An intelligent optimization method for distributed unmanned aerial vehicle jamming task allocation

Abstract

The application discloses an intelligent optimization method for distributed unmanned aerial vehicle jamming task allocation, sets an adaptive function by calculating the total number of distributed unmanned aerial vehicles and the total degree of jamming suppression, combines a sparse rate controllable Boolean particle swarm optimization algorithm and a real value particle swarm optimization algorithm to optimize the position of the start of the jamming of the distributed unmanned aerial vehicles and the jamming task allocation relationship, introduces a Pareto optimal theory to select an optimal solution, and realizes comprehensive consideration of multiple factors. The method effectively realizes solution of a multi-target optimization problem, reasonably allocates the jamming tasks of the distributed unmanned aerial vehicles, and obtains a smaller total number of distributed unmanned aerial vehicles and a higher total degree of jamming suppression.

Description

Technical Field

[0001] The present invention belongs to the field of electronic countermeasures, and in particular relates to an intelligent optimization method for distributed UAV jamming task allocation. Background Art

[0002] With the continuous development of electronic countermeasures technology and scheduling methods, traditional "one-on-one" jamming cannot achieve the desired effect, and the significance of coordinated jamming has become increasingly prominent. Distributed drones have the advantages of low cost and strong generation capabilities. The superior command post completes the allocation of distributed drones to radar jamming tasks, finds a suitable allocation strategy for the target radar to achieve the best jamming effect, maximize jamming efficiency, and complete coordinated jamming.

[0003] The distributed UAV jamming task allocation problem is a typical multi-objective optimization problem. Huang Jun et al. considered jamming coverage and energy consumption and solved the jamming task allocation problem using a heuristic genetic algorithm. Ran Huaming et al. established a collaborative jamming task allocation model and also used a genetic algorithm for optimization. Particle swarm optimization, as an intelligent optimization algorithm, has been widely used in solving nonlinear optimization problems due to its simple structure, few parameters, and fast convergence. Li Jun et al. proposed an improved particle swarm optimization algorithm that uses the total jamming utility as the objective function to optimize jamming task allocation. Most existing research on multi-jammer, multi-objective jamming task allocation considers a single fitness function, failing to comprehensively consider multiple optimization objectives. They also only consider the task allocation relationship and fail to factor in the number and location of distributed UAVs. Hu Hongbo et al. used weighted coefficient summation to transform multiple objectives into a single fitness function, taking into account multiple influencing factors. They then used an immune genetic algorithm to solve the task allocation problem. However, the optimization effect is significantly affected by the setting of the weighted coefficients.

[0004] From the above content, we can see that it is of great significance to comprehensively consider the impact of multiple factors on the final solution and achieve comprehensive optimization of the relationship between the number of drones and task allocation. Summary of the Invention

[0005] The present invention proposes an intelligent optimization method for the allocation of distributed UAV jamming tasks, which solves the problem of effectively jamming the target radar network while keeping the number of distributed UAVs as small as possible, and realizes the allocation of distributed UAV jamming tasks.

[0006] The technical solution to implement the present invention is: an intelligent optimization method for distributed UAV jamming task allocation, the steps are as follows:

[0007] Step 1: Set the target radar parameters and the effective cross-sectional area of ​​the distributed UAVs, and calculate the initial maximum detection range R of each target radar.kmax , target radar serial number k=1,…,K, and then calculate the detection coverage range Sq of the target radar network without interference max .

[0008] Step 2. Establish a multi-objective particle swarm optimization algorithm. The multi-objective particle swarm optimization algorithm consists of a sparse rate controllable Boolean particle swarm algorithm and a real-valued particle swarm optimization algorithm. Use the sparse rate controllable Boolean particle swarm algorithm to optimize the UAV jamming startup position, and use the real-valued particle swarm optimization algorithm to optimize the UAV jamming task allocation. That is, the particle position of the sparse rate controllable Boolean particle swarm algorithm is the position index matrix, and the particle position of the real-valued particle swarm optimization algorithm is the task allocation matrix. Set the parameters of the multi-objective particle swarm optimization algorithm, and randomly initialize the position index matrix, task allocation matrix and corresponding particle speed.

[0009] Step 3: Determine the location of the distributed UAV jammer according to the location index matrix, and determine the correspondence between the presence or absence of the distributed UAV and the target radar according to the task allocation matrix.

