Optimized scheduling method for launching multiple gliding aircrafts by aerial carrier

Abstract

The invention relates to an optimal scheduling method for a plurality of gliding aircrafts put on an aerial carrier. The invention relates to the optimal scheduling method for the plurality of gliding aircrafts put on the aerial carrier. The objective of the invention is to solve the problem that an existing method rarely considers resource scheduling and cannot give consideration to task timeliness when the number of gliding aircrafts is large. And the graffiti search algorithm mainly solves the problems that a continuous problem is easy to fall into local optimum, and an optimal scheme can not be found under the constraint of relatively short planning time. The method comprises the following steps of: 1, constructing an airborne launching scheduling model of a plurality of gliding aircrafts; 2, solving the time tijk of the i-th gliding aircraft flying from the j-th aircraft to the k-th target, and substituting the tijk into the airborne launching scheduling model of the multiple gliding aircrafts to calculate a distribution index; and 3, outputting the optimal positions in all the graffiti based on the distribution indexes and an improved mixed graffiti optimization algorithm, namely obtaining an optimal distribution strategy. The method is applied to the field of optimal scheduling of the multi-gliding aircraft launched by the aerial carrier.

Description

Technical Field

[0001] The invention relates to an optimization scheduling method for launching multiple gliding aircrafts by a carrier aircraft. Background Art

[0002] A glider is a medium- to long-range unmanned transport aircraft that can carry and airdrop important items such as cargo, medicine, food or strategic resources. Specifically, an aircraft can carry multiple gliders. After the aircraft reaches the target, it will drop the glider at a certain height, speed and angle. After that, the glider will fly unpowered to transport the cargo to the target. After releasing the cargo, it can be recycled and reused. Therefore, compared with the direct use of aircraft to drop, the use of gliders is more efficient, requires fewer pilots, and faces less pressure on airspace planning.

[0003] Disadvantages of existing technology

[0004] (1) It is of great significance to shorten the execution time of multiple gliders to transport cargo to multiple destinations. However, the existing glider collaborative planning methods rarely consider resource scheduling issues. When the number of gliders is large, it is impossible to take into account the timeliness of the task.

[0005] (2) The crow search algorithm has played an important role in the field of task planning, but it mainly solves continuous problems. The crow search algorithm is prone to fall into local optimality and may not be able to find the optimal solution under a shorter planning time constraint. Summary of the invention

[0006] The purpose of the present invention is to solve the problems that the existing glider collaborative planning methods give little consideration to resource scheduling issues and cannot take into account the timeliness of tasks when the number of gliders is large; and the crow search algorithm mainly solves continuous problems and is prone to fall into local optimality, and may not be able to find the optimal solution under a shorter planning time constraint, and thus proposes a carrier aircraft deployment optimization scheduling method for multiple gliders.

[0007] A method for optimizing the dispatching of multi-gliding aircraft by a carrier aircraft includes the following steps:

[0008] Step 1: Construct a multiple glider aircraft airborne delivery scheduling model;

[0009] Step 2: Find the time t that it takes for the i-th glider to fly from the j-th aircraft to the k-th target ijk , t ijk Bring in multiple glider aircraft onboard delivery scheduling models to calculate allocation indicators;

[0010] Step 3: Based on the allocation index and the improved hybrid crow optimization algorithm, the optimal position of all crows is output, that is, the optimal allocation strategy is obtained.

[0011] The beneficial effects of the present invention are:

[0012] The present invention proposes an optimized scheduling method for airborne delivery of glider aircraft, which adopts an improved hybrid crow optimization algorithm and considers that multiple aircraft with different altitudes, speeds, positions and headings can simultaneously deliver multiple glider aircraft from different angles, so as to achieve the shortest total time for multiple glider aircraft to deliver cargo to different targets.

[0013] The crow search algorithm plays an important role in the field of mission planning. Since the gliding aircraft airborne delivery scheduling problem of the present invention is a discrete problem, it cannot be directly processed by the hybrid crow optimization algorithm. The hybrid crow optimization algorithm needs to be improved. The improved hybrid crow optimization algorithm is used to solve the gliding aircraft airborne delivery scheduling problem of the present invention, and the optimal solution is found under the constraint of a shorter planning time. The improved hybrid crow optimization algorithm of the present invention takes into account the resource scheduling problem. When the number of gliding aircraft is large, the timeliness of the task is taken into account.

