Self-adaptive black-wing plinary optimization method for wireless energy transfer and communication of unmanned aerial vehicle

Abstract

The invention provides a self-adaptive black-wing optimization method for wireless energy transfer and communication of an unmanned aerial vehicle, and the method comprises the following steps: 1, building a joint optimization model in which the unmanned aerial vehicle drives a wireless node to carry out wireless energy transfer and communication, the system specifically comprises a system model, a wireless energy transfer model, a wireless communication model and a wireless energy transfer and communication joint optimization model. And step 2, solving the model established in the step 1 by adopting a self-adaptive black-wing optimization method. According to the invention, the technology of wireless energy transmission and communication for the wireless node through the unmanned aerial vehicle provides a powerful guarantee for stable operation 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 the wireless communication technology.

Description

Technical Field

[0001] The invention relates to an adaptive black kite optimization method for wireless energy transmission and communication of unmanned aerial vehicles. Background Art

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

[0003] Purpose of the invention: The technical problem to be solved by the present invention is to provide an adaptive black kite optimization method for wireless energy transmission and communication of unmanned aerial vehicles in view of the shortcomings of the prior art, including the following steps:

[0004] Step 1: Establish a joint optimization model for UAV-driven wireless nodes to perform wireless energy transmission and communication, which specifically includes a system model, a wireless energy transmission model, a wireless communication model, and a joint optimization model for wireless energy transmission and communication;

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

[0006] Step 1 includes the following steps:

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

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

[0009] Step 1.3, establishing a wireless communication model;

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

[0011] In step 1.1, the system model includes UAVs, wireless nodes, microwave wireless energy transmission and wireless communications; the UAV, as the transmitting end of energy transmission and communication, is mobile and controllable; the wireless nodes, as the receiving ends of energy reception and communication, are distributed in different locations in a certain area; microwave wireless energy transmission refers to the transmission of energy from the UAV to the wireless nodes through the microwave frequency band; and wireless communications refers to the transmission of information between the UAV and the wireless nodes.

[0012] Step 1.2 includes: the drone is driven at a constant power P tTransmit microwave energy, the power P received by the wireless node r It is expressed as:

[0013]

[0014] Among them G t and G r 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, including atmospheric attenuation, obstacle shielding, etc.;

[0015] Since microwaves are affected by various environmental factors during space propagation, resulting in losses, and wireless nodes have efficiency limitations when converting microwave signals, which reduces the actual received power of wireless nodes. Therefore, the final received power of wireless nodes is P r act for:

[0016]

[0017] where η E is the energy receiving efficiency.

[0018] Step 1.3 includes:

[0019] The communication between the drone and the wireless node adopts the additive white Gaussian noise (AWGN) channel model, and the signal-to-noise ratio (SNR) at the receiving end is expressed as:

[0020]

[0021] Where P c is the transmission power used by the drone for communication, N0 is the noise power spectral density, and B is the communication bandwidth;

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

[0023] V c =η C log2(1+SNR) (4)

[0024] where η C is the weighting factor for the energy communication rate.

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

[0026]

[0027]

[0028] Where st means restricted, w1 and w2 are weight factors, P max is the maximum transmission power of the drone; C1 represents the power of wireless energy transmission and wireless communication of the drone, which cannot exceed the maximum transmission power P of the drone. max ; C2 means that the wireless transmission power is non-negative and does not exceed the maximum transmission power P of the drone max ; C3 indicates that the wireless communication power is non-negative and does not exceed the maximum transmission power P of the drone max ; C4 represents the minimum distance d between the drone and the wireless node min and the maximum distance d max Between, 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 j-th dimension of the i-th black-winged kite is generated as follows:

[0031]

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

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

[0034]

[0035] Each row of P represents the position of a black kite, that is, an initial solution to the optimization problem; BK Nm represents the initial position of the mth dimension of the Nth black-winged kite, where N is the size of the population and m is the variable dimension of 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 represents the transmitting power of the UAV, d i Indicates the distance between the drone and the wireless node;

[0037] Step 2.2, in order to evaluate the quality of each black kite position in the population, it is necessary to design a fitness function Fit(BK):

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

[0039] where η E and η C They are the weight factors of energy receiving efficiency and communication rate respectively;

[0040] Step 2.3 proposes a collaborative predation strategy with enhanced development capabilities, in which the global leader and the contemporary leader work together to lead the black kite group to prey. Specifically, the global leader, with its optimal fitness, provides directional guidance for the black kite group in global search, helping the group to quickly lock in the potential optimal solution area. At the same time, the contemporary leader guides the black kite group to conduct a detailed search in a local area. Through this ingenious combination of global search and local search, the improved predation strategy maintains population diversity while ensuring efficient predation.

