CN115660339A - Static unmanned aerial vehicle cluster combat cooperative fire decision method based on improved particle swarm optimization - Google Patents

Static unmanned aerial vehicle cluster combat cooperative fire decision method based on improved particle swarm optimization Download PDF

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CN115660339A
CN115660339A CN202211298355.4A CN202211298355A CN115660339A CN 115660339 A CN115660339 A CN 115660339A CN 202211298355 A CN202211298355 A CN 202211298355A CN 115660339 A CN115660339 A CN 115660339A
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unmanned aerial
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丁赟
李银伢
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Nanjing University of Science and Technology
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Abstract

The invention discloses a static unmanned aerial vehicle cluster combat cooperative fire decision method based on an improved particle swarm algorithm, which comprises the following steps: firstly, establishing an unmanned aerial vehicle cluster static weapon target distribution model, secondly, encoding a weapon target distribution scheme, secondly initializing a particle swarm optimization algorithm, and finally, performing a particle swarm optimization algorithm and decoding the obtained feasible solution to obtain a possible distribution scheme. The static unmanned aerial vehicle cluster combat cooperative firepower decision method based on the improved particle swarm optimization has strong global search capability, can effectively avoid the invasion of local advantages, can search better solutions quickly, can further improve the quality of the solutions, and can effectively solve the problem that the optimization time consumption is too long in the existing algorithm.

Description

Static unmanned aerial vehicle cluster combat cooperative fire decision method based on improved particle swarm optimization
Technical Field
The invention relates to the field of weapon target distribution, in particular to a static unmanned aerial vehicle cluster combat cooperative fire decision method based on an improved particle swarm algorithm.
Background
The unmanned aerial vehicle cluster combat cooperative fire decision method is a key link of modern combat commanding, existing group members are reasonably distributed by researching how to study the weapon of one party, so that the best effect of the unmanned aerial vehicle weapon on striking and damaging the target of attack is achieved, the economic loss cost of the party is lowest through fire decision, and materials of the party can be protected. According to the number of weapons and ammunition of the unmanned aerial vehicle, the number of targets attacked, the threat degree of the targets attacked, the damage probability of the targets caused by weapons and other conditions, the weapons and ammunition suitable for hitting the targets are selected for distribution optimization, namely, the total amount constraint of various weapons and ammunitions is comprehensively considered, the suitable weapons and ammunitions are reasonably distributed to the targets in an optimal mode, and all the targets achieve the expected damage effect.
When the scale of the WTA problem is large, the problem of obviously overlong optimization time cannot be effectively solved. The WTA problem is an NP complete problem, and the position of a local excellent area is difficult to obtain through arithmetic operation. Under the conditions of various targets and multiple available weapon types, the algorithm of the existing method is easy to fall into the local optimal solution and consumes too long time, so that the optimal solution is difficult to obtain.
Until now, there are many optimization algorithms for the existing static WTA problem of unmanned aerial vehicle cluster, and the algorithms include particle swarm algorithm, ant colony algorithm, genetic algorithm, etc., which can obtain a satisfactory solution, but the algorithms have the problems of easy premature convergence and local optimum. The main reason for this problem is that the algorithm falls into a local optimal region during the optimization process, which lacks a means to quickly jump out of the local optimal solution. Especially when the particle size is huge, the existing algorithm is easy to fall into local optimization or needs a large number of iterations to obtain the optimal solution, and the correct distribution scheme cannot be obtained through rapid convergence.
Disclosure of Invention
The invention aims to provide a static unmanned aerial vehicle cluster combat cooperative fire decision method based on an improved particle swarm optimization, which accelerates the speed of jumping out of a local optimal solution in the solving process by carrying out encoding processing on particles, improving the value mode of weight coefficients of the particle swarm optimization, improving the updating mode of particle speed, updating the positions of the particles and the like.
