An Optimization Method for Unmanned and Human-aware Network Structure Based on Improved Secretary Bird Algorithm

Abstract

The present invention discloses an optimization method for the structure of an unmanned and manned perception network based on an improved secretary bird algorithm. First, according to the heterogeneous unmanned and manned collaborative perception network structure, a multi-objective optimization model is established based on the degree of nodes and the average value of the reciprocals of the shortest paths between node pairs. Then, the parameters of the improved secretary bird algorithm are initialized, the upper triangular coding method is adopted to form individuals in the population, and Tent chaotic mapping is introduced. The improved secretary bird algorithm is used to solve the multi-objective optimization model. Through two escape strategies of the secretary bird, the positions and fitness values of individuals are updated; Gaussian and Cauchy mutation perturbations are introduced to help the algorithm jump out of local optima and search for the global optimal solution; a greedy strategy is introduced to guide the algorithm to maintain the tracking of the optimal solution during the search process by comparing fitness values. The present invention can obtain the optimal heterogeneous unmanned and manned collaborative perception network structure under different tasks and improve the overall efficiency of the unmanned and manned system.

Description

Technical Field

[0001] The present invention belongs to the field of manned and unmanned collaborative sensing networks, and particularly relates to an optimization method for the structure of a manned and unmanned sensing network based on an improved secretary bird algorithm. Background Art

[0002] With the rapid development of unmanned systems, the future joint system will develop towards a "manned and unmanned system". When the manned and unmanned system performs collaborative sensing tasks, the network structure supporting the manned and unmanned system plays an important role. Especially in complex environments, the tasks are dynamically variable and each unit is heterogeneous, which poses higher requirements for the adaptability and timeliness of the network structure. However, when the existing network structure of the manned and unmanned system faces the target recognition task, its robustness and timeliness are poor. When facing the target tracking task, the energy consumption is too large and the connectivity is poor, which cannot meet the task requirements, and the network structure cannot adapt to the environmental changes. Therefore, the network structure of the manned and unmanned system needs to have the ability to flexibly adjust, and can be adjusted in real time according to the changes of the tasks, so that the network structure of the manned and unmanned system moves with the tasks, thereby ensuring the collaborative efficiency of the manned and unmanned system. At present, in the optimization of the network structure of the manned and unmanned collaborative sensing network, domestic and foreign research mainly focuses on model construction and algorithm design. There are still some deficiencies in the related technologies: in terms of modeling, node heterogeneity is not considered, and at the same time, multiple sensing scenarios are not considered, resulting in the architecture being unable to adapt to environmental changes; in terms of model solving, the convergence and efficiency of the algorithm still need to be improved; in terms of the comprehensive performance analysis of the network structure, the selected indicators are too single, and it is difficult to guarantee such comprehensive performance indicators of the network structure. Summary of the Invention

[0003] Aiming at the problems existing in the prior art, the present invention provides an optimization method for the structure of a manned and unmanned sensing network based on an improved secretary bird algorithm, which can establish an optimization model for the structure of a heterogeneous manned and unmanned collaborative sensing network with the goal of improving the robustness of the architecture, the timeliness of information, and reducing energy consumption according to the characteristics of the actual environment, and can meet the requirements of different tasks by adjusting the weights of the objective function. At the same time, the improved secretary bird algorithm is used for solving to obtain the optimal heterogeneous manned and unmanned collaborative sensing network structure under different tasks, so as to improve the overall efficiency of the manned and unmanned system.

[0004] To solve the above technical problems, the present invention provides the following technical solution: An optimization method for the structure of a manned and unmanned sensing network based on an improved secretary bird algorithm, comprising the following steps:

[0005] S1. Based on the heterogeneous human - unmanned collaborative sensing network structure, establish a multi - objective optimization model based on the degree of nodes and the average value of the reciprocal of the shortest path between node pairs. Specifically: construct objective functions aiming to maximize robustness, maximize information timeliness, and minimize energy consumption respectively; then, according to the task requirements, adjust the weight coefficients of the objective functions, and add the constraint conditions of the objective functions according to the characteristics of the heterogeneous human - unmanned collaborative sensing network structure.

[0006] S2. Initialize the parameters in the improved secretary bird algorithm; for the adjacency matrix of the heterogeneous human - unmanned collaborative sensing network structure, adopt the upper - triangular coding method to form individuals in the population; in the population initialization stage, introduce Tent chaotic mapping to increase the diversity of the population.

[0007] S3. Solve the multi - objective optimization model using the improved secretary bird algorithm, including the following steps:

[0008] S3.1. Start iteration, update the individual positions through three hunting strategies of the secretary bird.

