CN115755971A - Cooperative confrontation task allocation method for sea-air integrated unmanned intelligent equipment - Google Patents

Cooperative confrontation task allocation method for sea-air integrated unmanned intelligent equipment Download PDF

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CN115755971A
CN115755971A CN202211467805.8A CN202211467805A CN115755971A CN 115755971 A CN115755971 A CN 115755971A CN 202211467805 A CN202211467805 A CN 202211467805A CN 115755971 A CN115755971 A CN 115755971A
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quantum
naucrates
echeneis
sea
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高洪元
赵海军
孙志国
程建华
揣济阁
孙可歆
孙溶辰
李慧爽
马静雅
张震宇
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Harbin Engineering University
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Harbin Engineering University
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Abstract

The invention discloses a collaborative confrontation task allocation method for sea-air integrated unmanned intelligent equipment, which comprises the following steps of establishing a collaborative confrontation task allocation model for sea-air integrated unmanned intelligent equipment; step two, initializing quantum echeneis naucrates quantum positions and setting parameters; step three, calculating a quantum echeneis naucrates position fitness function value; step four, updating the quantum position of the quantum echeneis naucrates by using a free search strategy, judging whether the fitness value of the ith quantum echeneis naucrates is greater than the fitness value of the empirical position of the ith quantum, and if so, executing the step five; otherwise, executing the step six; step five, updating quantum positions of the quanta echeneis naucrates by using an adsorption whale strategy; step six, updating quantum positions of the quanta echeneis naucrates by using a host detachment strategy; and seventhly, iteratively updating to the maximum iteration number, mapping the optimal quantum echeneis naucrates position to a sea-air integrated unmanned cooperative countermeasure task allocation matrix and outputting the matrix. The invention reduces the problem solving difficulty, overcomes the defect of easy local convergence and improves the optimizing rate.

Description

Cooperative confrontation task allocation method for sea-air integrated unmanned intelligent equipment
Technical Field
The invention belongs to the field of unmanned intelligent equipment countermeasure, relates to a sea-air integrated unmanned intelligent equipment cooperative countermeasure task allocation method, and particularly relates to a quantum-based unmanned intelligent equipment cooperative countermeasure task allocation method
Figure BDA0003957080110000011
A cooperative confrontation task allocation method for a sea-air integrated unmanned intelligent device of a fish mechanism.
Background
The sea-air integrated unmanned intelligent device means that a ship-based unmanned aerial vehicle on a large ship and unmanned ships around the ship form a set of relatively complete sea-air unmanned countermeasure system, and under the cooperative countermeasure of the ship-based unmanned aerial vehicle and the unmanned ships, how to realize countermeasure task allocation is a research focus.
According to the existing literature, liang Guojiang and the like in the unmanned aerial vehicle cooperative multi-task allocation based on discrete particle swarm optimization published in computer simulation (2018,35 (2): 22-28), complex constraint conditions such as task time constraint and ammunition constraint are comprehensively considered on the basis of an unmanned aerial vehicle cooperative multi-task allocation model, and a multi-unmanned aerial vehicle cooperative multi-task allocation method based on a discrete particle swarm method is provided. The problem of task allocation of cooperative combat of multiple unmanned aerial vehicles can be solved under the complex multi-constraint condition by the method through experimental simulation verification, the problem of task allocation of countermeasure of the multiple unmanned aerial vehicles is solved to a certain extent by the designed discrete particle swarm task allocation method, but the method is easy to get into local convergence, the convergence speed is too low, and the optimal solution can be obtained only by a certain number of iterations.
Disclosure of Invention
Aiming at the prior art, the invention aims to solve the technical problem of providing a quantum-based unmanned confrontation scene in the sea and air
Figure BDA0003957080110000012
Sea-air integrated unmanned intelligent equipment cooperative confrontation task allocation method for fish mechanism and quantum coding design
Figure BDA0003957080110000013
The fish quantum position evolution mechanism obtains a new quantum
Figure BDA0003957080110000014
The fish mechanism method increases the optimizing rate and overcomes the defect that the prior method is easy to fall into local convergence.
In order to solve the technical problem, the invention provides a collaborative confrontation task allocation method for sea-air integrated unmanned intelligent equipment, which comprises the following steps:
step one, establishing a collaborative confrontation task allocation model of the sea-air integrated unmanned intelligent equipment;
step two, initial quantum
Figure BDA0003957080110000015
Quantum position of the fish and setting parameters;
step three, quantum computation
Figure BDA0003957080110000016
A fitness function value of the fish position;
step four, updating quanta by using a free search strategy
Figure BDA0003957080110000017
The quantum position of the fish is judged
Figure BDA0003957080110000018
Whether the fish fitness value is greater than the fitness value of its empirical position, i =1,2,3, …, K 1 When the greater than condition is satisfied, the ith quantum
Figure BDA0003957080110000019
The fish is locally searched through the fifth step; otherwise, the ith quantum
Figure BDA00039570801100000110
The fish is locally searched through the sixth step;
step five, updating quanta by using whale adsorption strategy
Figure BDA00039570801100000226
Performing a seventh step on the quantum position of the fish;
step six, updating quanta by using off-host strategy
Figure BDA00039570801100000227
Performing a seventh step on the quantum position of the fish;
step seven, judging whether the quantum is reached
Figure BDA00039570801100000228
Maximum number of iterations K of a fish 2 If yes, stopping iteration and optimizing quanta
Figure BDA00039570801100000229
Mapping the position of the fish to a sea-air integrated unmanned cooperative confrontation task allocation matrix and outputting the matrix; otherwise, enabling the iteration number k = k +1, and finding the quantum position corresponding to the maximum fitness value of the (k + 1) th iteration as the optimal quantum
Figure BDA00039570801100000230
Quantum position of fish
Figure BDA0003957080110000021
And continuing to execute the step four.