[0010] Step 4: Calculate the detection coverage Sq of the target radar network after interference based on the maximum detection distance of the target radar after interference, and calculate two fitness functions for distributed UAVs: the total number of distributed UAVs and the total degree of interference suppression, to obtain the distributed UAV interference task allocation optimization model.

[0011] Step 5: The two fitness functions are weightedly summed into a single fitness function, and the local optimal solution of the particle is updated according to the single fitness function.

[0012] Step 6: Select the global optimal solution of particles based on Pareto optimality theory.

[0013] Step 7: Update the particle position and particle velocity based on the sparse rate controllable Boolean particle swarm optimization algorithm and the real-valued particle swarm optimization algorithm.

[0014] Step 8: Determine whether the end condition is met. If not, return to step 3; otherwise, terminate the optimization and obtain the optimal archive, that is, the optimal Pareto front.

[0015] Step 9: According to the selection criteria, the optimal particles are selected from the optimal archive to determine the UAV jamming start-up position and allocate jamming tasks.

[0016] Compared with the prior art, the present invention has the following significant advantages:

[0017] (1) The sparse rate controllable Boolean particle swarm optimization algorithm and the real-valued particle swarm optimization algorithm are combined to simultaneously optimize the location of distributed UAV jammer startup and jammer task allocation.

[0018] (2) The Pareto optimality theory is introduced to effectively solve the multi-objective optimization problem, thereby obtaining fewer distributed drones and a greater degree of interference suppression. BRIEF DESCRIPTION OF THE DRAWINGS

[0019] Figure 1 Schematic diagram of the target radar and distributed drone locations.

[0020] Figure 2 This is a flow chart of an intelligent optimization method for distributed UAV jamming task allocation proposed by the present invention. DETAILED DESCRIPTION

[0021] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0022] The following will further introduce the specific implementation methods, as well as the technical difficulties and inventive points of this invention in combination with this design example.

[0023] The present invention provides an intelligent optimization method for distributed UAV interference task allocation, which takes the total number of distributed UAVs and the total degree of interference suppression as optimization targets, combines the real-valued particle swarm optimization algorithm and the sparse rate controllable Boolean particle swarm optimization algorithm with a multi-objective particle swarm optimization algorithm to optimize the distributed UAV interference start-up position and interference task allocation relationship.

[0024] The maximum number of distributed drones is L. On a rectangular plane with the coordinate origin as the reference point and containing M×N unit points, the distance between adjacent units is d c , randomly place K target radars, K≤M×NL, to form a target radar network. The schematic diagram of the target radar and distributed UAV positions is shown in Figure 1 As shown in the flow chart of the method Figure 2 The specific steps are as follows:

[0025] Step 1: Calculate the initial maximum detection range R of the target radar based on the target radar parameters and the effective cross-sectional area of ​​the distributed UAV. kmax , k=1,…,K, according to the target radar detection range, the detection coverage range Sq of the target radar network without interference can be calculated max .

[0026] Step 2: Establish a multi-objective particle swarm optimization algorithm, use the sparse rate controllable Boolean particle swarm algorithm to optimize the UAV jamming startup position, and use the real-valued particle swarm optimization algorithm to optimize the UAV jamming task allocation. That is, the particle position of the sparse rate controllable Boolean particle swarm algorithm is the position index matrix, and the particle position of the real-valued particle swarm optimization algorithm is the task allocation matrix. Set the multi-objective particle swarm optimization algorithm parameters, the number of particle groups is group, the number of iterations is G, the archive size is 1.5×group, the number of optimization targets is 2, and the number of grids in each dimension is s. Randomly generate a distributed UAV jamming startup position index matrix and the corresponding particle speed in is the set of natural numbers, is a set of real numbers, the element id in the mth row and nth column of the position index matrix ID m,n ∈{0,1}, set the unit point id of the kth target radar k = 0. Randomly generate interference task allocation matrix The corresponding particle velocity vector Maximum particle velocity v max =K+1, the value range of the elements in the task allocation matrix AI is [0,K+1].

