CN107239661B - A kind of remote sensing satellite observation mission planing method - Google Patents
A kind of remote sensing satellite observation mission planing method Download PDFInfo
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Abstract
The present invention provides a kind of remote sensing satellite observation mission planing method, and method includes：When becoming neighborhood tabu search algorithm using dynamic, initial taboo list T is set*, initial taboo list lengthDynamic becomes taboo list lengthAnd initial solution x0, since iterations k=1, by T*、And initial solution x0, the iterative process of tabu search algorithm is performed using modified neighborhood building method, and after stopping criterion is met, export and meet optimal solution x during stopping criterion*；Further according to T*、And x when meeting stopping criterion*, using the iterative process of adjusting type neighborhood method execution tabu search algorithm；After stopping criterion is met, using the optimal solution x to finally exporting*Optimal case of the more excellent solution of to be output one as the planning of remote sensing satellite observation mission is obtained using local search algorithm.The above method is the method for becoming neighborhood tabu search algorithm processing remote sensing satellite observation mission planning problem using dynamic, can improve the operational efficiency of algorithm, expand the hunting zone of solution and algorithm is not easy to be absorbed in cyclic search state.
Description
Technical Field
The invention relates to a computer technology, in particular to a method for processing the remote sensing satellite observation task planning problem by adopting a dynamic variable neighborhood tabu search algorithm.
Background
The remote sensing earth observation task planning problem can be briefly described as a group of remote sensing satellites and a group of observation tasks, wherein the completion of each observation task comprises two activities of data acquisition and data return, as shown in figure 1. Assigning a priority to each observation task; a group of available time windows are arranged between the ground target corresponding to the observation task and the satellite; one reference time range serves as the start-stop time of the mission plan.
In order to make the remote sensing satellite function better, the mission planning technology is particularly critical. The meaning of mission planning is the process of scheduling and resource matching of observation missions to be executed, determining the working time domain, space domain, mode and the like of the satellite and the load thereof, and making a detailed working plan, and aims to drive the satellite resources to execute the missions scientifically and efficiently. The remote sensing earth observation task planning is completed under complex constraint conditions and multiple optimization targets, so that the problem dimension is wide, the optimization space is large, and the approximate optimal solution is obtained by adopting an intelligent algorithm in the prior art. A tabu neighborhood search algorithm adopted in the prior art is a commonly used algorithm for solving the remote sensing earth observation task planning problem.
However, when the tabu neighborhood search algorithm in the prior art is used for processing the remote sensing satellite earth observation task planning problem, the search speed is slow, the remote sensing satellite earth observation task planning problem is easily trapped in a local loop search state, all solution spaces cannot be searched, and the optimal solution of all the solution spaces cannot be found.
Disclosure of Invention
Aiming at the defects in the prior art, the invention provides a method for processing the remote sensing satellite earth observation task planning problem by adopting a dynamic variable neighborhood tabu search algorithm.
In a first aspect, the invention provides a method for planning an observation task of a remote sensing satellite, which is a method for processing the problem of planning the observation task of the remote sensing satellite by adopting a dynamic variable neighborhood tabu search algorithm, and comprises the following steps:
s1, setting an initial tabu table T in the tabu search algorithm when the dynamic variable neighborhood tabu search algorithm is adopted to process the remote sensing satellite observation task planning problem*Length of initial tabu watchDynamic variable neighborhood tabu table lengthAnd the initial solution x0And let the optimal solution equal the initial solution x*＝x0；
S2, starting from the iteration number k equal to 1, according to the initial tabu table T*Dynamic change of tabu lengthAnd the initial solution x0Adopting an improved neighborhood construction method to execute an iteration process of a tabu search algorithm; after the iteration process meets the stop criterion of the tabu search algorithm, outputting the optimal solution x when the stop criterion is met*；
S3, according to the tabu list T*Dynamic change of tabu lengthOptimal solution x when the stop criterion is satisfied in step S2*Adopting an adjustment type neighborhood construction method to execute an iteration process of a tabu search algorithm;
s4, after the iterative process of the step S3 meets the stop criterion of the dynamic neighborhood change tabu search algorithm, the final output optimal solution x is processed*And obtaining a better solution to be output by adopting a local search algorithm as an optimal scheme for the remote sensing satellite observation task planning.