The solution of the method for realizing the invention is as follows: in a first aspect, the invention provides a static unmanned aerial vehicle cluster combat cooperative fire decision method based on an improved particle swarm algorithm, which comprises the following steps:
the method comprises the following steps: establishing an unmanned aerial vehicle cluster system combat static weapon target distribution model according to the number of unmanned aerial vehicle clusters and the number of attack target classes, and obtaining a corresponding fitness function according to constraint conditions;
step two: carrying out encoding processing on the particles and initializing the positions and the speeds of the particles;
step three: and performing optimization and optimization by using an improved particle swarm algorithm, and continuously updating the searching speed and the position of the particles in each dimension direction until an iteration termination condition is reached.
In a second aspect, the present invention provides a computer device comprising a memory, a processor and a computer program stored on the memory and executable on the processor, the processor implementing the steps of the method of the first aspect when executing the program.
Compared with the prior art, the method has the following remarkable advantages:
(1) The invention constructs a mathematical model of a static unmanned aerial vehicle cluster combat cooperative fire decision method based on an improved particle swarm algorithm, ensures calculation according to the threat degree of each target and the damage probability of a weapon to each target, and meets the idea of consistency of a weapon target distribution system, thereby realizing the maximization of the benefit of the whole distribution system;
(2) The method adopts the particle swarm optimization algorithm, improves the original foundation, effectively avoids the algorithm from falling into local optimization in the optimization process, ensures that the difference between the longest optimization time and the shortest optimization time is smaller, and can effectively solve the problem that the optimization time is too long in the algorithm. The efficiency of the unmanned aerial vehicle cluster carries out firepower decision and follow-up task is improved.
Drawings
FIG. 1 is a flow chart of a static unmanned aerial vehicle cluster combat cooperative fire decision method based on an improved particle swarm optimization.
FIG. 2 is a schematic diagram of a particle encoding scheme in accordance with the present invention.
FIG. 3 is a graph showing the convergence comparison in the embodiment of the present invention.
Detailed Description
As shown in fig. 1, a static unmanned aerial vehicle cluster combat cooperative fire decision method based on an improved particle swarm optimization comprises the following steps:
the method comprises the following steps: establishing an unmanned aerial vehicle cluster system combat static weapon target distribution model according to the number of unmanned aerial vehicle clusters and the number of attack target classes, and obtaining a corresponding fitness function according to constraint conditions;
step two: carrying out encoding processing on the particles and initializing the positions and the speeds of the particles;
step three: optimizing and optimizing by using an improved particle swarm algorithm, and continuously updating the searching speed and the position of the particles in each dimension direction until an iteration termination condition is reached;
further, the establishing of the unmanned aerial vehicle cluster weapon target assignment model in the step one includes:
analyzing the threat degree of the attacking target, and establishing a model f with the largest striking damage coefficient to the target during the unmanned aerial vehicle cluster battle 1 The function is expressed as:
Figure BDA0003903611730000031
wherein v is ij Representing the threat degree of the attacking target to the unmanned aerial vehicle of the same party, x ij =0 represents that the ith drone of my party strikes a weapon carried by the jth target without allocating a drone; on the contrary, x ij If 1, then, the assigned weapon is hit destroyed, p ij Representing the damage probability of the unmanned aerial vehicle to the incoming target;
the constraints on the model are as follows:
restraining one: the number of weapons that the ith drone platform can use most;
Figure BDA0003903611730000032
wherein r is i Represents an upper limit on the number of weapons that an ith drone platform can use;
and (2) constraining: the ith drone platform is the least capable weapon;
Figure BDA0003903611730000033
and (3) constraint three: how many unmanned aerial vehicle weapons are allocated at most for each incoming target;
Figure BDA0003903611730000034
wherein s is j Representing the upper number of unmanned aerial vehicle weapons to which each incoming target can be assigned;
and (4) constraining: the maximum number of unmanned aerial vehicle weapon allocations required by each attacking target;
Figure BDA0003903611730000035
and (5) constraining: when unmanned aerial vehicle weapons are distributed for operation, the actually distributed number cannot exceed the sum of the unmanned aerial vehicle cluster weapons;
Figure BDA0003903611730000036
further, the fitness function expression in the improved particle swarm optimization is as follows:
Figure BDA0003903611730000041
wherein, minF (x) ij ,δ k ) Is the minimum value, x, representing the function of solving the fitness ij Representing whether weapons of the drone are allocated, δ k Is a penalty factor, and δ k ＞0；α＝β＝χ＝η＝μ＝σ＝2。
Further, the particle coding scheme in step two is based on that the attack target is distributed to the weapons carried by the arranged unmanned aerial vehicle cluster in the form of integer coding, so as to perform coding processing, and the length D of the coding is the sum of all weapons of the unmanned aerial vehicle cluster.