[0009] S3.2. During the iteration process, update the individual positions and the fitness values through two escape strategies of the secretary bird.

[0010] S3.3. Introduce Gaussian and Cauchy mutation perturbations to update the fitness values, help the algorithm jump out of the local optimum, and continue to search for the global optimum solution.

[0011] S3.4. Based on the fitness values in S3.3, introduce a greedy strategy, and by comparing the fitness values, guide the algorithm to maintain tracking of the optimal solution during the search process.

[0012] S3.5. Judge whether the maximum number of iterations is satisfied. If so, output the heterogeneous human - unmanned collaborative sensing network structure corresponding to the individuals in the iterated population as the most excellent heterogeneous human - unmanned collaborative sensing network structure; otherwise, execute step S3.1.

[0013] Furthermore, in the aforementioned step S1, based on the degree of nodes k i , construct the objective function aiming to maximize the robustness R as follows:

[0014] ,

[0015] ,

[0016] In the formula, I i is the importance degree of node i , k i is the degree of the i th node, and N is the total number of nodes.

[0017] Furthermore, in the aforementioned step S1, based on the average value T of the reciprocals of the shortest path lengths between node pairs, a target function aiming to maximize information timeliness is constructed as follows:

[0018] ,

[0019] In the formula, d ij is the shortest path length between node i and node j .

[0020] Furthermore, in the aforementioned step S1, based on the distance between nodes, a target function aiming to minimize energy loss is constructed as follows:

[0021] ,

[0022] In the formula, d is the distance between two communication nodes; E elec represents the energy required to receive and send unit data; E fs represents the energy consumption coefficient of the free space mode circuit; E mp represents the energy consumption coefficient of the circuit in the multipath attenuation mode.

[0023] Furthermore, in the aforementioned step S1, the constraint conditions of the target function are as follows:

[0024] Constraint conditions for the edges in the heterogeneous manned-unmanned collaborative perception network structure:

[0025] ,

[0026] ,

[0027] In the formula, W C-R represents the number of connecting edges between the command node and the combat soldier, W R-R represents the number of connecting edges between combat soldiers, W R-UGV represents the connecting edge between a combat soldier and an unmanned vehicle, W R-UAV represents the connecting edge between a combat soldier and an unmanned aerial vehicle;

[0028] Constraint conditions for the degrees of nodes in the heterogeneous manned-unmanned collaborative perception network structure:

[0029] ,

[0030] ,

[0031] In the formula, k person represents the degree between the commander and the combat soldiers, k non-person represents the degree between the UAV and the unmanned vehicle, k const represents the node saturation;

[0032] Constraint conditions for network density in the heterogeneous unmanned collaborative perception network structure:

[0033] ,

[0034] In the formula, D represents the network density, D MAX represents the maximum network density threshold;

[0035] Constraint conditions for energy loss in the heterogeneous unmanned collaborative perception network structure:

[0036] ,

[0037] In the formula, E i represents the energy loss of node i ; E max represents the maximum energy capacity of node i ;

[0038] For the adjacency matrix A in the heterogeneous unmanned collaborative perception network structure, the value constraint condition is:

[0039] ,

[0040] In the formula, for the element A in the adjacency matrix a ij of the heterogeneous unmanned collaborative perception network structure, if its value is 1, it means the node is connected to the node, and if it is 0, it means the node is not connected to the node.

[0041] Furthermore, the parameters in the initialization of the improved secretary bird algorithm in the aforementioned step S2 include: population size P , number of iterations G , chaos coefficient t , position parameter μ , scale parameter γ, degree of freedom v , parameter r .

[0042] Furthermore, the three hunting strategies of the secretary bird in the aforementioned step S3.1 are divided into three time intervals, which are: , , , corresponding to the three stages of secretary bird hunting: searching for prey, consuming prey, and attacking prey;

[0043] When the number of iterations , its formula is as follows:

[0044] ,

[0045] In the formula, t represents the current iteration number, T represents the maximum iteration number, represents the new state of the i th secretary bird in the prey search stage, and are the randomly generated candidate solutions for the iteration in the prey search stage; R1 represents an array randomly generated in the interval [0,1] with a dimension of , where is the dimension of the solution space; represents the value of its jth dimension, represents the fitness value of its objective function;

[0046] When the number of iterations , its formula is as follows:

[0047] ,

[0048] In the formula, RB represents an array randomly generated from the standard normal distribution with a dimension of ; ，x best represents the current optimal value;

[0049] When the number of iterations , its formula is as follows:

[0050] ,

[0051] In the formula, RL represents the flight step size in Levy flight, t represents the current iteration number, T represents the maximum iteration number, represents the new state of the i th secretary bird in the prey search stage, represents the fitness value of its objective function.