[0014] The present invention solves the problems that the existing gliding aircraft collaborative planning methods pay little attention to resource scheduling issues and cannot take into account the timeliness of tasks when the number of gliding aircraft is large; and the crow search algorithm mainly solves continuous problems and is prone to fall into local optimality, and may not be able to find the optimal solution under a shorter planning time constraint.

[0015] Key technical points of the present invention

[0016] (1) Multi-glider airborne delivery scheduling model

[0017] Considering that multiple aircraft with different altitudes, speeds, positions and headings simultaneously launch multiple gliders from different angles, and considering the flight time of the glider aircraft from launch to landing, a glider aircraft airborne launch scheduling model is established with the goal of minimizing the total completion time and maximizing the efficiency of multiple glider aircraft performing attack missions on multiple targets.

[0018] (2) Improved Hybrid Crow Optimization Algorithm

[0019] An improved hybrid crow optimization algorithm is proposed. A high-quality initial crow population is obtained by initializing twice. At the same time, a chaotic perturbation strategy is introduced to perturb the positions of a few crows with excellent performance to avoid falling into local optimality.

[0020] The present invention relates to an optimization scheduling method for airborne delivery of multiple gliders. Considering that multiple aircraft with different altitudes, speeds, positions and headings simultaneously deliver multiple gliders from different angles, combined with the flight sequence and delivery conditions of each glider, the flight status of each carrier aircraft, and the position of each target, considering the flight time from delivery to landing of the glider, with the goal of minimizing the total completion time and maximizing the efficiency of multiple gliders performing attack tasks on multiple targets, an optimization model for airborne delivery scheduling of gliders is established. Furthermore, an improved hybrid crow optimization algorithm is proposed to improve the initial population quality and population diversity, promote the crow algorithm to quickly complete the optimization, and finally obtain the optimal allocation strategy. BRIEF DESCRIPTION OF THE DRAWINGS

[0021] Figure 1 Optimal scheduling model diagram for multi-glider aircraft airborne delivery;

[0022] Figure 2 Flowchart for the improved hybrid crow optimization algorithm. DETAILED DESCRIPTION

[0023] Specific implementation method 1: In this implementation method, a carrier aircraft deploys a multi-glider aircraft. The specific process is as follows:

[0024] Step 1: Construct a multiple glider aircraft airborne delivery scheduling model;

[0025] Step 2: Find the time t that it takes for the i-th glider to fly from the j-th aircraft to the k-th target ijk , t ijk Bring in multiple glider aircraft onboard delivery scheduling models to calculate allocation indicators;

[0026] Step 3: Based on the allocation index and the improved hybrid crow optimization algorithm, the optimal position of all crows is output, that is, the optimal allocation strategy is obtained.

[0027] Specific implementation method 2: This implementation method is different from the specific implementation method 1 in that: in step 1, a multiple glider aircraft airborne delivery scheduling model is constructed; the specific process is:

[0028] Step 1: Assume that N gliders are dropped by M aircraft onto Q targets;

[0029] The time it takes for the i-th glider to fly from the j-th aircraft to the k-th target is t ijk ;

[0030] i=1,2,…,N, j=1,2,…,M, k=1,2,…,Q;

[0031] N, M, and Q are all positive integers;

[0032] Step 1 and 2: Set the constraints of the multiple glider aircraft airborne delivery scheduling model; the conditions are as follows:

[0033] 1) N>Q, that is, the number of gliders is greater than the number of targets. Some targets may need to be assigned at least two gliders;

[0034] 2) Set the initial launch conditions of the carrier aircraft and limit the turning radius of the glider aircraft, that is, the glider aircraft cannot turn towards the target with too large a range;

[0035] The initial launch conditions of the carrier aircraft include altitude, speed, heading, position, and angle;

[0036] 3) A glider can only reach one target;

[0037] 4) A target can be assigned to any glider;

[0038] Step 13: Based on steps 11 and 12, construct a multi-glider aircraft airborne delivery scheduling model.

