CN113784366A - Intelligent clustering method based on coverage optimization of unmanned aerial vehicle cluster - Google Patents

Intelligent clustering method based on coverage optimization of unmanned aerial vehicle cluster Download PDF

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CN113784366A
CN113784366A CN202111092233.5A CN202111092233A CN113784366A CN 113784366 A CN113784366 A CN 113784366A CN 202111092233 A CN202111092233 A CN 202111092233A CN 113784366 A CN113784366 A CN 113784366A
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unmanned aerial
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CN113784366B (en
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姚媛媛
刘祁
董瑶瑶
乌云嘎
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Beijing Information Science and Technology University
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    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04W—WIRELESS COMMUNICATION NETWORKS
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Abstract

The invention discloses an intelligent clustering method based on unmanned aerial vehicle cluster coverage optimization, which optimizes the deployment problem of an unmanned aerial vehicle base station by using a cluster intelligent optimization algorithm to maximize the ground coverage of an unmanned aerial vehicle; the intelligent clustering method disclosed by the invention is an algorithm for quickly searching an optimal solution, can quickly find the optimal solution of the ground coverage rate of the unmanned aerial vehicle, theoretically controls the unmanned aerial vehicle base station to cover a task area by improving the particle swarm algorithm, realizes the improved particle swarm algorithm through a simulation result, can effectively optimize the aerial deployment of the unmanned aerial vehicle base station, can greatly improve the improved algorithm compared with the particle swarm algorithm, and provides a new idea for the deployment and coverage optimization problems of the unmanned aerial vehicle base station.

Description

Intelligent clustering method based on coverage optimization of unmanned aerial vehicle cluster
Technical Field
The invention relates to the technical field of unmanned aerial vehicle communication, in particular to an intelligent clustering method based on unmanned aerial vehicle cluster coverage optimization
Background
In the process of disaster area rescue and reconstruction, most of fixed base stations are damaged, large mobile communication equipment is hindered to different degrees in the transportation process, and Unmanned Aerial Vehicle (UAV) communication has unique advantages. In the face of such an emergency, a network consisting of a plurality of unmanned aerial vehicle base stations is required to fully cover a disaster-affected (task) area, so that communication services of all ground users can be guaranteed. The coverage rate problem of the multiple unmanned aerial vehicle base stations in the task area is one of important problems in the construction of the unmanned aerial vehicle base stations; however, a single drone base station has obvious disadvantages, such as limited coverage area and limited strain capacity in the face of emergency, and therefore, it is very important to solve the problems.
Disclosure of Invention
Aiming at the problems, the invention provides an intelligent clustering method based on unmanned aerial vehicle cluster coverage optimization, and provides a cluster intelligent optimization algorithm for optimizing the deployment problem of an unmanned aerial vehicle base station, so that the ground coverage of an unmanned aerial vehicle is maximized.
In order to realize the technical scheme, the invention provides an intelligent clustering method based on unmanned aerial vehicle cluster coverage optimization, which comprises the following steps:
the method comprises the following steps: analyzing a coverage scene of the unmanned aerial vehicle base station, deploying the man-machine base station, establishing a real model of the unmanned aerial vehicle base station by using an A2G channel model, and calculating to obtain the maximum communication radius of the unmanned aerial vehicle base station;
step two: compiling a coverage algorithm according to a real coverage scene of the unmanned aerial vehicle base station through a particle swarm algorithm, and then adding an artificial firefly algorithm to improve the particle swarm algorithm;
step three: selecting a particle swarm algorithm as a main function, adding a fitness function to obtain a multi-UAV coverage algorithm based on the particle swarm algorithm, wherein the fitness function is used for calculating the coverage area of the unmanned aerial vehicle base station to a task area, and adding an artificial firefly algorithm based on the particle swarm algorithm to form a distance constraint particle swarm algorithm;
step four: and (4) analyzing a simulation result, namely simulating the coverage environment of the unmanned aerial vehicle base station through MATLAB, comparing the distance constraint particle swarm algorithm with the PSO algorithm, and judging whether the optimization is successful or not.
