SD-MANET coverage optimization method based on improved dung beetle optimization algorithm

Abstract

The invention discloses an SD-MANET coverage optimization method based on an improved dung beetle optimization algorithm, and the method comprises the steps: taking combat soldiers, unmanned aerial vehicles and unmanned vehicles in a cooperative combat unit in a task area as combat nodes, taking the number of the combat nodes and a sensing radius as input, and taking the optimal deployment position of the cooperative combat unit as output; constructing an SD-MANET topology model based on a Boolean perception model in the task area; and improving a dung beetle optimization algorithm by combining Chebyshev chaotic mapping, an eagle optimization algorithm and adaptive t distribution variation, and optimizing the SD-MANET topology model by using the improved dung beetle optimization algorithm to obtain an optimal deployment position of the cooperative combat unit in the task area. According to the method, the convergence and the performance are improved while the algorithm complexity is not increased, and the efficient deployment of the active / unmanned cooperative combat unit in a complex battlefield environment and the initial deployment network coverage rate of the cooperative combat unit are ensured.

Description

Technical Field

[0001] The present invention relates to the intersection of software defined network technology and computational intelligence, and in particular to a SD-MANET coverage optimization method based on an improved dung beetle optimization algorithm. Background Art

[0002] In recent years, combat theories have been continuously innovated, and combat styles such as multi-domain warfare, drone swarm warfare, and mosaic warfare have been proposed one after another. Among them, manned / unmanned coordinated squad warfare is a typical representative of mosaic warfare, which reorganizes small and sophisticated combat units such as ground combat soldiers, aerial drones, ground unmanned vehicles, and robots based on combat missions and configures them on demand. Each combat unit is interconnected through a mobile ad hoc network (MANET), which has high flexibility and anti-destruction capabilities. However, in a complex battlefield environment with high mobility, strong confrontation, and changeable weather, the traditional MANET architecture has problems such as limited self-organization and autonomy, high communication overhead, and need to improve anti-destruction capabilities.

[0003] In order to improve the flexibility, scalability and survivability of tactical networks, it is proposed to introduce software-defined networking technology into MANET networks according to the various limitations of MANET in tactical networks. In addition, five aspects are proposed, including controller deployment and design, control signal design, network survivability improvement design, southbound protocol improvement and hybrid network control, combining the characteristics of tactical networks, which provides a design reference for the implementation of software-defined mobile ad hoc networks (SD-MANET).

[0004] At present, most of the topology management strategies of SD-MANET are to achieve the purpose of maximum coverage from the perspective of node deployment optimization. However, there are still problems such as uneven node distribution, redundant coverage, large holes, slow algorithm convergence, etc. These problems will lead to low initial deployment network coverage, uneven coverage and unreliability of manned / unmanned collaborative combat teams, making it difficult to ensure the successful execution of combat missions. Therefore, SD-MANET for manned / unmanned collaborative perception needs to use an efficient topology management module to efficiently plan the deployment location of each node before the mission begins, so as to achieve comprehensive and continuous perception of the mission area.

[0005] At present, most of the topology management modules in SD-MANET controllers only support real-time or scheduled acquisition of relevant information of the data plane layer through controller-to-switch type messages. A small number of studies have explored how to achieve maximum coverage of the task area by deploying relevant nodes in a fixed task area. At present, the research on this coverage optimization problem is roughly divided into two types, one based on virtual force technology and the other based on intelligent optimization algorithm technology. The current topology management strategies are difficult to apply to SD-MANET controllers that meet the needs of manned / unmanned collaborative perception in complex battlefield environments. Both the coverage effect and the algorithm convergence speed still need further research and improvement. Summary of the invention

[0006] Purpose of the invention: In view of the above problems, the purpose of the present invention is to provide a SD-MANET coverage optimization method based on the improved dung beetle optimization algorithm to ensure the rapid deployment and optimization of manned / unmanned collaborative combat teams, improve the coverage rate of manned / unmanned collaborative combat teams, and provide strong support for the efficient execution of combat missions.

