Cooperative jamming method based on intelligent optimization algorithm

Abstract

The application discloses a method for cooperative jamming based on intelligent optimization algorithm, comprising: constructing a jamming decision model, the jamming decision model comprising a cooperative jamming decision matrix, a gain matrix, a jamming matrix, a jamming gain matrix, a jamming bandwidth ratio factor, a jam-to-signal ratio, and a jamming benefit; establishing an objective function and a constraint condition of the jamming decision model according to the jamming benefit; and using an artificial bee colony algorithm to take the jamming benefit as a fitness function and optimize the cooperative jamming decision matrix A. In different complex electromagnetic spectrum environments such as limited spectrum resources and the same frequency band shared by jamming devices and illegal users, the limited jamming resources are reasonably distributed under the condition that the jamming device of the own side can normally communicate, so that greater jamming benefit is achieved; the algorithm convergence speed and search ability are improved, and the method is helpful for making a decision with higher jamming benefit in a shorter time.

Description

Technical Field

[0001] This invention belongs to the technical field of interference resource allocation methods, and relates to a cooperative interference method based on intelligent optimization algorithms. Background Technology

[0002] In recent years, electronic jamming technology has developed rapidly. Whether ensuring the smooth operation of major national events or the successful conduct of important examinations, jamming technology is essential for timely suppression of unauthorized users or signals, playing a crucial role in maintaining public stability. However, with the advancement of anti-jamming technology, limitations such as operating frequency and jamming power have made it increasingly difficult for a single jammer to effectively interfere with highly defended, networked targets. Consequently, multi-machine cooperative jamming has gradually become the mainstream method. Multiple jammers cooperate, coordinating their tasks and objectives according to a specific operating mode, effectively utilizing limited jamming resources to achieve optimal jamming results. There are two main types of cooperative jamming resource allocation methods: traditional combinatorial optimization methods, represented by dynamic programming, and intelligent optimization algorithms, represented by genetic algorithms. Traditional algorithms can solve small-scale jamming resource optimization problems, but as the number of jamming targets increases, the scale of the optimization problem grows exponentially, necessitating the use of intelligent optimization algorithms to solve it.

[0003] Furthermore, in civilian scenarios, jammers and unauthorized users share the same spectrum resources. In such complex electromagnetic environments, traditional high-power, wideband jamming methods suffer from low resource utilization. Existing cooperative jamming techniques and models are primarily designed for military scenarios, where both sides communicate using different frequency bands. They do not adequately consider the interference caused to the jammer's own machine when both sides share the same frequency band. Therefore, these methods are difficult to directly apply to civilian scenarios and more complex electromagnetic spectrum environments. Other existing cooperative jamming decision-making methods, including genetic algorithms, simulated annealing algorithms, particle swarm optimization algorithms, and their corresponding improvements, suffer from the drawback of easily getting trapped in local optima, slow convergence speed, and long processing time, making them unsuitable for making effective cooperative jamming decisions in a short period. Summary of the Invention

[0004] The purpose of this invention is to provide a cooperative interference method based on intelligent optimization algorithms, which solves the problem that existing technologies are difficult to directly apply to civilian scenarios.

[0005] The technical solution adopted in this invention is a cooperative interference method based on intelligent optimization algorithms, comprising the following steps:

[0006] Step 1: Construct an interference decision model. The interference decision model includes a collaborative interference decision matrix, a gain matrix, an interference matrix, an interference gain matrix, an interference bandwidth ratio factor, an interference-to-signal ratio, and interference benefits. Based on the interference benefits, establish the objective function and constraints of the interference decision model.

[0007] Step 2: Using the artificial bee colony algorithm, the interference benefit is used as the fitness function to optimize the cooperative interference decision matrix A.

[0008] The invention is further characterized by:

[0009] The cooperative interference decision matrix A in step 1 is:

[0010]

[0011] In the above formula, M represents the number of jammers, N represents the number of jamming targets, and A ij P represents the interference power of the i-th jammer. max This indicates the upper limit of the jamming power of the jammer.

