CN111176807B - Multi-star collaborative task planning method - Google Patents
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Abstract
The invention provides a multi-star collaborative task planning method, which comprises the following steps: step 1, setting coding rules for multi-star collaborative tasks; step 2, searching a suboptimal solution of the multi-star collaborative task by adopting an ant colony algorithm based on a coding rule; and 3, when the ant colony algorithm searches for the local optimum, using the suboptimal solution quickly searched by the ant colony algorithm as an initial solution of the simulated annealing algorithm, continuing to search by adopting the simulated annealing algorithm until the iteration step number is reached or an optimization target is reached, outputting a result, and determining an execution satellite corresponding to each task. According to the multi-star collaborative task planning method, the sub-optimal solution rapidly searched by the ant colony algorithm is used as the initial solution of the simulated annealing algorithm, so that the simulated annealing algorithm can start optimizing the problem at a higher starting point, the defects that the searching speed is low in the early stage of simulated annealing and the ant colony algorithm is easy to fall into the optimal are overcome, the optimal solution can be found while the optimizing speed is improved, and the task planning is more optimal.
Description
Technical Field
The invention belongs to the technical field of deep space exploration, relates to a satellite task planning method, and in particular relates to a multi-satellite collaborative task planning method.
Background
Earth observation satellites (Earth Observing Satellite, EOS) mainly acquire earth surface related information from space through satellite-borne sensors, and have the unique advantages of long running time, wide coverage range and the like. At present, earth observation satellites play an important role in various fields such as disaster prevention and control, weather forecast, environmental protection, military reconnaissance, modern agriculture, earth mapping and the like, and bring considerable social and economic benefits to human beings. At present, the rapid development of the earth observation field brings unprecedented opportunities and challenges, and the dependence of users on remote sensing information is increasingly deepened, so that observation targets are increasingly diversified, the observation range is wider, and the earth observation satellite is expected to complete more complex tasks. However, satellite resources for earth observation are still very limited with respect to the complex and huge number of observation task requirements. How to arrange earth observation satellite resources comprehensively, achieve the purposes of fully and reasonably utilizing space resources and maximally meeting user requirements, and become a problem to be solved urgently in the field of earth observation at present.
In the existing satellite mission planning research, most of the imaging satellite mission planning problems are modeled as optimization problems, and then various optimization algorithms are adopted for solving.
Each optimization algorithm has advantages and disadvantages when solving the multi-star collaborative task planning problem. The genetic algorithm has strong global searching capability, but has slow searching speed and poor local searching capability, and can often take more time when searching the global optimal solution. For the ant colony algorithm, the ant colony algorithm utilizes the positive feedback characteristic of the pheromone, and can search for a better solution in a faster time, but the algorithm is easy to fall into precocity, namely when the pheromone concentration of a certain path is obviously higher than that of other paths, the algorithm can converge on the path too fast. The simulated annealing algorithm has the defects of strong local searching capability, difficult sinking into a local optimal solution, slow convergence speed, long execution time, related algorithm performance to an initial value, sensitive parameters and the like. Particle swarm algorithms, though, have the ability to approach the optimal solution faster. However, since all particles fly toward the direction of the optimal solution, all particles tend to be identical (lose diversity) so that the later convergence speed is obviously slowed down, and when the algorithm converges to a certain precision, the optimization cannot be continued, and the achievable precision is not high.
Disclosure of Invention
In order to overcome the defects of the prior art, the inventor performs intensive research and provides a multi-star collaborative task planning method combining an ant colony algorithm and a simulated annealing algorithm, aiming at the problems that the early search speed of the ant colony algorithm is high but the early search speed is easy to fall into a premature stagnation state, the local search capacity of the simulated annealing algorithm is high but the early search speed is low, and the like.
