An adaptive black kite optimization method for wireless energy transmission and communication of unmanned aerial vehicles

Abstract

The application provides an adaptive black-winged kite optimization method for wireless energy transmission and communication of a UAV, and comprises the following steps: step 1, establishing a joint optimization model of a UAV-driven wireless node for wireless energy transmission and communication, specifically comprising a system model, a wireless energy transmission model, a wireless communication model and a wireless energy transmission and communication joint optimization model; step 2, using an adaptive black-winged kite optimization method to solve the model established in step 1. The technology of the application provides a strong guarantee for the stable operation of the wireless node through the UAV for wireless energy transmission and communication of the wireless node, lays a foundation for constructing a more intelligent, flexible and sustainable wireless network system, and further promotes the innovative development of wireless communication technology.

Description

TECHNICAL FIELD

[0001] The application relates to an adaptive black kite optimization method for wireless energy transmission and communication of a drone. BACKGROUND

[0002] With the rapid development of wireless communication technology, wireless nodes have been widely integrated into every corner of modern information society and have become indispensable infrastructure. However, wireless nodes face severe challenges in energy supply, especially in remote or hard-to-access areas of traditional power supply. In this context, drones have become an ideal aerial platform for solving the problem of wireless energy transmission and communication due to their high flexibility and rapid deployment capability. Drones can carry wireless energy transmission devices and achieve efficient wireless energy transmission and communication services by precisely flying close to or around wireless nodes. SUMMARY

[0003] The technical problem to be solved by the application is to provide an adaptive black kite optimization method for wireless energy transmission and communication of a drone, which solves the problems of the prior art.

[0004] Step 1, a joint optimization model of a drone driving a wireless node for wireless energy transmission and communication is established, which specifically includes a system model, a wireless energy transmission model, a wireless communication model and a wireless energy transmission and communication joint optimization model.

[0005] Step 2, an adaptive black kite optimization method is used to solve the model established in step 1.

[0006] Step 1 includes the following steps:

[0007] Step 1.1, a system model between the drone and the wireless sensor node is constructed;

[0008] Step 1.2, a wireless energy transmission model is established;

[0009] Step 1.3, a wireless communication model is established;

[0010] Step 1.4, a wireless energy transmission and communication joint optimization model is established.

[0011] In step 1.1, the system model includes a drone, a wireless node, microwave wireless energy transmission and wireless communication; wherein the drone serves as the transmitting end of energy transmission and communication and has mobility and controllability; the wireless node serves as the receiving end of energy reception and communication and is distributed at different positions in a certain area; microwave wireless energy transmission refers to energy transmission from the drone to the wireless node through a microwave frequency band; and wireless communication refers to information transmission between the drone and the wireless node.

[0012] Step 1.2 includes: the drone transmits energy to the wireless node at a constant power P tThe power P received by the wireless node after transmitting microwave energy. r Represented as:

[0013]

[0014] Among them G t and G r These are the antenna gain of the UAV and the antenna gain of the wireless node, respectively. λ is the wavelength of the microwave, d is the distance between the UAV and the wireless node, and L is the path loss factor, which includes atmospheric attenuation, obstacle occlusion, etc.

[0015] Microwave propagation in space is subject to various environmental interferences, resulting in losses. Furthermore, wireless nodes have efficiency limitations when converting microwave signals, leading to a reduction in the actual received power. Therefore, the final received power P of the wireless node will be affected. r act for:

[0016]

[0017] Where η E It refers to energy receiving efficiency.

[0018] Step 1.3 includes:

[0019] If the communication between the drone and the wireless node uses an additive white Gaussian noise (AWGN) channel model, then the signal-to-noise ratio (SNR) at the receiver is expressed as:

[0020]

[0021] Where P c N is the transmit power used by the UAV for communication, N0 is the noise power spectral density, and B is the communication bandwidth.

[0022] Obtain the communication rate V of the wireless node c for:

[0023] V c =η C ·log2(1+SNR) (4)

[0024] Where η C It is a weighting factor for energy communication rate.