The further improvement lies in that: in the first step, when the unmanned aerial vehicle base station is at the height H, the transmitting power of the unmanned aerial vehicle base station is PuThen the power of the user's transmissions received from the drone base station on the ground can be calculated from equation (2-1).
Figure BDA0003267966680000021
Wherein: pdminThe minimum power of a user receiving the communication signal of the unmanned aerial vehicle base station is shown, H is the flight height of the unmanned aerial vehicle base station at the moment, R is the communication radius of the unmanned aerial vehicle base station, alpha is a path loss index, and epsilon0Channel gain for unmanned aerial vehicle base station communication;
because the power received by the user needs to be larger than the signal power of the unmanned aerial vehicle base station after the loss, the communication radius of the unmanned aerial vehicle base station can be simplified according to the formula (2-1)
Figure BDA0003267966680000022
And the maximum value calculated by R is the maximum communication radius of the unmanned aerial vehicle base station.
The further improvement lies in that: in the second step, the fitness function is an algorithm for calculating the coverage ratio of the task area, the fitness function controls the size of the fitness value in the particle swarm algorithm, and the fitness value influences the position update and the speed update of the particles.
The further improvement lies in that: in the second step, the particle swarm algorithm represents the characteristics of the particles by using the positions, the speeds and the fitness values; the individual extreme value and the group extreme value are controlled through the fitness in the space, the position is updated through a formula, and the updating formula is as follows:
Figure BDA0003267966680000031
Figure BDA0003267966680000032
wherein: w is the inertial weight: in general, the algorithm with w equal to 0.4 and w equal to 0.9 has the best performance, and has better global search capability and local search capability, and c1、c2For acceleration constants, in general c1＝c2＝2。
The further improvement lies in that: in the second step, the particle swarm algorithm flow is as follows: firstly, initializing all parameters, calculating according to a fitness function, calculating an individual extreme value and a group extreme value, updating through a formula, and finally judging whether the iteration times meet the parameter setting in initialization; if the condition is not satisfied, the circulation is continued, otherwise, the optimal solution is output.
The further improvement lies in that: each iteration of the distance-constrained particle swarm algorithm consists of a fluorescein updating stage, a movement probability updating stage, a position updating stage and a neighborhood range updating stage; the distance constrained particle swarm algorithm comprises the following steps:
the method comprises the following steps: firefly deployment, the following data are initialized: the fluorescein of firefly is l0Dynamic decision field of r0Initialization step size s, neighborhood threshold ntDilution coefficient rho of fluorescein changing along with time, fluorescein update factor (fitness extraction ratio) gamma, dynamic decision domain update rate (neighborhood change rate) beta, firefly sensing domain rsIteration times M;
step two: fitness function J (x)i(t)) the following formula is used for the calculation:
Figure BDA0003267966680000041
wherein j ∈ Ni(t)＝{j:dij(t)<rd j(t),||xj(t)-xi(t)||>0 represents the set of neighboring drone base stations of drone base station i, dijRepresenting the Euclidean distance, r, between drone base station i and drone base station jd j(t) is the decision radius of the unmanned aerial vehicle base station j under the t iteration of the algorithm;
step three: fluorescein renewal phase
Calculating the fluorescein value of a single firefly individual as shown in formulas 3-4:
li(t+1)＝(1-ρ)li(t)-γJ(xi(t+1)) (3-4)
wherein li(t) fluorescein concentration calculated after t calculation cycles;
step four: probability of movement
In the probability calculation stage, to prevent the generation of fluorescein over-bounds and negative values, the probability of movement is calculated using the following formula:
Figure BDA0003267966680000042
step five: location update
In the stage of unmanned aerial vehicle base station movement, the calculation formula is as follows:
Figure BDA0003267966680000043
wherein s is the moving step length, and is taken
Figure BDA0003267966680000044
Is reasonable and is obtained by experiments
Figure BDA0003267966680000045
Is the optimal distance between unmanned aerial vehicle base stations;
step six: neighborhood range update phase
rdi(t+1)＝min{rs,max{0,rdi(t)+β(nt-|Ni(t)|)}} (3-7)
Wherein r isdi(t) represents the dynamic decision range of the ith firefly at time t, 0 ≦ rdi (t ≦ rs；
Step seven: and adding the algorithm improved in the steps into a particle swarm algorithm to obtain the artificial firefly algorithm.