The invention aims to provide the following technical scheme:
the invention provides a multi-star collaborative task planning method, which comprises the following steps:
step 1, setting coding rules for multi-star collaborative tasks;
step 2, searching a suboptimal solution of the multi-star collaborative task by adopting an ant colony algorithm based on a coding rule;
and 3, when the ant colony algorithm searches for the local optimum, using the suboptimal solution quickly searched by the ant colony algorithm as an initial solution of the simulated annealing algorithm, continuing to search by adopting the simulated annealing algorithm until the iteration step number is reached or an optimization target is reached, outputting a result, and determining an execution satellite corresponding to each task.
Further, the digital coding rule is: according to the number N of tasks t Setting digital codes with equal length, wherein each element on the codes is defined as a gene, and each gene is provided with a serial number of a corresponding task; the value of the gene is the corresponding time window number; the time window of the task corresponds to the satellite, and the satellite number selected by the task corresponding to the gene is determined after the time window number is determined;
the gene value is 0, and the corresponding task is not executed; when the gene value is not 0, the constraint conditions of the time window constraint planning window, the maneuvering constraint planning window, the resource constraint planning window, the storage constraint planning window and the like are needed to be judged, wherein the constraint windows are all of a length of N t 0-1 binary encoding of (2); 0 represents that the constraint is not satisfied, 1 represents that the constraint is satisfied, and when all planning windows corresponding to the task j are 1, the task j can be executed.
Further, step 2 may be implemented by steps comprising:
step 2.1, initial ant colony algorithm parameters and pheromone model: defining probability information of the tasks arranged in a certain time window as pheromones, respectively constructing the pheromones aiming at each task, and initializing the pheromones;
step 2.2, constructing a state transition matrix: the time window conflict factors are considered, the pheromones are corrected, and the probability that any time window is selected by the corrected task is obtained, namely the state transition matrix;
step 2.3, after calculating the probability of the task to all the windows, selecting a time window according to a roulette algorithm, sequentially selecting a time window for each task to construct a set of task schemes, further traversing m ants sequentially, and constructing corresponding task schemes for m ants;
step 2.4, sorting ants from small to large according to the comprehensive profit of the solved task;
step 2.5, updating the pheromone according to the sorting and updating strategies; the sorting strategy adopts a basic sorting ant colony algorithm strategy, namely that only ants ranked in the top omega position are allowed to release pheromone in each cycle;
and 2.6, circulating according to the steps 2.2 to 2.5, judging whether the ant colony algorithm falls into the local optimum, if not, turning to the step 2.2, otherwise, carrying out the simulated annealing algorithm.
Further, step 3 may be implemented by steps comprising:
step 3.1, outputting a suboptimal solution which is rapidly searched by the ant colony algorithm and is used as an initial solution of the simulated annealing algorithm;
step 3.2, initializing the initial temperature T of the simulated annealing parameter 0 ；
Step 3.3, disturbing the digital code to generate a new solution;
step 3.4, calculating a comprehensive objective function difference delta t corresponding to the old solution and the new solution;
step 3.5, judging whether the new solution is accepted;
step 3.6, when the new solution is confirmed to be accepted, replacing the current solution with the new solution, which is realized by only using a transformation part corresponding to the new solution generation in the current solution and correcting the objective function value; at this time, the current solution realizes one iteration, and the next test can be started on the basis; when the new solution is judged to be abandoned, continuing the next round of disturbance test on the basis of the original current solution;
step 3.7, reducing the current temperature once per disturbance test run, t=t 0 * q, q is the cooling coefficient;
step 3.8, judging whether an end condition is met, if not, turning to step 3.3, and if yes, turning to the next step;
and 3.9, outputting the current solution, obtaining the time window number selected by each task, and determining the execution satellite corresponding to each task.
The multi-star collaborative task planning method provided by the invention has the beneficial technical effects that:
the invention provides a multi-star collaborative task planning method combining an ant colony algorithm and a simulated annealing algorithm, which uses a suboptimal solution quickly searched by the ant colony algorithm as an initial solution of the simulated annealing algorithm, so that the simulated annealing algorithm can start optimizing a problem at a higher starting point, the defects that the searching speed is low in the early stage of simulated annealing and the ant colony algorithm is easy to fall into the optimal are overcome, the optimizing speed is improved, and meanwhile, a more optimal solution can be found, and the task planning is more optimal.