[0041] Step 2.4, a global and local collaborative leadership migration strategy is proposed, which combines the global and local collaborative leadership mechanisms. Specifically, if the fitness value of the i-th black kite is lower than the fitness value of a randomly selected black kite, which means that its current position is poor, then the i-th black kite is guided to migrate in the direction of the global leader and the current leader. Through this global and local collaborative guidance, the i-th black kite has the opportunity to find a better position. On the contrary, if the fitness value of the i-th black kite is not lower than the fitness value of the randomly selected black kite, which means that its current position is relatively good, then the i-th black kite is guided to migrate in the opposite direction of the current leader. This reverse migration strategy helps the algorithm to jump out of the local optimum and further explore other potential solutions in the solution space. Through this global and local collaborative leadership migration strategy, the migration efficiency of the black kite group is significantly enhanced, which not only improves the convergence speed of the algorithm, but also enhances its global search ability and robustness;

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

[0043] Step 2.6: Determine whether the maximum evolutionary generation G has been reached max (Generally, the value is 10 to 200). If it is reached, end; otherwise, go to step 2.2.

[0044] In step 2.3, the collaborative predation strategy with enhanced development capability is specifically:

[0045]

[0046] where y ij (t) is the position of the i-th black-winged kite in the j-th dimension in the t-th iteration, XL i (t) is the optimal position of the ith black kite in the first t iterations, X best (t) is the global optimal 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 led by global and local collaboration is expressed as:

[0048]

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

[0050] In step 2.5, the leader update strategy includes the optimal position update of the ith black kite in the first t iterations and the global optimal position update of the black kite group, which represent the local leader and the global leader respectively. 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 is the global optimal leader position, XL i (t) is the optimal position of the ith black kite in the first t iterations.

[0054] Beneficial effects: The present invention proposes an adaptive black kite optimization method for wireless energy transmission and communication of unmanned aerial vehicles. On the one hand, a joint optimization model of wireless energy transmission and communication is constructed, which integrates the wireless energy transmission model and the wireless communication model, and takes into account the key constraints such as the transmission power and communication range of the unmanned aerial vehicle. On the other hand, an adaptive black kite optimization algorithm is proposed, which integrates the collaborative predation strategy with enhanced development capability and the migration strategy of global-local collaborative leadership. The former ensures efficient predation while maintaining the diversity of the population through the collaborative leadership of the global leader and the contemporary leader; the latter significantly accelerates the convergence speed of the algorithm and improves its robustness by combining the global and local leadership mechanisms. Finally, the proposed adaptive black kite optimization method is used to optimize the constructed joint optimization model, which greatly improves the energy reception efficiency and communication rate between the unmanned aerial vehicle and the wireless node. The technology of wireless energy transmission and communication for wireless nodes by unmanned aerial vehicles not only provides a strong guarantee for the stable operation of wireless nodes, but also lays the foundation for building a more intelligent, flexible and sustainable wireless network system, and further promotes the innovative development of wireless communication technology. BRIEF DESCRIPTION OF THE DRAWINGS

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

[0056] Figure 1 It is a flow chart of the method of the present invention.

[0057] Figure 2 It is a schematic diagram of the results of an embodiment of the present invention. DETAILED DESCRIPTION

[0058] like Figure 1 As shown, an embodiment of the present invention provides an adaptive black kite optimization method for wireless energy transmission and communication of unmanned aerial vehicles, comprising the following steps:

[0059] Step 1: Establish a joint optimization model for UAV-driven wireless nodes to perform wireless energy transmission and communication, which specifically includes a system model, a wireless energy transmission model, a wireless communication model, and a joint optimization model for wireless energy transmission and communication;

[0060] Step 2, using an adaptive black kite optimization method to solve the model established in step 1.

