CN116684885A - Satellite communication link optimization method based on genetic ant colony simulated annealing hybrid algorithm - Google Patents
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Abstract
The invention relates to a satellite communication link optimization method based on a genetic ant colony simulated annealing hybrid algorithm, which comprises the following steps: s1, generating N initial link distribution matrixes; s2, calculating the adaptability of each matrix, and setting pheromones; s3, genetic selection; s4, genetic crossover: the pheromone is adjusted according to the adaptability of the matrix after crossing, and the better crossing direction and the solution matrix are determined; s5, carrying out genetic variation operation on the distribution matrix, and determining a better variation direction and a local optimal solution by combining an ant colony algorithm; s6, receiving adjustment according to an acceptance criterion of simulated annealing; s7, repeating the steps S3-S6, and outputting the global optimal solution in a loop iteration mode. The invention improves the convergence rate of the genetic algorithm by utilizing the local optimizing capability of the ant colony algorithm, and simultaneously determines whether to accept the crossover and mutation results by adding the acceptance solution of the simulated annealing algorithm so as to ensure that the genetic ant colony algorithm is prevented from falling into a local optimal solution trap and converging towards the global optimal value, thereby shortening the optimizing time and further improving the optimizing efficiency.
Description
Technical Field
The invention relates to the technical field of satellite communication link optimization, in particular to a satellite communication link optimization method based on a genetic ant colony simulated annealing hybrid algorithm.
Background
With the gradual application of satellite communication technology in important national strategic fields such as military science and technology and economy, the global satellite communication system occupies an important place in national security and economic development, and currently, the global main satellite communication system comprises a star link system, a GLONASS system, a Galileo system, a Beidou satellite navigation communication system, a GPS, a Beidou and GPS combined global satellite navigation communication system and the like, each large satellite navigation communication system is mainly maintained by directly communicating with a ground station, and the whole system has large instability. With the generation of inter-satellite link technology and the mature application of systems such as GPS, the establishment of a space measurement and control network by using inter-satellite links is a trend of development of various large satellite navigation communication networks.
There are two types of inter-satellite link antennas commonly used in the industry, one is inter-satellite link based on a reflector antenna and the second is inter-satellite link based on a spot beam phased array antenna. The first type of link technology is relatively mature, satellites in a satellite network realize satellite communication link establishment through a reflection antenna, but the link establishment mode adjustment mechanization lacks flexibility, and cannot adapt to a long-term self-running space environment. In contrast, the second inter-satellite link is based on a narrow beam phased array, has the characteristics of high antenna adjustment speed, flexible and efficient link establishment, high precision and the like, and is suitable for the working mode of the inter-satellite link. For China, a large number of ground stations cannot be arranged worldwide at present, so that inter-satellite link technology is greatly developed, distance measurement and communication between satellites are realized through the inter-satellite link technology, and autonomous operation of a satellite navigation communication system is realized.
In the prior art, the inter-satellite ranging and satellite communication performance in a satellite network are optimized through planning of inter-satellite links. The inter-satellite link planning algorithm is the basis of inter-satellite link networking, and the inter-satellite ranging requires that satellites can establish inter-satellite links with other satellites as much as possible, so that more inter-satellite ranging information is obtained, and the accuracy of orbit selection in autonomous navigation is improved; the requirement of communication among satellites requires the satellites to have links with optimal communication performance, and the optimal goal of whole network communication is met. The inter-satellite link planning algorithm in the prior art is a traditional genetic algorithm, wherein the selection of the cross rate and mutation rate parameters is mostly set by experience, and the algorithm searching speed is low even if feedback information in a structural network is utilized. Meanwhile, the conventional genetic algorithm has poor local searching capability, low searching probability in the later period of genetic evolution, easy generation of the problem of premature convergence, lack of parallelism capability, and low algorithm computing capability and operation efficiency.
Disclosure of Invention
In order to solve the problems that an inter-satellite link planning algorithm in the prior art lacks parallelism capability, has poor local searching capability and low speed, so that the optimization efficiency and accuracy of the algorithm are low, the invention provides a satellite link optimization method based on a genetic ant colony simulated annealing hybrid algorithm, and the optimization efficiency and accuracy are effectively improved through hybrid optimization formed by fusing the genetic algorithm, the ant colony algorithm and the simulated annealing algorithm, so that an accurate global optimal solution can be efficiently found.