Drawings
Fig. 1 shows a flow chart of a multi-star collaborative task planning method in a preferred embodiment of the invention.
Detailed Description
The invention is further described in detail below by means of the figures and examples. The features and advantages of the present invention will become more apparent from the description.
As shown in fig. 1, the invention provides a multi-star collaborative task planning method, which comprises the following steps:
step 1, setting coding rules for multi-star collaborative tasks;
step 2, searching a suboptimal solution of the multi-star collaborative task by adopting an ant colony algorithm based on a coding rule;
and 3, when the ant colony algorithm searches for the local optimum, using the suboptimal solution quickly searched by the ant colony algorithm as an initial solution of the simulated annealing algorithm, continuing to search by adopting the simulated annealing algorithm until the iteration step number is reached or an optimization target is reached, outputting a result, and determining an execution satellite corresponding to each task.
In step 1, the invention provides a general digital code in multi-star collaborative task planning, which can be applied to various optimization algorithms, such as genetic algorithm, ant colony algorithm, simulated annealing algorithm, particle swarm algorithm and the like, when solving the problem of multi-star collaborative task planning.
The digital coding rule is: according to the number N of tasks t Setting digital codes with equal length, wherein each element on the codes is defined as a gene, and each gene is provided with a serial number of a corresponding task; the value of the gene is the corresponding time window number; the time window of the task corresponds to the satellite, and the satellite number selected by the task corresponding to the gene is determined after the time window number is determined.
The gene value is 0 (the time window selected by the task conflicts with the time windows selected by other tasks, and the comprehensive benefit F of the task is smaller), and the corresponding task is not executed; when the gene value is not 0, the constraint conditions of the time window constraint planning window, the maneuvering constraint planning window, the resource constraint planning window, the storage constraint planning window and the like are needed to be judged, wherein the constraint windows are all of a length of N t 0-1 binary encoding of (2); 0 represents that the constraint is not satisfied, 1 represents that the constraint is satisfied, and when all planning windows corresponding to the task j are 1, the task j can be executed.
Wherein, "the value of the gene thereof is the corresponding time window number" means: each gene, i.e., each task, may correspond to a plurality of time windows, each time window being numbered, the value of the gene may be selected to be any number of time windows capable of performing the task.
The inventor knows that the ant colony algorithm firstly generates an initial population through random selection, sequences according to the path fitness of different individual selection in the initial population, and updates the pheromone by selecting paths with high fitness proportionally.
The implementation step of the simulated annealing algorithm is to encode the solution space of the actual problem, randomly generate an initial solution, then generate a new solution by a generating function, further construct an adaptability function according to the objective function of the required optimization problem, judge whether to accept the new solution, if so, accept the new solution S' as a new current solution S (T is the current temperature), then generate a new solution, judge again, and finally meet the ending condition and converge to a better solution. However, the algorithm has the defects of low convergence rate, long execution time, correlation of algorithm performance with an initial value, sensitivity of parameters and the like.
For this purpose, the ant colony algorithm is applied in combination with the simulated annealing algorithm to the multi-star mission plan. The condition of the simulated annealing algorithm is that when the ant colony algorithm is adopted to search for the local optimum, the simulated annealing algorithm is adopted to search.
The aim of the multi-star collaborative task planning problem is as follows: let N be s Satellite particles N t How to divide N into tasks t Reasonable allocation of individual tasks to N in a defined scheduling time s And the satellite can complete as many tasks as possible with as few resources as possible under the condition of meeting satellite constraint, so that the planned target income is maximized.
The algorithm flow diagrams involved in step 2 and step 3 are shown in fig. 1.