Further, the step one of establishing the collaborative countermeasure task allocation model of the sea-air integrated unmanned intelligent device comprises the following steps:
suppose that there are N carrier-borne unmanned aerial vehicles in the sea-air unmanned countermeasure group and
Figure BDA0003957080110000022
the unmanned surface vehicle can execute the confrontation task, and the set of the sea-air integrated unmanned intelligent equipment is defined as
Figure BDA0003957080110000023
Wherein, carrier-borne unmanned aerial vehicle U n Is U n ＝{v n ,l n ,w n ,r n }，
Figure BDA0003957080110000024
v n For carrier-borne unmanned aerial vehicle U n Speed of travel of l n For carrier-borne unmanned aerial vehicle U n I.e. the position of the large vessel, w n For carrier-borne unmanned aerial vehicle U n Amount of ammunition carried, r n For carrier-borne unmanned aerial vehicle U n Voyage of; unmanned surface vehicle
Figure BDA0003957080110000025
Is a set of attributes of
Figure BDA0003957080110000026
Figure BDA0003957080110000027
Figure BDA0003957080110000028
Is an unmanned surface boat
Figure BDA0003957080110000029
And the sailing speed of
Figure BDA00039570801100000210
Figure BDA00039570801100000211
Is an unmanned surface boat
Figure BDA00039570801100000212
In the initial position of the first and second movable parts,
Figure BDA00039570801100000213
is an unmanned surface boat
Figure BDA00039570801100000214
The amount of ammunition carried by the cartridge,
Figure BDA00039570801100000215
is unmanned surface boat
Figure BDA00039570801100000216
Voyage of; assuming that M sea surface objects are detected for an enemy, the set of sea surface objects is defined as T = { T = { 1 ,T 2 ,…,T M In which T is M Is Mth sea target;
the sea-air integrated unmanned cooperative confrontation task allocation matrix is
Figure BDA00039570801100000217
Wherein the content of the first and second substances,
Figure BDA00039570801100000218
when x n,m =1 denotes shipboard unmanned aerial vehicle U n Attack sea surface target T m ，m＝1,2,…,M，x n,m =0 denotes carrier-borne unmanned aerial vehicle U n Does not attack sea surface target T m ；
Figure BDA00039570801100000219
When x n,m =1 unmanned surface vehicle
Figure BDA00039570801100000220
Attack sea surface target T m ，x n,m =0 represents a water surface unmanned ship
Figure BDA00039570801100000221
Does not attack sea surface target T m ；
Establishment of maximum objective function of cooperative countermeasure task allocation of sea-air integrated unmanned intelligent equipment
Figure BDA00039570801100000222
Figure BDA00039570801100000223
Wherein M =1,2, …, M; e (-) is a judgment function when
Figure BDA00039570801100000224
When, the function returns the value 1, and
Figure BDA00039570801100000225
when so, the function returns a value of 0; c 1 For task constraint penalty terms, C 2 For ammunition constraining penalty terms, C 3 For voyage constraint penalty term, α 1 、α 2 And alpha 3 For being otherwise a constraint penalty term C 1 、C 2 And C 3 The weight factor of (2);
the established model needs to meet 3 constraint conditions, namely a task constraint condition, an ammunition constraint condition and a voyage constraint condition; the task constraint condition is
Figure BDA0003957080110000031
M =1,2, …, M, indicating that at most one ship-borne drone or one surface drone attacks the sea surface target T m (ii) a The ammunition constraint condition of the carrier-borne unmanned aerial vehicle is
Figure BDA0003957080110000032
Representing a shipboard unmanned aerial vehicle U n The number of the targets attacking the sea surface cannot exceed the carrying amount of ammunition; ammunition constraint of unmanned surface vehicle is
Figure BDA0003957080110000033
Figure BDA0003957080110000034
Unmanned surface vehicle
Figure BDA0003957080110000035
The number of the targets attacking the sea surface cannot exceed the carrying amount of ammunition; the range constraint condition of the carrier-borne unmanned aerial vehicle is D n ≤r n ，
Figure BDA0003957080110000036
Wherein D is n For carrier-borne unmanned aerial vehicle U n Total distance of flight of; suppose that the shipboard unmanned plane U n Attack sea surface target T in sequence 1 、T 2 And T 3 At this moment, the shipboard unmanned aerial vehicle U n Has a total flight distance D n ＝d 0,1 +d 1,2 +d 2,3 +d 0,3 ，d 0,1 For large vessels position and sea surface target T 1 Distance between d 1,2 For sea surface target T 1 With sea surface target T 2 Distance of d 2,3 For sea surface target T 2 With sea surface target T 3 Distance of d 0,3 For large vessels position and sea surface target T 3 Distance between, carrier-borne unmanned aerial vehicle U n The total distance of flight of (a) includes the return distance; the range constraint condition of the unmanned surface vehicle is
Figure BDA0003957080110000037
Figure BDA0003957080110000038
Is unmanned surface boat
Figure BDA0003957080110000039
Total distance traveled; unmanned surface vehicle
Figure BDA00039570801100000310
Attack sea surface target T in sequence 1 、T 2 And T 3 At the moment, the total sailing distance of the unmanned surface vehicle is
Figure BDA00039570801100000311
Figure BDA00039570801100000312
Is an unmanned surface boat
Figure BDA00039570801100000313
Initial position and sea surface targetT 1 Distance between d 1,2 For sea surface target T 1 With sea surface target T 2 Distance of d 2,3 For sea surface target T 2 With sea surface target T 3 The distance of (d);
converting task constraint conditions into task constraint penalty items
Figure BDA00039570801100000314
Wherein | is an absolute value function, and ammunition constraint conditions of the carrier-borne unmanned aerial vehicle and the surface unmanned ship are converted into ammunition constraint penalty terms
Figure BDA00039570801100000315
Converting range constraint conditions of carrier-borne unmanned aerial vehicle and surface unmanned ship into range constraint penalty items
Figure BDA00039570801100000316
For E (D) as a judgment function n ,r n ) If D is n ≥r n When the value of the function is 1, the function value returns; otherwise 0 is returned.
Further, the initial quantum in step two
Figure BDA00039570801100000317
The quantum position and parameter setting of the fish comprises:
setting population scale as K 1 Maximum number of iterations K 2 In the initial population, random initial quanta
Figure BDA00039570801100000418
Quantum position of fish, i quantum
Figure BDA00039570801100000419
The 1 st generation initial quantum position of the fish is
Figure BDA0003957080110000041
Figure BDA0003957080110000042
h＝1,2,…,S，i＝1,2,3,…,K 1 Where S is the maximum dimension of the quantum position vector, and any dimension of all quantum positions is [0,1 ]]Random number between, quantum
Figure BDA00039570801100000420
The position of the fish is obtained by quantum position measurement; if the ith quantum in the kth iteration
Figure BDA00039570801100000421
The quantum position of the fish is
Figure BDA0003957080110000043
i＝1,2,3,…,K 1 ，k∈{1,2,…,K 2 Get the ith quantum in the kth iteration by measurement
Figure BDA00039570801100000422
The position of the fish is
Figure BDA0003957080110000044
i＝1,2,…,K 1 ，k∈{1,2,…,K 2 The measurement rule is
Figure BDA0003957080110000045
Representing the ith quantum
Figure BDA00039570801100000423
The h-th dimension variable of the fish position,
Figure BDA0003957080110000046
is [0,1]H =1,2, …, S, K e {1,2, …, K 2 }。
Further, quantum is calculated in the third step
Figure BDA00039570801100000424
The fitness function value for the fish location includes:
ith quantum of kth generation
Figure BDA00039570801100000425
Location of fish
Figure BDA0003957080110000047
Mapping a sea-air integrated unmanned cooperative countermeasure task allocation matrix, wherein the mapping rule is as follows: will be provided with
Figure BDA0003957080110000048
Is
Figure BDA0003957080110000049
X corresponding to first row in sea-air integrated unmanned cooperative countermeasure task allocation matrix 1,1 ,x 1,2 ,…,x 1,M ；
Figure BDA00039570801100000410
X corresponding to the second row in sea-air integrated unmanned cooperative countermeasure task allocation matrix 2,1 ,x 2,2 ,…,x 2,M (ii) a By the way of analogy, the method can be used,
Figure BDA00039570801100000411
corresponding to last row in sea-air integrated unmanned cooperative countermeasure task allocation matrix
Figure BDA00039570801100000426
The constructed task assignment matrix is written as
Figure BDA00039570801100000412
Maximum dimension S satisfies
Figure BDA00039570801100000413
The ith quantum of the kth iteration