[0052] Furthermore, in the aforementioned step S3.2, the positions of its individuals are updated through two escape strategies of the secretary bird. The formulas for the two escape strategies of the secretary bird are as follows:

[0053] ,

[0054] Among them, R2 represents an array randomly generated from the normal distribution with a dimension of ;x random represents the random candidate solution of the current iteration, and K represents a random selection of the integer 1 or 2;

[0055] The fitness function in step S3.2 is as follows:

[0056] ,

[0057] In the formula, fit is the objective function, Fit is the fitness value, Ave_d ij is the number of path hops between nodes, R is the system robustness, T is the information timeliness, W ij is the node i and the node j the energy loss between them, w 1, w 2, w 3, and w 4 respectively represent the weight coefficients of the number of path hops, system robustness, information timeliness, and energy loss between nodes, is the penalty for violating the constraint conditions.

[0058] Furthermore, Gaussian and Cauchy mutation perturbations are introduced in the aforementioned step S3.3, and its formula is as follows:

[0059] ,

[0060] In the formula, is the updated solution, obeys the normal distribution with a mean of 0 and a variance of , r i obeys the location parameter , the scale parameter , the degree of freedom of the Cauchy distribution, and are the weight coefficients of the Gaussian distribution and the Cauchy distribution respectively, and t and T are the current iteration number and the maximum iteration number respectively.

[0061] Furthermore, the aforementioned method for optimizing the structure of an unmanned and human-aware network based on an improved secretary bird algorithm further includes the analysis of the performance of the optimal structure of the unmanned and human collaborative perception network, including: robustness, timeliness, connectivity, average path length, average betweenness centrality, clustering coefficient, and association tightness, as well as the robustness, timeliness, and average path length under node attacks.

[0062] Compared with the prior art, the beneficial technical effects of the present invention adopting the above technical solutions are as follows:

[0063] (1) The model is constructed by using system robustness, information timeliness, and energy loss, so as to ensure the comprehensive performance of the obtained heterogeneous manned / unmanned collaborative perception network structure;

[0064] (2) According to different task requirements, the weights of the objective function are adjusted to obtain the optimal network structure under different tasks, meet the requirements of the perception task, and improve the adaptability and timeliness of the network structure;

[0065] (3) Different nodes and their connecting edges are constrained in the model to reflect node heterogeneity and effectively simulate the characteristics of the actual environment;

[0066] (4) By improving the initial population of the secretary bird algorithm, introducing Gaussian and Cauchy mutation perturbations and greedy strategies, the algorithm has good convergence and search efficiency when solving the optimization problem of the heterogeneous manned / unmanned collaborative perception network structure;

[0067] (5) The robustness, timeliness, connectivity, average path length, average betweenness centrality, clustering coefficient, and correlation tightness indexes in complex network theory are used to compare and analyze the heterogeneous manned / unmanned collaborative perception network structure before and after optimization, and the effectiveness of the optimized network structure can be seen;

[0068] (6) The optimization method of the heterogeneous manned / unmanned collaborative perception network structure based on the improved secretary bird algorithm designed by the present invention is simple and efficient, and the optimal heterogeneous manned / unmanned collaborative perception network structure can be quickly obtained. Description of the Drawings

[0069] Figure 1 is a flowchart of the optimization method of the heterogeneous manned / unmanned collaborative perception network structure based on the improved secretary bird algorithm provided by the embodiment of the present invention.

[0070] Figure 2 is a schematic diagram of the upper triangular coding rule provided by the embodiment of the present invention.

[0071] Figure 3 is a comparison chart of the fitness convergence of the improved secretary bird algorithm (LSBOA), the basic secretary bird algorithm (SBOA), the dung beetle optimization algorithm (DBO), the grasshopper optimization algorithm (GOA), and the improved dung beetle optimization algorithm (TWDBO) under the target recognition task provided by the embodiment of the present invention.

[0072] Figure 4 is the optimal manned / unmanned collaborative perception network structure diagram obtained by the improved secretary bird algorithm under the target recognition task provided by the embodiment of the present invention.

[0073] Figure 5It is a comparison graph of the timeliness of the manned / unmanned collaborative perception network before and after optimization under random attacks on nodes in the target recognition task provided by the embodiments of the present invention.

[0074] Figure 6 It is a comparison graph of the robustness of the manned / unmanned collaborative perception network before and after optimization under random attacks on nodes in the target recognition task provided by the embodiments of the present invention.

