SD-MANET Coverage Optimization Method Based on Improved Dung Beetle Optimization Algorithm

Abstract

The present invention discloses an SD-MANET coverage optimization method based on an improved dung beetle optimization algorithm, including: regarding the combat soldiers, unmanned aerial vehicles and unmanned vehicles in the collaborative combat unit within the mission area as combat nodes, using the number and sensing radius of the combat nodes as inputs, and using the optimal deployment positions of the collaborative combat unit as outputs, constructing an SD-MANET topological model based on a Boolean sensing model within the mission area; jointly improving the dung beetle optimization algorithm by means of Chebyshev chaotic mapping, osprey optimization algorithm and adaptive t-distribution mutation, and using the improved dung beetle optimization algorithm to optimize the SD-MANET topological model to obtain the optimal deployment positions of the collaborative combat unit within the mission area. The present invention improves the convergence and performance without increasing the algorithm complexity, ensuring the efficient deployment of the manned / unmanned collaborative combat unit and the initial deployment network coverage rate of the collaborative combat unit in a complex battlefield environment.

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] Object of the Invention: Aiming at the above problems, the object of the present invention is to provide an SD-MANET coverage optimization method based on an 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 tasks.

[0007] Technical Solution: The SD-MANET coverage optimization method based on the improved dung beetle optimization algorithm of the present invention includes:

[0008] Regarding the combat soldiers, unmanned aerial vehicles, and unmanned ground vehicles in the collaborative combat team within the mission area as combat nodes, taking the number of combat nodes and the sensing radius as inputs and the optimal deployment positions of the collaborative combat team as outputs, constructing an SD-MANET topological model based on the Boolean sensing model within the mission area;

[0009] Combining the Chebyshev chaotic map, osprey optimization algorithm, and adaptive t-distribution mutation to improve the dung beetle optimization algorithm, and using the improved dung beetle optimization algorithm to optimize the SD-MANET topological model to obtain the optimal deployment positions of the collaborative combat team within the mission area.

[0010] Further, the process of constructing the SD-MANET topological model based on the Boolean sensing model within the mission area includes:

[0011] Describing the mission area as a two-dimensional plane with a length of L and a width of W , discretely dividing the two-dimensional plane into multiple monitoring point grids, and denoting the coordinates of the e th combat node in the collaborative combat team as , then the collaborative combat team is denoted as , where n represents the total number of combat nodes, denoting the set of monitoring points in the mission area as , calculating the distance between the combat node in the collaborative combat team and the h th monitoring point as ;

[0012] Calculating the sensing probability of a single combat node for a monitoring point according to the Boolean sensing model, and the formula is:

[0013] ,

[0014] In the formula, represents the sensing radius of the combat node within the collaborative combat team;

[0015] Calculate the joint perception probability of the entire cooperative combat unit for the monitoring point according to the perception probability of a single combat node. The formula is:

[0016] ,

[0017] Calculate the total coverage rate of the mission area according to the joint perception probability. The formula is:

[0018] ,

[0019] Take the total coverage rate within the mission area as the mathematical model of the SD-MANET topology model.

[0020] Furthermore, use the improved dung beetle optimization algorithm to optimize the SD-MANET topology model. The steps to obtain the optimal deployment positions of the cooperative combat unit within the mission area are as follows:

[0021] Step 21, set the number of dung beetle populations, the maximum number of iterations, and the dimension of each dung beetle individual, and take the total coverage rate within the mission area as the fitness function;

[0022] Step 22, initialize the dung beetle population using the Chebyshev chaotic map;

[0023] Step 23, calculate the fitness value of each dung beetle according to the fitness function, and divide the dung beetle population into four subgroups: rolling ball, breeding, foraging, and stealing according to the ratio;

[0024] Step 24, after updating the position according to the osprey optimization algorithm in the rolling ball dung beetle subgroup, calculate the first fitness value of the rolling ball dung beetle subgroup; after updating the position according to the adaptive t-distribution mutation perturbation in the foraging dung beetle subgroup, calculate the second fitness value of the foraging dung beetle subgroup; after updating the positions of the breeding dung beetle subgroup and the stealing dung beetle subgroup respectively, calculate the third fitness value of the breeding dung beetle subgroup and the third fitness value of the stealing dung beetle subgroup respectively, and select the maximum fitness value;

[0025] Step 25, repeat Step 24 until the maximum number of iterations is reached to obtain the optimal dung beetle position, and take the optimal dung beetle position as the optimal deployment position of the cooperative combat unit within the mission area.

