strategies to explore subpopulations Use generalized learning strategies; The speed update formula of the elite learning strategy is as follows:

[0046] The speed update formula of the coordinated learning strategy is as follows:

[0047] The speed update formula of the generalized learning strategy is as follows:

[0048] in is a random integer, For the The speed of the next generation of individuals, For the current The speed of an individual, and is an individual randomly selected from the parent population, It is one of the individuals in the population whose fitness function value ranks in the top preset percentage from small to large; is the current individual historical optimal solution; is a random number in the random value range of [-0.5,0.5], is the value range of each variable; and The random value range is [0,0.5].

[0049] S36: performing reflection out-of-bounds processing and position update on the updated sub-population; Step S36 includes: If the speed If the speed exceeds the limit, the reflection method with a certain interval is used to reflect the variable value that exceeds the range back to the legal range. The formula of the reflection process is as follows:

[0050] in The function is used to find the remainder of non-integer numbers. Indicates the minimum speed; Indicates the maximum speed; Update the position of the sensor node: according to the speed Calculate the position of the next generation of population individuals , for position Perform reflection out-of-bounds processing.

[0051] S37: Calculate the bootstrap probability, Centroid and guide step length; Step S37 includes: The formula for calculating the bootstrap probability is as follows:

[0052] in , used to control the steepness of the logistic function, represents the threshold that controls the maximum increase in the bootstrap probability, For continuous algebra without improvement; is the minimum bootstrap probability, is the maximum bootstrap probability; Indicates The bootstrap probability of the generation; According to the population position of the current generation, split Axis direction coordinate list and Y axis direction coordinate list, get the center list ; Calculate based on the circle center list Centroid , the formula is as follows:

[0053] in is the center of the circle list The X-axis coordinate of the coordinate, is the center of the circle list The Y-axis coordinate of the coordinate; Indicates the current The number of vertices of the polygon; Traverse the list of circle centers obtained from the population layout position, In the list, find the circle with the current center. The distance is minimum and smaller than the perception radius The guiding centroid ); The formula for calculating the bootstrap step length is as follows:

[0054] in The current center and Centroid distance; The center and The angle difference of the centroid, and is the guide step length and guide angle, is the guide vector; is a randomization function, cos and sin are trigonometric functions; is the function for calculating the angle.

[0055] Specifically, Figure 2 A schematic diagram of the boot area.

[0056] S38: Update the position of the sensor node and the current individual fitness value through the guidance probability and guidance step size; compare the current individual fitness value of the population with the optimal fitness value of the historical evolution algebra, update the optimal solution of the position of the sensor node until the maximum evolution algebra is reached, and output the global optimal position of each sensor node.

[0057] Step S38 includes: The formula for updating the position of the sensor node is as follows:

[0058] in, is the guide probability; is the randomization function.

[0059] This patent conducts simulation experiments on the proposed method on the MATLAB R2022b platform to perform performance tests. To ensure the fairness of the experiment, the same parameters are set for all algorithms during the experiment. The search area is 50 m × 50 m, the number of nodes is the theoretical number of nodes, which is 42 after calculation, the population size is 100, the perception radius is 5 m, and the number of iterations is set to 500. The WSN coverage effect of the improved particle swarm optimization algorithm guided by Voronoi diagram (VGPSO, Voronoi Guide Particle Swarm Optimization) is compared with the WSN coverage effect of the particle swarm algorithm (PSO), chaotic particle swarm algorithm (CPSO), fuzzy particle swarm algorithm (FPSO), unified particle swarm algorithm (UPSO) and extended particle swarm algorithm (XPSO).

[0060] Through Figure 3 and Figure 4 The comparative analysis shows that compared with the random deployment of sensor nodes, the deployment method based on random Halton sequence makes the distribution of sensor nodes more uniform in the initialization phase, thereby improving the initial coverage.

[0061] Figure 5 This is the coverage simulation diagram based on the improved particle swarm algorithm (VGPSO) of this application.

[0062] from Figure 6 The coverage rate change curves of the six algorithms in the WSN coverage optimization process are compared. From the analysis of the figure, it can be seen that the WSN coverage optimization based on particle swarm algorithm (PSO), cosine function interactive dynamic adjustment particle swarm algorithm (CPSO), nonlinear interactive adjustment particle swarm algorithm (FPSO), uniform search particle swarm algorithm (UPSO) and linear interactive dynamic adjustment particle swarm algorithm (XPSO) have improved the coverage rate of sensor nodes to a certain extent, but there are still some coverage blind areas and the coverage rate is not high. After the coverage optimization of the VGPSO algorithm guided by Halton sequence and Voronoi diagram, the distribution of sensor nodes is more uniform, the overlap is reduced, and the coverage rate is also higher than the previous methods.