[0039] The other steps and parameters are the same as those in the first embodiment.

[0040] Specific implementation method three: This implementation method is different from specific implementation methods one or two in that: in step one three, a multiple glider aircraft airborne delivery scheduling model is constructed based on step one one and step one two;

[0041] The expression of the multiple glider aircraft airborne delivery scheduling model is:

[0042]

[0043] Where: J is the allocation index, and the allocation index is used as the fitness;

[0044] k1 and k2 represent dimensionless weight coefficients, respectively;

[0045] t ijk is the time it takes for the i-th glider to fly from the j-th aircraft to the k-th target;

[0046] V ik is the decision variable. When the i-th glider does not fly to the k-th target, V ik =0, otherwise V ik =1;

[0047] The optimization variable is the decision variable V ik , that is, whether to perform allocation for glider aircraft i (i = 1, 2 ..., N) and target k (k = 1, 2 ..., Q), if V ik =1, otherwise V ik =0;

[0048] R ik represents the turning radius of the i-th glider flying to the k-th target. Obviously, R ik The smaller the better;

[0049] I k is the important attribute of the kth target, with a value range of [1,10]. More aircraft should be allocated to key targets during scheduling;

[0050] D k is the difficulty attribute of the kth target, such as terrain, weather, etc., with a value range of [1,10]. The greater the difficulty, the more difficult it is to transport, and more aircraft need to be allocated to it;

[0051] Time t ijk It mainly depends on the distance between the jth aircraft and the kth target.

[0052] The other steps and parameters are the same as those in the first or second embodiment.

[0053] Specific implementation method 4: This implementation method is different from one of the specific implementation methods 1 to 3 in that: in step 2, the time t required for the i-th glider to fly from the j-th aircraft to the k-th target is calculated. ijk , t ijk Bring in multiple glider aircraft onboard delivery scheduling models to calculate allocation indicators; the specific process is:

[0054] Step 21: Assume that the glider aircraft gradually lowers its altitude after being launched, and the flight altitude h of the glider aircraft changes with the remaining range R of the glider aircraft. L Change, the expression is:

[0055]

[0056] Among them, R L represents the remaining range of the glider aircraft, a1, a2, a3, a4 are polynomial coefficients, and h is the flight altitude of the glider aircraft;

[0057] Step 22: Flight altitude h of the glider and remaining range R of the glider L Satisfies the following relationship:

[0058] h′(t)=Vsinγ

[0059] R L ′(t)=-Vcosγ (3)

[0060] Where h′(t) represents the derivative of the flight altitude h of the glider with respect to time t;

[0061] R L ′(t) is the remaining range R of the glider aircraft L The derivative with respect to time t;

[0062] V and γ are the current speed and inclination angle of the glider aircraft respectively;

[0063] Step 2: From formula (2), we get:

[0064]

[0065] Step 24: From formula (3), we get:

[0066]

[0067] Step 25: Combining equations (4) and (5), we get:

[0068]

[0069] Step 26: Set the target point altitude and the remaining range R of the glider when it reaches the target L are all zero, and the inclination angle of the glider aircraft when it reaches the target is -80°;

[0070] Combined with the current flight altitude h of the glider aircraft and the current remaining range R of the glider aircraft L and the current inclination angle γ of the glider, combining equations (2) and (6):

[0071]

[0072] Based on formula (7), four unknown coefficients a1~a4 are solved;

[0073] e -h In formula 2 Take the logarithm,

[0074] Step 27: From formula (3), we get:

[0075]

[0076] Considering the uniform motion of a glider, integrate both sides of equation (8) and introduce the first equation of equation (7): Get time t;

[0077] Step 28: Set time t as the time t for the i-th glider to fly from the j-th aircraft to the k-th target ijk , t ijk Substitute into formula (1) to calculate the allocation index J.

[0078] The other steps and parameters are the same as those in Specific Embodiments 1 to 3.