[0012] The formula for calculating the dry-to-sin ratio in step 1 is:

[0013]

[0014] In the above formula, H i，j This indicates that the i-th jammer and the j-th jamming target are in the same frequency band as the jamming target's signal. Channel gain on, σ 2 p represents the ambient noise power in this scenario. J This indicates the transmission power of the target transmitter. This indicates that the jamming target and its transmitter are in the same frequency band. Channel gain.

[0015] The formula for calculating the interference benefit in step 1 is:

[0016]

[0017] In the above formula, the interference bandwidth ratio factor The expression is as follows:

[0018]

[0019] The objective function and constraints of the disturbance decision model in step 1 are as follows:

[0020]

[0021]

[0022] In the above formula, the interference matrix A jamThe cooperative interference decision matrix A indicates whether it interferes with the friendly jammer, and the interference gain matrix H represents the amount of interference. jam P represents the interference gain between jammers. th p represents the threshold power of interference generated by the jammer against the user's jamming system. i This indicates the jammer's transmission power;

[0023]

[0024] Step 2 specifically includes the following steps:

[0025] Step 2.1: Initialize the bee population size N0, the maximum number of iterations, and the maximum number of explorations for the bee population; generate N0 cooperative interference decision matrices A globally based on the interference decision model, corresponding to the N0 nectar sources in the algorithm, and use the interference benefit as the fitness function fit;

[0026] Step 2.2: Arrange the N0 different cooperative interference decision matrices in descending order of interference benefit, and treat the N0 / 2 bees with the greater interference benefit as foraging bees, and the remaining bees as observation bees;

[0027] Step 2.3: First, determine whether the current cooperative interference decision matrix A is a feasible solution based on the constraints. Then, select a suitable interference machine and reselect the interference target based on the judgment result to obtain a new cooperative interference decision matrix A′. Finally, update the cooperative interference decision matrix according to the greedy criterion.

[0028] Step 2.4: Observe the bees according to probability P. i The size of the bee that follows the selection is determined. First, it is judged whether the current cooperative interference decision matrix A is a feasible solution according to the constraints. Based on the judgment result, a suitable interference machine is selected and a new interference target is selected to obtain a new cooperative interference decision matrix. The cooperative interference decision matrix is ​​updated using a greedy strategy.

[0029] Step 2.5: If the number of neighborhood searches for a certain interference decision matrix is ​​greater than or equal to the upper limit of the number of searches but no solution with higher interference efficiency is found, the current interference decision matrix will be abandoned, the bee will become a scout bee, and a new interference decision matrix will be randomly generated in the global solution space and replaced.

[0030] Step 2.6: Compare the interference decision matrices obtained after the above local optimization, retain the collaborative interference decision matrix with the greatest interference benefit, return to step 2.3 again, until the maximum number of iterations is reached, save the collaborative interference decision matrix with the greatest interference benefit and output it.

[0031] In step 2.3, first determine whether the current cooperative interference decision matrix A is a feasible solution based on the constraints. Then, select a suitable jammer and reselect an interference target based on the judgment result. Specifically, first determine whether the current cooperative interference decision matrix A is a feasible solution based on the constraints. If it is, select the jammer with the least interference benefit and randomly assign an interference target to it. If not, further determine the reason for the infeasibility. If it is because the current cooperative interference decision matrix A interferes too much with the friendly jammer, then select the jammer that interferes the most with the friendly equipment and randomly assign an interference target to it. If it is because the jammers are too clustered or the jammer's transmission power exceeds the upper limit, then randomly select a friendly jammer and reselect an interference target for it.

[0032] In step 2.3, the greedy criterion is: if fit′ < fit, then retain the cooperative interference decision matrix A, and increment the current honey source search count by 1; otherwise, it will be replaced by the cooperative interference decision matrix A′, and the current honey source search count will be set to 0.