Further, the method for updating the search speed and the position of the particle in each dimension direction in the third step is as follows:
v id (t+1)＝ωv id (t)+c 1 r 1 (p id -x id (t))+c 2 r 2 (p gd -x id (t))
x id (t+1)＝x id (t)+v id (t+1)
in the formula, v id (t + 1) represents the velocity of particle movement at the t +1 th time, x id (t + 1) is the position of the particle at the time of the t +1 th iteration; and omega is an inertia factor of the movement of the particles, the value of the inertia factor is non-negative, when the value of the inertia factor is larger, the capability of the particles for searching the optimal solution globally is strong, and when the value of the inertia factor is smaller, the capability of the particles for searching the optimal solution globally is weak. Omega can be adjusted to achieve local or global optimum finding. p is a radical of id Is the optimal solution that the particle can find at the time of the t-th iteration, using p best Is represented by p gd Is the optimal solution found in the whole particle swarm, and g is used best And (4) showing. c. C 1 And c 2 Is the acceleration factor. r is a radical of hydrogen 1 And r 2 Is [0,1 ]]The random number of (c).
Further, the inertia factor w of the particle movement is improved, and a linearly decreasing inertia weight formula is established, which is specifically as follows:
Figure BDA0003903611730000042
wherein maxG and curG respectively represent the maximum iteration number of the current algorithm and the currently running iteration number, and generally take the value w max ＝0.9,w min ＝0.2。
Further, according to the definition of the distance between the particles, the search speed of the particles in each dimension is redefined:
Figure BDA0003903611730000051
wherein, S (x) i ,x j ) Is two particles x i And x j The similarity function of (c).
dis(p i -x i )＝h[ρ|f(p i )-f(x i )|]/C+υ(D-S(P i ,x i ))/D]
Where p and v are two positive numbers and p + v =1, respectively, for adjusting the difference in fitness of the function between two particles and the difference in coding between two particles, f (p) i ) And f (x) i ) Are all fitness values; h is an acceleration factor, h belongs to a positive integer, C is the maximum fitness function value within the current population, and D is the particle dimension.
v i ＝int[ωv i +c 1 r 1 dis(p i -x i )+c 2 r 2 dis(p g -x i )]
Wherein int [ ·]Denotes the fraction of integers taken of this formula, p i And p g Respectively, the optimal solution p of the individual extrema of the particle best And global optimal solution g best . The other parameter variables are set in the PSO algorithm, and the whole meaning is the same as the particle swarm optimization.
Further, the updated particle velocity can be synchronously applied to the positions of the updated particles, and the following update is made to the particle position formula:
Figure BDA0003903611730000052
wherein, maxT and CurT respectively represent the maximum iteration number of the current algorithm and the currently running iteration number.
Further, the feasible solution of the improved particle swarm optimization algorithm is decoded in an integer mode based on the hitting targets and weapons to obtain the optimal distribution scheme.
The static unmanned aerial vehicle cluster combat cooperative fire decision method based on the improved particle swarm optimization will be described in detail below with reference to the drawings and specific embodiments of the specification.
The static unmanned aerial vehicle cluster combat cooperative fire decision method based on the improved particle swarm optimization is applied to the static unmanned aerial vehicle weapon-target distribution problem. Different cases are set based on the same background, the method is used for optimizing and solving respectively, and model and algorithm optimization of final optimization of the method is compared with other methods.
The case background is that the number of unmanned aerial vehicles is 10, each unmanned aerial vehicle carries one weapon, the number of the attacking targets is 8, and 10 weapons strike 8 targets in total. The available amount of each weapon type and the maximum number of weapons that can be used per target are shown in table 1, and the threat level of each target to the drone is shown in table 2. The probability of damage to the target for each weapon is known as shown in table 3.