[0027] Step 3: Determine the location of the distributed UAV jammer based on the location index matrix ID, and determine the presence of the distributed UAV and its corresponding relationship with the target radar based on the task allocation matrix AI, as follows:

[0028] Step 3.1, use random mutation to control the total number of "1" in the position index matrix ID, that is, count the number of "1" in the position index matrix, denoted as N on If N on If the maximum number of drones L is greater than the specified number, randomly index N in the position index matrix. on -L elements that are "1" change from "1" to "0", so that the number of array elements N on Equal to the maximum number of drones specified. On the contrary, if N on If the number of drones is less than the specified maximum number, the LN on The elements that are "0" change from "0" to "1", making N on =L, thereby adjusting the position index matrix. The element with a value of "1" in the position index matrix W indicates that there is a distributed UAV interfering with the startup at that location;

[0029] Step 3.2, assign the lth element ai in the matrix AI according to the task l The value range of ai determines the presence or absence of the lth distributed UAV and the corresponding target radar: if 0≤ai l<1, the lth distributed UAV does not work, that is, there is no distributed UAV; if 1≤ai l <2, then the lth distributed UAV interferes with the first target radar; if K≤ai l ≤K+1, then the lth distributed UAV interferes with the Kth target radar.

[0030] Step 4: Calculate the detection coverage Sq of the target radar network after interference based on the maximum detection range of the target radar after interference, and calculate two fitness functions for distributed UAVs: the total number of distributed UAVs and the total degree of interference suppression, and obtain the distributed UAV interference task allocation optimization model, as follows:

[0031] Step 4.1. Calculate the distance x between the distributed UAV and its corresponding target radar lk , x lk The subscript indicates that the l-th UAV interferes with the k-th target radar. The specific calculation formula is as follows:

[0032]

[0033] Among them (m l ,n l ) is the position of the lth UAV, (m k ,n k ) is the position of the kth target radar. According to the distance between the kth target radar and the UAV that interferes with it and the relevant parameters of the distributed UAV, the maximum detection range R of the kth target radar after interference is calculated. k , k=1,…,K, and calculate the detection coverage range Sq after the target radar network is interfered.

[0034] Step 4.2: Calculate the total number of distributed UAVs TN and the total jamming suppression level P all The total number of distributed drones TN is the total number of values ​​not less than 1 in the task allocation matrix AI, and the total degree of interference suppression P all As shown in the following formula:

[0035]

[0036] Step 4.3: Consider the total number of distributed UAVs TN and the total jamming suppression level P all To optimize the objective, the optimization model of distributed UAV jamming task allocation can be expressed as:

[0037]

[0038] Step 5: The two fitness functions are weighted and summed into a single fitness function. Let the single fitness function be f = α·TN + β·P all, α and β are weight parameters, and the particle local optimal solution AI is updated according to a single fitness function pb 、ID pb .

[0039] Step 6: Select the global optimal solution based on Pareto optimality theory, as follows:

[0040] Step 6.1, according to the fitness function TN and P all Compare the current particle with the particles in the archive, and select the Pareto optimal solution to add to the archive.

[0041] Step 6.2: Constrain the size of the archive to ensure that the number of particles in the archive does not exceed the limit. Sort the particles in the archive from large to small according to the single fitness function f. If the total number of particles in the current archive exceeds the limit by a number Q, delete the Q particles with larger f before sorting to maintain a stable archive size.

[0042] Step 6.3: Based on the adaptive grid, the multi-objective optimization problem archive target space is divided into two parts according to the grid number s. 2 Divide into sub-regions of equal size, select the region with the least congestion, that is, the sub-region with the least number of particles, and arbitrarily select a particle as the leader, which is the global optimal solution AI gb 、ID gb .

[0043] Step 7: Update the particle position and particle velocity based on the sparse rate controllable Boolean particle swarm algorithm and the real-valued particle swarm algorithm, as follows:

[0044] Step 7.1: Use the sparse rate controllable Boolean particle swarm optimization algorithm to update the position index matrix ID and its corresponding particle velocity matrix V id , the particle position and particle velocity update formula of the sparse rate controllable Boolean particle swarm optimization algorithm is as follows:

[0045]

[0046] Where W 1 、 and are Boolean values ​​between 0 and 1, and their length is equivalent to the particle dimension and is the learning factor, represents the influence on the particle approaching the individual optimal solution, It is expressed as the influence on the particle approaching the global optimal solution, W 1 is the inertia weight. is the position index matrix of the i-th particle in the t-th iteration, i.e., the particle position of the sparse rate controllable Boolean particle swarm optimization algorithm, is the particle velocity of the i-th particle in the t-th iteration, represents the local optimal solution of the t-th iteration, represents the global optimal solution of the tth iteration. is the particle position of the i-th particle in the t+1th iteration, that is, the updated particle position, is the particle velocity of the i-th particle at the t+1th iteration, that is, the updated particle velocity.