[0079] Specific implementation method 5: This implementation method is different from the specific implementation methods 1 to 4 in that: in step 27, the uniform motion of the glider aircraft is considered, both sides of equation (8) are integrated, and the first equation of equation (7) is introduced to obtain the time t; the expression is:

[0080]

[0081] Among them, δR L Indicates that the flight interval [0,R L ] is divided into equal intervals, where δR L =5km; n represents a positive integer;

[0082] At this point, V, γ, h, and R can be determined based on the glider aircraft release position and target position. L , combined with formula (7) to obtain coefficients a1~a4, and finally use formula (9) to obtain t ijk , which makes it easier to calculate the planning index in formula (1).

[0083] Excellent initial population helps to improve algorithm performance. The present invention uses the important attributes of each target I k and difficulty attribute D k , combined with the time obtained by formula (9), the obviously unreasonable results are initially screened out, and the chaotic perturbation strategy is introduced in the iteration.

[0084] The other steps and parameters are the same as those in Specific Embodiments 1 to 4.

[0085] Specific implementation method 6: This implementation method is different from any one of specific implementation methods 1 to 5 in that: in step 3, the optimal position of all crows is output based on the allocation index and the improved hybrid crow optimization algorithm, that is, the optimal allocation strategy is obtained;

[0086] The optimal position for the crow is the allocation scheme, for example, target 1 is allocated to the first glider, which equals 1, and then target 2 is allocated to the second glider, which equals 2; Figure 2 ;

[0087] Crows are a type of bird with high IQ and strong memory, communication, storage, following and stealing abilities. Inspired by this, the crow optimization algorithm is based on four basic rules: ① Crows live in groups; ② Crows know where food is; ③ Crows follow and steal food from each other; ④ Crows protect food with a certain probability.

[0088] The specific process is:

[0089] Step 31: Set the number of iterations l = 1 and randomly initialize the positions of all crows;

[0090] Step 32: Randomly initialize the locations where all crows store food;

[0091] Step 3. Each crow randomly chooses an object to follow;

[0092] Step 34: Calculate the fitness J of each crow's position according to the multiple glider aircraft airborne delivery scheduling model (1);

[0093] Step 35: Update the current positions of all crows and update the locations of all crows' food storage;

[0094] Step 36: Add chaos disturbance to the current positions of all crows updated in step 35 to obtain the current positions of all crows updated again;

[0095] Step 37: Repeat steps 33-36 until the maximum number of iterations is reached, and output the optimal position of all crows, that is, obtain the optimal allocation strategy.

[0096] Position refers to which glider is assigned to which target, which is an allocation relationship and allocation scheme;

[0097] For example, 1 is for A, 2 is for B, 3 is for C, 4 is for D. This is a distribution scheme, defined as a position. Similarly, if 1 is for Z, 2 is for W, and 3 is for X, this is also another correspondence, which is also another position.

[0098] The position is related to the time t in formula 1, and the time t is obtained from the position;

[0099] The position is expressed as a number, for example, 1234, which is equivalent to crows 1 to 4 being assigned to the target, 1234 or 4321 or 1423, etc. With the assignment result, we can determine the assignment relationship between the aircraft and the target, and then further get the aircraft; according to the altitude, speed, inclination, remaining range, etc. of the glider, we can calculate the time t.

[0100] The other steps and parameters are the same as those in Specific Implementation Methods 1 to 5.

[0101] Specific implementation method 7: This implementation method is different from specific implementation methods 1 to 6 in that in step 31, the number of iterations l=1 is set, and the positions of all crows are randomly initialized; the specific process is:

[0102] 1a) Randomly select 20% of the crows;

[0103] 1b), randomly initialize the position of the crows selected in step 1a);

[0104] 1c), according to the multiple glider aircraft airborne delivery scheduling model (1), the fitness value J of each crow's position is calculated, and the fitness values ​​are sorted from low to high;

[0105] Position refers to which glider is assigned to which target, which is an allocation relationship and allocation scheme;

[0106] For example, 1 is for A, 2 is for B, 3 is for C, 4 is for D. This is a distribution scheme, defined as a position. Similarly, if 1 is for Z, 2 is for W, and 3 is for X, this is also another correspondence, which is also another position.