TABLE 1
Figure BDA0003903611730000061
TABLE 2
Figure BDA0003903611730000062
TABLE 3
Figure BDA0003903611730000071
And programming and simulating the WTA problem by using Matlab software and a PSO algorithm before improvement and a PSO algorithm after improvement respectively under the same software and hardware environment, and recording the average optimization consumption time (unit: second), the average iteration times and the minimum value of an average objective function (fitness function) of the common method and the method.
The programming and writing for optimization is performed with reference to the flow chart of fig. 1. Firstly, determining a static unmanned aerial vehicle weapon target distribution model and constraint conditions according to the conditions:
Figure BDA0003903611730000072
Figure BDA0003903611730000073
Figure BDA0003903611730000074
Figure BDA0003903611730000075
Figure BDA0003903611730000076
Figure BDA0003903611730000077
wherein r is i Expressed is an upper limit, s, on the number of weapons that the ith drone platform can use j The upper limit number of unmanned aerial vehicle weapons to which each incoming target can be assigned is represented, and the functional model satisfies the constraint condition.
Next, according to fig. 2, integer coding processing is performed on the feasible solution, where the coding dimension is D, the number of individuals in the unmanned aerial vehicle cluster is m, and r is 1 +r 2 +…+r m = D. A weapon can only hit a target once.
Initialize particle position, velocity, and set p best 、g best And the number of iterations.
And substituting the current position of each particle into a fitness function to calculate the fitness value of each particle. The calculation method comprises the following steps:
Figure BDA0003903611730000081
wherein, delta k Is a penalty factor, and δ k Is greater than 0. Typically, α = β = χ = η = μ = σ =2.
Then for each particle p i Make an evaluation and then update p best And g best 。
Recalculating individual particle position p using the definition of "distance" when following a new particle velocity i Respectively optimal solution p with individual best Global optimal solution g best The distance between the two sensors is calculated by the following method:
Figure BDA0003903611730000082
wherein, S (x) i ,x j ) Is two particles x i And x j The similarity function of (c).
dis(p i -x i )＝k[α|f(p i )-f(x i )|]/C+β(D-S(P i ,x i ))/D]
Where α and β are two positive numbers and α + β =1, the difference between the fitness functions of the two particles and the coding difference between the two particles, respectively, f (p) i ) And f (x) i ) Are all fitness values. k is an acceleration factor, k belongs to a positive integer, and C is the maximum fitness function value in the current population. D is the particle dimension.
The update of the particle in the search bit velocity in each dimension direction is as follows:
v i ＝int[ωv i +c 1 r 1 dis(p i -x i )+c 2 r 2 dis(p g -x i )]
wherein int [ ·]Denotes the fraction of integers taken of this formula, p i And p g Respectively, the optimal solution p of the individual extrema of the particle best And global optimal solution g best . The other parameter variables are set in the PSO algorithm, and the whole meaning is the same as the particle swarm optimization.
The update of the particle in the search bit position in each dimension direction is as follows:
Figure BDA0003903611730000091
wherein, maxT and CurT respectively represent the maximum iteration number of the current algorithm and the currently running iteration number.
And continuously updating the searching speed and the position of the particles in all dimension directions until an iteration termination condition is reached. When the seed termination condition is met, stopping the continuous iteration and outputting g best And obtaining the corresponding decision value x by coding and decoding ij And traversing the whole matrix record to update the decision matrix X so as to complete the fire decision of the unmanned aerial vehicle cluster. Otherwise, returning to the fitness function for recalculation and repeating the operation.
Table 4 accurately records the optimal allocation scheme obtained by applying the improved particle swarm optimization:
TABLE 4
Figure BDA0003903611730000092
Table 5 accurately records the results of the average time, average iteration number, and average objective function minimum for 100 runs of the program for both algorithms.
TABLE 5
Figure BDA0003903611730000093
According to the comparison analysis, the static unmanned aerial vehicle cluster combat cooperative fire decision method based on the improved particle swarm optimization can effectively solve the problem that optimization time is too long in the algorithm of the existing method, the solution quality can be further improved, the improved algorithm has a better convergence effect, and local optimization is not easy to occur. The scheme of fast distribution of the unmanned aerial vehicle cluster is provided, and the efficiency of completing tasks is improved.