[0063] In the process of wireless sensor network (WSN) coverage optimization based on VGPSO, the algorithm showed a significant convergence speed in the first 50 iterations, which was significantly better than other comparison algorithms. After 200 iterations, the convergence speed gradually slowed down. In the end, the VGPSO algorithm achieved an optimal coverage rate of 98.137% and an average coverage rate of 96.818%. Compared with the other five WSN coverage optimization algorithms, the VGPSO algorithm can achieve a higher coverage rate. Under the above experimental conditions, 20 cycles of experiments were conducted on the six algorithms, and the experimental results are shown in Table 1.

[0064] Table 1

[0065] The above are only exemplary embodiments of the present disclosure and cannot be used to limit the scope of the present disclosure. That is, any equivalent changes and modifications made according to the teachings of the present disclosure are still within the scope of the present disclosure. After considering the disclosure of the specification and the truth of practice, those skilled in the art will easily think of other embodiments of the present disclosure.

[0066] This application is intended to cover any variation, use or adaptation of the present disclosure, which follows the general principles of the present disclosure and includes common knowledge or customary technical means in the art not described in the present disclosure. The description and examples are to be regarded as exemplary only, and the scope and spirit of the present disclosure are defined by the claims.

Claims

1. A wireless sensor network node configuration optimization method based on VGPSO algorithm, characterized in that: The method comprises the following steps: S1: Define wireless sensor network node configuration optimization as a problem ; S2: Improved particle swarm algorithm through random Halton sequence, multi-factor cosine dynamic parameter adjustment strategy, multi-population and multi-strategy evolution method and Voronoi diagram guidance; S3: The improved particle swarm algorithm is used to solve the problem Solve and output the global optimal position of each sensor node in the wireless sensor network.

2. A wireless sensor network node configuration optimization method based on VGPSO algorithm as claimed in claim 1, characterized in that: Step S3 includes: S31: Setup Issues The basic parameters of and the parameters of improved particle swarm algorithm; S32: Determine the optimal number of sensor nodes in the wireless sensor network; S33: Initialize the population using a random Halton sequence; S34: Based on the cosine function, a control factor is introduced to design a multi-factor cosine dynamic parameter adjustment strategy; through the multi-factor cosine dynamic parameter adjustment strategy, the inertia weight and the global and individual learning factors are dynamically adjusted; S35: adopting a multi-population multi-strategy evolutionary method, dividing the initial population into three sub-populations according to the preset ratio according to the fitness function value of the individuals in the population; adopting a multi-population multi-strategy evolutionary method, designing a speed update strategy for each sub-population and updating the sub-population; S36: performing reflection out-of-bounds processing and position update on the updated sub-population; S37: Calculate the bootstrap probability, Centroid and guide step length; S38: Update the position of the sensor node and the current individual fitness value through the guidance probability and guidance step size; compare the current individual fitness value of the population with the optimal fitness value of the historical evolution algebra, update the optimal solution of the position of the sensor node until the maximum evolution algebra is reached, and output the global optimal position of each sensor node.

3. A wireless sensor network node configuration optimization method based on VGPSO algorithm as claimed in claim 2, characterized in that: Step S31 includes: Defining the problem The fitness function ,question The decision vector , Dimensions ,as well as The length of the search region in the problem. The upper boundary vector and the lower boundary vector , and the width upper boundary vector and the lower boundary vector ; Set the sensor's sensing radius to , number of nodes , the discrete granularity is 1; Determine the relevant parameters of the particle swarm algorithm, including: evolutionary generation Maximum speed , minimum speed , initialize the population size , control factor ; The problem M The search area in is discretized into pixels, if the wireless sensor network covers pixels, the coverage for .

4. A wireless sensor network node configuration optimization method based on VGPSO algorithm as claimed in claim 3, characterized in that: Step S32 includes: set up and They represent the locations of three sensor nodes in the wireless sensor network; and The triangle formed by three points in a two-dimensional plane is an equilateral triangle; let A represent the center point of the equilateral triangle; By line segment The radius , and As the center of the circle, construct three circles, point A is the intersection of the three circles, and point B is the center of the circle. and circle The intersection of Connect line segments and line segments , the intersection of the two line segments is point C; According to the properties of the circle , calculate the optimal number of sensor nodes , the formula is as follows: in Represents a line segment Length; Represents a line segment and line segments Angle.