[0007] Technical solution: The SD-MANET coverage optimization method based on the improved dung beetle optimization algorithm of the present invention comprises: The combat soldiers, UAVs and unmanned vehicles in the collaborative combat team in the mission area are taken as combat nodes, the number of combat nodes and the perception radius are taken as input, and the optimal deployment position of the collaborative combat team is taken as output to construct the SD-MANET topology model based on the Boolean perception model in the mission area. The Chebyshev chaos map, Osprey optimization algorithm and adaptive t-distribution mutation improved dung beetle optimization algorithm are combined, and the improved dung beetle optimization algorithm is used to optimize the SD-MANET topology model and obtain the optimal deployment position of the collaborative combat unit in the mission area.

[0008] Furthermore, the process of constructing the SD-MANET topology model based on the Boolean perception model in the task area includes: Describe the mission area as a long L ,Width W The two-dimensional plane is divided into multiple monitoring point grids, and the first e The coordinates of the combat nodes are , then the coordinated combat team is recorded as ,in n represents the total number of combat nodes, and the set of monitoring points in the mission area is recorded as , calculate the combat nodes and the h Monitoring points The distance is ; The perception probability of a single combat node for a monitoring point is calculated based on the Boolean perception model. The formula is: , In the formula, Indicates the perception radius of the combat node within the collaborative combat team; The joint perception probability of the entire coordinated combat team for the monitoring point is calculated based on the perception probability of a single combat node. The formula is: , The total coverage of the task area is calculated based on the joint perception probability. The formula is: , The total coverage in the mission area is taken as the mathematical model of the SD-MANET topology model.

[0009] Furthermore, the SD-MANET topology model is optimized using the improved dung beetle optimization algorithm to obtain the optimal deployment position of the collaborative combat team in the mission area, which includes the following steps: Step 21, setting the dung beetle population, the maximum number of iterations and the dimension of each dung beetle individual, and taking the total coverage rate in the task area as the fitness function; Step 22, initializing the dung beetle population using Chebyshev chaos mapping; Step 23, calculating the fitness value of each dung beetle according to the fitness function, and dividing the dung beetle population into four subgroups of rolling, breeding, foraging and stealing according to the proportion; Step 24, after the position of the rolling dung beetle subgroup is updated according to the osprey optimization algorithm, the first fitness value of the rolling dung beetle subgroup is calculated; after the position of the foraging dung beetle subgroup is updated according to the adaptive t-distribution variation disturbance, the second fitness value of the foraging dung beetle subgroup is calculated; after the positions of the breeding dung beetle subgroup and the thieving dung beetle subgroup are updated respectively, the third fitness value of the breeding dung beetle subgroup and the third fitness value of the thieving dung beetle subgroup are calculated respectively, and the maximum fitness value is selected; Step 25, repeat step 24 until the maximum number of iterations is reached, and the optimal dung beetle position is obtained, and the optimal dung beetle position is used as the optimal deployment position of the collaborative combat unit in the mission area.

[0010] Further, in step 22, initializing the dung beetle population using Chebyshev chaotic mapping includes: Generate a Chebyshev chaotic sequence Che , the formula is: , In the formula, i is the individual number in the population, j For each individual j dimensions, is an adjustable order parameter, Represents the entire dung beetle population Middle i Individual j The value of the dimension; The Chebyshev chaotic sequence Che Mapped into the solution space, the formula is: , In the formula, represents the minimum value of the variable to be optimized, Indicates the maximum value of the variable to be optimized.

[0011] Further, in step 24, after the position of the rolling ball dung beetle sub-group is updated according to the osprey optimization algorithm, the position update formula of the rolling ball dung beetle sub-group is: , In the formula, Indicates the current position of the dung beetle. is the current iteration number, is a random number in [0,1], is a random vector with elements in [1,2], is a hyperparameter used to adjust the effect of light intensity on the path of the rolling dung beetle. It indicates the better position of dung beetles in the dung beetle population and is used to guide the dung beetles to move to a better position.