[0026] Furthermore, initializing the dung beetle population using the Chebyshev chaotic map in Step 22 includes:

[0027] Generate a Chebyshev chaotic sequence Che , the formula is:

[0028] ,

[0029] In the formula, i is the individual serial number in the population, jFor the j th dimension of each individual, is an adjustable order parameter, representing the th individual in the entire dung beetle population i th j value of the dimension;

[0030] Map this Chebyshev chaotic sequence Che into the solution space, and the formula is:

[0031] ,

[0032] In the formula, represents the minimum value of the variable to be optimized, represents the maximum value of the variable to be optimized.

[0033] Furthermore, after the position update in the rolling dung beetle subgroup according to the osprey optimization algorithm in step 24, the position update formula of the rolling dung beetle subgroup is:

[0034] ,

[0035] In the formula, represents the current position of the dung beetle, is the current iteration number, is a random number within [0, 1], is a random vector within [1, 2], is a hyperparameter used to adjust the influence of the light source intensity on the path of the rolling dung beetle, represents the position of the better dung beetle in the dung beetle population, which is used to guide the dung beetle to move towards the better position.

[0036] Furthermore, after the position update in the foraging dung beetle subgroup according to the adaptive t-distribution mutation perturbation in step 24, the position update formula of the foraging dung beetle subgroup is:

[0037] ,

[0038] In the formula, is a random number following a normal distribution, is the lower limit of the optimal foraging area in the solution space, is a random vector within (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 the generated random number between [0, 1], represents the maximum number of iterations; Indicates the probability of selecting the t-distribution mutation perturbation, and the calculation formula is:

[0039] ,

[0040] In the formula, is the upper limit of the dynamically selected probability, is the change range of the probability.

[0041] Beneficial effects: Compared with the prior art, the significant advantages of the present invention are:

[0042] 1. The present invention combines the Chebyshev chaotic map, the osprey optimization algorithm, and the adaptive t-distribution mutation to improve the improved dung beetle optimization algorithm, improving the convergence and performance without increasing the algorithm complexity, ensuring the efficient deployment of the manned / unmanned collaborative combat unit in the complex battlefield environment and the initial deployment network coverage rate of the collaborative combat unit;

[0043] 2. The present invention introduces the Chebyshev chaotic map when initializing the population of the dung beetle optimization algorithm, strengthening the stability of the algorithm while improving the convergence of the algorithm;

[0044] 3. The present invention introduces the global exploration strategy of the osprey optimization algorithm in the dung beetle rolling ball stage, avoiding each dung beetle individual falling into the local optimal solution on the basis of the improved initialized population, encouraging each dung beetle to explore new solution spaces and compare to find the global optimal solution as much as possible, playing the role of leading the optimal value;

[0045] 4. The present invention introduces the adaptive t-distribution mutation in the dung beetle foraging stage, enabling the dung beetle to adaptively adjust parameters in combination with the solution space and the population state, and improving the effect that the manned / unmanned unit can still maintain an efficient assignment when facing topological changes;

[0046] 5. The present invention can be applied to the topology management module in the SD-MANET controller, enabling the topology management module to not only view the node positions and link states, but also efficiently optimize the node deployment positions, with stronger versatility and promotion. Description of the Drawings

[0047] Figure 1 is the flowchart of the SD-MANET coverage optimization method based on the improved dung beetle optimization algorithm;