[0107] The position is related to the time t in formula 1, and the time t is obtained from the position;

[0108] The position is expressed as a number, for example, 1234, which is equivalent to crows 1 to 4 being assigned to the target, 1234 or 4321 or 1423, etc. With the assignment result, we can determine the assignment relationship between the aircraft and the target, and then further get the aircraft; according to the altitude, speed, inclination, remaining range, etc. of the glider, we can calculate the time t.

[0109] 1d), repeat steps 1b) to 1c) for β times to obtain the position of each crow sorted from low to high in fitness value;

[0110] 1e), select the positions of the bottom 15% of crows in 1d) and delete them;

[0111] 1f), take the positions of the remaining crows as the positions of all randomly initialized crows.

[0112] The other steps and parameters are the same as those in Specific Embodiments 1 to 6.

[0113] Specific implementation eight: This implementation differs from any one of specific implementations one to seven in that, in 1d), β=10.

[0114] The other steps and parameters are the same as those in Specific Embodiments 1 to 7.

[0115] Specific implementation method 9: This implementation method is different from any one of specific implementation methods 1 to 8 in that the current positions of all crows are updated and the positions of all crows storing food are updated in step 35; the specific process is as follows:

[0116] Let the position of crow i in the lth iteration be x i l (Optimization variables),

[0117] as follows:

[0118]

[0119] in, is the position of crow i in the 1-dimensional space in the lth iteration, is the position of crow i in the 2D space at the lth iteration, is the position of crow i in the lth iteration; i = 1, 2…δ, δ is a positive integer;

[0120] Suppose crow j visits the location where crow j stores food in iteration l Crow i follows crow j and steals the food stored by crow j; j≠i;

[0121] If crow i is not found by crow j, crow i updates its position in the l+1th iteration according to the following result. The expression is:

[0122]

[0123] Among them, r i is a random number between [0,1];

[0124] L is the location m where crow j stores food l j distance;

[0125] is the position of crow i in the l+1th iteration;

[0126] is the position of crow i in the lth iteration;

[0127] If crow i has been discovered by crow j, crow j will adopt a deceptive strategy and fly to a location where crow j stores food. Unrelated random positions The expression is:

[0128]

[0129] in, is the position of crow j in the l+1th iteration, is the position of crow j in the lth iteration, R j is a random number between [-1,1];

[0130] The position of crow i after being deceived is:

[0131]

[0132] in, is the position of crow i in the l+1th iteration, is the position of crow i in the lth iteration, R i is a random number between [-1,1];

[0133] The crow i itself also stores food, and the crow i will compare the positions where the crow i stores food itself with the new positions found by the crow i following the crow j According to the allocation index J, select a better position (the position corresponding to the smaller value of the allocation index J) as the new food storage position of the crow i The expression is:

[0134]

[0135] Other steps and parameters are the same as those in any one of the first to eighth specific embodiments

[0136] Specific embodiment ten: The difference between this embodiment and any one of the first to ninth specific embodiments is that in step 36, chaotic perturbations are added to the current positions of all crows after the update in step 35 to obtain the current positions of all crows after being updated again; the specific process is as follows:

[0137] Use the Skew Tent map to generate a chaotic sequence:

[0138]

[0139] where x is a random number between [0, 1];

[0140] i corresponds to the crow number i;

[0141] y i is a random number between [0, 1] corresponding to the i-th crow, and y i+1 is a random number between [0, 1] corresponding to the (i + 1)-th crow;

[0142] It is considered that the system is in a chaotic state when 0 < x < 1 and 0 < y < 1;

[0143] Generate a chaotic sequence y1, y2... according to formula (15);

[0144] Use the Logistic map to generate chaotic random numbers and apply them to the first 15% of the crows:

[0145]

[0146] where is the position of the crow j in the l-th iteration, and y j is a random number between [0, 1] corresponding to the crow j

[0147] Other steps and parameters are the same as those in any one of the first to ninth specific embodiments

[0148] The present invention may also have many other embodiments. Without departing from the spirit and essence of the present invention, those skilled in the art may make various corresponding changes and modifications based on the present invention, but these corresponding changes and modifications should all fall within the scope of protection of the claims attached to the present invention.