[0061] Step 1 includes the following steps:

[0062] Step 1.1, build the system model between the drone and the wireless sensor node;

[0063] Step 1.2, establish a wireless energy transmission model;

[0064] Step 1.3, establishing a wireless communication model;

[0065] Step 1.4, establish a joint optimization model for wireless energy transmission and communication.

[0066] In step 1.1, the system model includes UAVs, wireless nodes, microwave wireless energy transmission and wireless communications; the UAV, as the transmitting end of energy transmission and communication, is mobile and controllable; the wireless nodes, as the receiving ends of energy reception and communication, are distributed in different locations in a certain area; microwave wireless energy transmission refers to the transmission of energy from the UAV to the wireless nodes through the microwave frequency band; and wireless communications refers to the transmission of information between the UAV and the wireless nodes.

[0067] Step 1.2 includes: the drone is driven at a constant power P t Transmit microwave energy, the power P received by the wireless node r It is expressed as:

[0068]

[0069] Among them G t and G r 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, including atmospheric attenuation, obstacle shielding, etc.;

[0070] Since microwaves are affected by various environmental factors during space propagation, resulting in losses, and wireless nodes have efficiency limitations when converting microwave signals, which reduces the actual received power of wireless nodes. Therefore, the final received power of wireless nodes is P r act for:

[0071]

[0072] where η E is the energy receiving efficiency.

[0073] Step 1.3 includes:

[0074] The communication between the drone and the wireless node adopts the additive white Gaussian noise (AWGN) channel model, and the signal-to-noise ratio (SNR) at the receiving end is expressed as:

[0075]

[0076] Where P c is the transmission power used by the drone for communication, N0 is the noise power spectral density, and B is the communication bandwidth;

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

[0078] V c =η C log2(1+SNR) (4)

[0079] where η C is the weighting factor for the energy communication rate.

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

[0081]

[0082]

[0083] Where st means restricted, w1 and w2 are weight factors, P max is the maximum transmission power of the drone; C1 represents the power of wireless energy transmission and wireless communication of the drone, which cannot exceed the maximum transmission power P of the drone. max ; C2 means that the wireless transmission power is non-negative and does not exceed the maximum transmission power P of the drone max ; C3 indicates that the wireless communication power is non-negative and does not exceed the maximum transmission power P of the drone max ; C4 represents the minimum distance d between the drone and the wireless node min and the maximum distance d max Between, 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 j-th dimension of the i-th black-winged kite is generated as follows:

[0086]

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

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

[0089]

[0090] Each row of P represents the position of a black kite, that is, an initial solution to the optimization problem; BK Nm represents the initial position of the mth dimension of the Nth black-winged kite, where N is the size of the population and m is the variable dimension of 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 represents the transmitting power of the UAV, d i Indicates the distance between the drone and the wireless node;

[0092] Step 2.2, in order to evaluate the quality of each black kite position in the population, it is necessary to design a fitness function Fit(BK):

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

[0094] where η E and η C They are the weight factors of energy receiving efficiency and communication rate respectively;

[0095] Step 2.3 proposes a collaborative predation strategy with enhanced development capabilities, in which the global leader and the contemporary leader work together to lead the black kite group to prey. Specifically, the global leader, with its optimal fitness, provides directional guidance for the black kite group in global search, helping the group to quickly lock in the potential optimal solution area. At the same time, the contemporary leader guides the black kite group to conduct a detailed search in a local area. Through this ingenious combination of global search and local search, the improved predation strategy maintains population diversity while ensuring efficient predation.

[0096] Step 2.4, a global and local collaborative leadership migration strategy is proposed, which combines the global and local collaborative leadership mechanisms. Specifically, if the fitness value of the i-th black kite is lower than the fitness value of a randomly selected black kite, which means that its current position is poor, then the i-th black kite is guided to migrate in the direction of the global leader and the current leader. Through this global and local collaborative guidance, the i-th black kite has the opportunity to find a better position. On the contrary, if the fitness value of the i-th black kite is not lower than the fitness value of the randomly selected black kite, which means that its current position is relatively good, then the i-th black kite is guided to migrate in the opposite direction of the current leader. This reverse migration strategy helps the algorithm to jump out of the local optimum and further explore other potential solutions in the solution space. Through this global and local collaborative leadership migration strategy, the migration efficiency of the black kite group is significantly enhanced, which not only improves the convergence speed of the algorithm, but also enhances its global search ability and robustness;

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

[0098] Step 2.6: Determine whether the maximum evolutionary generation G has been reached max (Generally, the value is 10 to 200). If it is reached, end; otherwise, go to step 2.2.