[0075] Figure 7 It is a comparison graph of the average path length of the manned / unmanned collaborative perception network before and after optimization under random attacks on nodes in the target recognition task provided by the embodiments of the present invention.

[0076] Figure 8 It is a comparison graph of the fitness convergence of the improved secretary bird algorithm (LSBOA) with the basic secretary bird algorithm (SBOA), dung beetle optimization algorithm (DBO), grasshopper optimization algorithm (GOA), and improved dung beetle optimization algorithm (TWDBO) in the target tracking task provided by the embodiments of the present invention.

[0077] Figure 9 It is the optimal structure diagram of the manned / unmanned collaborative perception network obtained by the improved secretary bird algorithm in the target tracking task provided by the embodiments of the present invention.

[0078] Figure 10 It is a comparison graph of the timeliness of the manned / unmanned collaborative perception network before and after optimization under random attacks on nodes in the target tracking task provided by the embodiments of the present invention.

[0079] Figure 11 It is a comparison graph of the robustness of the manned / unmanned collaborative perception network before and after optimization under random attacks on nodes in the target tracking task provided by the embodiments of the present invention.

[0080] Figure 12 It is a comparison graph of the average path length of the manned / unmanned collaborative perception network before and after optimization under random attacks on nodes in the target tracking task provided by the embodiments of the present invention. Detailed implementation manners

[0081] To better understand the technical content of the present invention, specific embodiments are hereby given and described in conjunction with the accompanying drawings as follows.

[0082] Aspects of the present invention are described with reference to the accompanying drawings, in which a number of illustrative embodiments are shown. Embodiments of the present invention are not limited to those described in the drawings. It should be understood that the present invention can be implemented by any one of the various concepts and embodiments introduced above, as well as the concepts and embodiments described in detail below, because the concepts and embodiments disclosed in the present invention are not limited to any embodiment. Additionally, some aspects disclosed in the present invention can be used alone or in any suitable combination with other aspects disclosed in the present invention.

[0083] Reference Figure 1 , the present invention provides an optimization method for an unmanned and manned perception network structure based on an improved secretary bird algorithm, including the following steps:

[0084] S1. Based on the heterogeneous unmanned and manned collaborative perception network structure, establish a multi-objective optimization model based on the degree of nodes and the average value of the reciprocals of the shortest paths between node pairs. Specifically: respectively construct objective functions with the goals of maximizing robustness, maximizing information timeliness, and minimizing energy loss; then, according to the task requirements, adjust the weight coefficients of the objective functions, and add constraint conditions to the objective functions according to the characteristics of the heterogeneous unmanned and manned collaborative perception network structure;

[0085] S2. Initialize the parameters in the improved secretary bird algorithm; for the adjacency matrix of the heterogeneous unmanned and manned collaborative perception network structure, use the upper triangular coding method to form individuals in the population; in the population initialization stage, introduce Tent chaos mapping to increase the diversity of the population;

[0086] S3. Use the improved secretary bird algorithm to solve the multi-objective optimization model, including the following steps:

[0087] S3.1. Start iteration, and update the individual positions through the three hunting strategies of the secretary bird;

[0088] S3.2. During the iteration process, update the individual positions through the two escape strategies of the secretary bird, and update its fitness value;

[0089] S3.3. Introduce Gaussian and Cauchy mutation perturbations to update the fitness value, help the algorithm jump out of the local optimum, and continue to search for the global optimum solution;

[0090] S3.4. Based on the fitness value in S3.3, introduce a greedy strategy, and by comparing the fitness values, guide the algorithm to maintain tracking of the optimal solution during the search process;

[0091] S3.5. Determine whether the maximum number of iterations is satisfied. If so, output the heterogeneous unmanned and manned collaborative perception network structure corresponding to the individuals in the population after iteration as the optimal heterogeneous unmanned and manned collaborative perception network structure; otherwise, execute step S3.1.

[0092] As a preferred embodiment of the present invention, in step S1, based on the degree of nodes, a target function aiming to maximize robustness is constructed as follows:

[0093] Based on the degree of nodes k i , a target function aiming to maximize the robustness R is constructed as follows:

[0094] ,

[0095] ,

[0096] In the formula, I i is the importance degree of node i , k i is the degree of the i th node, and N is the total number of nodes.

[0097] As a preferred embodiment of the present invention, in step S1, based on the average value T of the reciprocals of the shortest path lengths between node pairs, a target function aiming to maximize information timeliness is constructed as follows:

[0098] ,

[0099] In the formula, d ij is the shortest path length between node i and node j .