[0025] Step 1.4 includes: setting up wireless power transfer and communication between the UAV and the wireless node. In order to simultaneously improve the energy receiving efficiency and communication rate of the wireless node, and ensure that the UAV meets the predetermined power and energy limits, the following joint optimization model for wireless power transfer and communication is constructed:

[0026]

[0027]

[0028] Where st indicates constraint, w1 and w2 are weighting factors, and P max C1 represents the maximum transmit power of the drone; C1 represents the power of the drone for wireless power transmission and wireless communication, which cannot exceed the drone's maximum transmit power P. max C2 indicates that the wireless power transfer is non-negative and does not exceed the maximum transmit power P of the UAV. max C3 indicates that the wireless communication power is non-negative and does not exceed the maximum transmit power P of the UAV. max C4 represents the minimum distance d between the drone and the wireless node. min and maximum distance d max Between these two values, C5 indicates that the antenna gain is non-negative.

[0029] Step 2 includes:

[0030] Step 2.1, Initialization Phase: Establish an initial population P containing N random solutions, where the N random solutions represent the initial positions of N black-winged kites. The initial position of the i-th black-winged kite in the j-th dimension is generated as follows:

[0031]

[0032] Where i∈{1,2,…,N}, BK ij Let j be the position of the i-th black-winged kite in the j-th dimension. Let be the lower bound of the position of the j-th black-winged kite. is the upper bound of the position of the j-th black-winged kite, and rand is a random number between [0,1].

[0033] According to formula (5), the initial population P is obtained as follows:

[0034]

[0035] Each row in P represents the position of a black-winged kite, which is an initial solution to the optimization problem; BK Nm Let m represent the initial position of the Nth black-winged kite in the m-th dimension, where N is the population size and m is the dimension of the variable in the optimization problem;

[0036] The position of the i-th black-winged kite is BK. i =[P t,i ,d i ], where P t,i d represents the transmit power of the drone. i Indicates the distance between the drone and the wireless node;

[0037] Step 2.2, in order to evaluate the merits of each position of the black-winged kite in the population, a fitness function Fit(BK) needs to be designed:

[0038] Fit(BK) = w1·η E ·P r +w2·η C ·log2(1+SNR) (7)

[0039] Where η E and η C These are the weighting factors for energy receiving efficiency and communication rate, respectively.

[0040] Step 2.3 proposes an enhanced cooperative predation strategy. This strategy involves a global leader and a current leader collaboratively guiding the black-winged kite flock in predation. Specifically, the global leader, with its optimal fitness, provides directional guidance for the flock's global search, helping the group quickly locate potential optimal solution areas. Simultaneously, the current leader guides the flock in a more refined search within a local area. Through this ingenious combination of global and local searches, the improved predation strategy ensures efficient predation while maintaining population diversity.

[0041] Step 2.4 proposes a global and local collaborative guidance migration strategy, which combines global and local collaborative guidance mechanisms. Specifically, if the fitness value of the i-th black-winged kite is lower than that of a randomly selected black-winged kite, it means that its current position is poor. Therefore, the i-th black-winged kite is guided to migrate towards the global leader and the current leader. Through this global and local collaborative guidance, the i-th black-winged kite has the opportunity to find a better position. Conversely, if the fitness value of the i-th black-winged kite is not lower than that of a randomly selected black-winged kite, it means that its current position is relatively good. Therefore, the i-th black-winged kite is guided to migrate in the opposite direction to the current leader. This reverse migration strategy helps the algorithm escape local optima and further explore other potential solutions in the solution space. Through this global and local collaborative guidance migration strategy, the migration efficiency of the black-winged kite flock is significantly enhanced, not only improving the convergence speed of the algorithm but also enhancing its global search capability and robustness.

[0042] Step 2.5, Establish a leader update strategy;

[0043] Step 2.6: Determine whether the maximum number of generations G has been reached. max (The value is generally between 10 and 200). If the value is reached, the process ends; otherwise, proceed to step 2.2.