The specific scheme is as follows:
a satellite communication link optimization method based on a genetic ant colony simulated annealing hybrid algorithm comprises the following steps:
s1: determining a link planning model: generating a visual matrix V, compressing the visual matrix V into a matrix A, and splitting the visual matrix A to generate N initialization link distribution matrices R which are respectively R1= { R 11 ，R 12 ，…，R 1k }、R2＝{R 21 ，R 22 ，…，R 2k }、…、RN＝{R N1 ，R N2 ，…，R NK Setting different time slot numbers; wherein K is the total number of slots in one link allocation period, RNK = [ r ] i，j，k ] nxn The link allocation matrix, r, called slot k i，j，k =1 means that satellite i establishes an inter-satellite link with satellite j in slot k;
s2: the distributed parallelism mechanism of the ant colony algorithm and the positive feedback mechanism of the ant colony pheromone are utilized to screen out a better matrix and the number of time slots: firstly, each link distribution matrix R is regarded as an ant node in an ant colony, a plurality of ant nodes perform parallel optimizing operation, then the ant nodes are input into a fitness function F, an adaptation value is obtained according to the fitness function to set pheromones and generate a corresponding pheromone list, the positive feedback mechanism is that a link distribution matrix with higher pheromone concentration is selected as the preferred matrix according to a proportion m, m=N/s is rounded downwards, and s is iteration times;
s3, genetic selection: then, carrying out genetic selection on the link distribution matrix screened in the S2 by utilizing a genetic ant colony simulated annealing algorithm to generate a link distribution matrix with higher pheromone concentration as a better solution matrix, and selecting a genetic selection direction and a selection factor;
s4, genetic crossover: performing genetic cross operation on the link distribution matrix generated in the step S3, setting different cross modes, setting pheromones according to the adaptability of the cross solution matrix, receiving genetic cross adjustment according to a simulated annealing acceptance criterion, selecting a cross mode with higher pheromone concentration as a cross direction according to the pheromone concentration ranking, and generating a better link distribution matrix as a better solution matrix;
s5, genetic variation: performing genetic variation operation on the link distribution matrix generated in the step S4, setting a crossover operator to realize random variation of the transverse and longitudinal positions by combining an ant colony algorithm, and outputting a local optimal solution, namely an optimal link distribution matrix;
s6, receiving the cross variation in S5 according to the receiving criterion of simulated annealing to adjust the local optimal solution to trend to the global optimal solution;
s7, repeating the steps S3-S6 for loop iteration, and outputting a global optimal solution, namely a global optimal link allocation matrix, after the loop iteration reaches the maximum iteration number of the algorithm.
Step S1 comprises:
s11, determining a link planning model: generating a visual matrix A and the number of time slots K in a link allocation period 1 ，K 2 ，…，K n ；
The link planning model is as follows:
wherein TS stands for satellite, GS stands for ground station, d (TS- > GS) stands for transmission communication time delay between satellite and ground station;
the constraint conditions of the model are as follows:
s12, splitting the visual matrix A into n pieces of K i Matrix R of individual link allocation matrices i ＝{R 11 ，R 12 ，…，R 1ki I=1, 2,3, …, n; wherein the link allocation matrix satisfies a=r 11 +R 12 +…+R 1ki +α, where α is the remainder.