[0012] Furthermore, in step 24, after the position of the foraging dung beetle subgroup is updated according to the adaptive t-distribution variation disturbance, the position update formula of the foraging dung beetle subgroup is: , In the formula, is a random number that follows a normal distribution, is the lower limit of the optimal foraging area in the solution space, is a random vector in the range (0,1), is the upper limit of the optimal foraging area in the solution space, represents the optimal position within the current dung beetle population, is the t-distribution function, is a random number generated between [0,1], Indicates the maximum number of iterations; Indicates the probability of selecting the t-distribution variation disturbance, and the calculation formula is: , In the formula, is the upper limit of the probability of dynamic selection, is the magnitude of the probability change.

[0013] Beneficial effects: Compared with the prior art, the present invention has the following significant advantages: 1. The present invention combines Chebyshev chaos mapping, Osprey optimization algorithm and adaptive t-distribution variation to improve dung beetle optimization algorithm, which improves convergence and performance without increasing algorithm complexity, ensuring efficient deployment of manned / unmanned collaborative combat teams in complex battlefield environments and initial deployment network coverage of collaborative combat teams; 2. The present invention introduces Chebyshev chaos mapping when the dung beetle optimization algorithm performs population initialization, thereby improving the convergence of the algorithm and strengthening the stability of the algorithm; 3. The present invention introduces the global exploration strategy of the Osprey optimization algorithm in the dung beetle rolling ball stage, avoids each dung beetle individual from falling into the local optimal solution on the basis of the improved initialization population, encourages each dung beetle to explore more new solution spaces and compare them so as to find the global optimal solution as much as possible, and plays the role of optimal value guidance; 4. The present invention introduces adaptive t-distribution variation in the foraging stage of dung beetles, so that dung beetles can adaptively adjust parameters in combination with solution space and population status, which can improve the effect of maintaining efficient assignment of manned / unmanned teams in the face of topological changes; 5. The present invention can be applied to the topology management module in the SD-MANET controller, so that the topology management module can not only check the node location and link status, but also efficiently optimize the node deployment location, and has stronger versatility and promotion. BRIEF DESCRIPTION OF THE DRAWINGS

[0014] Figure 1 It is a flowchart of the SD-MANET coverage optimization method based on the improved dung beetle optimization algorithm; Figure 2 The performance of the improved DBO proposed by the present invention and other comparison algorithms on the CEC2005 test function F1; Figure 3 The performance of the improved DBO proposed by the present invention and other comparison algorithms on the CEC2005 test function F2; Figure 4 The performance of the improved DBO proposed by the present invention and other comparison algorithms on the CEC2005 test function F7; Figure 5 The performance of the improved DBO proposed by the present invention and other comparison algorithms on the CEC2005 test function F14; Figure 6 This is a diagram showing the effect of random deployment strategy; Figure 7 This is the effect diagram of the SD-MANET topology management strategy based on improved DBO proposed in the present invention. DETAILED DESCRIPTION

[0015] In order to make the objectives, technical solutions and advantages of the present application more clearly understood, the present application is further described in detail below in conjunction with the accompanying drawings and embodiments.

[0016] The SD-MANET coverage optimization method based on the improved dung beetle optimization algorithm described in this embodiment includes: The combat soldiers, UAVs and unmanned vehicles in the collaborative combat team in the mission area are taken as combat nodes, the number of combat nodes and the perception radius are taken as input, and the optimal deployment position of the collaborative combat team is taken as output to construct the SD-MANET topology model based on the Boolean perception model in the mission area. The Chebyshev chaos map, Osprey optimization algorithm and adaptive t-distribution mutation improved dung beetle optimization algorithm are combined, and the improved dung beetle optimization algorithm is used to optimize the SD-MANET topology model and obtain the optimal deployment position of the collaborative combat unit in the mission area.