[0048] Figure 2 is the performance of the improved DBO proposed by the present invention and other comparison algorithms on the CEC2005 test function F1;

[0049] Figure 3 is the performance of the improved DBO proposed by the present invention and other comparison algorithms on the CEC2005 test function F2;

[0050] Figure 4 The performance of the improved DBO proposed by the present invention and other comparison algorithms on the CEC2005 test function F7;

[0051] Figure 5 The performance of the improved DBO proposed by the present invention and other comparison algorithms on the CEC2005 test function F14;

[0052] Figure 6 The effect diagram of the random deployment strategy;

[0053] Figure 7 The effect diagram of the SD-MANET topology management strategy based on the improved DBO proposed by the present invention. Detailed implementation manners

[0054] In order to make the objectives, technical solutions and advantages of the present application clearer, the present application will be further described in detail below with reference to the accompanying drawings and embodiments.

[0055] The SD-MANET coverage optimization method based on the improved dung beetle optimization algorithm described in this embodiment includes:

[0056] Regarding the combat soldiers, unmanned aerial vehicles and unmanned vehicles in the coordinated combat unit within the mission area as combat nodes, taking the number of combat nodes and the sensing radius as inputs, and taking the optimal deployment positions of the coordinated combat unit as outputs, a SD-MANET topology model based on the Boolean sensing model within this mission area is constructed;

[0057] Combining the Chebyshev chaotic map, the osprey optimization algorithm and the adaptive t-distribution mutation to improve the dung beetle optimization algorithm, and using the improved dung beetle optimization algorithm to optimize the SD-MANET topology model to obtain the optimal deployment positions of the coordinated combat unit within the mission area.

[0058] 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 the efficient deployment of heterogeneous manned / unmanned coordinated combat units composed of unmanned systems such as combat soldiers, unmanned aerial vehicles, unmanned vehicles and ground robots, effectively support various activities involved in coordinated combat, and can achieve efficient coverage of the mission area.

[0059] For a mission area within a fixed range, the coordinated combat unit within this mission area is a manned / unmanned coordinated combat unit, and this manned / unmanned coordinated combat unit includes three types of heterogeneous nodes, namely soldier, unmanned aerial vehicle and unmanned vehicle. Taking the number of these three types of heterogeneous nodes and their corresponding sensing radii as inputs, and taking the optimal deployment positions of the manned / unmanned coordinated combat unit as outputs, a SD-MANET topology model based on the Boolean sensing model within this mission area is constructed.

[0060] Further, the process of constructing the SD-MANET topology model based on the Boolean perception model in the mission area includes:

[0061] Describe the mission area as a two-dimensional plane with a length of L and a width of W . Discretely divide this two-dimensional plane into multiple monitoring point grids. Denote the coordinates of the e th combat node in the cooperative combat unit as . The geometric center of each combat node is the coverage optimization target position. Then, the cooperative combat unit is denoted as , where n represents the total number of combat nodes. Denote the set of monitoring points in the mission area as . Calculate the distance between the combat node in the cooperative combat unit and the h th monitoring point as ; where ;

[0062] Calculate the perception probability of a single combat node for a monitoring point according to the Boolean perception model. The formula is:

[0063] ,

[0064] In the formula, represents the perception radius of the combat node in the cooperative combat unit;

[0065] Calculate the joint perception probability of the entire cooperative combat unit for a monitoring point according to the perception probability of a single combat node. The formula is:

[0066] ,

[0067] Calculate the total coverage rate of the mission area according to the joint perception probability. The formula is:

[0068] ,

[0069] Take the total coverage rate in the mission area as the mathematical model of the SD-MANET topology model.