The invention has the beneficial effects that: the intelligent algorithm disclosed by the invention is an algorithm for quickly searching the optimal solution, and can quickly find the optimal solution of the unmanned aerial vehicle to the ground coverage rate. And theoretically, the unmanned aerial vehicle base station is controlled to cover the task area through an improved particle swarm algorithm. The particle swarm algorithm with the improved simulation result can effectively optimize the aerial deployment of the unmanned aerial vehicle base station, compared with the particle swarm algorithm, the improved algorithm can be greatly improved, and a new thought is provided for the problem of deployment and coverage optimization of the unmanned aerial vehicle base station.
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FIG. 1 is a particle swarm algorithm flow of the present invention;
FIG. 2 shows the final coverage result of the unmanned aerial vehicle base station after optimization by the particle swarm optimization;
FIG. 3 is a distance-constrained particle swarm algorithm flow of the present invention;
fig. 4-6 show the initial coverage, the number of iterations, and the final coverage of the drone base station, respectively, under the PSO algorithm;
FIGS. 7-9 show the initial coverage rate, the number of iterations, and the final coverage rate of the UAV base station under the distance-constrained particle swarm optimization
Fig. 10-12 show the initial coverage, the number of iterations, and the final coverage of the drone base stations, respectively, for the PSO algorithm;
fig. 13-15 show the initial coverage, the number of iterations, and the final coverage of the drone base stations, respectively, under the PSO algorithm; as can be seen from the figure, nine drone base stations can basically cover the task area;
fig. 16-18 show the initial coverage, the number of iterations, and the final coverage of the drone base stations, respectively, under the distance constrained particle swarm algorithm;
fig. 19 to fig. 21 show the initial coverage, the number of iterations, and the final coverage of the base station of the unmanned aerial vehicle under the distance-constrained particle swarm optimization, respectively;
fig. 22 is drone base station coverage.
Detailed Description
In order to enhance the understanding of the present invention, the present invention will be further described with reference to the following examples, which are only illustrative and not intended to limit the scope of the present invention.
The embodiment provides an intelligent clustering method based on unmanned aerial vehicle cluster coverage optimization, which is characterized by comprising the following steps:
the method comprises the following steps: analyzing a coverage scene of the unmanned aerial vehicle base station, deploying the man-machine base station, establishing a real model of the unmanned aerial vehicle base station by using an A2G channel model, and calculating to obtain the maximum communication radius of the unmanned aerial vehicle base station;
step two: compiling a coverage algorithm according to a real coverage scene of the unmanned aerial vehicle base station through a particle swarm algorithm, and then adding an artificial firefly algorithm to improve the particle swarm algorithm;
step three: selecting a particle swarm algorithm as a main function for use, adding a fitness function to obtain a multi-UAV coverage algorithm based on the particle swarm algorithm, wherein the fitness function is used for calculating the coverage area of an unmanned aerial vehicle base station to a task area, adding an artificial firefly algorithm on the basis of the particle swarm algorithm to form a distance constraint particle swarm algorithm, writing the coverage algorithm by utilizing an optimization scheme for communication coverage of the multi-UAV base station in an intelligent swarm algorithm according to the actual coverage scene of the unmanned aerial vehicle base station through the particle swarm algorithm, and finally adding the artificial firefly algorithm to make up the deficiency in the particle swarm algorithm so as to improve the coverage rate of the unmanned aerial vehicle base station;
step four: and (4) simulation result analysis, namely simulating the coverage environment of the unmanned aerial vehicle base station through MATLAB, comparing the distance constraint particle swarm algorithm with the PSO algorithm, and judging whether the optimization is successful or not.