[0099] In step 2.3, the collaborative predation strategy with enhanced development capability is specifically:

[0100]

[0101] where y ij (t) is the position of the i-th black-winged kite in the j-th dimension in the t-th iteration, XL i (t) is the optimal position of the ith black kite in the first t iterations, X best (t) is the global optimal 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 led by global and local collaboration is expressed as:

[0103]

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

[0105] In step 2.5, the leader update strategy includes the optimal position update of the ith black kite in the first t iterations and the global optimal position update of the black kite group, which represent the local leader and the global leader respectively. 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 is the global optimal leader position, XL i (t) is the optimal position of the ith black kite in the first t iterations.

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

[0110] Step 1, initialization parameters;

[0111] 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; the transmission power range is: [10W, 100W]; the transmission distance range is: [50m, 200m], the weight coefficient w1=1, w2=0.5.

[0112] Step 2, initialize the population;

[0113] Using formula (6), N initial candidate solutions are randomly generated, that is, 50 groups [P t ,d].

[0114] Step 3, evaluate fitness;

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

[0116] Step 4, update the population;

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

[0118] Step 5, iteration;

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

[0120] Step 6, output the result;

[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 the present invention found an optimal solution with a transmission power of 70.1893W and a transmission distance of 50m. The fitness value of the optimal solution is 1024. Compared with the optimal fitness value of 454 in the initial population, the optimized fitness value is increased by 125.6%. Therefore, the optimized result achieves a better balance between energy receiving efficiency and communication rate. The specific results are as follows: Figure 2 As shown, the horizontal axis represents the number of iterations and the vertical axis represents the optimal fitness value in each generation.

[0123] The present invention solves the problem of joint optimization of wireless energy transmission and communication of unmanned aerial vehicles. In particular, when there are multiple variables and constraints, the adaptive black kite optimization algorithm can quickly find the global or approximate global optimal solution, thereby greatly improving the energy reception efficiency and communication rate. Compared with traditional optimization methods, the adaptive black kite optimization algorithm has higher efficiency and better global search capabilities when dealing with high-dimensional and complex problems. The algorithm in the present invention can be applied to similar wireless transmission system parameter optimization problems, providing a new method for system design and performance optimization.

[0124] The present invention provides an adaptive black kite optimization method for wireless energy transmission and communication of unmanned aerial vehicles. There are many methods and ways to implement the technical solution. The above is only a preferred embodiment of the present invention. It should be pointed out that for ordinary technicians in this technical field, several improvements and modifications can be made without departing from the principle of the present invention. These improvements and modifications should also be regarded as the protection scope of the present invention. All components not specified in this embodiment can be implemented by existing technologies.

Claims

1. An adaptive black kite optimization method for wireless energy transmission and communication of unmanned aerial vehicles, characterized in that: The following steps are involved: Step 1: Establish a joint optimization model for UAV-driven wireless nodes to perform wireless energy transmission and communication, which specifically includes a system model, a wireless energy transmission model, a wireless communication model, and a joint optimization model for wireless energy transmission and communication; Step 2, using an adaptive black kite optimization method to solve the model established in step 1.

2. The method according to claim 1, characterized in that Step 1 includes the following steps: Step 1.1, build the system model between the drone and the wireless sensor node; Step 1.2, establish a wireless energy transmission model; Step 1.3, establishing a wireless communication model; Step 1.4, establish a joint optimization model for wireless energy transmission and communication.

3. The method according to claim 2, characterized in that In step 1.1, the system model includes UAVs, wireless nodes, microwave wireless energy transmission and wireless communications; the UAVs serve as the transmitter of energy transmission and communication; the wireless nodes serve as the receivers of energy reception and communication and are distributed in different locations in the region; microwave wireless energy transmission refers to the UAV transmitting energy to the wireless nodes through the microwave frequency band; and wireless communications refers to the information transmission between the UAV and the wireless nodes.