[0070] Further, use the improved dung beetle optimization algorithm to optimize the SD-MANET topology model. The steps to obtain the optimal deployment position of the cooperative combat unit in the mission area are as follows:

[0071] Step 21, set the number of dung beetle populations, the maximum number of iterations, and the dimension of each dung beetle individual. Take the total coverage rate in the mission area as the fitness function; where the dimension can be set to twice the number of combat nodes in the cooperative combat unit;

[0072] Step 22: Initialize the dung beetle population using the Chebyshev chaotic map;

[0073] Step 23: Calculate the fitness value of each dung beetle according to the fitness function, and divide the dung beetle population into four subgroups, namely, ball-rolling, breeding, foraging, and stealing, according to a ratio such as 6:6:7:11.

[0074] Step 24: After updating the positions according to the osprey optimization algorithm in the ball-rolling dung beetle subgroup, calculate the first fitness value of the ball-rolling dung beetle subgroup; after updating the positions according to the adaptive t-distribution mutation perturbation in the foraging dung beetle subgroup, calculate the second fitness value of the foraging dung beetle subgroup; after updating the positions of the breeding dung beetle subgroup and the stealing dung beetle subgroup respectively, calculate the third fitness value of the breeding dung beetle subgroup and the third fitness value of the stealing dung beetle subgroup respectively, and select the maximum fitness value.

[0075] Step 25: Repeat Step 24 until the maximum number of iterations is reached to obtain the optimal position of the dung beetle, and use the optimal position of the dung beetle as the optimal deployment position of the coordinated combat team in the mission area.

[0076] The flow chart of the coverage optimization method described in this example is as Figure 1As shown, it mainly includes two aspects, namely the construction of the optimization model and the algorithm optimization. During the construction of the optimization model, first, the SD-MANET controller obtains the data plane layer information through Controller-to-Switch, that is, the controller-to-switch message. Then, it constructs an 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 largely affect the optimization process of the algorithm in the solution space. Therefore, in this example, the Chebyshev chaotic map (Chebyshev chaotic map) is introduced during the initialization of the dung beetle population 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. Second, in the rolling ball stage of the traditional dung beetle optimization algorithm, the absolute value of the difference between the dung beetle individual and the global worst position is used to simulate the change of intensity, reducing the possibility of the algorithm falling into the local optimum. In this example, the global exploration strategy of the osprey optimization algorithm is adopted to replace the formula in the rolling ball stage, making up for the problems of the dung beetle algorithm in the rolling ball behavior that only depends on the worst value, cannot communicate with other dung beetles in time, and has more parameters. On the basis of the improved initialized population, it avoids each dung beetle falling into the local optimum solution, encourages each dung beetle to explore new solution spaces and compare, so as to find the global optimum solution as much as possible, and further improve the initial deployment effect of heterogeneous manned / unmanned collaborative combat teams. Finally, in this example, an adaptive t-distribution mutation perturbation with the number of iteration rounds as a parameter is used to perturb the foraging behavior of the dung beetles, which can enhance the global exploration ability of the algorithm in the early stage and also have good local search ability in the later stage. The adaptive t-distribution mutation perturbation strategy can increase random interference in the early stage of algorithm optimization to avoid the algorithm falling into the local optimum solution prematurely. However, if the t-distribution mutation perturbation is performed in each iteration all the time, it will lead to a decrease in the convergence of the algorithm. Therefore, this example proposes to adaptively select whether to adopt the t-distribution mutation perturbation strategy according to the probability, which will achieve a better balance between algorithm optimization and convergence, and improve the robustness of the algorithm at the same time, so that the algorithm can maintain a high stability even in the task area with obstacle interference; because the adaptive t-distribution mutation can adaptively adjust parameters by combining the solution space and the population state, it can improve the efficient assignment effect of heterogeneous manned / unmanned teams in the face of topological changes.

[0077] Further, the initialization of the dung beetle population using the Chebyshev chaotic map in step 22 includes:

[0078] Generate a Chebyshev chaotic sequence Che , and the formula is:

[0079] ,

[0080] wherein, i is the individual serial number in the population, j is the j th dimension of each individual, is an adjustable order parameter, represents the entire dung beetle population in the i th individual j th dimension value; the dimension of each individual is 2 times the number of nodes N, the first N values are the abscissas of N nodes, and the last N values are the ordinates of N nodes. Each individual is a deployment scheme;

[0081] Map this Chebyshev chaotic sequence Che into the solution space, and the formula is:

[0082] ,

[0083] wherein, represents the minimum value of the variable to be optimized, represents the maximum value of the variable to be optimized. The variable to be optimized is X, and its actual meaning is the position coordinate of the node. lb and ub are the minimum and maximum values of the coordinate respectively.