[0047] Step 7.2: Use the real-valued particle swarm optimization algorithm to update the interference task allocation matrix AI and its corresponding particle velocity matrix V ai , the real-valued particle swarm optimization algorithm particle position and particle velocity update formula is as follows

[0048]

[0049] Among them, W 2 、 and are all real values, rand is a random number in the interval [0,1], usually learning factors and The values ​​in are all 2.05. 2 The value w in 2 As the number of iterations decreases from 0.9 to 0.4. is the task assignment matrix of the i-th particle in the t-th iteration, i.e., the particle position of the real-valued particle swarm optimization algorithm, is the particle velocity of the i-th particle in the t-th iteration, represents the local optimal solution of the t-th iteration, represents the global optimal solution of the tth iteration. is the particle position of the i-th particle in the t+1th iteration, that is, the updated particle position, is the particle velocity of the i-th particle at the t+1th iteration, that is, the updated particle velocity.

[0050] When the updated position of a particle is not within the constraint range, boundary processing is required to ensure that the particle position is within the constraint range. Common boundary processing methods include absorbing boundaries.

[0051] Step 8: Determine whether the end condition is met. If so, terminate the optimization and obtain the optimal archive, that is, the optimal Pareto frontier. If not, return to step 3.

[0052] Step 9: According to the selection criteria, the particle with the smallest single fitness function value f in the optimal archive is selected as the optimal particle, and the optimal solution ID of the position index matrix and the task allocation matrix is ​​obtained. opt and AI opt, thereby determining the distributed UAV jamming start-up location and jamming task allocation relationship.

[0053] In summary, the present invention innovatively proposes an intelligent optimization method for distributed UAV interference task allocation. The method optimizes the distributed UAV interference startup position and interference task allocation based on the multi-objective particle swarm optimization algorithm, and combines the sparse rate controllable Boolean particle swarm optimization algorithm and the real-valued particle swarm optimization algorithm to optimize the position index and task allocation based on the Pareto optimal theory to obtain the optimal solution. The influence of multiple factors on the optimal solution is comprehensively considered to obtain a lower total number of distributed UAVs and a larger total degree of interference suppression. The total number of distributed UAVs decreases by more than 20% compared with the maximum number of distributed UAVs, and the total degree of interference suppression reaches more than 50%.

Claims

1. An intelligent optimization method for distributed UAV jamming task allocation, characterized by: Here are the steps: Step 1: Set the target radar parameters and the effective cross-sectional area of ​​the distributed UAVs, and calculate the initial maximum detection range R of each target radar. kmax , the target radar serial number k=1,…,K, and then calculate the detection coverage range Sq of the target radar network without interference max ; Step 2: Establish a multi-objective particle swarm optimization algorithm, which consists of a sparse rate controllable Boolean particle swarm algorithm and a real-valued particle swarm algorithm. Use the sparse rate controllable Boolean particle swarm algorithm to optimize the UAV jamming startup position, and use the real-valued particle swarm optimization algorithm to optimize the UAV jamming task allocation. That is, the particle position of the sparse rate controllable Boolean particle swarm algorithm is the position index matrix, and the particle position of the real-valued particle swarm optimization algorithm is the task allocation matrix. Set the parameters of the multi-objective particle swarm optimization algorithm, and randomly initialize the position index matrix, task allocation matrix, and corresponding particle speeds. Step 3: Determine the location of the distributed UAV jammer according to the location index matrix, and determine the correspondence between the presence or absence of the distributed UAV and the target radar according to the task allocation matrix; Step 4: Calculate the detection coverage Sq of the target radar network after interference based on the maximum detection range of the target radar after interference, and calculate two fitness functions for distributed UAVs: the total number of distributed UAVs and the total degree of interference suppression, to obtain a distributed UAV interference task allocation optimization model; Step 5: The two fitness functions are weighted and summed into a single fitness function, and the local optimal solution of the particle is updated according to the single fitness function; Step 6: Select the global optimal solution of particles based on Pareto optimality theory; Step 7: Update the particle position and particle velocity based on the sparse rate controllable Boolean particle swarm optimization algorithm and the real-valued particle swarm optimization algorithm; Step 8: Determine whether the end condition is met. If not, return to step 3; otherwise, terminate the optimization and obtain the optimal archive, that is, the optimal Pareto frontier. Step 9: According to the selection criteria, the optimal particles are selected from the optimal archive to determine the UAV jamming start-up position and allocate jamming tasks.