Objective function 1
Objective function 2
F＝αF 1 +βF 2 Comprehensive objective function
F 1 Maximizing satellite observed target returns, F 2 Minimizing satellite energy consumption，x ij As decision variable, x ij =1 means that task j is observed by satellite i, V j Representing task revenue for task j, R j Indicating the resource consumption required to complete task j. Alpha and beta respectively represent the weight coefficients of the objective function 1 and the objective function 2, and the final result of the planning implementation is to realize the comprehensive benefit Fmax.
In the invention, in the step 2, based on the coding rule, the sub-optimal solution of the multi-star cooperative task is searched by adopting an ant colony algorithm, and the method is implemented by the following steps:
step 2.1, defining probability information of tasks arranged in a certain time window as pheromones by using initial ant colony algorithm parameters and a pheromone model, and respectively constructing the pheromones for each task; suppose task i shares O i Time window, pheromone tau ik Representing the probability of arranging task i in window k; firstly initializing pheromones, and setting the initial probability of selecting each time window for a task to be equalThe sum of probabilities is 1;
initializing pheromones:
k∈[1，Oi]
step 2.2, constructing a state transition matrix: and correcting the pheromone to obtain the probability of selecting the kth time window by the corrected task i, namely the state transition matrix. To increase the convergence rate, a heuristic factor eta is increased ik ，η ik Representing the number of overlapping times, eta, of the kth time window of the task i and other task time windows ik The larger the task i selects the higher the possibility that the time window collides with other tasks, the worse the task is completed;
the probability of task i selecting the kth time window is:
i∈[1,N t ],k∈[1,O i ]
alpha represents a pheromone heuristic factor, and beta represents a task conflict heuristic factor;
step 2.3, after calculating the probability of the task i on all windows, selecting a time window according to a roulette algorithm, sequentially selecting a time window for each task to construct a set of task schemes, further traversing m ants in sequence, and constructing corresponding task schemes for m ants (the determination of each set of task schemes is equivalent to the determination of a digital code set according to the task, the determination of the task digital code and the determination of a solution of the task plan);
step 2.4, sorting ants from small to large according to the comprehensive profit of the solved task;
step 2.5, the pheromone tau is carried out according to the sorting and updating strategy ik Updating. The ordering strategy adopts a basic ordering ant colony algorithm strategy, namely that only ants ranked in the top omega position are allowed to release pheromone in each cycle. The purpose of the pheromone global updating is to strengthen the pheromones of certain paths according to the characteristics of the current optimal solution, so that the pheromones are converged more directionally. The update strategy is as follows:
τ ik (t+1)＝ρτ ik (t)+w r Δτ1≤r≤ω
ρ represents forgetting factor, Δτ represents pheromone increment, w r Representing coefficients ranked in the r-th solution, w the higher the rank is r The larger the value, t represents the number of cycles;
the paths after the other ranking omega bits are multiplied by forgetting factors rho, tau ik (t+1)＝ρτ ik (t)1≤r≤ω；
Step 2.6, circulating according to the steps 2.2 to 2.5, judging whether the ant colony algorithm falls into local optimum, if not, turning to the step 2.2, otherwise, carrying out a simulated annealing algorithm; after the ant colony algorithm iterates for a certain number of times, the pheromone difference among a plurality of time windows is overlarge due to continuous updating of the pheromone, and the search is easy to sink into local optimum.