Step S2 includes:
s21, taking the link distribution matrix generated in S1 as an ant node in the ant colony, inputting the ant node into the fitness function F,adjusting the pheromone according to the adaptive value of the function F to generate a corresponding pheromone list; j=a (i, k) means that satellite i establishes a link with satellite j at time slot k;
s22, screening out a link distribution matrix with higher pheromone concentration according to the pheromone list generated in S21 and the proportion m, wherein m=s/N, and S is the iteration number;
step S3 includes:
s31, genetic selection is carried out on the link distribution matrixes screened in the S2 by using a roulette selection method, wherein the selection probability of each link distribution matrix is proportional to the fitness value of each link distribution matrix,
according to the equationSelecting; f is fitness;
s32, calculating the sum F of the fitness values of the t-th generation link matrix sum (R i ) And their respective Pi;
s33, generating a random number rand (i) by using a random function rand (), and letting sum=rand (i) ×f sum ；
S34, obtainingThe k-th individual is selected if k is the smallest;
s35, performing operations of S33 and S34 for N times to obtain N individuals and generate a t+1st generation link matrix R t+1 ；
At the initial time, the information amount of random numbers generated in each optimization direction is equal, and τ is set ij (0) C (C is a constant); link allocation matrix k (k=1, 2, …, s); in the moving process, the moving direction is determined according to the concentration of the pheromone in each path direction, p k ij (t) represents the rotation of ant k from position i at time t after one iterationThe probability of moving to position j, namely:
wherein allowed k = { rand (1), rand (2), …, rand (i) …, rand(s) } represents the random number direction that ant k next allows to select, and the pheromone adjustment formula in each path direction is as follows:
τ ij (t+n)＝ρ·τ ij (t)+ΔT ij
wherein Deltaτ ij k Represents the amount of information, deltaT, left on the direction path ij in the present iteration for the kth ant ij Represents the pheromone increment on path ij in this iteration, F (R k ) And (3) representing the fitness value of the current iteration star-link matrix, and taking a constant Q, generating the following model:
s36, iterating for S times, wherein s=N×m, generating S t+1 generation link matrixes, setting pheromone according to the fitness value, and carrying out secondary screening according to the concentration of the pheromone as required.
Step S4 includes:
s41, inputting the link distribution matrix R and the dynamic cross operator pc generated in the S3, and formulating a cross strategy;
s42 using round (rand. P) y ) Operation acquisition 0-p y A random integer value in between;
s43, randomly determining one position in the link distribution matrix to start exchanging function values, generating a new link distribution matrix, receiving genetic cross adjustment according to a simulated annealing acceptance criterion, setting corresponding pheromones according to matrix fitness values, acquiring pheromone concentrations in different genetic cross modes, and screening out an optimal genetic cross mode according to the pheromone concentration ranking.
Step S5 includes:
s51, inputting the link allocation matrix and the mutation operator pm generated in S4, setting different crossover operators, and using round (rand. P) x ) And round (rand. P) y ) The operation achieves random variation at the lateral and longitudinal positions.
Step S6, using simulated annealing receiving criteria to receive genetic variation adjustment: randomly searching a global optimal solution of the objective function from a certain initial local optimal solution in the local optimizing process;
s61: generating an initial optimal solution a and calculating a fitness function F (a);
s62: the disturbance generates a new solution a ', and an fitness function value F (a') is calculated;
s63: calculating Δf=f (a) -F (a '), accepting the new solution a=a ', F (a) =f (a ') when Δf < 0; otherwise, accept the new solution according to Metropolis criterion.
Step S7 includes:
and S71, judging whether the maximum iteration times are reached, if so, ending, otherwise, modifying the temporary solution delta E with a small amplitude, continuing to transfer to S3-S6 for iterative optimization, and finally generating a link planning matrix which enables the average communication time delay of the satellite network to be minimum as an output global optimal solution.
Compared with the prior art, the invention has the beneficial effects that: the invention provides a satellite communication link optimization method based on a genetic ant colony simulated annealing hybrid algorithm, which comprises the following steps of S1: determining a link planning model: generating a visual matrix A and splitting the visual matrix A to generate N initialization link distribution matrixes R; s2: performing parallel optimizing operation; the distributed parallelism mechanism of the ant colony algorithm is introduced, so that the diversity of knowledge is increased, and the operation capability of the algorithm is improved; the positive feedback mechanism of the ant colony pheromone is introduced, namely, the direction with higher pheromone concentration is selected, so that the genetic selection, crossing and mutation operations are more accurate and efficient; s3, the link distribution matrix performs genetic selection by using a genetic ant colony simulated annealing algorithm to generate the link distribution matrix and select an optimal genetic selection direction, the algorithm enables a genetic crossing algorithm to get rid of risks of being trapped in a local optimal trap, and meanwhile the defect of low searching efficiency in the later