Figure BDA00039570801100000428
Location of fish
Figure BDA00039570801100000414
Mapping is unmanned cooperative confrontation task allocation matrix of sea-air integration
Figure BDA00039570801100000436
Obtaining the ith quantum of the kth iteration
Figure BDA00039570801100000433
Fitness function value of fish
Figure BDA00039570801100000415
i＝1,2,…,K 1 (ii) a By comparing all quanta
Figure BDA00039570801100000429
Finding the quantum position corresponding to the maximum fitness value of the kth iteration as the optimal quantum by the fish fitness function value
Figure BDA00039570801100000427
Quantum position of fish
Figure BDA00039570801100000416
Further, in the fourth step, the quantum is updated by using a free search strategy
Figure BDA00039570801100000430
The quantum position of the fish is judged
Figure BDA00039570801100000431
Whether the fish fitness value is greater than the fitness value of its empirical position, i =1,2,3, …, K 1 When the greater than condition is satisfied, the ith quantum
Figure BDA00039570801100000432
The fish is locally searched through the fifth step; otherwise, the ith quantum
Figure BDA00039570801100000434
The fish local search through the sixth step comprises:
in free search strategy, i quantum
Figure BDA00039570801100000435
The h-dimension quantum rotation angle of the fish is
Figure BDA00039570801100000417
i＝1,2,…,K 1 H =1,2, …, S, ε is [1,K 1 ]Random integer between, ζ i,h 、
Figure BDA0003957080110000051
Is [0,1]A random number in between, and a random number,
Figure BDA0003957080110000052
is an epsilon quantum
Figure BDA00039570801100000533
The h-th dimension variable of the fish position,
Figure BDA0003957080110000053
for the kth iteration of the optimal quantum
Figure BDA00039570801100000534
H dimension variable of fish position;
updating ith quantum in free search strategy by using quantum revolving gate
Figure BDA00039570801100000535
H-dimensional quantum position of fish:
Figure BDA0003957080110000054
h＝1,2,…,S，i＝2,3,…,K 1 (ii) a For quantum position according to measurement rule
Figure BDA0003957080110000055
Measure the position in each dimension of
Figure BDA0003957080110000056
Then calculate
Figure BDA0003957080110000057
Fitness function value of
Figure BDA0003957080110000058
And to
Figure BDA0003957080110000059
And assigning, wherein the assignment rule is as follows:
Figure BDA00039570801100000510
quantum
Figure BDA00039570801100000536
When the fish is attached to the swordfish moving at high speed, the position of the swordfish can be adjusted, and the ith quantum is
Figure BDA00039570801100000538
The h dimension quantum rotation angle of the fish experience quantum position is
Figure BDA00039570801100000511
Wherein h =1,2, …, S, i =2,3, …, K 1 ，
Figure BDA00039570801100000512
Is the ith quantum
Figure BDA00039570801100000537
H-dimension variable, xi, of fish at previous generation position i,h Is a Gaussian random number satisfying that the mean value is 0 and the variance is 1; updating ith quantum by quantum revolving gate
Figure BDA00039570801100000539
H-dimension empirical quantum position of fish
Figure BDA00039570801100000513
I quanta of
Figure BDA00039570801100000549
Empirical quantum position of fish
Figure BDA00039570801100000514
The measurement being an empirical position
Figure BDA00039570801100000515
And calculate
Figure BDA00039570801100000516
Fitness function value of
Figure BDA00039570801100000517
Comparison
Figure BDA00039570801100000518
And
Figure BDA00039570801100000519
the size of (1) when
Figure BDA00039570801100000520
When larger, the ith quantum
Figure BDA00039570801100000540
Fish is locally searched through the fifth step; when in use
Figure BDA00039570801100000521
Greater than or equal to
Figure BDA00039570801100000522
While the ith quantum
Figure BDA00039570801100000541
The fish is locally searched through step six.
Further, in the fifth step, quantum is updated by using a whale adsorption strategy
Figure BDA00039570801100000542
The quantum positions of fish include:
quantum of quantum
Figure BDA00039570801100000543
When the fish host is changed from swordfish to whale, food residue on whale is used as food, and quantum is used
Figure BDA00039570801100000544
Fish adopts whale adsorption strategy to update quanta
Figure BDA00039570801100000545
Quantum position of the fish; in the whale adsorption strategy, the ith quantum
Figure BDA00039570801100000546
The h-dimension quantum rotation angle of the fish is
Figure BDA00039570801100000523
Wherein h =1,2, …, S, i =2,3, …, K 1 ，
Figure BDA00039570801100000524
Is composed of
Figure BDA00039570801100000525
And
Figure BDA00039570801100000526
the Euclidean distance of (a) is,
Figure BDA00039570801100000527
Figure BDA00039570801100000528
is [0,1]A random number in between;
method for renewing and adsorbing ith quantum in whale strategy by using quantum revolving door
Figure BDA00039570801100000547
H-dimensional quantum position of fish:
Figure BDA00039570801100000529
then calculate
Figure BDA00039570801100000530
Fitness function value of
Figure BDA00039570801100000531
And for the ith quantum of the (k + 1) th iteration
Figure BDA00039570801100000548
Quantum position of fish
Figure BDA00039570801100000532
The assignment is carried out according to the following assignment rule
Figure BDA0003957080110000061
Further, in step six, the quantum is updated by using an off-host strategy
Figure BDA0003957080110000069
The quantum positions of fish include:
quantum of quantum
Figure BDA00039570801100000610
While the fish host is still a swordfish, and swordfish has found a food rich sea area, quantum
Figure BDA00039570801100000611
The fish will leave the host to take food, and quantum will be at this time
Figure BDA00039570801100000612
Fish updating quanta using off-host strategy
Figure BDA00039570801100000613
Quantum position of fish, i quantum in off-host strategy
Figure BDA00039570801100000614
The h dimension quantum rotation angle of the fish is
Figure BDA0003957080110000062
h＝1,2,…,S，i＝2,3,…,K 1 And lambda is a decision value,
Figure BDA0003957080110000063
is [0,1]A random number in between;
updating ith quantum in host-off strategy by using quantum revolving gate
Figure BDA00039570801100000615
H-dimensional quantum position of fish:
Figure BDA0003957080110000064
then calculate
Figure BDA0003957080110000065
Fitness function value of
Figure BDA0003957080110000066
And for the ith quantum of the (k + 1) th iteration
Figure BDA00039570801100000616
Quantum position of fish
Figure BDA0003957080110000067
The assignment is carried out according to the following assignment rule
Figure BDA0003957080110000068
The invention has the beneficial effects that: the invention designs a quantum-based
Figure BDA00039570801100000617
A cooperative confrontation task allocation method for a sea-air integrated unmanned intelligent device of a fish mechanism. In a sea-air unmanned confrontation scene, a ship-based unmanned aerial vehicle and unmanned ship sea-air integrated cooperative confrontation strategy on a large ship is designed, a plurality of complex constraint conditions are comprehensively considered, the task efficiency is taken as a target function, confrontation tasks are distributed to the ship-based unmanned aerial vehicle and the unmanned ship, and finally the confrontation tasks of cooperative strike of a plurality of targets are realized.
Compared with the prior art, the invention has the beneficial effects that:
(1) The invention discloses a sea-air integrated unmanned intelligent equipment collaborative confrontation task allocation model, and aims to solve the problem that few aerial unmanned aerial vehicles in the past literature are collaboratively confronted in the large-scale ship confrontation background.
(2) The invention adopts a penalty term function method, converts a plurality of constraint conditions into penalty terms and substitutes the penalty terms into the objective function, so that the constrained problem with a plurality of complex constraint conditions is converted into an unconstrained problem, the model is simplified, and the problem solving difficulty is reduced.
(3) The invention designs quantum coding
Figure BDA00039570801100000618
The fish quantum position evolution mechanism obtains a new quantum
Figure BDA00039570801100000619
Fish mechanism method, quantum
Figure BDA00039570801100000620
The three strategies are used for cooperatively optimizing the fitness function, so that the defect that the traditional method is easy to fall into local convergence is overcome, and the optimizing rate of an evolution mechanism is also improved.