[0100] As a preferred embodiment of the present invention, in step S1, based on the distance between nodes, a target function aiming to minimize energy loss is constructed as follows:

[0101] ,

[0102] In the formula, d is the distance between two communication nodes; E elec represents the energy required to receive and send unit data; E fs represents the energy consumption coefficient of the free space mode circuit; E mp represents the energy consumption coefficient of the circuit in the multipath attenuation mode.

[0103] As a preferred embodiment of the present invention, in step S1, the constraint conditions of the target function are as follows:

[0104] Constraint conditions of the edges in the heterogeneous unmanned collaborative perception network structure:

[0105] ,

[0106] ,

[0107] In the formula, W C-R represents the number of edges between the command node and the combat soldiers, W R-R represents the number of edges between combat soldiers, W R-UGV represents the edge between a combat soldier and an unmanned vehicle, W R-UAV represents the edge between a combat soldier and an unmanned aerial vehicle;

[0108] Constraint conditions for the degree of nodes in the heterogeneous manned-unmanned collaborative perception network structure:

[0109] ,

[0110] ,

[0111] In the formula, k person represents the degree of the commander and combat soldiers, k non-person represents the degree of unmanned aerial vehicles and unmanned vehicles, k const represents the node saturation;

[0112] Constraint conditions for the network density in the heterogeneous manned-unmanned collaborative perception network structure:

[0113] ,

[0114] In the formula, D represents the network density, D MAX represents the maximum network density threshold;

[0115] Constraint conditions for the energy loss in the heterogeneous manned-unmanned collaborative perception network structure:

[0116] ,

[0117] In the formula, E i represents the energy loss of node i ; E max represents the maximum energy capacity of node i ;

[0118] Value constraint conditions for the adjacency matrix in the heterogeneous manned-unmanned collaborative perception network structure are: A are:

[0119] ,

[0120] In the formula, the adjacency matrix of the heterogeneous human - unmanned collaborative sensing network structure A The element in a ij , whose value being 1 indicates that the node is connected to the node, and being 0 indicates that the node is not connected to the node.

[0121] As a preferred embodiment of the present invention, the parameters initialized in step S2 in the improved secretary bird algorithm include: population size P=100 , number of iterations G=500 , chaos coefficient t=1.1 , position parameter μ = 0 , scale parameter γ = 1, degree of freedom v=1 , parameter r= 0.5 .

[0122] Refer to Figure 2 , the upper triangular coding method. Specifically: the adjacency matrix A of the heterogeneous human - unmanned collaborative sensing network structure is binary - coded, with 1 and 0 respectively indicating whether each node in the network structure is connected or not. For the m - th row in the adjacency matrix A of the heterogeneous human / unmanned collaborative sensing network structure, the first m elements in the m - th row are deleted, and the remaining elements are arranged as a row vector to form the secretary bird in the improved secretary bird algorithm.

[0123] As a preferred embodiment of the present invention, the three hunting strategies of the secretary bird in step S3.1 are divided into three time intervals, which are respectively: , , , corresponding to the three stages of the secretary bird preying: searching for prey, consuming prey, and attacking prey;

[0124] When the number of iterations , its formula is as follows:

[0125] ,

[0126] In the formula, t represents the current number of iterations, T represents the maximum number of iterations, represents the new state of the i -th secretary bird in the prey - searching stage, and are the randomly - selected candidate solutions for iteration in the prey - searching stage; R1 represents an array randomly generated in the interval [0, 1] with a dimension of , where is the dimension of the solution space; represents the value of its j - th dimension, represents the fitness value of its objective function;

[0127] When the number of iterations , its formula is as follows:

[0128] ,

[0129] wherein, RB represents an array randomly generated from a standard normal distribution with a dimension of ; ，x best represents the current optimal value;

[0130] When the number of iterations , its formula is as follows:

[0131] ,

[0132] wherein, RL represents the flight step length in Lévy flight, t represents the current iteration number, T represents the maximum iteration number, represents the i new state of the secretary bird in the prey search stage, represents the fitness value of its objective function.

[0133] As a preferred embodiment of the present invention, in step S3.2, the position of the secretary bird is updated through two escape strategies of the secretary bird, and the formulas of the two escape strategies of the secretary bird are as follows:

[0134] ,

[0135] wherein, R2 represents an array randomly generated from a normal distribution with a dimension of ; x random represents the random candidate solution of the current iteration, and K represents a random selection of the integer 1 or 2;

[0136] The fitness function in step S3.2 is as follows:

[0137] ,

[0138] wherein, fit is the objective function, Fit is the fitness value, Ave_d ij is the number of path hops between nodes, R is the system robustness, T is the information timeliness, W ij is the node i and the node j the energy loss between, w 1, w 2, w 3, and w 4 respectively represent the weight coefficients of the number of path hops, system robustness, information timeliness, and energy loss between nodes, is the penalty for violating the constraint condition.