period of genetic algorithm evolution is overcome; s4, performing genetic crossing operation and determining a crossing direction; s5, performing genetic variation operation, setting a crossover operator to realize random variation of the transverse and longitudinal positions and outputting an optimal solution, namely an optimal link distribution matrix; s6, enabling the local optimal solution to trend to the global optimal solution according to an acceptance criterion of simulated annealing; the algorithm is fused with a simulated annealing algorithm, so that the algorithm gets rid of the risk of being trapped in a local optimal trap, and the defect of low searching efficiency in the later evolutionary stage of the genetic algorithm in S4 and S5 is overcome; s7, performing loop iteration to output a global optimal solution. According to the invention, the convergence rate of the genetic algorithm is improved through the fusion of the ant colony algorithm and the genetic algorithm and the local optimizing capability of the ant colony algorithm, meanwhile, whether the crossover and mutation results are accepted or not is solved through the simulated annealing acceptance, the genetic ant colony algorithm is further optimized through the addition of the simulated annealing algorithm, the trap of the local optimal solution is prevented from being trapped, the trap can be converged towards the global optimal solution direction, and meanwhile, the simulated annealing criterion is fused with the genetic and ant colony algorithm, so that the optimal approximate solution can be obtained in a shorter time, and the optimizing efficiency is further improved. In conclusion, the genetic, ant colony and simulated annealing criteria are fused, so that the hybrid algorithm has high efficiency and strong operation capability; meanwhile, compared with the conventional algorithm, the hybrid algorithm has the advantages that a parallel mechanism is added, synchronous parallel operation can be conveniently performed, the optimization speed is increased, the algorithm parameter setting is more reasonable, the selection of crossover factors and mutation factors is more reasonable, good individuals are reserved while the diversity of the population is maintained, in conclusion, the algorithm organically integrates the genetic algorithm, the ant colony algorithm and the simulated annealing algorithm, and the three algorithms are mutually complementary and are synthesized into a more efficient and accurate optimization algorithm.
Drawings
Fig. 1 is a flowchart of a satellite communication link optimization method based on a genetic ant colony simulated annealing hybrid algorithm.
FIG. 2 is a flow chart of a simulated annealing acceptance criteria in the present invention.
Fig. 3 is a schematic diagram of the present invention from local optimal solution to global optimal solution.
Fig. 4 shows a compression matrix a according to an embodiment of the present invention.
Detailed Description
The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to fig. 1 to 4.
The invention provides a satellite communication link optimization method based on a genetic ant colony simulated annealing hybrid algorithm, which is shown in fig. 1, and comprises the following steps:
s1, establishing a satellite network transmission link planning model through analysis, setting a proper objective function, and generating an initialization solution.
Step S1 comprises:
s11, establishing a link planning model, wherein an objective function is as follows:
satellite-ground integrated network communication transmission delay:
where d (TS- > GS) represents the transmission communication delay between the satellite and the ground satellite and between the satellite and the ground station, which is customized, the value of which is only related to the choice of the ground satellite, here we set it to a fixed value; and the number of links between satellites is determined by the average number of different links of the satellite in one period.
The transmission link optimization model is established by analysis as follows:
the model is established by taking inter-satellite visibility, inter-satellite link establishment quantity and the like as constraint conditions and taking average communication time delay as an optimization target, and by solving the model, the minimum time delay of average transmission communication of a satellite network can be obtained, so that the satellite-ground integrated transmission performance is optimized;
s12, using the given visual matrix V and the time slot number K in one link allocation period i (K i Is variable, different slot numbers may bring different optimization effects), we get the link planning matrix R, r= { R 1 、R 2 、…、R Ki ' Link allocation matrix R j Is a 0-1 matrix, each row has only one element of 1, each link is allocated with matrix R j Representing the link establishment relation among satellites in the time slot, wherein the initial matrix must meet the s.t. constraint condition, namely, the visibility constraint, the inter-satellite link establishment quantity constraint and the symmetry constraint;
to save memory space, we compress it into matrix a, which is shown in fig. 4: the row in matrix a represents satellite i, the matrix array represents time slot k, and element a (i, k) =j in compressed matrix a represents that satellite i establishes a link with satellite j when time slot k;
s2, performing preliminary distributed parallel selection operation on the initialized link distribution matrix generated in the step S1 by utilizing an ant colony positive feedback mechanism, and reserving a better solution;
step S2 includes:
s21, taking the initialized link distribution matrix generated in S1 as an ant node in the ant colony, inputting the ant node into the fitness function F,adjusting the pheromone according to the adaptive value of the function F to generate a corresponding pheromone list;