Drawings
FIG. 1 shows a quantum-based design of the present invention
Figure BDA00039570801100000621
A schematic diagram of a cooperative countermeasure task allocation method of a sea-air integrated unmanned intelligent device of a fish mechanism;
FIG. 2 is
Figure BDA0003957080110000071
The positions of the unmanned intelligent equipment and the sea surface target are distributed;
FIG. 3 is a drawing showing
Figure BDA0003957080110000072
The positions of the unmanned intelligent equipment and the sea surface target are distributed;
FIG. 4 is a drawing showing
Figure BDA0003957080110000073
The positions of the unmanned intelligent equipment and the sea surface target are distributed;
FIG. 5 is a drawing showing
Figure BDA0003957080110000074
The convergence curve of the objective function of (1).
Detailed Description
The invention is further described with reference to the drawings and examples.
With reference to fig. 1, the present invention comprises the following steps:
step one, establishing a collaborative confrontation task allocation model of the sea-air integrated unmanned intelligent equipment.
Suppose that there are N carrier-borne unmanned aerial vehicles in the sea-air unmanned countermeasure group and
Figure BDA0003957080110000075
the unmanned surface vehicle can execute the confrontation task, and the set of the sea-air integrated unmanned intelligent equipment is defined as
Figure BDA0003957080110000076
Wherein, carrier-borne unmanned aerial vehicle U n Is U n ＝{v n ,l n ,w n ,r n }，
Figure BDA0003957080110000077
v n For carrier-borne unmanned aerial vehicle U n Speed of travel of l n For carrier-borne unmanned aerial vehicle U n I.e. the position of the large vessel, w n For carrier-borne unmanned aerial vehicle U n Amount of ammunition carried, r n For carrier-borne unmanned aerial vehicle U n The voyage of. Unmanned surface vehicle
Figure BDA0003957080110000078
Is a set of attributes of
Figure BDA0003957080110000079
Figure BDA00039570801100000710
Figure BDA00039570801100000711
Is an unmanned surface boat
Figure BDA00039570801100000712
And the sailing speed of
Figure BDA00039570801100000713
Figure BDA00039570801100000714
Is an unmanned surface boat
Figure BDA00039570801100000715
In the initial position of the first and second movable parts,
Figure BDA00039570801100000716
is an unmanned surface boat
Figure BDA00039570801100000717
The amount of ammunition carried by the cartridge,
Figure BDA00039570801100000718
is an unmanned surface boat
Figure BDA00039570801100000719
The voyage of. Assuming that M sea surface objects are detected by the enemy, of sea surface targets set is defined as T = { T = { (T) } 1 ,T 2 ,…,T M In which T is M The Mth sea surface target.
The sea-air integrated unmanned cooperative confrontation task allocation matrix is
Figure BDA00039570801100000720
Wherein the content of the first and second substances,
Figure BDA00039570801100000721
when x n,m =1 denotes shipboard unmanned aerial vehicle U n Attack sea surface target T m ，m＝1,2,…,M，x n,m =0 denotes ship-borne unmanned aerial vehicle U n Does not attack sea surface target T m ；
Figure BDA00039570801100000722
When x n,m =1 unmanned surface vehicle
Figure BDA00039570801100000723
Attack sea surface target T m ，x n,m =0 unmanned surface vehicle
Figure BDA00039570801100000724
Does not attack sea surface target T m 。
Establishment of maximum objective function of cooperative countermeasure task allocation of sea-air integrated unmanned intelligent equipment
Figure BDA00039570801100000725
Figure BDA0003957080110000081
Wherein M =1,2, …, M. E (-) is a judgment function when
Figure BDA0003957080110000082
When the function returns a value of 1, and
Figure BDA0003957080110000083
when the function returns a value of 0.C 1 For task constraint penalty terms, C 2 For ammunition constraining penalty terms, C 3 For voyage constraint penalty term, α 1 、α 2 And alpha 3 For being otherwise a constraint penalty term C 1 、C 2 And C 3 The weight factor of (2).
The established model needs to meet 3 constraint conditions, namely a task constraint condition, an ammunition constraint condition and a voyage constraint condition. The task constraint condition is
Figure BDA0003957080110000084
M =1,2, …, M, indicating that at most one ship-borne drone or one surface drone attacks the sea surface target T m . The ammunition constraint condition of the carrier-borne unmanned aerial vehicle is
Figure BDA0003957080110000085
Representing a shipboard unmanned aerial vehicle U n The number of the targets attacking the sea surface cannot exceed the carrying amount of ammunition; ammunition constraint of unmanned surface vehicle is
Figure BDA0003957080110000086
Figure BDA0003957080110000087
Unmanned surface vehicle
Figure BDA0003957080110000088
The number of sea targets attacked cannot exceed the amount of ammunition carried. The range constraint condition of the carrier-borne unmanned aerial vehicle is D n ≤r n ，
Figure BDA0003957080110000089
Wherein D is n For carrier-borne unmanned aerial vehicle U n Total distance of flight. Suppose that the shipboard unmanned plane U n Attack sea surface target T in sequence 1 、T 2 And T 3 At this moment, the shipboard unmanned aerial vehicle U n Has a total flight distance D n ＝d 0,1 +d 1,2 +d 2,3 +d 0,3 ，d 0,1 For large vessels position and sea surface target T 1 Distance between d 1,2 For sea surface target T 1 With sea surface target T 2 Distance of d 2,3 For sea surface target T 2 With sea surface target T 3 Distance of d 0,3 For large vessels position and sea surface target T 3 Due to the ship-borne unmanned plane U n All tasks of the unmanned aerial vehicle are executed and the unmanned aerial vehicle still needs to return to the home, so that the carrier-borne unmanned aerial vehicle U n The total distance of flight of (a) includes the return distance. The range constraint condition of the unmanned surface vehicle is
Figure BDA00039570801100000810
Figure BDA00039570801100000811
Figure BDA00039570801100000812
Is unmanned surface boat
Figure BDA00039570801100000813
Total distance traveled. Unmanned surface vehicle
Figure BDA00039570801100000814
Attack sea surface target T in sequence 1 、T 2 And T 3 At the moment, the total sailing distance of the unmanned surface vehicle is
Figure BDA00039570801100000815
Figure BDA00039570801100000816
Is an unmanned surface boat
Figure BDA00039570801100000817
Initial position and sea surface target T 1 Distance between d 1,2 For sea surface target T 1 With sea surface target T 2 Distance of d 2,3 For sea surface target T 2 With sea surface target T 3 The distance of (c).
Converting task constraint conditions into task constraint penalty items
Figure BDA00039570801100000818
Where | is a function of absolute value. Converting ammunition constraint conditions of carrier-borne unmanned aerial vehicle and surface unmanned ship into ammunition constraint penalty items
Figure BDA0003957080110000091
Converting range constraint conditions of carrier-borne unmanned aerial vehicle and surface unmanned ship into range constraint penalty items
Figure BDA0003957080110000092
E (-) is the judgment function. With E (D) n ,r n ) For example, if D n ≥r n When the value of the function is 1, the function value returns; otherwise 0 is returned.
Step two, initial quantum
Figure BDA00039570801100000920
The quantum position of the fish and setting parameters.