[0044] In step 2.3, the enhanced cooperative predation strategy specifically refers to:

[0045]

[0046] Where y ij (t) represents the position of the i-th black-winged kite in the j-th dimension during the t-th iteration, XL i (t) represents the optimal position of the i-th black-winged kite in the first t iterations, X best (t) represents the optimal global leader position, r is a random number between [0,1], and p is a constant with a value of 0.4.

[0047] In step 2.4, the migration strategy guided by global and local collaboration is expressed as follows:

[0048]

[0049] Where F i Let F be the fitness value of the i-th black-winged kite. rand X represents the fitness value of a black-winged kite randomly selected from the population. best (t) represents the optimal global leader position, and C(0,1) is a one-dimensional Cauchy heterogeneous distribution.

[0050] In step 2.5, the leader update strategy includes updating the optimal position of the i-th black-winged kite in the first t iterations and updating the globally optimal position of the black-winged kite flock. These represent the local leader and the global leader, respectively, and the specific updates are as follows:

[0051] XL i (t)=min(XL i (1), XL i (2),…,XL i (t)) (10)

[0052] X best =min(XL1,XL2,…,XL) N (11)

[0053] Where X best For the optimal leader position globally, XL i (t) represents the optimal position of the i-th black-winged kite in the first t iterations.

[0054] Beneficial Effects: This invention proposes an adaptive black-winged kite optimization method for wireless power transfer and communication of unmanned aerial vehicles (UAVs). Firstly, it constructs a joint optimization model for wireless power transfer and communication, integrating the wireless power transfer model and the wireless communication model, and considering key constraints such as UAV transmit power and communication range. Secondly, it proposes an adaptive black-winged kite optimization algorithm, which combines a cooperative predation strategy with enhanced development capabilities and a global-local cooperative migration strategy. The former ensures efficient predation while maintaining population diversity through the cooperative guidance of a global leader and a current leader; the latter significantly accelerates the algorithm's convergence speed and improves its robustness by combining global and local leadership mechanisms. Finally, the proposed adaptive black-winged kite optimization method is used to optimize the constructed joint optimization model, greatly improving the energy reception efficiency and communication rate between the UAV and the wireless node. The technology of using UAVs for wireless power transfer and communication not only provides strong support for the stable operation of wireless nodes but also lays the foundation for building a more intelligent, flexible, and sustainable wireless network system, further promoting the innovative development of wireless communication technology. Attached Figure Description

[0055] The present invention will be further described in detail below with reference to the accompanying drawings and specific embodiments, and the advantages of the present invention in the above and / or other aspects will become clearer.

[0056] Figure 1 This is a flowchart of the method of the present invention.

[0057] Figure 2 This is a schematic diagram showing the results of an embodiment of the present invention. Detailed Implementation

[0058] like Figure 1 As shown, this embodiment of the invention provides an adaptive black-winged kite optimization method for wireless power transmission and communication of unmanned aerial vehicles (UAVs), comprising the following steps:

[0059] Step 1: Establish a joint optimization model for UAV-driven wireless nodes to perform wireless power transfer and communication, which includes a system model, a wireless power transfer model, a wireless communication model, and a joint optimization model for wireless power transfer and communication.

[0060] Step 2: An adaptive black-winged kite optimization method is used to solve the model established in Step 1.

[0061] Step 1 includes the following steps:

[0062] Step 1.1: Construct a system model between the UAV and the wireless sensor nodes;

[0063] Step 1.2, establish a wireless power transfer model;

[0064] Step 1.3: Establish a wireless communication model;

[0065] Step 1.4: Establish a joint optimization model for wireless power transmission and communication.

[0066] In step 1.1, the system model includes a drone, a wireless node, microwave wireless power transmission, and wireless communication; wherein the drone, as the transmitter of energy transmission and communication, has mobility and controllability; the wireless node, as the receiver of energy and communication, is distributed in different locations in a certain area; microwave wireless power transmission refers to the drone transmitting energy to the wireless node through the microwave frequency band; wireless communication refers to the information transmission between the drone and the wireless node.