[0139] As a preferred embodiment of the present invention, in the aforementioned step S3.3, Gaussian and Cauchy mutation perturbations are introduced, and the formula is as follows:

[0140] ,

[0141] In the formula, is the updated solution, obeys a normal distribution with a mean of 0 and a variance of , r i obeys a Cauchy distribution with a location parameter , a scale parameter , and a degree of freedom , and are the weight coefficients of the Gaussian distribution and the Cauchy distribution respectively, and t and T are the current iteration number and the maximum iteration number respectively.

[0142] As a preferred embodiment of an optimization method for an unmanned and manned perception network structure based on an improved secretary bird algorithm of the present invention, it further includes an analysis of the performance of the optimal unmanned and manned collaborative perception network structure, including: robustness, timeliness, connectivity, average path length, average betweenness centrality, clustering coefficient, and association tightness, as well as robustness, timeliness, and average path length under node attacks.

[0143] Table 1. Comparison table of the optimal architecture in terms of robustness, timeliness, connectivity, and average path length under the target recognition task. Table 2. Comparison table of the optimal architecture in terms of average betweenness centrality, global clustering coefficient, and association tightness under the target recognition task.

[0144] Table 1

[0145] Architecture type Centralized architecture 1.1659 0.4684 0.0439 2.4000 DBO optimized architecture 3.3842 0.5737 0.1683 2.1895 SBOA optimized architecture 4.0503 0.6737 0.2120 1.6632 LSBOA optimized architecture 6.2583 0.7235 0.3486 1.2341

[0146] Table 2

[0147] Architecture type Average betweenness centrality Global clustering coefficient Associative compactness Centralized architecture 0 0.006 0.2312 DBO optimized architecture 3.3000 0.1326 0.3679 SBOA optimized architecture 6.3500 0.2956 0.5876 LSBOA optimized architecture 7.2500 0.3567 0.6979

[0148] Figure 4 shows the optimal unmanned and manned collaborative perception network structure diagram obtained by the improved secretary bird algorithm under the target recognition task provided by the embodiment of the present invention. It can be seen from the figure that under the target recognition task, the network structure optimized by LSBOA is more evenly distributed. Figure 5 shows the comparison diagram of the timeliness of the optimized and non-optimized unmanned / manned collaborative perception network under random attacks on nodes under the target recognition task provided by the embodiment of the present invention. It can be seen from the figure that except for the distributed network structure under ideal conditions, the optimal network structure considering heterogeneity has better timeliness when facing the target recognition task. Figure 6It is a comparison graph of the robustness of the manned / unmanned collaborative perception network before and after optimization under a target recognition task according to an embodiment of the present invention when random attacks are carried out on nodes. It can be seen from the graph that, except for the distributed network structure under ideal conditions, the optimal network structure considering heterogeneity has better robustness when facing the target recognition task. Figure 7 It is a comparison graph of the average path length of the manned / unmanned collaborative perception network before and after optimization under a target recognition task according to an embodiment of the present invention when random attacks are carried out on nodes. It can be seen from the graph that, except for the distributed network structure under ideal conditions, the optimal network structure considering heterogeneity has a lower average path length when facing the target recognition task. Figure 8 It is a comparison graph of the fitness convergence of the improved secretary bird algorithm (LSBOA), the basic secretary bird algorithm (SBOA), the dung beetle optimization algorithm (DBO), the grasshopper optimization algorithm (GOA), and the improved dung beetle optimization algorithm (TWDBO) under a target tracking task according to an embodiment of the present invention. It can be seen from the graph that when facing the target tracking task, the LSBOA algorithm has better convergence and a lower fitness value. Figure 9 It is a structure diagram of the optimal manned / unmanned collaborative perception network obtained by the improved secretary bird algorithm under a target tracking task according to an embodiment of the present invention. It can be seen from the graph that under the target tracking task, the network structure distribution optimized by LSBOA is more uniform.