The further improvement lies in that: in the first step, when the unmanned aerial vehicle base station is at the height H, the transmitting power of the unmanned aerial vehicle base station is PuThen the power of the user's transmissions received from the drone base station on the ground can be calculated from equation (2-1).
Figure BDA0003267966680000071
Wherein: pdminThe minimum power of a user receiving the communication signal of the unmanned aerial vehicle base station is shown, H is the flight height of the unmanned aerial vehicle base station at the moment, R is the communication radius of the unmanned aerial vehicle base station, alpha is a path loss index, and epsilon0Channel gain for unmanned aerial vehicle base station communication;
because the power received by the user needs to be larger than the signal power of the unmanned aerial vehicle base station after the loss, the communication radius of the unmanned aerial vehicle base station can be simplified according to the formula (2-1)
Figure BDA0003267966680000072
And the maximum value calculated by R is the maximum communication radius of the unmanned aerial vehicle base station.
The further improvement lies in that: in the second step, the fitness function is an algorithm for calculating the coverage ratio of the task area, the fitness function controls the size of the fitness value in the particle swarm algorithm, and the fitness value influences the position update and the speed update of the particles.
The further improvement lies in that: in the second step, the particle swarm algorithm represents the characteristics of the particles by using the positions, the speeds and the fitness values; the individual extreme value and the group extreme value are controlled through the fitness in the space, the position is updated through a formula, and the updating formula is as follows:
Figure BDA0003267966680000081
Figure BDA0003267966680000082
wherein: w is the inertial weight: in general, the algorithm with w equal to 0.4 and w equal to 0.9 has the best performance, has better global search capability and local search capability,c1、c2for acceleration constants, in general c1＝c2＝2。
As shown in fig. 1, in the second step, the particle swarm algorithm flow is: firstly, initializing all parameters, calculating according to a fitness function, calculating an individual extreme value and a group extreme value, updating through a formula, and finally judging whether the iteration times meet the parameter setting in initialization; if the condition is not satisfied, the circulation is continued, otherwise, the optimal solution is output, and the particle swarm algorithm has the advantages that: firstly, the algorithm is an algorithm with better global search capability in an intelligent group algorithm, and secondly, the phase particle swarm algorithm requires few parameters in the initialization process and is convenient to adjust; meanwhile, the particle swarm optimization algorithm has the property of being easy to converge, so that the calculation amount of the algorithm can be effectively reduced, and finally, the particle swarm optimization algorithm has good compatibility and large improvement space.
As shown in fig. 2, in order to finally cover the result of the unmanned aerial vehicle base station optimized by the particle swarm algorithm, multiple unmanned aerial vehicles are overlapped in the figure, which indicates that the phenomenon of prematurity occurs, and the particle swarm algorithm has shortcomings, and is easy to fall into a local optimal solution, so that the algorithm is prematurity, and the circulation is ended before the algorithm is advanced, which has great influence on the result of people, so that the people introduce the improved firefly algorithm according to the shortcomings.
An artificial firefly group optimization algorithm (GSO for short) is a novel group intelligent optimization algorithm proposed by Indian scholars. The GSO algorithm assigns the same fluorescein value and dynamic decision radius at the initialization stage. The firefly's decision radius is constantly changing, which affects the size of its own decision radius based on the number of other fireflies around it. This makes it have better local search capability.