[0017] The SD-MANET coverage optimization method based on the improved dung beetle optimization algorithm described in the present invention can be applied to the topology management module in the SD-MANET controller to achieve efficient deployment of heterogeneous manned / unmanned collaborative combat teams composed of unmanned systems such as combat soldiers, unmanned aerial vehicles, unmanned vehicles and ground robots, effectively support various activities involved in collaborative combat, and achieve efficient coverage of the mission area.

[0018] For a mission area within a fixed range, the collaborative combat team in the mission area is a manned / unmanned collaborative combat team, which includes three types of heterogeneous nodes, namely combat soldiers, drones and unmanned vehicles. Taking the number of the three types of heterogeneous nodes and their corresponding perception radius as input and the optimal deployment position of the manned / unmanned collaborative combat team as output, the SD-MANET topology model based on the Boolean perception model in the mission area is constructed.

[0019] Furthermore, the process of constructing the SD-MANET topology model based on the Boolean perception model in the task area includes: Describe the mission area as a long L ,Width W The two-dimensional plane is divided into multiple monitoring point grids, and the first e The coordinates of the combat nodes are , the geometric center of each combat node is the coverage optimization target position, then the coordinated combat team is recorded as ,in n represents the total number of combat nodes, and the set of monitoring points in the mission area is recorded as , calculate the combat nodes and the h Monitoring points The distance is ;in ; The perception probability of a single combat node for a monitoring point is calculated based on the Boolean perception model. The formula is: , In the formula, Indicates the perception radius of the combat node within the collaborative combat team; The joint perception probability of the entire coordinated combat team for the monitoring point is calculated based on the perception probability of a single combat node. The formula is: , The total coverage of the task area is calculated based on the joint perception probability. The formula is: , The total coverage in the mission area is taken as the mathematical model of the SD-MANET topology model.

[0020] Furthermore, the SD-MANET topology model is optimized using the improved dung beetle optimization algorithm to obtain the optimal deployment position of the collaborative combat team in the mission area, which includes the following steps: Step 21, setting the population size of dung beetles, the maximum number of iterations, and the dimension of each dung beetle individual, and taking the total coverage rate in the mission area as the fitness function; wherein the dimension can be set to twice the number of combat nodes in the collaborative combat unit; Step 22, initializing the dung beetle population using Chebyshev chaos mapping; Step 23, calculating the fitness value of each dung beetle according to the fitness function, and dividing the dung beetle population into four subgroups of rolling, breeding, foraging and stealing according to a ratio of 6:6:7:11, Step 24, after the position of the rolling dung beetle subgroup is updated according to the osprey optimization algorithm, the first fitness value of the rolling dung beetle subgroup is calculated; after the position of the foraging dung beetle subgroup is updated according to the adaptive t-distribution variation disturbance, the second fitness value of the foraging dung beetle subgroup is calculated; after the positions of the breeding dung beetle subgroup and the thieving dung beetle subgroup are updated respectively, the third fitness value of the breeding dung beetle subgroup and the third fitness value of the thieving dung beetle subgroup are calculated respectively, and the maximum fitness value is selected; Step 25, repeat step 24 until the maximum number of iterations is reached, and the optimal dung beetle position is obtained, and the optimal dung beetle position is used as the optimal deployment position of the collaborative combat unit in the mission area.