5. A wireless sensor network node configuration optimization method based on VGPSO algorithm as claimed in claim 4, characterized in that: Step S33 includes: exist Within the scope, use Sequence initialization random generation Individuals, that is The solution vector of each individual is , where the odd-numbered bits range from ], the range of even-numbered digits is ; individual Together they constitute the parent population, and the formula for initializing the parent population is as follows: Is The range of sequence generation values ​​is A randomly distributed variable, The formula for the process is as follows: in and Represent the upper and lower boundaries of odd-numbered digits, respectively. and Respectively represent the upper and lower boundaries of even-numbered digits; Indicates the population An odd-dimensional variable of individuals, Indicates the population An even-dimensional variable of individuals, and constitutes an individual ; To generate the The prime number of the cardinality of the dimension, Represents an integer In the cardinality The next Number of digits; Is the cardinality The next No. Dimensional Sequence value, is an integer In the cardinality The index of the highest digit under ; is an integer In the cardinality The next Number of digits.

6. A wireless sensor network node configuration optimization method based on VGPSO algorithm as claimed in claim 5, characterized in that: Step S34 includes: Introducing control factors , controlled by the factor and the current generation of the particle swarm algorithm calculate ; Use the cosine function to adjust the inertia weight , the formula is as follows: Using Inertia Weight and , calculate the global and individual learning factors and , the formula is as follows: in ; Indicates an intermediate parameter.

7. A wireless sensor network node configuration optimization method based on VGPSO algorithm as claimed in claim 6, characterized in that: Step S35 includes: According to the fitness function value of the individuals in the population, the initial population is divided into three sub-populations according to the preset ratio. , according to the fitness level, they are: development sub-population, moderate sub-population and exploration sub-population; Speed ​​update strategies include: elite learning strategy, coordinated learning strategy and generalized learning strategy; Development subpopulation Use elite learning strategy, moderate subpopulation Using coordinated learning strategies to explore subpopulations Use generalized learning strategies; The speed update formula of the elite learning strategy is as follows: The speed update formula of the coordinated learning strategy is as follows: The speed update formula of the generalized learning strategy is as follows: in is a random integer, For the The speed of the next generation of individuals, For the current The speed of an individual, and is an individual randomly selected from the parent population, It is one of the individuals in the population whose fitness function value ranks in the top preset percentage from small to large; is the current individual historical optimal solution; is a random number in the random value range of [-0.5,0.5], is the value range of each variable; and The random value range is [0,0.5].

8. A wireless sensor network node configuration optimization method based on VGPSO algorithm as claimed in claim 7, characterized in that: Step S36 includes: If the speed If the speed exceeds the limit, the reflection method with a certain interval is used to reflect the variable value that exceeds the range back to the legal range. The formula of the reflection process is as follows: in The function is used to find the remainder of non-integer numbers. Indicates the minimum speed; Indicates the maximum speed; Update the position of the sensor node: according to the speed Calculate the position of the next generation of population individuals , for position Perform reflection out-of-bounds processing.

9. A wireless sensor network node configuration optimization method based on VGPSO algorithm as claimed in claim 8, characterized in that: Step S37 includes: The formula for calculating the bootstrap probability is as follows: in , used to control the steepness of the logistic function, represents the threshold that controls the maximum increase in the bootstrap probability, For continuous algebra without improvement; is the minimum bootstrap probability, is the maximum bootstrap probability; Indicates The bootstrap probability of the generation; According to the population position of the current generation, split Axis direction coordinate list and Y axis direction coordinate list, get the center list ; Calculate based on the circle center list Centroid , the formula is as follows: in is the center of the circle list The X-axis coordinate of the coordinate, is the center of the circle list The Y-axis coordinate of the coordinate; Indicates the current The number of vertices of the polygon; Traverse the list of circle centers obtained from the population layout position, In the list, find the circle with the current center. The distance is minimum and smaller than the perception radius The guiding centroid ); The formula for calculating the bootstrap step length is as follows: in The current center and Centroid distance; The center and The angle difference of the centroid, and is the guide step length and guide angle, is the guide vector; is a randomization function, cos and sin are trigonometric functions; is the function for calculating the angle.

10. A wireless sensor network node configuration optimization method based on VGPSO algorithm as claimed in claim 9, characterized in that: Step S38 includes: The formula for updating the position of the sensor node is as follows: in, is the guide probability; is the randomization function.

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