Setting population scale as K 1 Maximum number of iterations K 2 . In the initial population, random initial quanta
Figure BDA00039570801100000919
Quantum position of fish, i quantum
Figure BDA00039570801100000921
The 1 st generation initial quantum position of the fish is
Figure BDA0003957080110000093
h＝1,2,…,S，i＝1,2,3,…,K 1 Where S is the maximum dimension of the quantum position vector, and any dimension of all quantum positions is [0,1 ]]Random number between, quantum
Figure BDA00039570801100000922
The position of the fish can be measured by quantum position. If the ith quantum in the kth iteration
Figure BDA00039570801100000932
The qubit of the fish is
Figure BDA0003957080110000094
i＝1,2,3,…,K 1 ，k∈{1,2,…,K 2 Get the ith quantum in the kth iteration by measurement
Figure BDA00039570801100000923
The fish being in a position of
Figure BDA0003957080110000095
i＝1,2,…,K 1 ，k∈{1,2,…,K 2 The measurement rule is
Figure BDA0003957080110000096
Representing the ith quantum
Figure BDA00039570801100000924
The h-th dimension variable of the fish position,
Figure BDA0003957080110000097
is [0,1]H =1,2, …, S, K e {1,2, …, K 2 }。
Step three, quantum computation
Figure BDA00039570801100000926
A fitness function value for the fish position.
Ith quantum of kth generation
Figure BDA00039570801100000925
Location of fish
Figure BDA0003957080110000098
Mapping a sea-air integrated unmanned cooperative confrontation task allocation matrix, wherein the specific mapping rule is as follows: will be provided with
Figure BDA0003957080110000099
Is/are as follows
Figure BDA00039570801100000910
Corresponding to the first row in the sea-air integrated unmanned cooperative countermeasure task allocation matrix
Figure BDA00039570801100000911
X corresponding to the second row in sea-air integrated unmanned cooperative countermeasure task allocation matrix 2,1 ,x 2,2 ,…,x 2,M (ii) a By the way of analogy, the method can be used,
Figure BDA00039570801100000912
corresponding to last row in sea-air integrated unmanned cooperative countermeasure task allocation matrix
Figure BDA00039570801100000913
The constructed task assignment matrix is written as
Figure BDA00039570801100000914
Therefore, the maximum dimension S is to be satisfied
Figure BDA00039570801100000915
The ith quantum of the kth iteration
Figure BDA00039570801100000927
Location of fish
Figure BDA00039570801100000916
Mapping to sea-air integrated unmanned cooperative confrontation task allocation matrix
Figure BDA00039570801100000917
Obtaining the ith quantum of the kth iteration
Figure BDA00039570801100000928
Fitness function value of fish
Figure BDA00039570801100000918
i=1,2,…,K 1 . By comparing all quanta
Figure BDA00039570801100000930
Finding the quantum position corresponding to the maximum adaptability value of the kth iteration as the optimal quantum by the fish adaptability function value
Figure BDA00039570801100000931
Quantum position of fish
Figure BDA0003957080110000101
Step four, updating the quantity by using a free search strategySeed of Japanese apricot
Figure BDA00039570801100001031
Quantum position of fish.
Updating quanta according to a free search strategy
Figure BDA00039570801100001032
Quantum position of fish, i quantum in free search strategy
Figure BDA00039570801100001034
The h-dimension quantum rotation angle of the fish is
Figure BDA0003957080110000102
i＝1,2,…,K 1 H =1,2, …, S, ε is [1,K 1 ]Random integer between, ζ i,h 、
Figure BDA0003957080110000103
Is [0,1]A random number in between, and a random number,
Figure BDA0003957080110000104
is an epsilon quantum
Figure BDA00039570801100001035
The h-th dimension variable of the fish position,
Figure BDA0003957080110000105
for the kth iteration of the optimal quantum
Figure BDA00039570801100001033
H-dimension variable of fish position.
Updating ith quantum in free search strategy by using quantum revolving gate
Figure BDA00039570801100001049
H-dimensional quantum position of fish:
Figure BDA0003957080110000106
h＝1,2,…,S，i＝2,3,…,K 1 . Root of herbaceous plantsAccording to measurement rule to quantum position
Figure BDA0003957080110000107
Measure the position in each dimension of
Figure BDA0003957080110000108
Then calculate
Figure BDA0003957080110000109
Fitness function value of
Figure BDA00039570801100001010
And to
Figure BDA00039570801100001011
The assignment is carried out according to the following assignment rule
Figure BDA00039570801100001012
Quantum
Figure BDA00039570801100001038
When the fish is attached to the swordfish moving at a high speed, the position of the swordfish can be adjusted. Quantum i
Figure BDA00039570801100001037
The h-dimension quantum rotation angle of the fish experience quantum position is
Figure BDA00039570801100001013
Wherein h =1,2, …, S, i =2,3, …, K 1 ，
Figure BDA00039570801100001014
Is the ith quantum
Figure BDA00039570801100001039
H-dimension variable, xi, of fish at previous generation position i,h Is a gaussian random number satisfying a mean value of 0 and a variance of 1. Updating ith quantum by quantum revolving gate
Figure BDA00039570801100001036
H-dimension empirical quantum position of fish
Figure BDA00039570801100001015
I quanta of
Figure BDA00039570801100001040
Empirical quantum position of fish
Figure BDA00039570801100001016
Measured as an empirical position
Figure BDA00039570801100001017
And calculate
Figure BDA00039570801100001018
Fitness function value of
Figure BDA00039570801100001019
Comparison
Figure BDA00039570801100001020
And
Figure BDA00039570801100001021
the size of (1) when
Figure BDA00039570801100001022
When larger, the ith quantum
Figure BDA00039570801100001044
Fish is locally searched through the fifth step; when in use
Figure BDA00039570801100001023
Greater than or equal to
Figure BDA00039570801100001024
When the ith quantum
Figure BDA00039570801100001041
The fish is searched locally through step six.
Step five, updating quanta by using whale adsorption strategy
Figure BDA00039570801100001042
Quantum position of fish.
Quantum of quantum
Figure BDA00039570801100001043
When the host of the fish is changed from swordfish to whale, the food residue on the whale is taken as food, and then quantum is added
Figure BDA00039570801100001045
Fish adopts whale adsorption strategy to update quanta
Figure BDA00039570801100001047
Quantum position of fish. In the whale adsorption strategy, the ith quantum
Figure BDA00039570801100001046
The h-dimension quantum rotation angle of the fish is
Figure BDA00039570801100001025
Wherein h =1,2, …, S, i =2,3, …, K 1 ，
Figure BDA00039570801100001026
Is composed of
Figure BDA00039570801100001027
And
Figure BDA00039570801100001028
the Euclidean distance of (a) is,
Figure BDA00039570801100001029
Figure BDA00039570801100001030
is [0,1]A random number in between.
Method for renewing and adsorbing ith quantum in whale strategy by using quantum revolving door
Figure BDA00039570801100001048
H-dimensional quantum position of fish:
Figure BDA0003957080110000111
then calculate
Figure BDA0003957080110000112
Fitness function value of
Figure BDA0003957080110000113
And for the ith quantum of the (k + 1) th iteration
Figure BDA00039570801100001114
Quantum position of fish
Figure BDA0003957080110000114
The assignment is carried out according to the following assignment rule
Figure BDA0003957080110000115
Step six, updating quanta by using off-host strategy
Figure BDA00039570801100001115
Quantum position of fish.
Quantum of quantum
Figure BDA00039570801100001116
While the fish host is still a swordfish, and swordfish has found a food rich sea area, quantum
Figure BDA00039570801100001117
The fish will take food out of the host. At this time quantum
Figure BDA00039570801100001118
Fish updating quanta using off-host strategy
Figure BDA00039570801100001119
Amount of fishA sub-position. Quantum i in off-host strategy
Figure BDA00039570801100001120
The h dimension quantum rotation angle of the fish is
Figure BDA0003957080110000116
h＝1,2,…,S，i＝2,3,…,K 1 And lambda is a decision value,
Figure BDA0003957080110000117
is [0,1]A random number in between.