Optionally, step S2 includes:
set taboo tableInitial tabu length Starting from the iteration number k equal to 1, randomly selecting an initial solution x in the feasible solution space0And calculating an objective function value f (x) of the initial solution0)；
Neighborhood solution set N for obtaining initial solution x0 by improved neighborhood method1(x) For N (x) is formed as N1(x) The fitness function to solve is f [ n (x)]According to f [ n (x)]Screening out n*(x)，n*(x) Is a set N1(x) The solution which maximizes the fitness function value is obtained;
the selection rule of the solution is as follows:
if n*(x)∈T*And n is*(x) When scofflaw criterion is satisfied, order x*＝n*(x)，n*(x)→T*；
if n*(x)∈T*And n is*(x) If scofflaw criterion is not satisfied, order x*N '(x), where f [ n' (x)]＝opt{f[n(x)],n(x)∈(N1(x)-T*)}；
③ if:then let x*＝n*(x)，n*(x)→T*；
Judging whether the stop criterion is met, if yes, stopping outputting x*Else, for the current x*Continuously adopting an improved neighborhood construction method to generate disturbance, and continuously iterating;
in will be connected with n*(x) Tabu table T is put into corresponding neighborhood moving method*In the middle, the taboo length is set asAnd the tabu table T will be used after each subsequent iteration*In and n*(x) The length of the taboo of the corresponding neighborhood shift method is reduced by 1.
Optionally, step S2 includes:
initial tabu table T*And the initial solution x0Dynamic change of tabu lengthStarting iteration times k equal to 0; randomly adopting new task insertion or task replacement method to x0Generation of random perturbation neighborhood solution space N1(x) In N at1(x) To obtain a new better solution j1Judging whether to accept a new better solution j1(ii) a Repetition ofIterating until a stopping rule is satisfied, and obtaining a better solution x when the stopping rule is satisfied*；
Step S3 includes:
using reassignment tasks on better solutions x when the stopping criterion is met in step S2*Generating random disturbance to generate neighborhood solution space N2(x) In N at2(x) To obtain a new better solution j2Judging whether to accept a new better solution j2(ii) a The iterative process is repeated until a stopping rule is satisfied.
Optionally, the stopping criterion of the dynamic neighborhood change tabu search algorithm includes:
determining the step number termination criterion: taking the current optimal solution recorded when the step number reaches a threshold value as the optimal solution output by the dynamic variable neighborhood tabu search algorithm;
or,
frequency control principle: when the frequency of a certain solution sequence or the optimal solution of the objective function value in the iterative process exceeds a given threshold value, the calculation is stopped;
or,
target control principle: and within the given step number, the current optimal value is not changed any more, or the changed difference value is within a preset range, and the calculation is terminated.
Optionally, each preferred solution comprises:
executing the satellite identification, the observation starting time point and the observation ending time point of each task;
the starting time point and the ending time point of the interaction between each satellite and at least one ground station;
and/or the presence of a gas in the gas,
the improved neighborhood construction method is characterized in that a new task is inserted into a current solution sequence or one or more tasks are replaced, and a new solution space is constructed; the new or replacement tasks inserted are: a user who receives in advance waits for at least one satellite to execute an observation/downloading task;
the adjusting neighborhood construction method is that the current solution sequence reassignment task is that a certain observation task in a certain satellite solution sequence is adjusted from one visible time window to another visible time window of the satellite;
and/or the presence of a gas in the gas,
the method further comprises the following steps:
and sending the task instruction corresponding to each satellite in the optimal scheme to the satellite, so that a remote sensor of the satellite executes according to a planned observation task.