Claims

1. A method for optimizing the dispatching of multiple glider aircraft by a carrier aircraft, characterized in that: The specific process of the method is: Step 1: Construct a multiple glider aircraft airborne delivery scheduling model; Step 2: Find the time t that it takes for the i-th glider to fly from the j-th aircraft to the k-th target ijk , t ijk Bring in multiple glider aircraft onboard delivery scheduling models to calculate allocation indicators; Step 3: Based on the allocation index and the improved hybrid crow optimization algorithm, the optimal position of all crows is output, that is, the optimal allocation strategy is obtained.

2. The method for optimizing the dispatching of multi-glider aircraft by a carrier aircraft according to claim 1, characterized in that: In the step 1, a multiple glider aircraft airborne delivery scheduling model is constructed; the specific process is as follows: Step 1: Assume that N gliders are dropped by M aircraft onto Q targets; The time it takes for the i-th glider to fly from the j-th aircraft to the k-th target is t ijk ; i=1,2,…,N, j=1,2,…,M, k=1,2,…,Q; Step 1 and 2: Set the constraints of the multiple glider aircraft airborne delivery scheduling model; the conditions are as follows: 1) N>Q, that is, the number of gliders is greater than the target number; 2) Set the initial launch conditions of the carrier aircraft and limit the turning radius of the glider aircraft; The initial launch conditions of the carrier aircraft include altitude, speed, heading, position, and angle; 3) A glider can only reach one target; 4) A target can be assigned to any glider; Step 13: Based on steps 11 and 12, construct a multi-glider aircraft airborne delivery scheduling model.

3. The method for optimizing the dispatching of multi-glider aircraft by a carrier aircraft according to claim 2, characterized in that: In the step 13, a multiple glider aircraft airborne delivery scheduling model is constructed based on the steps 11 and 12; The expression of the multiple glider aircraft airborne delivery scheduling model is: Where: J is the allocation index, and the allocation index is used as the fitness; k1 and k2 represent weight coefficients respectively; t ijk is the time it takes for the i-th glider to fly from the j-th aircraft to the k-th target; V ik is the decision variable. When the i-th glider does not fly to the k-th target, V ik =0, otherwise V ik =1; R ik represents the turning radius of the i-th glider flying towards the k-th target; I k is the important attribute of the kth target, with a value range of [1,10]; D k is the difficulty attribute of the kth target, and its value range is [1,10].

4. The method for optimizing the dispatching of multi-glider aircraft by a carrier aircraft according to claim 3, characterized in that: In step 2, the time t required for the i-th glider to fly from the j-th aircraft to the k-th target is calculated. ijk , t ijk Bring in multiple glider aircraft onboard delivery scheduling models to calculate allocation indicators; the specific process is: Step 21: The flight altitude h of the glider aircraft changes with the remaining range R of the glider aircraft. L Change, the expression is: Among them, R L represents the remaining range of the glider aircraft, a1, a2, a3, a4 are polynomial coefficients, and h is the flight altitude of the glider aircraft; Step 22: Flight altitude h of the glider and remaining range R of the glider L Satisfies the following relationship: h′(t)=Vsinγ R L ′(t)=-Vcosγ (3) Where h′(t) represents the derivative of the flight altitude h of the glider with respect to time t; R L ′(t) is the remaining range R of the glider aircraft L The derivative with respect to time t; V and γ are the current speed and inclination angle of the glider aircraft respectively; Step 2: From formula (2), we get: Step 24: From formula (3), we get: Step 25: Combining equations (4) and (5), we get: Step 26: Set the target point altitude and the remaining range R of the glider when it reaches the target L are all zero, and the inclination angle of the glider aircraft when it reaches the target is -80°; Combined with the current flight altitude h of the glider aircraft and the current remaining range R of the glider aircraft L and the current inclination angle γ of the glider, combining equations (2) and (6): Based on formula (7), four unknown coefficients a1~a4 are solved; Step 27: From formula (3), we get: Considering the uniform motion of a glider, integrate both sides of equation (8) and introduce the first equation of equation (7): Get time t; Step 28: Set time t as the time t for the i-th glider to fly from the j-th aircraft to the k-th target ijk , t ijk Substitute into formula (1) to calculate the allocation index J.