Updating ith quantum in disengagement host strategy by using quantum revolving gate
Figure BDA00039570801100001121
H-dimensional quantum position of fish:
Figure BDA0003957080110000118
then calculate
Figure BDA0003957080110000119
Fitness function value of
Figure BDA00039570801100001110
And for the ith quantum of the (k + 1) th iteration
Figure BDA00039570801100001122
Quantum position of fish
Figure BDA00039570801100001111
The assignment is carried out according to the following assignment rule
Figure BDA00039570801100001112
Step seven, judging whether the quantum is reached
Figure BDA00039570801100001123
Maximum number of iterations K of a fish 2 If yes, stopping iteration and optimizing quanta
Figure BDA00039570801100001124
Mapping the position of the fish to a sea-air integrated unmanned cooperative confrontation task allocation matrix and outputting the matrix; otherwise, let k = k +1, find the quantum position corresponding to the maximum fitness value of the (k + 1) th iteration as the optimal quantum
Figure BDA00039570801100001125
Quantum position of fish
Figure BDA00039570801100001113
And continuing to execute the step four.
The present invention is further illustrated below with reference to specific parameters.
Quantum dot
Figure BDA00039570801100001126
The fish optimization method QRO, and the discrete pigeon flock optimization method is recorded as DPIO. To verify quantum-based
Figure BDA00039570801100001127
The invention provides performance of a sea mission planning method by a plurality of unmanned aerial vehicles of a fish mechanism, and three groups of simulation tests and two convergence curve simulation graphs are carried out. Setting population scale as K 1 ＝100，λ＝0.2，α＝0.01，α 1 ＝-1，α 2 ＝-1，α 3 =1, the targets are randomly distributed in an area of 5km × 5km, assuming that all the ship-borne drones and the surface drones sail at one speed in the whole course, the unit of the speed is m/s, and the position of the large ship is (2500,0), and the unit is m. The task execution time for each sea surface target is 10s. Table 1 gives the attribute parameters of the carrier-borne drone.
TABLE 1 Attribute parameters of Carrier-borne unmanned aerial vehicles
Figure BDA0003957080110000121
Table 2 gives the attribute parameters of the surface unmanned boat.
Table 2 attribute parameters of unmanned surface vehicle
Figure BDA0003957080110000122
In a first set of experiments, N =2 carrier-borne drones and
Figure BDA0003957080110000123
an unmanned surface vessel performs the task of confrontation of M =9 sea surface targets. The information of the carrier-borne unmanned aerial vehicle is numbered 1 and 2 in the table 1, the information of the surface unmanned ship is numbered 1 and 2 in the table 2, and the target position is shown in fig. 2. Setting population scale as K 1 =100, number of iterations K 2 =500. Table 3 shows the mission planning schemes measured by the QRO method and the GA method optimal solution. It can be seen that, compared with the DPIO method, the optimal task allocation scheme generated by the QRO method is more reasonable, and in the task allocation scheme, several adjacent tasks are divided into a group, such as a sea surface target T 2 And T 6 And T 1 And T 8 And the navigation distance of the unmanned intelligent equipment is shortest.
TABLE 3 task allocation scheme measured by optimal solution of QRO method and DPIO method
Figure BDA0003957080110000124
Figure BDA0003957080110000131
In a second set of experiments, N =4 carrier-borne drones and
Figure BDA0003957080110000134
an unmanned surface vessel performs the confrontational tasks of M =18 sea surface targets. The carrier-borne unmanned aerial vehicle information is numbers 1,2,3 and 4 in the table 1, the surface unmanned aerial vehicle information is numbers 1 and 2 in the table 2, and the target position is shown in fig. 3. Setting population scale as K 1 =100, number of iterations K 2 =500. Table 4 shows the optimal solution measurement of QRO method and DPIO methodAnd (4) outputting a task planning scheme. It can be seen that the optimal task allocation matrix generated by the DPIO method does not satisfy the constraint condition, and therefore the optimal solution is far smaller than the optimal solution generated by the QRO method.
TABLE 4 mission planning scheme for optimal solution measurement of QRO method and DPIO method
Figure BDA0003957080110000132
In the third set of experiments, N =8 carrier-borne drones and
Figure BDA0003957080110000133
an unmanned surface vessel performs the confrontational tasks of M =30 sea surface targets. The information of the carrier-borne unmanned aerial vehicle is shown in table 1, the information of the surface unmanned ship is shown in table 2, and the target position is shown in fig. 4. Setting population scale as K 1 =100, number of iterations K 2 =500. Table 5 shows a mission planning scheme measured by the QRO method and the DPIO method optimal solution. When the task scale is gradually increased, the difference between the objective function values of the two methods is increasingly large, even if one task is repeatedly executed by a plurality of unmanned intelligent devices in the task planning scheme measured by the optimal solution of the DPIO method, the task planning scheme measured by the optimal solution of the QRO method can still meet 3 task constraint penalty terms, and the optimal solution can basically reach a critical optimal value.
TABLE 5 mission planning scheme for optimal solution measurement of QRO method and DPIO method
Figure BDA0003957080110000141
To further verify the convergence of QRO, the DPIO method was chosen for comparative simulation, the convergence analysis is shown in figure 5-figure 5 at N =4,
Figure BDA0003957080110000142
on the scale of (2), the number of iterations is set to K 2 =500, population size K 1 =100, 100 independent replicates were performedAnd averaging simulation results. It can be seen that the convergence performance of the QRO method is significantly better than the DPIO method.

Claims (7)

1. A collaborative confrontation task allocation method for sea-air integrated unmanned intelligent equipment is characterized by comprising the following steps:
step one, establishing a collaborative confrontation task allocation model of the sea-air integrated unmanned intelligent equipment;
initializing the quantum position of the quantum echeneis naucrates and setting parameters;
step three, calculating a fitness function value of the quantum echeneis naucrates position;
step four, updating the quantum position of the quantum echeneis naucrates by using a free search strategy, and judging whether the fitness value of the ith quantum echeneis naucrates is larger than the fitness value of the empirical position, i =1,2,3, …, K 1 When the condition is more than the condition, the i-th quantum echeneis naucrates carries out local search through the fifth step; otherwise, the ith quantum echeneis naucrates carries out local search through the sixth step;
step five, updating the quantum position of the quantum echeneis naucrates by using a whale adsorption strategy, and executing step seven;
step six, updating the quantum position of the quantum echeneis naucrates by using a host-off strategy, and executing step seven;
step seven, judging whether the maximum iteration number K of the quantum echeneis naucrates is reached 2 If so, terminating iteration, mapping the position of the optimal quantum echeneis naucrates into a sea-air integrated unmanned cooperative countermeasure task allocation matrix and outputting the matrix; otherwise, the iteration times k = k +1, and the quantum position corresponding to the maximum fitness value of the (k + 1) th iteration is found to be the quantum position of the optimal quantum echeneis naucrates
Figure FDA0003957080100000011
And continuing to execute the step four.