[0084] Furthermore, after the position update is performed according to the osprey optimization algorithm in the rolling dung beetle subgroup in step 24, the position update formula of the rolling dung beetle subgroup is:

[0085] ,

[0086] wherein, represents the current position of the dung beetle, is the current iteration number, is a random number within [0, 1], is a random vector within [1, 2], is a hyperparameter used to adjust the influence of the light source intensity on the path of the rolling dung beetle, represents the position of the better dung beetle in the dung beetle population, which is used to guide the dung beetle to move towards the better position.

[0087] Furthermore, after the position update is performed according to the adaptive t-distribution mutation perturbation in the foraging dung beetle subgroup in step 24, the position update formula of the foraging dung beetle subgroup is:

[0088] ,

[0089] wherein, is a random number subject to the normal distribution, is the lower limit of the optimal foraging area in the solution space, is a random vector within the range of (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], represents the maximum number of iterations; represents the probability of whether to select the t-distribution mutation perturbation, and the calculation formula is:

[0090] ,

[0091] In the formula, is the dynamically selected upper limit of the probability, is the change range of the probability. Exemplarily and The values of are taken as 0.5 and 0.1 respectively.

[0092] Use the improved dung beetle optimization algorithm to optimize the SD-MANET topology model. Take the position of the dung beetle in the dung beetle population corresponding to the best global fitness as the best position, and take the best position in the dung beetle population as the optimal deployment position of the manned / unmanned collaborative combat unit. The optimal deployment position can be transmitted to each combat node in the data plane layer through the OpenFlow protocol, and each combat node deploys according to the received deployment position. Through the above process, ensure the rapid deployment and optimization of the manned / unmanned collaborative combat unit, improve the coverage rate of the manned / unmanned collaborative combat unit, and provide strong support for the efficient execution of combat tasks.

[0093] To verify the effectiveness of the improved dung beetle optimization algorithm (OTDBO) proposed in the present invention, in this example, it was tested on the classic function test set CEC2005 released by the Institute of Electrical and Electronics Engineers International Conference on Evolutionary Computation (CEC) 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 grey wolf optimization algorithm (GWO), and the currently better improved sparrow search algorithm, the adaptive spiral flight 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 is as follows Figures 2 to 5As shown in the figure, the left figure is the function image of the CEC test function with coordinates, which is used to illustrate the unimodal, multimodal, and complex optimization situations of the test function. The right figure is the convergence curve. It can be seen from Figures 2 to 5 that the dung beetle optimization algorithm improved by the combined Chebyshev chaotic mapping, the global exploration strategy of the osprey algorithm, and the adaptive t-distribution mutation proposed in the present invention has good performance while converging quickly.

[0094] Simulate the SD-MANET topology management strategy based on the improved dung beetle optimization algorithm proposed in the present invention, as Figures 6 to 7 shown, where Figure 6 is the schematic diagram of random deployment, Figure 7 is the 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, and at the same time ensure the connectivity of the self-organizing communication network of the teams.

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 to optimize the SD-MANET topology model using the improved dung beetle optimization algorithm to obtain the optimal deployment position of the collaborative combat unit in the mission area. 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 used as the mathematical model of the SD-MANET topology model; 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.

2. The SD-MANET coverage optimization method based on the improved dung beetle optimization algorithm according to claim 1 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.

3. The SD-MANET coverage optimization method based on the improved dung beetle optimization algorithm according to claim 2 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.

4. The SD-MANET coverage optimization method based on the improved dung beetle optimization algorithm according to claim 3 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.

Images