FIG. 3 shows that the improved particle swarm optimization has a faster convergence rate than the general particle swarm optimization by using the method of the present invention, and the iteration number of the improved algorithm is smaller than that before the improvement. The improved algorithm is easier to converge and has better effect.
The foregoing has described in detail the principles, essential features and advantages of the invention. It will be understood by those skilled in the art that the present invention is not limited to the embodiments described above, which are described in the specification and illustrated only to illustrate the principle of the present invention, but that various changes and modifications may be made therein without departing from the spirit and scope of the present invention, which fall within the scope of the invention as claimed. The scope of the invention is defined by the appended claims and equivalents thereof.

Claims (10)

1. A static unmanned aerial vehicle cluster combat cooperative fire decision method based on an improved particle swarm algorithm is characterized by comprising the following steps:
the method comprises the following steps: establishing an unmanned aerial vehicle cluster system combat static weapon target distribution model according to the number of unmanned aerial vehicle clusters and the number of attack target classes, and obtaining a corresponding fitness function according to constraint conditions;
step two: carrying out encoding processing on the particles and initializing the positions and the speeds of the particles;
step three: and performing optimization and optimization by using an improved particle swarm algorithm, and continuously updating the searching speed and the position of the particles in each dimension direction until an iteration termination condition is reached.
2. The improved particle swarm optimization-based static unmanned aerial vehicle cluster combat cooperative fire decision method according to claim 1, wherein the establishment of the unmanned aerial vehicle cluster system combat static weapon target distribution model in the first step is as follows:
analyzing the threat degree of the attacking target and establishing the unmannedModel f with maximum striking damage coefficient on target during battle of airplane cluster 1 The function is expressed as:
Figure FDA0003903611720000011
wherein v is ij Representing the threat degree of the attacking target to the unmanned aerial vehicle of the same party, x ij =0 represents that the ith drone of my party strikes a weapon carried by the jth target without allocating a drone; on the contrary, x ij If 1, then, the assigned weapon is hit destroyed, p ij Representing the damage probability of the unmanned aerial vehicle to the incoming target;
the constraints on the model are as follows:
restraining one: the number of weapons that the ith drone platform can use most;
Figure FDA0003903611720000012
wherein r is i Represents an upper limit on the number of weapons that an ith drone platform can use;
and (2) constraining: the ith drone platform has a least usable weapon;
Figure FDA0003903611720000013
and (3) constraining: the maximum number of unmanned aerial vehicle weapon allocations required by each attacking target;
Figure FDA0003903611720000014
wherein s is j Representing the upper number of drone weapons to which each incoming target can be assigned;
and (4) constraint: how many unmanned aerial vehicle weapons are allocated at most for each incoming target;
Figure FDA0003903611720000021
and (5) constraint: when unmanned aerial vehicle weapons are distributed for operation, the actually distributed number cannot exceed the sum of the unmanned aerial vehicle cluster weapons;
Figure FDA0003903611720000022
3. the improved particle swarm algorithm-based static unmanned aerial vehicle cluster combat cooperative fire decision method according to claim 2, wherein the fitness function expression in the improved particle swarm algorithm is as follows:
Figure FDA0003903611720000023
wherein, minF (x) ij ,δ k ) Is the minimum value, x, representing the function of solving the fitness ij Representing whether weapons of the drone are allocated, δ k Is a penalty factor, and δ k ＞0；α＝β＝χ＝η＝μ＝σ＝2。
4. The improved particle swarm optimization-based static unmanned aerial vehicle cluster battle cooperative fire decision method as claimed in claim 1, wherein the particle encoding scheme in the second step is based on that the attack target is distributed to the weapons carried by the arranged unmanned aerial vehicle cluster in an integer encoding mode, so as to perform encoding processing, and the length D of the encoding is the sum of all the weapons of the unmanned aerial vehicle cluster.