In a second aspect, the present invention further provides an apparatus for processing a remote sensing satellite observation mission plan, comprising:
the processor is connected with the receiver;
the receiver receives at least one observation task executed by the satellite remote sensor;
the processor adopts a dynamic variable neighborhood tabu search algorithm to process the planning of the observation task of the remote sensing satellite, and specifically comprises the following steps: when a dynamic neighborhood change tabu search algorithm is adopted to process the remote sensing satellite observation task planning problem, an initial tabu table T in the tabu search algorithm is set*Length of initial tabu watchDynamic variable neighborhood tabu table lengthAnd the initial solution x0And let the optimal solution equal the initial solution x*＝x0；
Starting from the iteration number k equal to 1 according to the initial tabu table T*Dynamic change of tabu lengthAnd the initial solution x0Adopting an improved neighborhood construction method to execute an iteration process of a tabu search algorithm; after the iteration process meets the stop criterion of the tabu search algorithm, outputting the optimal solution x when the stop criterion is met*；
According to the tabu table T*Dynamic change of tabu lengthOptimal solution x when the stop criterion is satisfied in step S2*Adopting an adjustment type neighborhood construction method to execute an iteration process of a tabu search algorithm;
after the iteration process of the last step meets the stopping criterion of the dynamic variable neighborhood tabu search algorithm, the final output optimal solution x is subjected to*And obtaining a better solution to be output by adopting a local search algorithm as an optimal scheme for the remote sensing satellite observation task planning.
Optionally, the method further comprises: a transmitter coupled to the processor;
the transmitter sends the task instruction of each satellite to the satellite so that a remote sensor of the satellite executes the task according to the planned observation task;
wherein each preferred solution comprises:
executing the satellite identification, the observation starting time point and the observation ending time point of each task;
a start time point and an end time point of each satellite interacting with at least one ground station.
And/or the presence of a gas in the gas,
the improved neighborhood construction method specifically executed by the processor is that a new task is inserted into the current solution sequence or one or more tasks are replaced, so that a new solution space is constructed; the new or replacement tasks inserted are: a user who receives in advance waits for at least one satellite to execute an observation/downloading task;
and/or the adjusting neighborhood construction method specifically executed by the processor is that the current solution sequence reassignment task is to adjust a certain observation task in a certain satellite solution sequence from one visible time window to another visible time window of the satellite.
Optionally, the stopping criterion of the dynamic neighborhood change tabu search algorithm includes:
determining the step number termination criterion: taking the current optimal solution recorded when the step number reaches a threshold value as the optimal solution output by a tabu search algorithm;
or,
frequency control principle: when the frequency of a certain solution sequence or the optimal solution of the objective function value in the iterative process exceeds a given threshold value, the calculation is stopped;
or,
target control principle: and within the given step number, the current optimal value is not changed any more, or the changed difference value is within a preset range, and the calculation is terminated.
Optionally, the processor is specifically configured to:
set taboo tableInitial tabu length Starting from the iteration number k equal to 1, randomly selecting an initial solution x in the feasible solution space0And calculating an objective function value f (x) of the initial solution0)；
Obtaining an initial solution x using an improved neighborhood approach0N of the neighborhood solution set1(x) For N (x) is formed as N1(x) The fitness function to solve is f [ n (x)]According to f [ n (x)]Screening out n*(x)，n*(x) Is a set N1(x) The solution which maximizes the fitness function value is obtained;
the selection rule of the solution is as follows:
if n*(x)∈T*And n is*(x) When scofflaw criterion is satisfied, order x*＝n*(x)，n*(x)→T*；
if n*(x)∈T*And n is*(x) If scofflaw criterion is not satisfied, order x*N '(x), where f [ n' (x)]＝opt{f[n(x)],n(x)∈(N1(x)-T*)}；
③ if:then let x*＝n*(x)，n*(x)→T*；
Judging whether the stop criterion is met, if yes, stopping outputting x*Else, for the current x*Continuously adopting an improved neighborhood construction method to generate disturbance, and continuously iterating;
in will be connected with n*(x) Tabu table T is put into corresponding neighborhood moving method*In the middle, the taboo length is set asAnd the tabu table T will be used after each subsequent iteration*In and n*(x) The length of the taboo of the corresponding neighborhood shift method is reduced by 1.