In this embodiment, the GSO algorithm is improved once and added to the particle swarm algorithm, and the improved particle swarm algorithm is named as a distance constrained particle swarm algorithm. Each iteration consists of a fluorescein updating stage, a moving probability updating stage, a position updating stage and a neighborhood range updating stage;
as shown in fig. 3, the distance-constrained particle swarm algorithm comprises the following steps:
the method comprises the following steps: firefly deployment, the following data are initialized: the fluorescein of firefly is l0Dynamic decision field of r0Initialization step size s, neighborhood threshold ntDilution coefficient rho of fluorescein changing along with time, fluorescein update factor (fitness extraction ratio) gamma, dynamic decision domain update rate (neighborhood change rate) beta, firefly sensing domain rsIteration times M;
step two: fitness function J (x)i(t)) the following formula is used for the calculation:
Figure BDA0003267966680000091
wherein j ∈ Ni(t)＝{j:dij(t)<rd j(t),||xj(t)-xi(t)||>0 represents the set of neighboring drone base stations of drone base station i, dijRepresenting the Euclidean distance, r, between drone base station i and drone base station jd j(t) is the decision radius of the unmanned aerial vehicle base station j under the t iteration of the algorithm;
step three: fluorescein renewal phase
Calculating the fluorescein value of a single firefly individual as shown in formulas 3-4:
li(t+1)＝(1-ρ)li(t)-γJ(xi(t+1)) (3-4)
wherein li(t) fluorescein concentration calculated after t calculation cycles.
Step four: probability of movement
In the probability calculation stage, to prevent the generation of fluorescein over-bounds and negative values, the probability of movement is calculated using the following formula:
Figure BDA0003267966680000101
step five: location update
In the stage of unmanned aerial vehicle base station movement, the calculation formula is as follows:
Figure BDA0003267966680000102
wherein s is the moving step length, and is taken
Figure BDA0003267966680000103
Is more reasonable. Through experiments, the product
Figure BDA0003267966680000104
Is the optimal distance between unmanned aerial vehicle base stations;
step six: neighborhood range update phase
rdi(t+1)＝min{rs,max{0,rdi(t)+β(nt-|Ni(t)|)}} (3-7)
Wherein r isdi(t) represents the dynamic decision range of the ith firefly at time t, 0 ≦ rdi (t ≦ rs；
Step seven: and adding the algorithm improved in the steps into a particle swarm algorithm to obtain the artificial firefly algorithm.
Example two
And analyzing a simulation result, and simulating by the algorithm. And comparing the PSO algorithm with the distance constraint particle swarm algorithm to judge whether the optimization is successful.
First of all, initializing
When P is presentu1.25W, the height of the unmanned plane is 20m, and let epsilon0/PdWhen 1000, the communication radius can be approximated by equation (2-7) to be 25m (an approximation is used for the simulation).
PSO algorithm initialization: the area boundary is 100m multiplied by 100m, the number of unmanned aerial vehicles is 8, the communication radius is 25m, the discrete granularity is 1, the iteration number is 300, and the weight coefficient wmax＝0.9；wmin0.4, self-cognition parameter, social cognition parameter c1＝c 22 max speed 2, min speed-2, position max 50, position min 0.
Initializing a distance constraint particle swarm algorithm: the fluorescein volatility factor is 0.9, the fitness extraction ratio is 0.1, the neighbor change rate is 0.58, the number of field unmanned aerial vehicles is 6, and the initial fluorescein concentration is 400.
Table 1 coverage of PSO algorithm and distance constrained particle swarm algorithm.
Figure BDA0003267966680000111
As can be seen in fig. 4-9: table 1 shows coverage rates of the PSO algorithm and the distance-constrained particle swarm algorithm, and a comparison shows that final coverage rates of both the PSO algorithm and the distance-constrained particle swarm algorithm are improved to a certain extent compared with initial coverage rates. But the final coverage distance constrained particle swarm algorithm is significantly higher than the PSO algorithm.
In the comparison experiment, we can obviously find that the final coverage rate of the distance-constrained particle swarm optimization is obviously higher than that of the PSO algorithm when the positions of the initial randomly-distributed unmanned aerial vehicles and the initial conditions are the same. The distance constraint particle swarm algorithm obtained by the iterative process is easier to converge, the optimizing capability of the distance constraint particle swarm algorithm is stronger, and the optimal solution can be found in a short time.
EXAMPLE III
The embodiment discloses a group of contrast tests, which mainly comprise the steps of discussing the influence of the number of unmanned aerial vehicle base stations on the coverage rate, and judging whether the distance constraint particle swarm algorithm achieves the optimization effect or not through the final coverage rate of the unmanned aerial vehicle base stations.