[0033] The beneficial effects of this invention are as follows: The cooperative interference method based on intelligent optimization algorithms constructs a cooperative interference resource allocation model. Addressing complex electromagnetic spectrum environments such as limited spectrum resources and the sharing of the same frequency band between jammers and unauthorized users, it rationally allocates limited interference resources while ensuring normal communication for the jammer's own equipment, achieving greater interference benefits. In the algorithm fitness evaluation stage, a novel design of the fitness function combines the constraints of the optimization problem with fitness, transforming different levels of solution quality into corresponding fitness values, gradually guiding infeasible solutions towards feasible solution regions. Furthermore, it improves upon the traditional artificial bee colony algorithm by employing differentiated neighborhood search strategies based on the quality of different solutions in the current population during the bee collection and observation phases, thereby enhancing the algorithm's convergence speed and search capability, and helping to make decisions with higher interference benefits in a shorter time. Attached Figure Description

[0034] Figure 1 This is a flowchart of the cooperative interference method based on intelligent optimization algorithm of the present invention;

[0035] Figure 2 This is a schematic diagram of the cooperative interference model of the cooperative interference method based on intelligent optimization algorithm of the present invention;

[0036] Figure 3 This is a comparison of experimental simulation results between the cooperative interference method based on intelligent optimization algorithm of this invention and the cooperative interference resource allocation method of traditional artificial bee colony algorithm;

[0037] Figure 4 This is a diagram showing the efficiency of a single interfering machine in the cooperative interference resource allocation method of the traditional artificial bee colony algorithm.

[0038] Figure 5 This is a diagram illustrating the efficiency of a single jammer in the collaborative jamming method based on intelligent optimization algorithms of this invention. Detailed Implementation

[0039] The present invention will now be described in detail with reference to the accompanying drawings and specific embodiments.

[0040] Cooperative interference methods based on intelligent optimization algorithms, such as Figure 1 As shown, it includes the following steps:

[0041] Step 1: Construct an interference decision model, such as Figure 2 As shown, the dashed lines represent circles with radius d centered on each jammer, indicating the effective jamming range of that jammer. The maximum bandwidth that a single jammer can interfere with is defined as W. max The total bandwidth of the spectrum is W total The spectrum is then divided into L = W. total / W max In this scenario, each interfering target selects a frequency band for communication without repetition, thus defining the set of interfering target frequency bands. This indicates that the set of bandwidths of the interfering target signal is W = {w1, w2, ..., w...} N The jammer on our side also selects a frequency band for communication without repetition, using the set F = {f1, f2, ..., f...}. M The interference decision model includes the collaborative interference decision matrix, gain matrix, interference matrix, interference gain matrix, interference bandwidth ratio factor, interference-to-signal ratio, and interference benefit; the objective function and constraints of the interference decision model are established based on the interference benefit.

[0042] The cooperative jamming decision matrix represents the matching relationship between the friendly jammer and the jamming target, and its expression is:

[0043]

[0044] In the above formula, if A ij =0 indicates that the i-th jammer does not jam the j-th target. If A ij ≠0 indicates that the i-th jammer interferes with the j-th target, and the i-th jammer selects the same frequency band as the j-th target's signal. The transmitted noise signal, A ij The value represents the jamming power of the i-th jammer, M represents the number of jammers, N represents the number of jamming targets, and P max This indicates the upper limit of the jamming power of the jammer. If the jammer's transmission power exceeds this value during jamming, the jammer's target will be exposed, and the power must be reduced.