[0021] The flowchart of the coverage optimization method described in this example is as follows Figure 1As shown in the figure, it mainly includes two aspects, namely, the construction of the optimization model and the optimization of the algorithm. In the process of optimizing the model, the SD-MANET controller first obtains the data plane layer information through the Controller-to-Switch message from the controller to the switch, and then constructs the SD-MANET topology model based on the Boolean perception model in the task area, optimizes the dung beetle optimization algorithm, sets parameters, and uses the optimized dung beetle optimization algorithm to optimize the SD-MANET topology model. In this example, the improvement of the traditional dung beetle optimization algorithm mainly includes three parts. First, the traditional dung beetle optimization algorithm has a large dependence on the initialized population. The quality of the initialized population can greatly affect the optimization process of the algorithm in the solution space. Therefore, this example introduces the Chebyshev chaos map when the dung beetle population is initialized to enhance the randomness and diversity of the dung beetles, so as to optimize the initialized dung beetle population, accelerate the convergence of the algorithm, and make the algorithm performance more stable. Secondly, in the rolling ball stage, the traditional dung beetle optimization algorithm uses the absolute value of the difference between the individual dung beetle and the global worst position to simulate the change of intensity, reducing the possibility of the algorithm falling into the local optimum. This example uses the global exploration strategy of the Osprey optimization algorithm to replace the formula of the rolling ball stage to make up for the problem that the dung beetle algorithm only relies on the worst value in the rolling ball behavior, cannot communicate with other dung beetles in time, and has many parameters. On the basis of the improved initial population, each dung beetle is prevented from falling into the local optimal solution, and each dung beetle is encouraged to explore new solution spaces and compare them to find the global optimal solution as much as possible, further improving the initial deployment effect of heterogeneous manned / unmanned collaborative combat units. Finally, in this example, the adaptive t-distribution variation perturbation with the number of iterations as a parameter is used to perturb the foraging behavior of dung beetles, which can strengthen the global exploration ability of the algorithm in the early stage and have good local search ability in the later stage. The adaptive t-distribution mutation perturbation strategy can add random interference in the early stage of algorithm optimization to prevent the algorithm from falling into the local optimal solution too early. However, if the t-distribution mutation perturbation is performed in each iteration, the convergence of the algorithm will decrease. Therefore, this example proposes to adaptively choose whether to use the t-distribution mutation perturbation strategy based on probability. This will achieve a better trade-off between algorithm optimization and convergence, while improving the robustness of the algorithm, so that the algorithm can maintain a high stability even in the task area where obstacles interfere. Since the adaptive t-distribution mutation can combine the solution space and the population state to adaptively adjust the parameters, it can improve the effect of maintaining an efficient assignment for heterogeneous manned / unmanned teams in the face of topology changes.

[0022] Further, in step 22, initializing the dung beetle population using Chebyshev chaotic mapping includes: Generate a Chebyshev chaotic sequence Che , the formula is: , In the formula, i is the individual number in the population, j For each individual j dimensions, is an adjustable order parameter, Represents the entire dung beetle population Middle i Individual j The dimension of each individual is 2 times the number of nodes N. The first N values ​​are the horizontal coordinates of the N nodes, and the last N values ​​are the vertical coordinates of the N nodes. Each individual is a deployment plan. The Chebyshev chaotic sequence Che Mapped into the solution space, the formula is: , In the formula, represents the minimum value of the variable to be optimized, Indicates the maximum value of the variable to be optimized. The variable to be optimized is X, which actually means the position coordinate of the node. lb and ub are the minimum and maximum values ​​of the coordinates respectively.

[0023] Further, in step 24, after the position of the rolling ball dung beetle sub-group is updated according to the osprey optimization algorithm, the position update formula of the rolling ball dung beetle sub-group is: , In the formula, Indicates the current position of the dung beetle. is the current iteration number, is a random number in [0,1], is a random vector with elements in [1,2], is a hyperparameter used to adjust the effect of light intensity on the path of the rolling dung beetle. It indicates the better position of dung beetles in the dung beetle population and is used to guide the dung beetles to move to a better position.

[0024] Furthermore, in step 24, after the position of the foraging dung beetle subgroup is updated according to the adaptive t-distribution variation disturbance, the position update formula of the foraging dung beetle subgroup is: , In the formula, is a random number that follows a normal distribution, is the lower limit of the optimal foraging area in the solution space, is a random vector in the range (0,1), is the upper limit of the optimal foraging area in the solution space, represents the optimal position within the current dung beetle population, is the t-distribution function, is a random number generated between [0,1], Indicates the maximum number of iterations; Indicates the probability of selecting the t-distribution variation disturbance, and the calculation formula is: , In the formula, is the upper limit of the probability of dynamic selection, is the magnitude of the probability change. and The values ​​of are 0.5 and 0.1 respectively.