[0067] Step 1.2 includes: the drone operating at a constant power P t The power P received by the wireless node after transmitting microwave energy. r Represented as:

[0068]

[0069] Among them G t and G r These are the antenna gain of the UAV and the antenna gain of the wireless node, respectively. λ is the wavelength of the microwave, d is the distance between the UAV and the wireless node, and L is the path loss factor, which includes atmospheric attenuation, obstacle occlusion, etc.

[0070] Microwave propagation in space is subject to various environmental interferences, resulting in losses. Furthermore, wireless nodes have efficiency limitations when converting microwave signals, leading to a reduction in the actual received power. Therefore, the final received power P of the wireless node will be affected. r act for:

[0071]

[0072] Where η E It refers to energy receiving efficiency.

[0073] Step 1.3 includes:

[0074] If the communication between the drone and the wireless node uses an additive white Gaussian noise (AWGN) channel model, then the signal-to-noise ratio (SNR) at the receiver is expressed as:

[0075]

[0076] Where P c N is the transmit power used by the UAV for communication, N0 is the noise power spectral density, and B is the communication bandwidth.

[0077] Obtain the communication rate V of the wireless node c for:

[0078] V c =η C ·log2(1+SNR) (4)

[0079] Where η C It is a weighting factor for energy communication rate.

[0080] Step 1.4 includes: setting up wireless power transfer and communication between the UAV and the wireless node. In order to simultaneously improve the energy receiving efficiency and communication rate of the wireless node, and ensure that the UAV meets the predetermined power and energy limits, the following joint optimization model for wireless power transfer and communication is constructed:

[0081]

[0082]

[0083] Where st indicates constraint, w1 and w2 are weighting factors, and P max C1 represents the maximum transmit power of the drone; C1 represents the power of the drone for wireless power transmission and wireless communication, which cannot exceed the drone's maximum transmit power P. max C2 indicates that the wireless power transfer is non-negative and does not exceed the maximum transmit power P of the UAV. max C3 indicates that the wireless communication power is non-negative and does not exceed the maximum transmit power P of the UAV. max C4 represents the minimum distance d between the drone and the wireless node. min and maximum distance d max Between these two values, C5 indicates that the antenna gain is non-negative.

[0084] Step 2 includes:

[0085] Step 2.1, Initialization Phase: Establish an initial population P containing N random solutions, where the N random solutions represent the initial positions of N black-winged kites. The initial position of the i-th black-winged kite in the j-th dimension is generated as follows:

[0086]

[0087] Where i∈{1,2,…,N}, BK ij Let j be the position of the i-th black-winged kite in the j-th dimension. Let be the lower bound of the position of the j-th black-winged kite. is the upper bound of the position of the j-th black-winged kite, and rand is a random number between [0,1].

[0088] According to formula (5), the initial population P is obtained as follows:

[0089]

[0090] Each row in P represents the position of a black-winged kite, which is an initial solution to the optimization problem; BK Nm Let m represent the initial position of the Nth black-winged kite in the m-th dimension, where N is the population size and m is the dimension of the variable in the optimization problem;

[0091] The position of the i-th black-winged kite is BK. i =[P t,i ,d i ], where P t,i d represents the transmit power of the drone. i Indicates the distance between the drone and the wireless node;

[0092] Step 2.2, in order to evaluate the merits of each position of the black-winged kite in the population, a fitness function Fit(BK) needs to be designed:

[0093] Fit(BK) = w1·η E ·P r +w2·η C ·log2(1+SNR) (7)

[0094] Where η E and η C These are the weighting factors for energy receiving efficiency and communication rate, respectively.