s22, screening a link distribution matrix with higher pheromone concentration according to the pheromone list generated in S21 and the proportion m, wherein m=s/N, and determining the screening proportion by S, thereby screening a better solution group at the position;
s3, further optimizing and screening the solution generated in the step S2 by utilizing the selection operation of the mixing algorithm:
step S3 includes:
s31, for the link allocation matrices screened in S2, we use roulette selection (roulette wheel selection), where the probability of selection of each link allocation matrix is proportional to its fitness value,
according to the equationSelecting;
s32, calculating the sum F of the fitness values of the t-th generation link matrix sum (R i ) And their respective Pi;
s33, generating S different random numbers { … rand (i) … } by using a random function rand (, so that sum=rand (i) ×F) sum ；
S34, obtainingMinimum k, then->The kth individual is selected;
s35, performing operations of S33 and S34 for N times to obtain N individuals and generate a t+1st generation link matrix R t+1 The method comprises the steps of carrying out a first treatment on the surface of the Iterating for s times, wherein s=n×m, generating s t+1 generation link matrixes, updating the pheromone concentration under different random numbers according to the fitness value of each link matrix, and setting the pheromone quantity for each link matrix, wherein the adjustment mode of the link matrix pheromone is consistent with the adjustment mode of the directional path pheromone;
at the initial time, the information amounts in the respective optimization directions (generated random numbers) are equal, and τ is set ij (0) C (C is a constant); during the moving process of ants (link distribution matrix) k (k=1, 2, …, s), the moving direction is determined according to the pheromone concentration in each path direction (different random numbers correspond to different paths), and p k ij (t) represents the probability that ant k transitions from position i to position j at time t after one iteration, i.e
Wherein allowed k = { rand (1), rand (2), …, rand (i) …, rand(s) } represents the follow-up that ant k next allows selectionThe number direction and the pheromone adjustment formula in each path direction are as follows:
τ ij (t+n)＝ρ·τ ij (t)+Δτ ij
wherein Deltaτ ij k Represents the amount of information, Δτ, left on the direction path ij in the present iteration for the kth ant ij Represents the pheromone increment on path ij in this iteration, F (R k ) And (3) representing the fitness value of the current iteration star-link matrix, and taking a constant Q, generating the following model:
s36, selecting a better link distribution matrix according to the pheromone concentration ranking, and recording a better genetic selection direction.
S4, performing mixed genetic crossover operation on the link distribution matrix generated in the S3, obtaining a better solution and selecting a better genetic crossover direction;
step S4 includes:
s41, inputting the link distribution matrix R and the crossover operator pc (dynamic crossover operator, selecting a proper crossover operator according to the concentration of the pheromone, and formulating a proper crossover strategy) generated in S3
S42 using round (rand. P) y ) Operation acquisition 0-p y Random integer values between
S43, randomly determining a position to start exchanging function values, generating more link distribution matrixes, receiving genetic cross adjustment according to a simulated annealing acceptance criterion, adjusting corresponding pheromones (including setting adjustment information quantity for the link distribution matrixes and setting adjustment information quantity for different cross path directions) according to matrix fitness values, selecting different genetic cross modes, screening out a better genetic cross mode according to the pheromone concentration ranking, and simultaneously screening out a better solution matrix according to the pheromone concentration ranking, wherein the information quantity adjustment mode is consistent with that in S3, and the improved genetic ant colony hybrid cross mode increases cross diversity, is beneficial to jumping out of a local optimal solution and improves global optimality.
Step S5 and step S6 include:
inputting the link distribution matrix screened in the S4, performing mixed genetic variation operation to obtain more solutions, and further obtaining better solutions;
s51, inputting the link allocation matrix and the mutation operator pm generated in S4, setting different crossover operators, and using round (rand. P) x ) And round (rand. P) y ) The random variation at the transverse and longitudinal positions is realized by operation, so that the variation diversity is increased, and the local optimal solution can be more easily jumped out;
s61, using a simulated annealing acceptance criterion to accept genetic variation adjustment, namely enabling the hybrid algorithm to jump out of local optimum in a probabilistic way and finally trend to a global optimum solution;
s62, carrying out fitness evaluation on the link distribution matrix generated in S61, setting and adjusting pheromones, and selecting a more appropriate variation mode and a more optimal link distribution matrix according to the pheromone ranking;
s7, judging according to the iteration times and the cycle ending conditions:
in step S7, S71, determining whether the maximum iteration number is reached, if so, ending, otherwise continuing to operate in steps S2, S3, S4, S5 and S6, generating a better link planning matrix through continuous iterative optimization, so that the average communication delay of the satellite network is minimum, and finally outputting a global optimal solution.