In the invention, the step 3 is implemented by comprising the following specific steps:
step 3.1, outputting a suboptimal solution which is rapidly searched by the ant colony algorithm and is used as an initial solution of the simulated annealing algorithm;
step 3.2, initializing simulated annealing parameters and an initial temperature T 0 Unlike the basic simulated annealing algorithm, which is set too high, a moderate value is chosen in order to accept a poor solution with a moderate probability (about 20%); preferably, the initial temperature T 0 Taking values in the range of 200-300;
step 3.3, disturbing the digital code to generate a new solution, wherein the specific method is to randomly select a gene position, and conduct gene mutation on the gene position, wherein the mutation value is a value between 1 and the total time window number of the corresponding task of the gene and is not equal to the original value;
step 3.4, calculating a comprehensive objective function difference delta t corresponding to the old solution and the new solution;
step 3.5, judging whether the new solution is accepted or not, wherein the judging basis is an acceptance criterion, and the most commonly used acceptance criterion is a Metropolis criterion: if delta T >0, S 'is accepted as a new current solution S, otherwise, S' is accepted as a new current solution S by probability exp (delta T/T);
step 3.6, when the new solution is confirmed to be accepted, replacing the current solution with the new solution, which is realized by only using a transformation part corresponding to the new solution generation in the current solution and correcting the objective function value; at this time, the current solution realizes one iteration, and the next test can be started on the basis; when the new solution is judged to be abandoned, continuing the next round of disturbance test on the basis of the original current solution;
step 3.7, reducing the current temperature once per disturbance test run, t=t 0 * q, q is the cooling coefficient;
step 3.8, judging whether an end condition (the end condition can be the iteration step number or an optimization target) is met, if not, turning to step 3.3, and if so, turning to the next step;
and 3.9, outputting the current solution, obtaining the time window number selected by each task, and determining the execution satellite corresponding to each task.
Examples1
The simulation verification is carried out on the method, the number of satellites is 3, the number of observation targets is 20, and the number of time windows of each satellite for tasks is shown in table 1.
TABLE 1 number of time windows for each satellite to task
The encoding gene of the final planning result is [3 7 2 14 3 2 12 4 15 11 3 2 11 2 3];
satellite 1 performs tasks 4, 7, 8, 11, 13, and 18, respectively.
Satellite 2 performs tasks 3, 5, 9, 10, 12, 14, 17, and 19, respectively.
Satellite 3 performs tasks 1, 2, 6, 15, 16, and 20, respectively.
The invention has been described above in connection with preferred embodiments, which are, however, exemplary only and for illustrative purposes. On this basis, the invention can be subjected to various substitutions and improvements, and all fall within the protection scope of the invention.
Claims (9)
1. The multi-star collaborative task planning method is characterized by comprising the following steps of:
step 1, setting coding rules for multi-star collaborative tasks;
step 2, searching a suboptimal solution of the multi-star collaborative task by adopting an ant colony algorithm based on a coding rule;
step 3, when the ant colony algorithm searches for the local optimum, using the sub-optimal solution quickly searched by the ant colony algorithm as an initial solution of the simulated annealing algorithm, continuing to search by adopting the simulated annealing algorithm until the iteration step number is reached or an optimization target is reached, outputting a result, and determining an execution satellite corresponding to each task;
step 3 is implemented by steps comprising:
step 3.1, outputting a suboptimal solution which is rapidly searched by the ant colony algorithm and is used as an initial solution of the simulated annealing algorithm;
step 3.2, initializing the initial temperature T of the simulated annealing parameter 0 ；
Step 3.3, disturbing the digital code to generate a new solution;
step 3.4, calculating a comprehensive objective function difference delta t corresponding to the old solution and the new solution;
step 3.5, judging whether the new solution is accepted;
step 3.6, when the new solution is confirmed to be accepted, replacing the current solution with the new solution, which is realized by only using a transformation part corresponding to the new solution generation in the current solution and correcting the objective function value; at this time, the current solution realizes one iteration, and the next test can be started on the basis; when the new solution is judged to be abandoned, continuing the next round of disturbance test on the basis of the original current solution;
step 3.7, reducing the current temperature once per disturbance test run, t=t 0 * q, q is the cooling coefficient;
step 3.8, judging whether an end condition is met, if not, turning to step 3.3, and if yes, turning to the next step;
and 3.9, outputting the current solution, obtaining the time window number selected by each task, and determining the execution satellite corresponding to each task.