[0095] Step 2.3 proposes an enhanced cooperative predation strategy. This strategy involves a global leader and a current leader collaboratively guiding the black-winged kite flock in predation. Specifically, the global leader, with its optimal fitness, provides directional guidance for the flock's global search, helping the group quickly locate potential optimal solution areas. Simultaneously, the current leader guides the flock in a more refined search within a local area. Through this ingenious combination of global and local searches, the improved predation strategy ensures efficient predation while maintaining population diversity.

[0096] Step 2.4 proposes a global and local collaborative guidance migration strategy, which combines global and local collaborative guidance mechanisms. Specifically, if the fitness value of the i-th black-winged kite is lower than that of a randomly selected black-winged kite, it means that its current position is poor. Therefore, the i-th black-winged kite is guided to migrate towards the global leader and the current leader. Through this global and local collaborative guidance, the i-th black-winged kite has the opportunity to find a better position. Conversely, if the fitness value of the i-th black-winged kite is not lower than that of a randomly selected black-winged kite, it means that its current position is relatively good. Therefore, the i-th black-winged kite is guided to migrate in the opposite direction to the current leader. This reverse migration strategy helps the algorithm escape local optima and further explore other potential solutions in the solution space. Through this global and local collaborative guidance migration strategy, the migration efficiency of the black-winged kite flock is significantly enhanced, not only improving the convergence speed of the algorithm but also enhancing its global search capability and robustness.

[0097] Step 2.5, Establish a leader update strategy;

[0098] Step 2.6: Determine whether the maximum number of generations G has been reached. max (The value is generally between 10 and 200). If the value is reached, the process ends; otherwise, proceed to step 2.2.

[0099] In step 2.3, the enhanced cooperative predation strategy specifically refers to:

[0100]

[0101] Where y ij (t) represents the position of the i-th black-winged kite in the j-th dimension during the t-th iteration, XL i (t) represents the optimal position of the i-th black-winged kite in the first t iterations, X best (t) represents the optimal global leader position, r is a random number between [0,1], and p is a constant with a value of 0.4.

[0102] In step 2.4, the migration strategy guided by global and local collaboration is expressed as follows:

[0103]

[0104] Where F i Let F be the fitness value of the i-th black-winged kite. rand X represents the fitness value of a black-winged kite randomly selected from the population. best (t) represents the optimal global leader position, and C(0,1) is a one-dimensional Cauchy heterogeneous distribution.

[0105] In step 2.5, the leader update strategy includes updating the optimal position of the i-th black-winged kite in the first t iterations and updating the globally optimal position of the black-winged kite flock. These represent the local leader and the global leader, respectively, and the specific updates are as follows:

[0106] XL i (t)=min(XL i (1), XL i (2),…,XL i (t)) (10)

[0107] X best =min(XL1,XL2,…,XL) N (11)

[0108] Where X best For the optimal leader position globally, XL i (t) represents the optimal position of the i-th black-winged kite in the first t iterations.

[0109] In another embodiment of the present invention, an adaptive black-winged kite optimization method for wireless power transfer and communication of unmanned aerial vehicles is provided, comprising the following steps:

[0110] Step 1, initialize parameters;

[0111] The variable dimension m = 2, representing the transmit power P. t And transmission distance d; population size N = 50 (i.e., 50 candidate solutions); maximum number of iterations G max =10; Transmission power range: [10W, 100W]; Transmission distance range: [50m, 200m]; Weighting coefficients w1 = 1, w2 = 0.5.

[0112] Step 2, initialize the population;

[0113] Using equation (6), N initial candidate solutions are randomly generated, i.e., 50 sets [P] t ,d].

[0114] Step 3, assess fitness;

[0115] The fitness value of each candidate solution is calculated using equation (7).

[0116] Step 4, update the population;

[0117] The population is updated using the adaptive black-winged kite optimization method, i.e., equations (8) to (11).

[0118] Step 5, iteration;

[0119] Repeat steps 3 and 4 until the maximum number of iterations G is reached. max .

[0120] Step 6, output the results;

[0121] Output the optimal solution, that is, the solution with the largest fitness value and its corresponding transmission power and transmission distance.