5. The method for optimizing the dispatching of multi-glider aircraft by a carrier aircraft according to claim 4, characterized in that: In step 27, the uniform motion of the glider is considered, both sides of equation (8) are integrated, and the first equation of equation (7) is introduced to obtain the time t; the expression is: Among them, δR L Indicates that the flight interval [0,R L ] equally divided intervals; n represents a positive integer.

6. The method for optimizing the dispatching of multi-glider aircraft by a carrier aircraft according to claim 5, characterized in that: In the step 3, the optimal position of all crows is output based on the allocation index and the improved hybrid crow optimization algorithm, that is, the optimal allocation strategy is obtained; The specific process is: Step 31: Set the number of iterations l = 1 and randomly initialize the positions of all crows; Step 32: Randomly initialize the locations where all crows store food; Step 3. Each crow randomly chooses an object to follow; Step 34: Calculate the fitness J of each crow's position according to the multiple glider aircraft airborne delivery scheduling model (1); Step 35: Update the current positions of all crows and update the locations of all crows' food storage; Step 36: Add disturbance to the current positions of all crows updated in step 35 to obtain the current positions of all crows updated again; Step 37: Repeat steps 33-36 until the maximum number of iterations is reached, and output the optimal position of all crows, that is, obtain the optimal allocation strategy.

7. The method for optimizing the dispatching of multi-glider aircraft by a carrier aircraft according to claim 6, characterized in that: In step 31, let the number of iterations l=1, and randomly initialize the positions of all crows; the specific process is: 1a) Randomly select 20% of the crows; 1b), randomly initialize the position of the crows selected in step 1a); 1c), according to the multiple glider aircraft airborne delivery scheduling model (1), the fitness value J of each crow's position is calculated, and the fitness values ​​are sorted from low to high; 1d), repeat steps 1b) to 1c) for β times to obtain the position of each crow sorted from low to high in fitness value; 1e), select the positions of the bottom 15% of crows in 1d) and delete them; 1f), take the positions of the remaining crows as the positions of all randomly initialized crows.

8. The method for optimizing the dispatching of multi-glider aircraft by a carrier aircraft according to claim 7, characterized in that: In the above 1d), β=10.

9. The method for optimizing the dispatching of multiple glider aircraft by a carrier aircraft according to claim 8, characterized in that: In step 35, the current positions of all crows are updated and the positions of all crows storing food are updated; the specific process is: Suppose the position of crow i in the lth iteration is Suppose crow j visits the location where crow j stores food in iteration l Crow i follows crow j and steals the food stored by crow j; j≠i; If crow i is not found by crow j, crow i updates its position in the l+1th iteration according to the following result. The expression is: Among them, r i is a random number between [0,1]; L is the location where crow j stores food distance; is the position of crow i in the l+1th iteration; is the position of crow i in the lth iteration; If crow i has been discovered by crow j, crow j will adopt a deceptive strategy and fly to a location where crow j stores food. Unrelated random positions The expression is: in, is the position of crow j in the l+1th iteration, is the position of crow j in the lth iteration, R j is a random number between [-1,1]; The position of crow i after being deceived is: in, is the position of crow i in the l+1th iteration, is the position of crow i in the lth iteration, R i is a random number between [-1,1]; Crow i also stores food, and crow i will compare the location of crow i's own food storage and crow i follows the new position found by crow j According to the allocation index J, a better location is selected as the new food storage location of crow i. The expression is:

10. The method for optimizing the dispatching of multiple gliders by a carrier aircraft according to claim 9, characterized in that: In step 36, disturbance is added to the current positions of all crows updated in step 35 to obtain the current positions of all crows updated again; the specific process is: Generate random numbers and apply them to the first 15% of crows: Among them, x j l is the position of crow j in the lth iteration, y j is a random number between [0,1] corresponding to crow j.

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