Optionally, the processor is further configured to:
initial tabu table T*And the initial solution x0Dynamic change of tabu lengthThe iteration number k is 1; randomly adopting new task insertion or task replacement method to x0Generation of random perturbation neighborhood solution space N1(x) In N at1(x) To obtain a new better solution j1Judging whether to accept a new better solution j1(ii) a Repeating the iteration process until a stopping rule is met, and obtaining a better solution x when the stopping rule is met*；
And the number of the first and second groups,
better solution x when stopping criterion is satisfied by using redistribution task pair*Generating random disturbance to generate neighborhood solution space N2(x) In N at2(x) To obtain a new better solution j2Judging whether to accept a new better solution j2(ii) a The iterative process is repeated until a stopping rule is satisfied.
The method for processing the remote sensing satellite earth observation task planning problem by adopting the dynamic variable neighborhood tabu search algorithm remarkably improves the solving performance of the problem. Meanwhile, by alternately using the two types of neighborhood structures, the searching capability of the algorithm to the solution space and the capability of avoiding the local optimal solution are enhanced, and the solving quality and efficiency are improved.
Drawings
FIG. 1 is a schematic diagram of a prior art interaction of satellite observations with a ground station;
FIG. 2 is a schematic diagram of a time window for a current satellite observation;
FIG. 3 is a schematic flow chart of a method according to an embodiment of the present invention;
fig. 4 is a schematic diagram of an optimal solution provided by an embodiment of the present invention.
Detailed Description
For the purpose of better explaining the present invention and to facilitate understanding, the present invention will be described in detail by way of specific embodiments with reference to the accompanying drawings.
Currently, the remote sensing satellite earth observation needs to satisfy the following constraints:
(1) imaging of a terrestrial target must be performed when the satellite moves over the target in an orbital revolution, where the satellite's remote sensor will be able to see the target for a period of time called a time window (as shown in fig. 2). In a given planning period, there is generally more than one time window between the satellite and the target, the observation of the target by the satellite needs to be completed within one of the time windows, and the time window for the target to observe is generally smaller than the visible time window, and the start time and the end time of the observation time window are shown in fig. 2.
(2) When one satellite executes 2 successive observation tasks, a certain transition time is needed in the middle to make the satellite remote sensor well adjust. When the ground station receives the data from the satellite, the data downloading is also required to be completed within a time window as well as the observation task.
(3) The number of side-looking adjustments of the satellite is limited during each on-off time. The side-looking adjustment times are the side-looking swing angles of the satellite adjustment remote sensors to observe targets.
(4) The satellite has a fixed capacity on-board memory, and temporarily stores the observed target image data in the memory. After the data is transmitted back to the ground station, the memory capacity of the memory is released. The real-time capacity of the memory is thus dynamically changed throughout the observation.
(5) The satellite consumes energy both during the observation of the target and during the data download, and the energy available to the satellite in each orbital turn is limited, so that the energy consumption in each turn cannot exceed the maximum energy limit during the scheduling process.
The taboo search algorithm is an extension of local neighborhood search, some experienced operations are taboo by setting a taboo table, and some excellent solutions are rewarded by utilizing scofflaw criteria, so that a global optimization algorithm is realized, and the time complexity of the taboo search algorithm depends on the size of a search neighborhood and the evaluation cost of determining movement. The tabu search algorithm is optimized step by step for a single solution, so the algorithm execution efficiency is high. The taboo search algorithm adopts a flexible storage structure and corresponding taboo criteria to avoid circular search, releases some taboo solutions through scofflaw criteria, further ensures that the solution space diversification algorithm jumps out of local optimal solutions, and finally realizes global optimization.
The tabu search algorithm is a neighborhood search algorithm that starts from an initial solution, screens candidate solutions one by one in the neighborhood of the current initial solution, and finds the best candidate solution. If the found best candidate solution is better than the current solution, the current solution is replaced. If the best candidate solution found is inferior to the current solution, the best non-contraindicated candidate solution is selected to replace the current solution.
The tabu search algorithm is an algorithm with strong applicability and can solve a plurality of problems. In the application of the method to the remote sensing satellite mission planning problem, the coding mode and the construction of a neighborhood solution are mainly concerned.
In addition, the application of the tabu search algorithm to the satellite mission planning problem is briefly described below to better understand the algorithm execution process.