[0122] After 10 iterations, the method of this invention found an optimal solution with a transmit power of 70.1893W and a transmission distance of 50m. The fitness value of this optimal solution is 1024. Compared with the optimal fitness value of 454 in the initial population, the optimized fitness value is improved by 125.6%, thus achieving a better balance between energy reception efficiency and communication rate. Specific results are as follows... Figure 2 As shown in the figure, the horizontal axis represents the number of iterations, and the vertical axis represents the optimal fitness value in each generation.

[0123] This invention solves the joint optimization problem of wireless power transfer and communication for unmanned aerial vehicles (UAVs). Particularly when multiple variables and constraints exist, the adaptive black-winged kite optimization algorithm can quickly find the global or near-global optimum, thereby significantly improving energy reception efficiency and communication speed. Compared to traditional optimization methods, the adaptive black-winged kite optimization algorithm exhibits higher efficiency and better global search capability when handling high-dimensional and complex problems. The algorithm in this invention can be applied to similar wireless transmission system parameter optimization problems, providing a new method for system design and performance optimization.

[0124] This invention provides an adaptive black-winged kite optimization method for wireless power transmission and communication in unmanned aerial vehicles (UAVs). Many methods and approaches exist for implementing this technical solution; the above description is merely a preferred embodiment of the invention. It should be noted that those skilled in the art can make various improvements and modifications without departing from the principles of this invention, and these improvements and modifications should also be considered within the scope of protection of this invention. All components not explicitly stated in this embodiment can be implemented using existing technologies.

Claims

1. An adaptive black-winged kite optimization method for wireless power transfer and communication in unmanned aerial vehicles, characterized in that, Includes the following steps: Step 1: Establish a joint optimization model for UAV-driven wireless nodes to perform wireless power transfer and communication, which includes a system model, a wireless power transfer model, a wireless communication model, and a joint optimization model for wireless power transfer and communication. Step 2: An adaptive black-winged kite optimization method is used to solve the model established in Step 1. Step 1 includes the following steps: Step 1.1: Construct a system model between the UAV and the wireless sensor nodes; Step 1.2, establish a wireless power transfer model; Step 1.3: Establish a wireless communication model; Step 1.4: Establish a joint optimization model for wireless power transmission and communication; Step 1.4 includes: setting up wireless power transfer and communication between the UAV and the wireless node, and constructing the following joint optimization model for wireless power transfer and communication: in V represents the final received power of the wireless node. c P represents the communication rate of a wireless node. t G represents the constant power of the drone. t and G r These are the antenna gains of the drone and the wireless node, respectively; d is the distance between the drone and the wireless node; and P is the antenna gain of the wireless node. r This represents the power received by the wireless node, where st indicates that it is constrained, w1 and w2 are weighting factors, and P max C1 represents the maximum transmit power of the drone; C1 represents the power of the drone for wireless power transmission and wireless communication, which cannot exceed the drone's maximum transmit power P. max C2 indicates that the wireless power transfer is non-negative and does not exceed the maximum transmit power P of the UAV. max C3 indicates that the wireless communication power is non-negative and does not exceed the maximum transmit power P of the UAV. max C4 represents the minimum distance d between the drone and the wireless node. min and maximum distance d max Between these two values, C5 indicates that the antenna gain is non-negative.

2. The method according to claim 1, characterized in that, In step 1.1, the system model includes a drone, a wireless node, microwave wireless power transmission, and wireless communication; wherein the drone serves as the transmitter for energy transmission and communication; the wireless node serves as the receiver for energy reception and communication, and is distributed in different locations within the area; microwave wireless power transmission refers to the drone transmitting energy to the wireless node via the microwave frequency band; and wireless communication refers to the information transmission between the drone and the wireless node.

3. The method according to claim 2, characterized in that, Step 1.2 includes: the drone operating at a constant power P t The power P received by the wireless node after transmitting microwave energy. r Represented as: Where λ is the wavelength of the microwave, and L is the path loss factor; The final received power of the wireless node for: Where η E It refers to energy receiving efficiency.