[0045] The interference-to-signal ratio (ISR) describes the degree of influence of each jammer on the target. The higher the ISR of the signal received at the target's receiver, the greater the impact of the jamming on the target. The calculation formula is:

[0046]

[0047] In the above formula, H i，j This indicates that the i-th jammer and the j-th jamming target are in the same frequency band as the jamming target's signal. The channel gain is used to simulate channel conditions in complex environments such as urban areas, and its value usually follows a Rayleigh distribution with a certain mean; if the distance between the jammer and the jamming target exceeds the upper limit d of the jamming range, then H... i，j A value of 0 indicates that the target cannot be effectively interfered with; σ 2 p represents the ambient noise power in this scenario. J This indicates the transmission power of the target transmitter. This indicates that the jamming target and its transmitter are in the same frequency band. Channel gain.

[0048] The formula for calculating the interference benefit of interference decision on a single interference target is as follows:

[0049]

[0050] In the above formula, the interference bandwidth ratio factor This indicates the impact of the jammer's frequency band bandwidth on the jamming effectiveness. The smaller the value, the less impact the targeting interference has on the target. The expression is as follows:

[0051]

[0052] The jamming effectiveness of a jammer against a target is related to factors such as jamming range, interference-to-signal ratio, and jamming bandwidth ratio. Based on reconnaissance and comprehensive intelligence analysis, the threat level index ω = [ω1, ω2, ..., ω] of each jamming target can be obtained. N Based on the interference benefits, establish the objective function and constraints of the interference decision-making model:

[0053]

[0054]

[0055] In the above formula, the interference matrix A jam This indicates whether the cooperative interference decision matrix A interferes with the friendly interference machine. This indicates that in the current interference decision, the i-th jammer will not interfere with the j-th jammer, and vice versa; the interference gain matrix H jamP represents the interference gain between jammers. th p represents the threshold power of interference generated by the jammer against the user's jamming system. i This indicates the jammer's transmission power;

[0056]

[0057] C1 indicates whether the i-th jammer interferes with the j-th target; C2 indicates that due to the limitations of the jammer's own physical parameters, each jammer can only intelligently jam one target at most; C3 indicates that to avoid excessive concentration of limited jamming resources, which would make it difficult to achieve the overall jamming effect, the cooperative jamming decision scheme should jam at least half of the targets; C4 indicates that the jamming transmission power of each jammer cannot exceed the maximum jamming power limit; C5 indicates that under this jamming decision, the total jamming power generated by the jammers on their own jamming system cannot exceed the jamming threshold P. th Exceeding this value may affect normal communication between jammers.

[0058] Step 2: Using the artificial bee colony algorithm, the interference benefit is used as the fitness function to optimize the cooperative interference decision matrix A.

[0059] Step 2.1: Initialize the bee population size N0, the maximum number of iterations maxCycle, and the maximum exploration limit; based on the cooperative interference decision model, randomly generate N0 cooperative interference decision matrices A globally, corresponding to the N0 nectar sources in the algorithm, and use the interference benefit as the fitness function fit. The interference benefit value fit corresponds to the amount of nectar for each nectar source in the algorithm. This invention designs a new fitness function for the optimization problem, transforming the optimization problem into a function of the interference decision matrix A, as shown below:

[0060]

[0061] g1(A) = -countp (9);

[0062]

[0063]

[0064] Where countp represents the number of friendly jammers whose transmit power exceeds P. max The quantity, and then the above formula is further transformed:

[0065]

[0066] In the above formula, a0, a1, and a2 represent integers, and a0 > a1 > a2. To follow the principle that better solutions have greater fitness, the fitness function can be expressed as:

[0067]

[0068] The fitness function can effectively indicate the superiority or inferiority of different solutions, and can gradually guide infeasible solutions to feasible solutions during the algorithm iteration process, and finally obtain the optimal solution of the optimization problem.