2. The method for allocating cooperative combat tasks of the sea-air integrated unmanned intelligent equipment according to claim 1, wherein the method comprises the following steps: step one, establishing a collaborative confrontation task allocation model of the sea-air integrated unmanned intelligent equipment comprises the following steps:
suppose that there are N carrier-borne unmanned aerial vehicles in the sea-air unmanned countermeasure group and
Figure FDA0003957080100000012
the unmanned surface vehicle can execute the confrontation task, and the set of the sea-air integrated unmanned intelligent equipment is defined as
Figure FDA0003957080100000013
Wherein, carrier-borne unmanned aerial vehicle U n Is U n ＝{v n ，l n ，w n ，r n }，
Figure FDA0003957080100000014
v n For carrier-borne unmanned aerial vehicle U n Speed of travel of l n For carrier-borne unmanned aerial vehicle U n I.e. the position of the large vessel, w n For carrier-borne unmanned aerial vehicle U n Amount of ammunition carried, r n For carrier-borne unmanned aerial vehicle U n Voyage of; unmanned surface vehicle
Figure FDA0003957080100000015
Is a set of attributes of
Figure FDA0003957080100000016
Figure FDA0003957080100000017
Figure FDA0003957080100000018
Is an unmanned surface boat
Figure FDA0003957080100000019
And the sailing speed of
Figure FDA00039570801000000110
Figure FDA00039570801000000111
Is an unmanned surface boat
Figure FDA00039570801000000112
In the initial position of the first and second movable parts,
Figure FDA00039570801000000113
is an unmanned surface boat
Figure FDA00039570801000000114
The amount of ammunition carried by the cartridge,
Figure FDA00039570801000000115
is an unmanned surface boat
Figure FDA00039570801000000116
Voyage of; assuming that M sea surface objects are detected for an enemy, the set of sea surface objects is defined as T = { T = { 1 ，T 2 ，…，T M In which T M Is Mth sea surface target;
the sea-air integrated unmanned cooperative confrontation task allocation matrix is
Figure FDA00039570801000000117
Wherein the content of the first and second substances,
Figure FDA00039570801000000118
when x n，m =1 denotes shipboard unmanned aerial vehicle U n Attack sea surface target T m ，m＝1，2，…，M，x n，m =0 denotes ship-borne unmanned aerial vehicle U n Does not attack sea surface target T m ；
Figure FDA0003957080100000021
When x n，m =1 unmanned surface vehicle
Figure FDA0003957080100000022
Attack sea surface target T m ，x n，m =0 represents a water surface unmanned ship
Figure FDA0003957080100000023
Does not attack sea surface target T m ；
Establishment of maximum objective function of cooperative countermeasure task allocation of sea-air integrated unmanned intelligent equipment
Figure FDA0003957080100000024
Figure FDA0003957080100000025
Wherein M =1,2, …, M; e (-) is a judgment function when
Figure FDA0003957080100000026
When, the function returns the value 1, and
Figure FDA0003957080100000027
when so, the function returns a value of 0; c 1 For task constraint penalty terms, C 2 For ammunition constraining penalty terms, C 3 For voyage constraint penalty term, α 1 、α 2 And alpha 3 For being otherwise a constraint penalty term C 1 、C 2 And C 3 The weight factor of (2);
the established model needs to meet 3 constraint conditions, namely a task constraint condition, an ammunition constraint condition and a voyage constraint condition; the task constraint condition is
Figure FDA0003957080100000028
M =1,2, …, M, indicating that at most one ship-borne drone or one surface drone attacks the sea surface target T m (ii) a The ammunition constraint condition of the carrier-borne unmanned aerial vehicle is
Figure FDA0003957080100000029
Representing a shipboard unmanned aerial vehicle U n The number of the targets attacking the sea surface cannot exceed the carrying amount of ammunition; ammunition constraint of unmanned surface vehicle is
Figure FDA00039570801000000210
Figure FDA00039570801000000211
Unmanned surface vehicle
Figure FDA00039570801000000212
The number of the targets attacking the sea surface cannot exceed the carrying amount of ammunition; the range constraint condition of the carrier-borne unmanned aerial vehicle is D n ≤r n ，
Figure FDA00039570801000000213
Wherein D is n For carrier-borne unmanned aerial vehicle U n Total distance of flight of; suppose that the shipboard unmanned plane U n Attack sea surface target T in sequence 1 、T 2 And T 3 At this moment, the shipboard unmanned aerial vehicle U n Has a total flight distance D n ＝d 0，1 +d 1，2 +d 2，3 +d 0，3 ，d 0，1 For large vessels position and sea surface target T 1 Distance between d 1，2 For sea surface target T 1 With sea surface target T 2 Distance of d 2，3 For sea surface target T 2 With sea surface target T 3 Distance of d, d 0，3 For large vessels position and sea surface target T 3 Distance between, carrier-borne unmanned aerial vehicle U n The total distance of flight of (a) includes the return distance; the range constraint condition of the unmanned surface vehicle is
Figure FDA00039570801000000214
Figure FDA00039570801000000215
Is an unmanned surface boat
Figure FDA00039570801000000216
Total distance traveled; unmanned surface vehicle
Figure FDA00039570801000000217
Attack sea surface target T in sequence 1 、T 2 And T 3 At the moment, the total sailing distance of the unmanned surface vehicle is
Figure FDA00039570801000000218
Figure FDA00039570801000000219
Is an unmanned surface boat
Figure FDA00039570801000000220
Initial position and sea surface target T 1 Distance between d, d 1，2 For sea surface target T 1 With sea surface target T 2 Distance of d 2，3 For sea surface target T 2 With sea surface target T 3 The distance of (d);
converting task constraint conditions into task constraint penalty items
Figure FDA0003957080100000031
Wherein | is an absolute value function, and ammunition constraint conditions of the carrier-borne unmanned aerial vehicle and the surface unmanned ship are converted into ammunition constraint penalty terms
Figure FDA0003957080100000032
Converting range constraint conditions of carrier-borne unmanned aerial vehicle and surface unmanned ship into range constraint penalty items
Figure FDA0003957080100000033
E (-) is a judgment function for E (D) n ，r n ) If D is n ≥r n When the value of the function is 1, the function value returns; otherwise 0 is returned.