2. The intelligent optimization method for distributed UAV jamming task allocation according to claim 1 is characterized in that: Assume that M×N unit points are evenly divided in a rectangular plane, the reference point is the coordinate origin, the maximum number of distributed drones is L, and the distance between adjacent units on the rectangular plane containing M×N unit points with the coordinate origin as the reference point is d c , randomly place K target radars, K≤M×NL, to form a target radar network.

3. The intelligent optimization method for distributed UAV jamming task allocation according to claim 2 is characterized in that: In step 2, a multi-objective particle swarm optimization algorithm is established as follows: Set the parameters of the multi-objective particle swarm optimization algorithm, the number of particle groups is group, the number of iterations is G, the archive size is 1.5×group, the number of optimization targets is 2, and the number of grids in each dimension is s; randomly generate the distributed UAV interference start-up position index matrix and the corresponding particle speed in is the set of natural numbers, is a set of real numbers, the element id in the mth row and nth column of the position index matrix ID m,n ∈{0,1}, set the unit point id of the kth target radar k =0; Randomly generate interference task allocation matrix The corresponding particle velocity vector Maximum particle velocity v max =K+1, the value range of the elements in the task allocation matrix AI is [0,K+1].

4. The intelligent optimization method for distributed UAV jamming task allocation according to claim 3 is characterized in that: In step 3, the location of the distributed UAV jammer is determined based on the location index matrix, and the correspondence between the presence or absence of the distributed UAV and the target radar is determined based on the task allocation matrix, as follows: Step 3.1, use random mutation to control the total number of "1" in the position index matrix ID, that is, count the number of "1" in the position index matrix, denoted as N on ; If N on If the maximum number of drones L is greater than the specified number, randomly index N in the position index matrix. on -L elements that are "1" change from "1" to "0", so that the number of array elements N on Equal to the maximum number of drones specified; On the contrary, if N on If the number of drones is less than the specified maximum number, the LN on The elements that are "0" change from "0" to "1", making N on =L, thereby adjusting the position index matrix. The element with a value of "1" in the position index matrix W indicates that there is a distributed UAV interfering with the startup at that location; Step 3.2, assign the lth element ai in the matrix AI according to the task l The value range of ai determines the presence or absence of the lth distributed UAV and the corresponding target radar: if 0≤ai l <1, the lth distributed UAV does not work, that is, there is no distributed UAV; if 1≤ai l <2, then the lth distributed UAV interferes with the first target radar; if K≤ai l ≤K+1, then the lth distributed UAV interferes with the Kth target radar.

5. The intelligent optimization method for distributed UAV jamming task allocation according to claim 4 is characterized in that: In step 4, the detection coverage range Sq of the target radar network after interference is calculated based on the maximum detection range of the target radar after interference, and two fitness functions for distributed UAVs are calculated: the total number of distributed UAVs and the total degree of interference suppression, to obtain the distributed UAV interference task allocation optimization model, as follows: Step 4.

1. Calculate the distance x between the distributed UAV and its corresponding target radar lk , the specific calculation formula is as follows: Among them (m l ,n l ) is the position of the lth UAV, (m k ,n k ) is the position of the kth target radar; According to the distance between the kth target radar and the UAV that interferes with it and the relevant parameters of the distributed UAV, the maximum detection distance R of the kth target radar after interference is calculated. k , k=1,…,K, and calculate the detection coverage Sq of the target radar network after being interfered; Step 4.2: Calculate the total number of distributed UAVs TN and the total jamming suppression level P all The total number of distributed drones TN is the total number of values ​​not less than 1 in the task allocation matrix AI, and the total degree of interference suppression P all As shown in the following formula: Sq max Indicates the detection coverage of the target radar network without interference; Step 4.3: Consider the total number of distributed UAVs TN and the total jamming suppression level P all To optimize the goal, the optimization model of distributed UAV jamming task allocation is expressed as:

6. The intelligent optimization method for distributed UAV jamming task allocation according to claim 5 is characterized in that: In step 5, the two fitness functions are weighted and summed into a single fitness function, and the local optimal solution of the particle is updated according to the single fitness function, as follows: Assume that the single fitness function is f = α·TN + β·P all , α and β are weight parameters, and the particle local optimal solution AI is updated according to a single fitness function pb 、ID pb .