TABLE 2 initial and final coverage for PSO and distance-constrained particle swarm optimization
Figure BDA0003267966680000121
As can be seen in fig. 10-22: table 2 shows that the initial coverage and the final coverage of the PSO algorithm and the distance-constrained particle swarm algorithm are as shown in the above table under the condition of different numbers of drones.
Fig. 22 is coverage of base stations of the unmanned aerial vehicle, which is a line graph produced according to table 2, and changes of the initial coverage and the final coverage of the PSO algorithm, and the initial coverage and the final coverage of the distance-constrained particle swarm algorithm along with the number of base stations of the unmanned aerial vehicle can be more visually seen through the line graph.
According to simulation data, the initial coverage rate and the final coverage rate of the PSO algorithm and the distance-constrained particle swarm algorithm are increased while the number of the unmanned aerial vehicles is increased. This indicates that an increase in the number of drones contributes to the increase in coverage. And comparing the distance constraint particle swarm algorithm with the PSO algorithm, and showing that the coverage rate of the distance constraint particle swarm algorithm is superior to that of the PSO algorithm.
The simulation result can show that the distance constraint particle swarm algorithm is superior to the PSO algorithm, and the premature phenomenon in the PSO algorithm is solved. The coverage rate of the PSO algorithm can be increased by increasing the number of the unmanned aerial vehicles, but the coverage rate is unstable and cannot reach high coverage rate. With the continuous increase of the unmanned aerial vehicle base station, the coverage rate of the distance constraint particle swarm algorithm is continuously increased, even reaching ninety-eight percent. However, blind utilization of increasing the number of drones to achieve full coverage is an incorrect method, because when the coverage rate of a drone base station is not considered alone, the planning of a drone path and resource problems are also important.
The foregoing illustrates and describes the principles, general features, and advantages of the present 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 as defined in the appended claims. The scope of the invention is defined by the appended claims and equivalents thereof.

Claims (6)

1. An intelligent clustering method based on unmanned aerial vehicle cluster coverage optimization is characterized by comprising the following steps:
the method comprises the following steps: analyzing a coverage scene of the unmanned aerial vehicle base station, deploying the man-machine base station, establishing an actual model of the unmanned aerial vehicle base station by using an A2G channel model, and calculating to obtain the maximum communication radius of the unmanned aerial vehicle base station;
step two: compiling a coverage algorithm according to a real coverage scene of the unmanned aerial vehicle base station through a particle swarm algorithm, and then adding an artificial firefly algorithm to improve the particle swarm algorithm;
step three: selecting a particle swarm algorithm as a main function, adding a fitness function to obtain a multi-UAV coverage algorithm based on the particle swarm algorithm, wherein the fitness function is used for calculating the coverage area of an unmanned aerial vehicle base station to a task area, and adding an artificial firefly algorithm based on the particle swarm algorithm to form a distance constraint particle swarm algorithm;
step four: and (4) analyzing a simulation result, namely simulating the coverage environment of the unmanned aerial vehicle base station through MATLAB, comparing the distance constraint particle swarm algorithm with the PSO algorithm, and judging whether the optimization is successful or not.
2. The intelligent clustering method based on unmanned aerial vehicle cluster coverage optimization according to claim 1, wherein: in the first step, when the unmanned aerial vehicle base station is at the height H, the transmitting power of the unmanned aerial vehicle base station is PuThen the user receives the power formula (2-1) transmitted by the unmanned aerial vehicle base station on the ground
Figure FDA0003267966670000011
Wherein: pdminThe minimum power of a user receiving the communication signal of the unmanned aerial vehicle base station is shown, H is the flight height of the unmanned aerial vehicle base station at the moment, R is the communication radius of the unmanned aerial vehicle base station, alpha is a path loss index, and epsilon0Channel gain for unmanned aerial vehicle base station communication;
because the power received by the user needs to be larger than the signal power of the unmanned aerial vehicle base station after the loss, the communication radius of the unmanned aerial vehicle base station can be simplified according to the formula (2-1)
Figure FDA0003267966670000021
And the maximum value calculated by R is the maximum communication radius of the unmanned aerial vehicle base station.