[0069] Step 2.2: Arrange the N0 different cooperative interference decision matrices in descending order of interference benefit, and treat the N0 / 2 bees with the greater interference benefit as foraging bees, and the remaining bees as observation bees;

[0070] Step 2.3, Bee-collecting stage: First, determine whether the current cooperative interference decision matrix A is a feasible solution based on the constraints. Based on the determination result, select a suitable interference machine and reselect the interference target. Specifically, first determine whether the current cooperative interference decision matrix A is a feasible solution based on the constraints. If it is, select the interference machine with the smallest interference benefit and reassign a random interference target to it. If not, further determine the reason for the infeasibility. If it is because the current cooperative interference decision matrix A interferes too much with the friendly interference machine, select the interference machine that interferes the most with the friendly equipment and reassign a random interference target to it. If it is because the interference machines are too clustered or the interference machine's transmission power exceeds the upper limit, randomly select a friendly interference machine and reassign a interference target to it, resulting in a new cooperative interference decision matrix A′. According to the greedy criterion, if fit′ < fit, retain the cooperative interference decision matrix A and increment the current honey source search count by 1. Otherwise, it will be replaced by the cooperative interference decision matrix A′ and the current honey source search count will be set to 0.

[0071] Step 2.4: Observe the bees according to probability P. i The size of the interference decision matrix determines the probability of the observing bee choosing to follow it. The higher the interference benefit value of a certain cooperative interference decision matrix, the greater the probability that the observing bee will choose to follow it. First, determine whether the current cooperative interference decision matrix A is a feasible solution based on the constraints. Based on the determination result, select a suitable interference machine and reselect the interference target to obtain a new cooperative interference decision matrix. Then, use a greedy strategy to update the cooperative interference decision matrix; probability P... i The expression is as follows:

[0072]

[0073] Step 2.5: If the number of neighborhood searches for a certain interference decision matrix is ​​greater than or equal to the upper limit of the number of searches but no solution with higher interference efficiency is found, the current interference decision matrix will be abandoned, the bee will become a scout bee, and a new interference decision matrix will be randomly generated in the global solution space and replaced.

[0074] Step 2.6: Compare the interference decision matrices obtained after the above local optimization, retain the collaborative interference decision matrix with the greatest interference benefit, return to step 2.3 again, until the maximum number of iterations is reached, save the collaborative interference decision matrix with the greatest interference benefit and output it.

[0075] Through the above methods, the cooperative interference method based on intelligent optimization algorithms of this invention constructs a cooperative interference resource allocation model. Addressing the complex electromagnetic spectrum environment in current civilian scenarios, such as limited spectrum resources and the sharing of the same frequency band between jammers and illegal users, it rationally allocates limited interference resources while ensuring normal communication of friendly jammer equipment, thereby improving the interference effectiveness against illegal users. It uses multiple low-power, targeted jammers instead of the traditional high-power, wide-bandwidth blocking and suppression jamming methods, comprehensively considering the interference effectiveness against illegal users in the spatial, frequency, and power domains, and cooperating to complete the interference task. In the algorithm fitness evaluation stage, a novel design of the fitness function is implemented, combining the constraints of the optimization problem with fitness, transforming different solutions of varying quality into corresponding fitness values, gradually guiding infeasible solutions towards feasible solution regions. Improvements are made to the traditional artificial bee colony algorithm, using differentiated neighborhood search strategies based on the quality of different solutions in the current population during the bee collection and observation phases, thereby improving the algorithm's convergence speed and search capability, and helping to make decisions with higher interference effectiveness in a shorter time.

[0076] Example

[0077] The cooperative interference method based on intelligent optimization algorithm of this invention was used in a simulation experiment, and the parameters were set as follows:

[0078] In the experiment, the population size N0 = 60, the maximum number of algorithm iterations maxCycle = 500, and the maximum number of explorations per nectar source limit = 1000. The number of friendly jammers M = 8, the number of jamming targets N = 6, the number of frequency bands L = 10, and the jamming bandwidth ratio factor W... J = [1, 1, 1, 0.85, 1, 1], Interference threshold P th =0.1, Interference target frequency band F j= [2, 4, 9, 8, 3, 5] and the jammer's communication frequency band F = [9, 2, 5, 1, 8, 4, 3, 10]. After reconnaissance and assessment, the threat level index of the jamming target is ω = [0.2, 0.3, 0.3, 0.3, 0.6, 0.4]. The channel gain matrix H follows a Rayleigh distribution with a mean of 1, and the jamming gain matrix H... jam It follows a Rayleigh distribution with a mean of 0.1.