[0025] The SD-MANET topology model is optimized using the improved dung beetle optimization algorithm. The dung beetle position in the dung beetle population corresponding to the best global fitness is taken as the best position, and the best position in the dung beetle population is taken as the optimal deployment position of the manned / unmanned collaborative combat unit. The optimal deployment position can be transmitted to each combat node at the data plane layer through the OpenFlow protocol, and each combat node is deployed according to the received deployment position. The above process ensures the rapid deployment and optimization of manned / unmanned collaborative combat units, improves the coverage rate of manned / unmanned collaborative combat units, and provides strong support for the efficient execution of combat missions.

[0026] In order to verify the effectiveness of the improved dung beetle optimization algorithm (OTDBO) proposed in the present invention, it was tested on the classic function test set CEC2005 released by the International Conference on Evolutionary Computation (CEC) of the Institute of Electrical and Electronics Engineers in 2005, and compared with four currently popular baseline algorithms. The four baseline algorithms are the original dung beetle optimization algorithm (DBO), the sparrow search algorithm (SSA), the gray wolf optimization algorithm (GWO) and the currently better improved sparrow search algorithm adaptive spiral flying sparrow search algorithm (ASFSSA). The present invention selected four representative test functions from the CEC2005 function test set, namely the F1, F2, F7 and F14 test functions. The performance of the improved DBO proposed in this example and other comparison algorithms on the CEC2005 test functions F1, F2, F7 and F14 are shown as follows: Figures 2 to 5 As shown, the left figure is the function graph of the CEC test function, which is used to illustrate the single peak, multi-peak and complex optimization of the test function. The right figure is the convergence curve. Figures 2 to 5 It can be seen that the improved dung beetle optimization algorithm proposed by the present invention, which combines Chebyshev chaos mapping, Osprey algorithm global exploration strategy and adaptive t distribution mutation three strategies, has fast convergence and good performance.

[0027] The SD-MANET topology management strategy based on the improved dung beetle optimization algorithm proposed in the present invention is simulated. Figure 6 to Figure 7 As shown, Figure 6 This is a schematic diagram of random deployment. Figure 7 This is a schematic diagram of the deployment scheme based on the improved dung beetle optimization algorithm proposed in the present invention. It can be seen from the simulation effect diagram that the method proposed in the present invention can greatly improve the network coverage rate of heterogeneous manned / unmanned collaborative combat teams, while also ensuring the connectivity of the team's self-organized communication network.

Claims

1. The SD-MANET coverage optimization method based on the improved dung beetle optimization algorithm is characterized by: include: The combat soldiers, UAVs and unmanned vehicles in the collaborative combat team in the mission area are taken as combat nodes, the number of combat nodes and the perception radius are taken as input, and the optimal deployment position of the collaborative combat team is taken as output to construct the SD-MANET topology model based on the Boolean perception model in the mission area. The Chebyshev chaos map, Osprey optimization algorithm and adaptive t-distribution mutation improved dung beetle optimization algorithm are combined, and the improved dung beetle optimization algorithm is used to optimize the SD-MANET topology model and obtain the optimal deployment position of the collaborative combat unit in the mission area.

2. The SD-MANET coverage optimization method based on the improved dung beetle optimization algorithm according to claim 1 is characterized in that: The process of constructing the SD-MANET topology model based on the Boolean perception model in the mission area includes: Describe the mission area as a long L ,Width W The two-dimensional plane is divided into multiple monitoring point grids, and the first e The coordinates of the combat nodes are , then the coordinated combat team is recorded as ,in n represents the total number of combat nodes, and the set of monitoring points in the mission area is recorded as , calculate the combat nodes and the h Monitoring points The distance is ; The perception probability of a single combat node for a monitoring point is calculated based on the Boolean perception model. The formula is: ， In the formula, Indicates the perception radius of the combat node within the collaborative combat team; The joint perception probability of the entire coordinated combat team for the monitoring point is calculated based on the perception probability of a single combat node. The formula is: ， The total coverage of the task area is calculated based on the joint perception probability. The formula is: ， The total coverage in the mission area is taken as the mathematical model of the SD-MANET topology model.