7. The intelligent optimization method for distributed UAV jamming task allocation according to claim 6 is characterized in that: In step 6, the global optimal solution of particles is selected based on the Pareto optimality theory, as follows: Step 6.1, according to the fitness function TN and P all Compare the current particle with the particles in the archive, and select the Pareto optimal solution to add to the archive; Step 6.2: Constrain the size of the archive to ensure that the number of particles in the archive does not exceed the limit. Sort the particles in the archive from large to small according to the single fitness function f. If the total number of particles in the current archive exceeds the limit by Q, delete the Q particles with larger f before sorting to maintain a stable archive size. Step 6.3: Based on the adaptive grid, the multi-objective optimization problem archive target space is divided into two parts according to the grid number s. 2 Divide into sub-regions of equal size, select the region with the least congestion, that is, the sub-region with the least number of particles, and arbitrarily select a particle as the leader, which is the global optimal solution AI gb 、ID gb .

8. The intelligent optimization method for distributed UAV jamming task allocation according to claim 7 is characterized in that: In step 7, the particle positions and particle velocities are updated based on the sparse rate controllable Boolean particle swarm optimization algorithm and the real-valued particle swarm optimization algorithm, as follows: Step 7.1: Use the sparse rate controllable Boolean particle swarm optimization algorithm to update the position index matrix ID and its corresponding particle velocity matrix V id , the particle position and particle velocity update formula of the sparse rate controllable Boolean particle swarm optimization algorithm is as follows: Among them, W 1 、 and are Boolean values ​​of 0 and 1, and their length is equivalent to the particle dimension. and is the learning factor, represents the influence on the particle approaching the individual optimal solution, It is expressed as the influence on the particle approaching the global optimal solution, W 1 is the inertia weight; is the position index matrix of the i-th particle in the t-th iteration, i.e., the particle position of the sparse rate controllable Boolean particle swarm optimization algorithm, is the particle velocity of the i-th particle in the t-th iteration, represents the local optimal solution of the t-th iteration, represents the global optimal solution of the tth iteration; is the particle position of the i-th particle in the t+1th iteration, that is, the updated particle position, is the particle velocity of the i-th particle in the t+1th iteration, that is, the updated particle velocity; Step 7.2: Use the real-valued particle swarm optimization algorithm to update the interference task allocation matrix AI and its corresponding particle velocity matrix V ai , the real-valued particle swarm optimization algorithm particle position and particle velocity update formula is as follows: Among them, W 2 、 and are all real values, rand is a random number in the interval [0,1], usually learning factors and The values ​​in are all 2.05; the inertia weight W 2 The value w in 2 As the number of iterations decreases from 0.9 to 0.4; is the task assignment matrix of the i-th particle in the t-th iteration, i.e., the particle position of the real-valued particle swarm optimization algorithm, is the particle velocity of the i-th particle in the t-th iteration, represents the local optimal solution of the t-th iteration, represents the global optimal solution of the tth iteration; is the particle position of the i-th particle in the t+1th iteration, that is, the updated particle position, is the particle velocity of the i-th particle in the t+1th iteration, that is, the updated particle velocity; When the updated position of a particle is not within the constraint range, boundary processing is required to ensure that the particle position is within the constraint range. Common boundary processing methods include absorbing boundaries.

9. The intelligent optimization method for distributed UAV jamming task allocation according to claim 8 is characterized in that: In step 9, according to the selection criteria, the optimal particles are selected from the optimal archive to determine the UAV jamming start-up position and allocate the jamming tasks, as follows: According to the selection criteria, the particle with the smallest single fitness function value f in the optimal archive is selected as the optimal particle, and the optimal solution ID of the position index matrix and the task allocation matrix is ​​obtained. opt and AI opt , thereby determining the distributed UAV jamming startup position and jamming task allocation relationship.

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