5. The improved particle swarm algorithm-based static unmanned aerial vehicle cluster battle cooperative fire decision method as claimed in claim 1, wherein the method for updating the search speed and position of the particles in each dimension direction in step three is as follows:
v id (t+1)＝ωv id (t)+c 1 r 1 (p id -x id (t))+c 2 r 2 (p gd -x id (t))
x id (t+1)＝x id (t)+v id (t+1)
in the formula, v id (t + 1) represents the velocity of particle movement at the t +1 th time, x id (t + 1) is the position of the particle at the time of the t +1 th iteration; omega is an inertia factor of particle movement, the value of omega is non-negative, and the adjustment of omega realizes local optimum search or global optimum search; p is a radical of formula id Is the optimal solution that the particle can find at the time of the t-th iteration, using p best Is represented by the formula p gd Is the optimal solution found in the whole particle swarm, and g is used best Representing; c. C 1 And c 2 Is an acceleration factor; r is 1 And r 2 Is [0,1 ]]The random number of (2).
6. The improved particle swarm algorithm-based static unmanned aerial vehicle cluster battle cooperative fire decision method according to claim 5, wherein the improvement of the inertia factor w of particle movement is performed, and a linear decreasing inertia weight formula is established, specifically as follows:
Figure FDA0003903611720000031
wherein, maxG and curG respectively represent the maximum iteration times of the current algorithm and the iteration times currently running, and take value w max ＝0.9,w min ＝0.2。
7. The improved particle swarm algorithm-based static unmanned aerial vehicle cluster battle cooperative fire decision method as claimed in claim 5, wherein the search speed of the particles in each dimension is redefined according to the definition of the "distance" between the particles:
Figure FDA0003903611720000032
wherein, S (x) i ,x j ) Is two particles x i And x j A similarity function of (a);
dis(p i -x i )＝h[ρ|f(p i )-f(x i )|]/C+υ(D-S(P i ,x i ))/D]
where p and v are two positive numbers and p + v =1, respectively, for adjusting the difference in fitness of the function between two particles and the coding difference between two particles, f (p) i ) And f (x) i ) Are all fitness values; h is an acceleration factor, h belongs to a positive integer, C is the maximum fitness function value in the current population, and D is the particle dimension;
v i ＝int[ωv i +c 1 r 1 dis(p i -x i )+c 2 r 2 dis(p g -x i )]
wherein int [ ·]Indicating that the integer part, p, is taken for this formula i And p g Respectively, the optimal solution p of the individual extrema of the particle best And global optimal solution g best (ii) a The other parameter variables are the same as the PSO algorithm, and the integral meaning is the same as the particle swarm algorithm.
8. The improved particle swarm algorithm-based static unmanned aerial vehicle cluster battle cooperative fire decision method as claimed in claim 7, wherein the updated particle velocity can be synchronously applied to the updated particle position, and the following update is made to the particle position formula:
Figure FDA0003903611720000041
wherein, maxT and CurT respectively represent the maximum iteration number of the current algorithm and the currently running iteration number.
9. The improved particle swarm optimization-based static unmanned aerial vehicle cluster combat cooperative fire decision method according to claim 8, wherein decoding feasible solutions of optimization algorithms in an integer manner based on hit targets and weapons results in an optimal distribution scheme.
10. A computer device comprising a memory, a processor and a computer program stored on the memory and executable on the processor, characterized in that the steps of the method of any of claims 1-9 are implemented when the program is executed by the processor.
CN202211298355.4A 2022-10-23 2022-10-23 Static unmanned aerial vehicle cluster combat cooperative fire decision method based on improved particle swarm optimization Pending CN115660339A (en)

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Publication number Priority date Publication date Assignee Title
CN116684273A (en) * 2023-06-08 2023-09-01 中国人民解放军国防科技大学 Automatic planning method and system for mobile communication network structure based on particle swarm

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* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN116684273A (en) * 2023-06-08 2023-09-01 中国人民解放军国防科技大学 Automatic planning method and system for mobile communication network structure based on particle swarm
CN116684273B (en) * 2023-06-08 2024-01-30 中国人民解放军国防科技大学 Automatic planning method and system for mobile communication network structure based on particle swarm

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