2. The multi-star collaborative mission planning method according to claim 1, wherein in step 1, the digital encoding rules are: according to the number N of tasks t Setting digital codes with equal length, wherein each element on the codes is defined as a gene, and each gene is provided with a serial number of a corresponding task; the value of the gene is the corresponding time window number; the time window of the task corresponds to the satellite, and the number of the time window is determined, namely the sanitation selected by the task corresponding to the gene is determinedStar numbering;
the gene value is 0, and the corresponding task is not executed; when the gene value is not 0, the constraint conditions of the time window constraint planning window, the maneuvering constraint planning window, the resource constraint planning window, the storage constraint planning window and the like are needed to be judged, wherein the constraint windows are all of a length of N t 0-1 binary encoding of (2); 0 represents that the constraint is not satisfied, 1 represents that the constraint is satisfied, and when all planning windows corresponding to the task j are 1, the task j can be executed.
3. The multi-star collaborative mission planning method of claim 1, wherein step 2 is implemented by steps comprising:
step 2.1, initial ant colony algorithm parameters and pheromone model: defining probability information of the tasks arranged in a certain time window as pheromones, respectively constructing the pheromones aiming at each task, and initializing the pheromones;
step 2.2, constructing a state transition matrix: the time window conflict factors are considered, the pheromones are corrected, and the probability that any time window is selected by the corrected task is obtained, namely the state transition matrix;
step 2.3, after calculating the probability of the task to all the windows, selecting a time window according to a roulette algorithm, sequentially selecting a time window for each task to construct a set of task schemes, further traversing m ants sequentially, and constructing corresponding task schemes for m ants;
step 2.4, sorting ants from small to large according to the comprehensive profit of the solved task;
step 2.5, updating the pheromone according to the sorting and updating strategies; the sorting strategy adopts a basic sorting ant colony algorithm strategy, namely that only ants ranked in the top omega position are allowed to release pheromone in each cycle;
and 2.6, circulating according to the steps 2.2 to 2.5, judging whether the ant colony algorithm falls into the local optimum, if not, turning to the step 2.2, otherwise, carrying out the simulated annealing algorithm.
4. The multi-star collaborative task planning method according to claim 3, wherein in step 2.1, an pheromone is initialized, and the initial probability of each time window for task selection is equal, and the sum of probabilities is 1:
suppose task i shares O i Time window, pheromone tau ik Representing the probability of arranging task i in window k, initializing pheromones, and the initial probability of selecting each time window by the task is equalThe sum of probabilities is 1.
5. The multi-star collaborative mission planning method according to claim 4, wherein in step 2.2, the specific steps of constructing a state transition matrix are:
an heuristic factor eta is added ik ，η ik Representing the number of overlapping times, eta, of the kth time window of the task i and other task time windows ik The larger the task i selects the higher the possibility that the time window collides with other tasks, the worse the task is completed;
the probability of task i selecting the kth time window is:
where α represents a pheromone heuristic and β represents a task conflict heuristic.
6. The multi-star collaborative mission planning method of claim 5, wherein in step 2.5, the update strategy is as follows:
τ ik (t+1)＝ρτ ik (t)+w r Δτ 1≤r≤ω
ρ represents forgetting factor, Δτ represents pheromone increment, w r Representing coefficients ranked in the r-th solution, w the higher the rank is r The larger the value, t represents the number of cycles;
the paths after the other ranking omega bits are multiplied by forgetting factors rho, tau ik (t+1)＝ρτ ik (t) 1≤r≤ω。
7. The multi-star collaborative mission planning method of claim 1, wherein in step 3.2, an initial temperature T 0 The value is within the range of 200-300.
8. The multi-star collaborative mission planning method according to claim 7, wherein in step 3.3, the perturbation method is: randomly selecting a gene position, carrying out gene mutation on the gene position, wherein the mutation value is a value between 1 and the total time window number of the corresponding task of the gene and is not equal to the original value.
9. The multi-star collaborative mission planning method according to claim 8, wherein in step 3.5, the basis of the determination is an acceptance criterion, and the acceptance criterion is a Metropolis criterion: if deltat >0 then S' is accepted as the new current solution S, otherwise the probability exp (deltat/T) is accepted as the new current solution S.
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