While the invention has been described with reference to certain preferred embodiments, it will be understood by those skilled in the art that various changes and substitutions of equivalents may be made and equivalents will be apparent to those skilled in the art without departing from the scope of the invention. Therefore, the protection scope of the invention is subject to the protection scope of the claims.
Claims (8)
1. A satellite communication link optimization method based on a genetic ant colony simulated annealing hybrid algorithm is characterized by comprising the following steps:
s1: determining a link planning model: generating a visual matrix V, compressing the visual matrix V into a matrix A, and splitting the visual matrix A to generate N initialization link distribution matrices R which are respectively R1= { R 11 ，R 12 ，…，R 1K }、R2＝{R 21 ，R 22 ，…，R 2K }、…、RN＝{R N1 ，R N2 ，…，R NK Setting different time slot numbers; wherein K is the total number of time slots in one link allocation period, R NK ＝[r i，j，k ] nxn The link allocation matrix, r, called slot k i，j，k =1 means that satellite i establishes an inter-satellite link with satellite j in slot k;
s2: the distributed parallelism mechanism of the ant colony algorithm and the positive feedback mechanism of the ant colony pheromone are utilized to screen out a better matrix and the number of time slots: firstly, each link distribution matrix R is regarded as an ant node in an ant colony, a plurality of ant nodes perform parallel optimizing operation, then the ant nodes are input into a fitness function F, an adaptation value is obtained according to the fitness function to set pheromones and generate a corresponding pheromone list, the positive feedback mechanism is to select a link distribution matrix with higher pheromone concentration as the preferred matrix according to a proportion m, whereins is the iteration number;
s3, genetic selection: then, carrying out genetic selection on the link distribution matrix screened in the S2 by utilizing a genetic and simulated annealing algorithm to generate a link distribution matrix with higher pheromone concentration as a better solution matrix, and selecting a genetic selection direction and a selection factor;
s4, genetic crossover: performing genetic cross operation on the link distribution matrix generated in the step S3, setting different cross modes, setting pheromones according to the adaptability of the cross solution matrix, receiving genetic cross adjustment according to a simulated annealing acceptance criterion, selecting a cross mode with higher pheromone concentration as a cross direction according to the pheromone concentration ranking, and generating a better link distribution matrix as a better solution matrix;
s5, genetic variation: performing genetic variation operation on the link distribution matrix generated in the step S4, setting a crossover operator to realize random variation of the transverse and longitudinal positions by combining an ant colony algorithm, and outputting a local optimal solution, namely a local optimal link distribution matrix;
s6, receiving the cross variation in S5 according to the receiving criterion of simulated annealing to adjust the local optimal solution to trend to the global optimal solution;
s7, loop iteration: repeating the steps S3-S6 for loop iteration, and outputting a global optimal solution, namely a global optimal link distribution matrix, after the loop iteration reaches the maximum iteration times of the algorithm.
2. The method for simulated annealing hybrid optimization based on genetic ant colony as claimed in claim 1, wherein step S1 comprises:
s11, determining a link planning model: generating a visual matrix A and the number of time slots K in a link allocation period 1 ，K 2 ，…，K n ；
The link planning model is as follows:
wherein TS stands for satellite, GS stands for ground station, d (TS- > GS) stands for transmission communication time delay between satellite and ground station;
the constraint conditions of the model are as follows:
s12, splitting the visual matrix A into n pieces of K i Matrix R of individual link allocation matrices i ＝{R 11 ，R 12 ，…，R 1ki I=1, 2,3, …, n; wherein the link allocation matrix satisfies a=r 11 +R 12 +…+R 1ki +α, where αIs the remainder.