3. The intelligent clustering method based on unmanned aerial vehicle cluster coverage optimization according to claim 1, wherein: in the second step, the fitness function is an algorithm for calculating the coverage ratio of the task area, the fitness function controls the size of the fitness value in the particle swarm algorithm, and the fitness value influences the position update and the speed update of the particles.
4. The intelligent clustering method based on unmanned aerial vehicle cluster coverage optimization according to claim 1, wherein: in the second step, the particle swarm algorithm represents the characteristics of the particles by using the positions, the speeds and the fitness values; the individual extreme value and the group extreme value are controlled through the fitness in the space, the position is updated through a formula, and the updating formula is as follows:
Figure FDA0003267966670000022
Figure FDA0003267966670000023
wherein: w is the inertial weight: in general, the algorithm with w equal to 0.4 and w equal to 0.9 has the best performance, and has better global search capability and local search capability, and c1、c2For acceleration constants, in general c1＝c2＝2。
5. The intelligent clustering method based on unmanned aerial vehicle cluster coverage optimization according to claim 1, wherein: in the second step, the particle swarm algorithm flow is as follows: firstly, initializing all parameters, calculating according to a fitness function, calculating an individual extreme value and a group extreme value, updating through a formula, and finally judging whether the iteration times meet the parameter setting in initialization; and if the condition is not met, continuing to circulate, otherwise, outputting the optimal solution.
6. The intelligent clustering method based on unmanned aerial vehicle cluster coverage optimization according to claim 1 or 5, wherein: each iteration of the distance-constrained particle swarm algorithm consists of a fluorescein updating stage, a moving probability updating stage, a position updating stage and a neighborhood range updating stage; the distance constrained particle swarm algorithm comprises the following steps:
the method comprises the following steps: firefly deployment, the following data are initialized: the fluorescein of firefly is l0Dynamic decision field of r0Initialization step size s, neighborhood threshold ntDilution coefficient rho of fluorescein changing along with time, fluorescein update factor gamma, dynamic decision domain update rate beta and firefly sensing domain rsIteration times M;
step two: fitness function J (x)i(t)) the following formula is used for the calculation:
Figure FDA0003267966670000031
wherein j ∈ Ni(t)＝{j:dij(t)<rd j(t),||xj(t)-xi(t)||>0 represents the set of neighboring drone base stations of drone base station i, dijRepresenting the Euclidean distance, r, between drone base station i and drone base station jd j(t) is the decision radius of the unmanned aerial vehicle base station j under the t iteration of the algorithm;
step three: fluorescein renewal phase
Calculating the fluorescein value of a single firefly individual as shown in formulas 3-4:
li(t+1)＝(1-ρ)li(t)-γJ(xi(t+1)) (3-4)
wherein li(t) fluorescein concentration calculated after t calculation cycles;
step four: probability of movement
In the probability calculation stage, to prevent the generation of fluorescein over-bounds and negative values, the probability of movement is calculated using the following formula:
Figure FDA0003267966670000041
step five: location update
In the stage of unmanned aerial vehicle base station movement, the calculation formula is as follows:
Figure FDA0003267966670000042
wherein s is the moving step length, and is taken
Figure FDA0003267966670000043
Is reasonable and is obtained by experiments
Figure FDA0003267966670000044
Optimal distance between unmanned aerial vehicle base stations;
step six: neighborhood range update phase
rdi(t+1)＝min{rs,max{0,rdi(t)+β(nt-|Ni(t)|)}} (3-7)
Wherein r isdi(t) represents the dynamic decision range of the ith firefly at time t, 0 ≦ rdi (t ≦ rs；
Step seven: and adding the algorithm improved in the steps into a particle swarm algorithm to obtain the artificial firefly algorithm.
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