4. The method according to claim 3, characterized in that Step 1.2 includes: the drone is driven at a constant power P t Transmit microwave energy, the power P received by the wireless node r It is expressed as: Among them G t and G r are the antenna gain of the UAV and the antenna gain of the wireless node, 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; The final received power of the wireless node for: where η E is the energy receiving efficiency.

5. The method according to claim 4, characterized in that Step 1.3 includes: The communication between the drone and the wireless node adopts the additive Gaussian white noise channel model, and the signal-to-noise ratio (SNR) at the receiving end is expressed as: Where P c is the transmission power used by the drone for communication, N0 is the noise power spectral density, and B is the communication bandwidth; Get the communication rate V of the wireless node c for: V c =η C ·log2(1+SNR) (4) where η C is the weighting factor for the energy communication rate.

6. The method according to claim 5, characterized in that Step 1.4 includes: setting up the UAV and wireless nodes to perform wireless energy transmission and communication, and constructing the following wireless energy transmission and communication joint optimization model: Where st means restricted, w1 and w2 are weight factors, P max is the maximum transmission power of the drone; C1 represents the power of wireless energy transmission and wireless communication of the drone, which cannot exceed the maximum transmission power P of the drone. max ; C2 means that the wireless transmission power is non-negative and does not exceed the maximum transmission power P of the drone max ; C3 indicates that the wireless communication power is non-negative and does not exceed the maximum transmission power P of the drone max ; C4 represents the minimum distance d between the drone and the wireless node min and the maximum distance d max Between, C5 indicates that the antenna gain is non-negative.

7. The method according to claim 6, 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 j-th dimension of the i-th black-winged kite is generated as follows: where i∈{1,2,…,N}, BK ij is the j-th dimension position of the i-th black-winged kite, is the lower bound of the position of the black-winged kite in the jth dimension, is the upper bound of the position of the black-winged kite in the jth dimension, and rand is a random number between [0,1]; According to formula (5), the initial population P is: Each row of P represents the position of a black kite, that is, an initial solution to the optimization problem; BK Nm represents the initial position of the mth dimension of the Nth black-winged kite, where N is the size of the population and m is the variable dimension of the optimization problem; The position of the i-th black-winged kite is BK i =[P t,i ,d i ], where P t,i represents the transmitting power of the UAV, d 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 They are the weight factors of energy receiving efficiency and communication rate respectively; Step 2.3, propose a collaborative predation strategy with enhanced development capability, in which the global leader and the contemporary leader work together to lead the black kite group to prey: the global leader provides directional guidance for the black kite group in global search with the optimal fitness, helping the group to lock in the potential optimal solution area; at the same time, the contemporary leader guides the black kite group to search in a local range; Step 2.4, propose a migration strategy of global and local collaborative leadership: if the fitness value of the ith black kite is lower than the fitness value of a randomly selected black kite, guide the ith black kite to migrate in the direction of the global leader and the current leader; if the fitness value of the ith black kite is not lower than the fitness value of the randomly selected black kite, guide the ith black 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 evolutionary generation G has been reached max , if it is reached, then it ends, otherwise go to step 2.

2.

8. The method according to claim 7, characterized in that In step 2.3, the collaborative predation strategy with enhanced development capability is specifically: where y ij (t) is the position of the i-th black-winged kite in the j-th dimension in the t-th iteration, XL i (t) is the optimal position of the ith black kite in the first t iterations, X best (t) is the global optimal leader position, r is a random number between [0,1], and p is a constant.

9. The method according to claim 8, characterized in that In step 2.4, the migration strategy led by global and local collaboration is expressed as: Among them, F i is the fitness value of the i-th black-winged kite, F rand is the fitness value of a black-winged kite randomly selected from the population, X best (t) is the global optimal leader position, and C(0,1) is a one-dimensional Cauchy heterogeneous distribution.

10. The method according to claim 9, characterized in that In step 2.5, the leader update strategy includes the optimal position update of the i-th black kite in the previous t iterations and the global optimal position update of the black kite group. 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 is the global optimal leader position, XL i (t) is the optimal position of the ith black kite in the first t iterations.

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