[0079] The matching schemes for interference decision-making based on the model and the scenario conditions are shown in the table below:

[0080]

[0081] The table above shows that this cooperative interference decision implements interference on five out of the six interference targets, achieving an overall interference effect on the targets and avoiding excessive concentration of interference resources. The simulation results of the interference decision matrix obtained by the cooperative interference method PMABC based on intelligent optimization algorithm of this invention and the interference decision matrix obtained by the cooperative interference resource allocation method ABC using the existing artificial bee colony algorithm are shown below. Figure 3 , Figure 4 , Figure 5 As shown. By Figure 3 It can be seen that the improved algorithm converges after 100 iterations, while the traditional algorithm takes nearly 200 iterations to reach stability. Figure 4-5 It can be seen that the interference effect of each interfering machine in the improved algorithm has a smaller oscillation amplitude and a faster convergence speed. Therefore, the improved artificial bee colony algorithm has a higher convergence speed and optimization capability.

Claims

1. A cooperative interference method based on intelligent optimization algorithms, characterized in that, Includes the following steps: Step 1: Construct an interference decision model, which includes a collaborative interference decision matrix, a gain matrix, an interference matrix, an interference bandwidth ratio factor, an interference-to-signal ratio, and interference benefits. Based on the interference benefits, establish the objective function and constraints of the interference decision model. The cooperative jamming decision matrix represents the matching relationship between the friendly jammer and the jamming target, and its expression is: （1）； In the above formula, if This indicates that the i-th jammer does not interfere with the j-th target. This indicates that the i-th jammer interferes with the j-th target, and the i-th jammer selects a frequency band that is the same as the frequency band of the j-th target's signal. The transmitted noise signal The value represents the jamming power of the i-th jammer, M represents the number of jammers, and N represents the number of jamming targets. This indicates the upper limit of the jamming power of the jammer. If the jammer's transmission power exceeds this value during jamming, the jammer will be exposed as a target and the power must be reduced. The interference-to-signal ratio (ISR) describes the degree of influence of each jammer on the target. The higher the ISR of the signal received at the target's receiver, the greater the impact of the jamming on the target. The calculation formula is: （2）； In the above formula, This indicates that the i-th jammer and the j-th jamming target are in the same frequency band as the jamming target's signal. The channel gain, used to simulate channel conditions in complex environments such as urban areas, typically follows a Rayleigh distribution with a certain mean. If the distance between the jammer and the target exceeds the upper limit d of the jamming range, then... A value of 0 indicates that the target cannot be effectively interfered with; This represents the ambient noise power in this scenario. This indicates the transmission power of the target transmitter. This indicates that the jamming target and its transmitter are in the same frequency band. Channel gain; The formula for calculating the interference benefit of interference decision on a single interference target is as follows: （3）； In the above formula, the interference bandwidth ratio factor This indicates the impact of the jammer's frequency band bandwidth on the jamming effectiveness. The smaller the value, the less impact the targeting interference has on the target. The expression is as follows: （4）； The jamming effectiveness of a jamming device against a target is related to factors such as jamming range, interference-to-signal ratio, and jamming bandwidth ratio. Based on reconnaissance and comprehensive intelligence analysis, a threat level index for each jamming target can be obtained. Based on the interference benefits, establish the objective function and constraints of the interference decision-making model: （5）； （6）； In the above formula, the interference matrix This indicates whether the cooperative interference decision matrix A interferes with the friendly interference machine. This indicates that in the current interference decision, the i-th jammer will not interfere with the j-th jammer, and vice versa; the interference gain matrix indicates that the i-th jammer will interfere with the j-th jammer. This indicates the interference gain between jammers. This indicates the threshold of interference power generated by the jammer on the user's own jammer system. This indicates the jammer's transmission power; （7）； This indicates whether the i-th jammer interferes with the j-th jamming target. This indicates that due to limitations in the physical parameters of the jammer itself, each jammer can only jam a maximum of one target. This means that, in order to avoid the excessive concentration of limited interference resources, which would make it difficult to achieve the overall interference effect on the target, a coordinated interference decision-making scheme should interfere with at least half of the targets. This means that the jamming transmission power of each jammer cannot exceed the maximum jamming power limit. This means that under this interference decision-making condition, the total interference power generated by the jammer on the friendly jammer system cannot exceed the interference threshold. Exceeding this value may affect normal communication between jammers; Step 2: Using the artificial bee colony algorithm, the interference benefit is used as the fitness function to optimize the cooperative interference decision matrix A.