3. The method for allocating cooperative combat tasks of the sea-air integrated unmanned intelligent equipment according to claim 1, wherein the method comprises the following steps: step two, the quantum position of the initial quantum echeneis naucrates and the parameter setting comprise:
setting population scale as K 1 Maximum number of iterations K 2 In the initial population, the quantum position of random initial quantum echeneis naucrates and the 1 st generation initial quantum position of the i-th quantum echeneis naucrates are
Figure FDA0003957080100000034
h＝1，2，…，S，i＝1，2，3，…，K 1 Where S is the maximum dimension of the quantum position vector, and any dimension of all quantum positions is [0,1 ]]The position of the quantum echeneis naucrates is obtained by quantum position measurement; if the quantum position of the ith quantum echeneis naucrates in the kth iteration is set as
Figure FDA0003957080100000035
i＝1，2，3，…，K 1 ，k∈{1，2，…，K 2 The position of the ith quantum echeneis naucrates in the kth iteration is obtained by measurement
Figure FDA0003957080100000036
i＝1，2，…，K 1 ，k∈{1，2，…，K 2 The measurement rule is
Figure FDA0003957080100000037
An h-dimension variable representing the position of the i-th quantum echeneis naucrates,
Figure FDA0003957080100000038
is [0,1]H =1,2, …, S, K e {1,2, …, K 2 }。
4. The method for allocating cooperative combat tasks of the sea-air integrated unmanned intelligent equipment according to claim 1, wherein the method comprises the following steps: step three, calculating the fitness function value of the quantum echeneis naucrates position comprises the following steps:
the position of the ith quantum echeneis naucrates in the kth generation
Figure FDA0003957080100000039
Mapping a sea-air integrated unmanned cooperative countermeasure task allocation matrix, wherein the mapping rule is as follows: will be provided with
Figure FDA00039570801000000310
Is/are as follows
Figure FDA00039570801000000311
X corresponding to first row in sea-air integrated unmanned cooperative countermeasure task allocation matrix 1，1 ，x 1，2 ，…，x 1，M ；
Figure FDA00039570801000000312
X corresponding to the second row in sea-air integrated unmanned cooperative countermeasure task allocation matrix 2，1 ，x 2，2 ，…，x 2，M (ii) a By the way of analogy, the method can be used,
Figure FDA00039570801000000313
corresponding to last row in sea-air integrated unmanned cooperative countermeasure task allocation matrix
Figure FDA00039570801000000314
The constructed task assignment matrix is written as
Figure FDA00039570801000000315
Maximum dimension S satisfies
Figure FDA00039570801000000316
The position of the ith quantum echeneis naucrates of the kth iteration
Figure FDA0003957080100000041
Mapping is unmanned cooperative confrontation task allocation matrix of sea-air integration
Figure FDA0003957080100000042
Obtaining the fitness function value of the ith quantum echeneis naucrates of the kth iteration
Figure FDA0003957080100000043
i＝1，2，…，K 1 (ii) a The quantum position of the optimal quantum echeneis naucrates corresponding to the maximum fitness value of the kth iteration is found by comparing fitness function values of all quantum echeneis naucrates
Figure FDA0003957080100000044
5. The method for allocating cooperative combat tasks of the sea-air integrated unmanned intelligent equipment according to claim 1, wherein the method comprises the following steps: step four, updating the quantum position of the quantum echeneis naucrates by using a free search strategy, and judging whether the fitness value of the i-th quantum echeneis naucrates is larger than the fitness value of the empirical position of the i-th quantum, i =1,2,3, …, K 1 When the condition is more than the condition, the i-th quantum echeneis naucrates carries out local search through the fifth step; otherwise, the local search of the ith quantum echeneis naucrates in the sixth step comprises the following steps:
in the free search strategy, the h-dimension quantum rotation angle of the i-th quantum echeneis naucrates is
Figure FDA0003957080100000045
i＝1，2，…，K 1 H =1,2, …, S, ε is [1,K 1 ]A random integer between the number of the first and second integers,
Figure FDA0003957080100000046
is [0,1]A random number in between, and a random number,
Figure FDA0003957080100000047
is the h-dimension variable of the position of the epsilon quantum echeneis naucrates,
Figure FDA0003957080100000048
h dimension variable of optimal quantum echeneis naucrates position for k iteration;
updating h-dimension qubit of i-th quantum echeneis naucrates in free search strategy by using quantum revolving gatePlacing:
Figure FDA0003957080100000049
h =1,2, …, S, i =2,3, …, K1; for quantum position according to measurement rule
Figure FDA00039570801000000410
Measure the position in each dimension of
Figure FDA00039570801000000411
Then calculate
Figure FDA00039570801000000412
Fitness function value of
Figure FDA00039570801000000413
And are aligned with
Figure FDA00039570801000000414
And assigning, wherein the assignment rule is as follows:
Figure FDA00039570801000000415
when the quantum echeneis naucrates is attached to a swordfish body moving at a high speed, the position of the swordfish body can be adjusted, and the h-dimension quantum rotation angle of the empirical quantum position of the ith quantum echeneis naucrates is
Figure FDA00039570801000000416
Wherein h =1,2, …, S, i =2,3, …, K 1 ，
Figure FDA00039570801000000417
Is the h-dimension variable xi of the ith quantum echeneis naucrates in the previous generation i，h Is a Gaussian random number satisfying that the mean value is 0 and the variance is 1; updating the h-dimension empirical quantum position of the i-th quantum echeneis naucrates by using the quantum revolving gate
Figure FDA00039570801000000418
Empirical quantum position of i quanta echeneis naucrates
Figure FDA00039570801000000419
Measured as an empirical position
Figure FDA00039570801000000420
And calculate
Figure FDA00039570801000000421
Fitness function value of
Figure FDA00039570801000000422
Comparison
Figure FDA00039570801000000423
The size of (1) when
Figure FDA00039570801000000424
When the fish is larger, the i-th quantum echeneis naucrates carries out local search through the fifth step; when in use
Figure FDA00039570801000000425
Greater than or equal to
Figure FDA00039570801000000426
In the meantime, the i-th quantum echeneis naucrates locally searches through step six.
6. The method for allocating cooperative combat tasks of the sea-air integrated unmanned intelligent equipment according to claim 1, wherein the method comprises the following steps: step five, updating the quantum position of the quantum echeneis naucrates by using an adsorption whale strategy comprises the following steps:
when the host of the quantum echeneis naucrates is changed from swordfish to whale, food residues on the whale are taken as food, and then the quantum echeneis naucrates adopts a whale adsorption strategy to update the quantum position of the quantum echeneis naucrates; in the whale adsorption strategy, the h-dimension quantum rotation angle of the i-th quantum echeneis naucrates is
Figure FDA0003957080100000051
Wherein h =1,2, …, S, i =2,3, …, K 1 ，
Figure FDA0003957080100000052
Is composed of
Figure FDA0003957080100000053
And
Figure FDA0003957080100000054
the Euclidean distance of (a) is,
Figure FDA0003957080100000055
Figure FDA0003957080100000056
is [0,1]A random number in between;
updating the h-dimensional quantum position of the ith quantum echeneis naucrates in the whale adsorption strategy by using a quantum revolving door:
Figure FDA0003957080100000057
then calculate
Figure FDA0003957080100000058
Fitness function value of
Figure FDA0003957080100000059
And for the (k + 1) th iteration, the quantum position of the (i) th quantum echeneis naucrates
Figure FDA00039570801000000510
The assignment is carried out according to the following assignment rule
Figure FDA00039570801000000511
7. The method for allocating collaborative combat tasks of sea-air integrated unmanned intelligent equipment according to claim 1, characterized by comprising the following steps: step six, updating the quantum position of the quantum echeneis naucrates by using the off-host strategy comprises the following steps:
when the host of the quantum echeneis naucrates is still the swordfish and the swordfish has found a sea area rich in food, the quantum echeneis naucrates can be separated from the host to take food, and then the quantum echeneis naucrates adopts a separation-host strategy to update the quantum position of the quantum echeneis naucrates, wherein the h-dimensional quantum rotation angle of the i-th quantum echeneis naucrates in the separation-host strategy is
Figure FDA00039570801000000512
h＝1，2，…，S，i＝2，3，…，K 1 And lambda is a decision value, wherein,
Figure FDA00039570801000000513
is [0,1]A random number in between;
updating the h-dimensional quantum position of the i-th quantum echeneis naucrates in the off-host strategy by using a quantum revolving gate:
Figure FDA00039570801000000514
then calculate
Figure FDA00039570801000000515
Fitness function value of
Figure FDA00039570801000000516
And for the (k + 1) th iteration, the quantum position of the (i) th quantum echeneis naucrates
Figure FDA00039570801000000517
The assignment is carried out according to the following assignment rule
Figure FDA00039570801000000518
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* Cited by examiner, † Cited by third party
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* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN116149166A (en) * 2023-04-19 2023-05-23 济南大学 Unmanned rescue boat course control method based on improved beluga algorithm

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