[0149] Figure 10 It is a comparison graph of the timeliness of the manned / unmanned collaborative perception network before and after optimization under a target tracking task according to an embodiment of the present invention when random attacks are carried out on nodes. It can be seen from the graph that, except for the distributed network structure under ideal conditions, the optimal network structure considering heterogeneity has better timeliness when facing the target tracking task. Figure 11 It is a comparison graph of the robustness of the manned / unmanned collaborative perception network before and after optimization under a target tracking task according to an embodiment of the present invention when random attacks are carried out on nodes. It can be seen from the graph that, except for the distributed network structure under ideal conditions, the optimal network structure considering heterogeneity has better robustness when facing the target tracking task. Figure 12 It is a comparison graph of the average path length of the manned / unmanned collaborative perception network before and after optimization under a target tracking task according to an embodiment of the present invention when random attacks are carried out on nodes. It can be seen from the graph that, except for the distributed network structure under ideal conditions, the optimal network structure considering heterogeneity has a lower average path length when facing the target tracking task.

[0150] In summary, the present invention selects system robustness, information timeliness, and energy loss as optimization objectives, adjusts the weights of the objective function according to different task requirements, and proposes an optimization method for the structure of an unmanned and manned perception network based on an improved secretary bird algorithm. The upper triangular coding method is used to form the secretary bird. Tent chaos mapping is introduced in the population initialization of the algorithm. During the iteration process, Gaussian and Cauchy mutation perturbations are introduced. Through the greedy strategy, the fitness value is updated to accelerate the convergence of the algorithm. Finally, the optimal heterogeneous unmanned and manned collaborative perception network structure is obtained, and the network structures before and after optimization are compared and analyzed. This optimization method for the network structure of the command and control system based on the improved secretary bird algorithm is simple and efficient, and can quickly obtain the heterogeneous unmanned and manned collaborative perception network structure with the highest robustness and timeliness and the lowest energy loss under the conditions of limited network density and degree constraints.

[0151] Although the present invention has been described above with reference to preferred embodiments, it is not intended to limit the present invention. Those of ordinary skill in the art to which the present invention pertains may make various modifications and refinements without departing from the spirit and scope of the present invention. Therefore, the protection scope of the present invention shall be determined by the scope defined in the claims.

Claims

1. An optimization method for the structure of an attended and unattended sensing network based on an improved secretary bird algorithm, characterized in that It includes the following steps: S1. Based on the heterogeneous human - unmanned collaborative sensing network structure, establish a multi - objective optimization model based on the degree of nodes and the average value of the reciprocals of the shortest path lengths between node pairs. Specifically: construct objective functions aiming to maximize robustness, maximize information timeliness, and minimize energy consumption respectively; then, according to the task requirements, adjust the weight coefficients of the objective functions, and add the constraint conditions of the objective functions according to the characteristics of the heterogeneous human - unmanned collaborative sensing network structure. Among them, based on the distance between nodes, construct the objective function aiming to minimize energy consumption as follows: ， where d is the distance between two communication nodes; E elec represents the energy required to receive the data of the sending unit; E fs represents the energy consumption coefficient of the free space mode circuit; E mp represents the energy consumption coefficient of the circuit in the multipath attenuation mode; The constraint conditions of the objective function are as follows: Constraint conditions of the edges in the heterogeneous human - unmanned collaborative sensing network structure: ， ， In the formula, W C-R represents the number of edges between the command node and the combat soldiers, W R-R represents the number of edges between combat soldiers, W R-UGV represents the edge between a combat soldier and an unmanned vehicle, W R-UAV represents the edge between a combat soldier and an unmanned aerial vehicle; Constraint conditions of the degrees of nodes in the heterogeneous human - unmanned collaborative sensing network structure: , , Wherein, k person represents the degree between the commander and the combat soldiers, k non-person represents the degree between the UAV and the unmanned vehicle, k const represents the node saturation; Constraint conditions of the network density in the heterogeneous human - unmanned collaborative sensing network structure: , where D represents the network density, D MAX represents the maximum network density threshold; Constraint conditions of the energy consumption in the heterogeneous human - unmanned collaborative sensing network structure: ， In the formula, E i represents the energy loss of the node i ; E max represents the maximum energy capacity of the node i ; Adjacency Matrix in the Heterogeneous Human-Robot Collaborative Sensing Network Structure A The value constraint conditions are as follows: ， In the formula, the adjacency matrix of the heterogeneous human - unmanned collaborative perception network structure A The element in a ij , whose value is 1 indicating that the node is connected to the node, and 0 indicating that the node is not connected to the node; S2. Initialize the parameters in the improved vulture algorithm; for the adjacency matrix of the heterogeneous human - unmanned collaborative sensing network structure, adopt the upper - triangular coding method to form individuals in the population; in the population initialization stage, introduce Tent chaotic mapping to increase the diversity of the population. S3. Use the improved vulture algorithm to solve the multi - objective optimization model, including the following steps: S3.