4. The method according to claim 3, characterized in that, Step 1.3 includes: If the communication between the drone and the wireless node uses an additive white Gaussian noise channel model, then the signal-to-noise ratio (SNR) at the receiver is expressed as: Where P c N is the transmit power used by the UAV for communication, N0 is the noise power spectral density, and B is the communication bandwidth. Obtain the communication rate V of the wireless node c for: V c =η C ·log2(1+SNR) (4) Where η C It is a weighting factor for energy communication rate.

5. The method according to claim 4, characterized in that, Step 2 includes: Step 2.1, Initialization Phase: Establish an initial population P containing N random solutions, where the N random solutions represent the initial positions of N black-winged kites. The initial position of the i-th black-winged kite in the j-th dimension is generated as follows: Where i∈{1,2,…,N}, BK ij Let j be the position of the i-th black-winged kite in the j-th dimension. Let be the lower bound of the position of the j-th black-winged kite. is the upper bound of the position of the j-th black-winged kite, and rand is a random number between [0,1]. According to formula (5), the initial population P is obtained as follows: Each row in P represents the position of a black-winged kite, which is an initial solution to the optimization problem; BK Nm Let m represent the initial position of the Nth black-winged kite in the m-th dimension, where N is the population size and m is the dimension of the variable in the optimization problem; The position of the i-th black-winged kite is BK. i =[P t,i ,d i ], where P t,i d represents the transmit power of the drone. i Indicates the distance between the drone and the wireless node; Step 2.2, Design the fitness function Fit(BK): Fit(BK)=w1·η E ·P r +w2·h C ·log2(1+SNR) (7) Where η E and η C These are the weighting factors for energy receiving efficiency and communication rate, respectively. Step 2.3 proposes a cooperative predation strategy that enhances the development capabilities of black-winged kites. This strategy involves the global leader and the current leader working together to guide the black-winged kite flock in predation: the global leader, with its optimal fitness, provides directional guidance for the global search, helping the flock to locate potential optimal solution areas; meanwhile, the current leader guides the black-winged kite flock to search within a local area. Step 2.4 proposes a migration strategy that coordinates global and local leadership: if the fitness value of the i-th black-winged kite is lower than the fitness value of a randomly selected black-winged kite, then guide the i-th black-winged kite to migrate in the direction of the global leader and the current leader; if the fitness value of the i-th black-winged kite is not lower than the fitness value of a randomly selected black-winged kite, then guide the i-th black-winged kite to migrate in the opposite direction of the current leader. Step 2.5, Establish a leader update strategy; Step 2.6: Determine whether the maximum number of generations G has been reached. max If the condition is met, the process ends; otherwise, proceed to step 2.

2.

6. The method according to claim 5, characterized in that, In step 2.3, the enhanced cooperative predation strategy specifically refers to: Where y ij (t) represents the position of the i-th black-winged kite in the j-th dimension during the t-th iteration, XL i (t) represents the optimal position of the i-th black-winged kite in the first t iterations, X best (t) represents the optimal global leader position, r is a random number between [0,1], and p is a constant.

7. The method according to claim 6, characterized in that, In step 2.4, the migration strategy guided by global and local collaboration is expressed as follows: Where F i Let F be the fitness value of the i-th black-winged kite. rand X represents the fitness value of a black-winged kite randomly selected from the population. best (t) represents the optimal global leader position, and C(0,1) is a one-dimensional Cauchy heterogeneous distribution.

8. The method according to claim 7, characterized in that, In step 2.5, the leader update strategy includes updating the optimal position of the i-th black-winged kite in the first t iterations, and updating the global optimal position of the black-winged kite flock. The specific updates are as follows: XL i (t)=min(XL i (1),XL i (2),…,XL i (t)) (10) X best =min(XL1,XL2,…,XL N ) (11) Where X best For the optimal leader position globally, XL i (t) represents the optimal position of the i-th black-winged kite in the first t iterations.

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