3. The hybrid optimization algorithm of simulated annealing based on genetic ant colony as claimed in claim 1, wherein step S2 comprises:
s21, taking the link distribution matrix generated in S1 as an ant node in the ant colony, inputting the ant node into the fitness function F,adjusting the pheromone according to the adaptive value of the function F to generate a corresponding pheromone list; j=a (i, k) means that satellite i establishes a link with satellite j at time slot k;
s22, screening out a link distribution matrix with higher pheromone concentration according to the pheromone list generated in S21 and the proportion m, wherein m=s/N, and S is the iteration number.
4. The method for optimizing a satellite communication link based on the hybrid genetic ant colony simulated annealing algorithm of claim 1, wherein step S3 comprises:
s31, genetic selection is carried out on the link distribution matrixes screened in the S2 by using a roulette selection method, wherein the selection probability of each link distribution matrix is proportional to the fitness value of each link distribution matrix,
according to the equationSelecting; f is fitness;
s32, calculating the sum F of the fitness values of the t-th generation link matrix sum (R i ) And their respective Pi;
s33, generating a random number rand (i) by using a random function rand (), and letting sum=rand (i) ×f sum ；
S34, obtainingThe k-th individual is selected if k is the smallest;
s35, performing operations of S33 and S34 for N times to obtain N individuals and generate t+1st generation link momentArray R t+1 ；
At the initial time, the information amount of random numbers generated in each optimization direction is equal, and τ is set ij (0) C (C is a constant); link allocation matrix k (k=1, 2, …, s); in the moving process, the moving direction is determined according to the concentration of the pheromone in each path direction, p k ij (t) represents the probability that ant k transitions from position i to position j at time t after one iteration, namely:
wherein allowed k = { rand (1), rand (2), …, rand (i) …, rand(s) } represents the random number direction that ant k next allows to select, and the pheromone adjustment formula in each path direction is as follows:
τ ij (t+n)＝ρ·τ ij (t)+Δτ ij
wherein Deltaτ ij k Represents the amount of information, Δτ, left on the direction path ij in the present iteration for the kth ant ij Represents the pheromone increment on path ij in this iteration, F (R k ) And (3) representing the fitness value of the current iteration star-link matrix, and taking a constant Q, generating the following model:
s36, iterating for S times, wherein s=N×m, generating S t+1 generation link matrixes, setting pheromone according to the fitness value, and carrying out secondary screening according to the concentration of the pheromone as required.
5. The method for optimizing a satellite communication link based on the hybrid genetic ant colony simulated annealing algorithm of claim 1, wherein step S4 comprises:
s41, inputting the link distribution matrix R and the dynamic cross operator pc generated in the S3, and formulating a cross strategy;
s42 using round (rand. P) y ) Operation acquisition 0-p y A random integer value in between;
s43, randomly determining one position in the link distribution matrix to start exchanging function values, generating a new link distribution matrix, receiving genetic cross adjustment according to a simulated annealing acceptance criterion, setting corresponding pheromones according to matrix fitness values, acquiring pheromone concentrations in different genetic cross modes, and screening out an optimal genetic cross mode according to the pheromone concentration ranking.
6. The method for optimizing a satellite communication link based on the genetic ant colony simulated annealing algorithm as claimed in claim 1, wherein step S5 comprises:
s51, inputting the link allocation matrix and the mutation operator pm generated in S4, setting different crossover operators, and using round (rand. P) x ) And round (rand. P) y ) The operation achieves random variation at the lateral and longitudinal positions.
7. The method for optimizing a satellite communication link based on a simulated annealing algorithm of claim 1, wherein step S6 uses simulated annealing acceptance criteria to accept genetic variation adjustments: randomly searching a global optimal solution of the objective function from a certain initial local optimal solution in the local optimizing process;
s61: generating an initial optimal solution a and calculating a fitness function F (a);
s62: the disturbance generates a new solution a ', and an fitness function value F (a') is calculated;
s63: calculating Δf=f (a) -F (a '), accepting the new solution a=a ', F (a) =f (a ') when Δf < 0; otherwise, accept the new solution according to Metropolis criterion.
8. The method for optimizing a satellite communication link based on the hybrid genetic ant colony simulated annealing algorithm of claim 1, wherein step S7 comprises:
and S71, judging whether the maximum iteration times are reached, if so, ending, otherwise, modifying the temporary solution delta E with a small amplitude, continuing to transfer to S3-S6 for iterative optimization, and finally generating a link planning matrix which enables the average communication time delay of the satellite network to be minimum as an output global optimal solution.
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