1. Start iteration, and update the individual positions through three hunting strategies of the vulture. S3.

2. During the iteration process, update the individual positions and their fitness values through two escape strategies of the vulture. Specifically: update the individual positions through two escape strategies of the vulture, and the formulas of the two escape strategies of the vulture are as follows: , Among them, R2 represents an array randomly generated from a normal distribution with a dimension of , x random represents the random candidate solution of the current iteration, and K represents a random selection of the integer 1 or 2; The fitness function in step S3.2 is as follows: ， In the formula, fit is the objective function, Fit is the fitness value, Ave_d ij is the number of path hops between nodes, R is the system robustness, and T is the information timeliness, W ij is the node i and the node j is the energy loss between them, w 1, w 2, w 3, and w 4 respectively represent the weight coefficients of the number of path hops, system robustness, information timeliness, and energy loss between nodes, is the penalty for violating the constraint conditions; S3.

3. Introduce Gaussian and Cauchy mutation perturbations to update the fitness value, help the algorithm jump out of the local optimum, and continue to search for the global optimum solution. S3.

4. Based on the fitness value in S3.3, introduce a greedy strategy, and by comparing the fitness values, guide the algorithm to keep tracking the optimal solution during the search process. S3.

5. Judge whether the maximum number of iterations is satisfied. If so, output the heterogeneous human - unmanned collaborative sensing network structure corresponding to the individuals in the iterated population as the most excellent heterogeneous human - unmanned collaborative sensing network structure; otherwise, execute step S3.

1.

2. The optimization method for the structure of the manned and unmanned perception network based on the improved secretary bird algorithm according to claim 1, wherein In step S1, based on the degree of nodes k i , construct an objective function aiming to maximize the robustness R as follows: , , Wherein, I i is the importance degree of the node i , k i is the degree of the i th node, and N is the total number of nodes.

3. An optimization method for an unmanned perception network structure based on an improved secretary bird algorithm according to claim 1, characterized in that In step S1, based on the average value T of the reciprocals of the shortest path lengths between node pairs, construct the objective function aiming to maximize information timeliness as follows: ， In the formula, d ij is the i and j the shortest path length between nodes.

4. An optimization method for an unmanned perception network structure based on an improved secretary bird algorithm according to claim 1, characterized in that, The parameters initialized in the improved secretary bird algorithm in step S2 include: population size P , number of iterations G , chaos coefficient t , position parameter μ , scale parameter γ, degree of freedom v , parameter r .

5. An optimization method for an unmanned perception network structure based on an improved secretary bird algorithm according to claim 1, characterized in that, The three hunting strategies of the secretary bird in step S3.1 are divided into three time intervals, namely: , , , corresponding to the three stages of the secretary bird's predation: searching for prey, exhausting prey, and attacking prey; When the number of iterations is reached, its formula is as follows: ， where \(t\) represents the current iteration number and \(T\) represents the maximum iteration number. denotes the i new state of the secretary bird in the prey search stage, and is a randomly generated candidate solution for the iteration in the prey search stage; \(R1\) represents an array randomly generated in the interval \([0, 1]\) with a dimension of where is the dimension of the solution space; represents the value of its \(j\)-th dimension, represents the fitness value of its objective function. When the number of iterations is as follows: ， wherein, RB represents an array randomly generated from a standard normal distribution with a dimension of ; ，x best represents the current optimal value; When the number of iterations is reached, its formula is as follows: ， Wherein, RL represents the flight step length in Levy flight, t represents the current iteration number, and T represents the maximum iteration number. represents the i new state of the secretary bird in the prey search stage, and represents the fitness value of its objective function.

6. The optimization method for the structure of the manned and unmanned perception network based on the improved secretary bird algorithm according to claim 1, wherein, The Gaussian and Cauchy mutation perturbations introduced in step S3.3 are as follows: ， wherein, is the updated solution, obeys a normal distribution with a mean of 0 and a variance of , r i obeys a Cauchy distribution with a location parameter, a scale parameter , and a degree of freedom , and are the weight coefficients of the Gaussian distribution and the Cauchy distribution respectively, and t and T are the current iteration number and the maximum iteration number respectively.

7. An optimization method for an unmanned perception network structure based on an improved secretary bird algorithm according to claim 1, characterized in that, It also includes the analysis of the performance of the most excellent heterogeneous human - unmanned collaborative sensing network structure, including: robustness, timeliness, connectivity, average path length, average betweenness centrality, clustering coefficient, and correlation tightness, as well as robustness, timeliness, and average path length under node attacks.

Images