3. The SD-MANET coverage optimization method based on the improved dung beetle optimization algorithm according to claim 2 is characterized in that: Using the improved dung beetle optimization algorithm to optimize the SD-MANET topology model, the optimal deployment position of the collaborative combat team in the mission area includes the following steps: Step 21, setting the dung beetle population, the maximum number of iterations and the dimension of each dung beetle individual, and taking the total coverage rate in the task area as the fitness function; Step 22, initializing the dung beetle population using Chebyshev chaos mapping; Step 23, calculating the fitness value of each dung beetle according to the fitness function, and dividing the dung beetle population into four subgroups of rolling, breeding, foraging and stealing according to the proportion; Step 24, after the position of the rolling dung beetle subgroup is updated according to the osprey optimization algorithm, the first fitness value of the rolling dung beetle subgroup is calculated; after the position of the foraging dung beetle subgroup is updated according to the adaptive t-distribution variation disturbance, the second fitness value of the foraging dung beetle subgroup is calculated; after the positions of the breeding dung beetle subgroup and the thieving dung beetle subgroup are updated respectively, the third fitness value of the breeding dung beetle subgroup and the third fitness value of the thieving dung beetle subgroup are calculated respectively, and the maximum fitness value is selected; Step 25, repeat step 24 until the maximum number of iterations is reached, and the optimal dung beetle position is obtained, and the optimal dung beetle position is used as the optimal deployment position of the collaborative combat unit in the mission area.

4. The SD-MANET coverage optimization method based on the improved dung beetle optimization algorithm according to claim 3 is characterized in that: In step 22, the dung beetle population is initialized using the Chebyshev chaos map, including: Generate a Chebyshev chaotic sequence Che , the formula is: ， In the formula, i is the individual number in the population, j For each individual j dimensions, is an adjustable order parameter, Represents the entire dung beetle population Middle i Individual j The value of the dimension; The Chebyshev chaotic sequence Che Mapped into the solution space, the formula is: ， In the formula, represents the minimum value of the variable to be optimized, Indicates the maximum value of the variable to be optimized.

5. The SD-MANET coverage optimization method based on the improved dung beetle optimization algorithm according to claim 4 is characterized in that: After the position of the rolling ball dung beetle sub-swarm is updated according to the osprey optimization algorithm in step 24, the position update formula of the rolling ball dung beetle sub-swarm is: ， In the formula, Indicates the current position of the dung beetle. is the current iteration number, is a random number in [0,1], is a random vector with elements in [1,2], is a hyperparameter used to adjust the effect of light intensity on the path of the rolling dung beetle. It indicates the better position of dung beetles in the dung beetle population and is used to guide the dung beetles to move to a better position.

6. The SD-MANET coverage optimization method based on the improved dung beetle optimization algorithm according to claim 5 is characterized in that: After the position of the foraging dung beetle subgroup is updated according to the adaptive t-distribution variation disturbance in step 24, the position update formula of the foraging dung beetle subgroup is: ， In the formula, is a random number that follows a normal distribution, is the lower limit of the optimal foraging area in the solution space, is a random vector in the range (0,1), is the upper limit of the optimal foraging area in the solution space, represents the optimal position within the current dung beetle population, is the t-distribution function, is a random number generated between [0,1], Indicates the maximum number of iterations; Indicates the probability of selecting the t-distribution variation disturbance, and the calculation formula is: ， In the formula, is the upper limit of the probability of dynamic selection, is the magnitude of the probability change.

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