2. The cooperative interference method based on intelligent optimization algorithm according to claim 1, characterized in that, Step 2 specifically includes the following steps: Step 2.1: Determine the population size of the bee colony. Initialize the maximum number of iterations and the maximum number of explorations; generate globally random values ​​based on the interference decision model. A cooperative interference decision matrix A, and in the algorithm Each honey source is matched, and the interference benefit is used as the fitness function. ; Step 2.2, The different cooperative interference decision matrices are arranged from high to low interference effectiveness, with those having the highest interference effectiveness being... One bee was used as a foraging bee, and the rest were used as observation bees; Step 2.3: First, determine whether the current cooperative interference decision matrix A is a feasible solution based on the constraints. Then, select an interference machine and reselect interference targets based on the determination result to obtain a new cooperative interference decision matrix. And update the collaborative interference decision matrix according to the greedy criterion; Step 2.4: Observe the bees based on probability. The size of the bee that follows the selection is determined by first judging whether the current cooperative interference decision matrix A is a feasible solution based on the constraints. Based on the judgment result, the interference machine is selected and the interference target is reselected to obtain a new cooperative interference decision matrix. The cooperative interference decision matrix is ​​then updated using a greedy strategy. Step 2.5: If the number of neighborhood searches for a certain interference decision matrix is ​​greater than or equal to the upper limit of the number of searches but no solution with higher interference efficiency is found, the current interference decision matrix will be abandoned, the bee will become a scout bee, and a new interference decision matrix will be randomly generated in the global solution space and replaced. Step 2.6: Compare the interference decision matrices obtained after local optimization, retain the collaborative interference decision matrix with the greatest interference benefit, return to step 2.3 again, until the maximum number of iterations is reached, save the collaborative interference decision matrix with the greatest interference benefit and output it.

3. The cooperative interference method based on intelligent optimization algorithm according to claim 2, characterized in that, Step 2.3 describes first determining whether the current cooperative interference decision matrix A is a feasible solution based on the constraints, and then selecting an interference machine and reselecting an interference target based on the determination result. Specifically, it involves: first determining whether the current cooperative interference decision matrix A is a feasible solution based on the constraints; if so, selecting the interference machine with the least interference benefit and randomly assigning it an interference target; if not, further determining the reason for the infeasibility; if the current cooperative interference decision matrix A interferes too much with the friendly interference machine, then selecting the interference machine that interferes most with the friendly equipment and randomly assigning it an interference target; if the interference machines are too clustered or the interference machine's transmission power exceeds the upper limit, then randomly selecting a friendly interference machine and reselecting an interference target for it.

4. The cooperative interference method based on intelligent optimization algorithm according to claim 2, characterized in that, The greedy criterion described in step 2.3 is: if Then retain the collaborative interference decision matrix. At the same time, the current search count for honey sources is incremented by 1; otherwise, it will be affected by the collaborative interference decision matrix. Replace it and set the current honey source search count to 0.

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