In the remote sensing satellite task planning problem, a resource (satellite) based coding mode is adopted, namely, tasks are numbered uniformly, and a task list of the satellite imaged in an orbit circle is compiled into iterative codes. For example, the coding (i.e., one feasible solution) of a certain satellite Sat1 in one iteration Sat 1: 10 → 2 → 4 → 42 → 5 → 27 shows that the Sat1 satellite performs tasks 10, 2, 4, 42, 5, 27 in sequence.
② in ② addition ②, ② aiming ② at ② the ② particularity ② of ② the ② remote ② sensing ② task ② planning ② problem ②, ② various ② neighborhood ② structures ② are ② designed ②, ② such ② as ② ① ② inserting ② neighborhood ②, ② namely ② ① ② inserting ② certain ② meta ② task ② into ② a ② task ② list ② of ② a ② certain ② satellite ② as ② an ② independent ② observation ② activity ②, ② deleting ② the ② neighborhood ②, ② namely ② directly ② removing ② the ② certain ② observation ② activity ② from ② the ② observation ② activity ② list ② of ② the ② certain ② satellite ②, ② replacing ② a ② task ② neighborhood ②, ② namely ② replacing ② a ② certain ② meta ② task ② which ② is ② arranged ② in ② the ② current ② solution ② by ② a ② certain ② meta ② task ② which ② is ② not ② arranged ②, ② and ② redistributing ② the ② task ② neighborhood ②, ② namely ② exchanging ② the ② position ② and ② the ② sequence ② of ② two ② meta ② tasks ② in ② the ② current ② solution ② to ② realize ② neighborhood ② reconstruction ②. ②
Further, tabu search is a neighborhood search algorithm, the basis of which iteration is to continuously construct a neighborhood solution. And when the neighborhood solution is constructed every time, the constraint conditions can be contrasted to ensure that the constructed neighborhood solution is a feasible solution. Therefore, in each iteration of the tabu search algorithm for solving the remote sensing satellite mission planning problem, the whole iteration is realized by constructing a feasible neighborhood solution.
In a traditional implementation, solving the remote sensing satellite earth observation task planning problem by a tabu search algorithm can be described as follows:
and setting the whole search space as S, the tabu table as T, and the current solution as x, wherein x belongs to S. And setting the current solved neighborhood solution set as N (x), wherein N (x) belongs to N (x), and the solved fitness function is f [ x ]]If n is present*(x) So that:
f[n*(x)]＝opt{f[n(x)],n(x)∈(N(x)-T)}
then use n*(x) The iteration continues with x as the new solution. The above process is repeated until the termination condition is satisfied. In the iteration process, if the movement of a certain neighborhood is selected, the movement neighborhood is placed into a tabu table T so as to avoid falling into local minimum in the next iteration process. If f [ n ]*(x)]Is high enough to satisfy scofflaw criteria, even if n*(x) E.g. T, also select n*(x) As the current new solution.
According to the above description, the basic flow of the tabu search algorithm can be summarized as follows:
step 1: initializing algorithm parameters to generate an initial solution x, let x*＝x，
That is, given the algorithm parameters, an initial solution i is randomly generated and the tabu table is empty.
Step 2: constructing N (x) according to the candidate solution construction method;
step 3: according to f [ n (x)]Screening out n*(x)，
If: n is*(x) E is T and n*(x) When scofflaw criterion is satisfied, order x*＝n*(x)，n*(x)→T；
If: n is*(x) E is T and n*(x) If scofflaw criterion is not satisfied, order x*N '(x), where f [ n' (x)]＝opt{f[n(x)],n(x)∈(N(x)-T)}；
If:then let x*＝n*(x)，n*(x)→T。
Step 4: updating T, and removing the failed object;
step 5: and judging whether the termination condition is met, if so, ending and outputting an optimization result, and otherwise, turning to Step 2.
The tabu search algorithm has the following defects:
firstly, when solving the remote sensing satellite earth observation task planning problem by using a tabu search algorithm, the tabu search effect by using a fixed neighborhood search structure is not ideal, and the search speed is slow.
the prior art adopts a fixed taboo length in the whole searching process, the excessively long taboo length increases the calculation amount and influences the operation efficiency of the algorithm, and the excessively short taboo length is easy to fall into a circular searching state.
and thirdly, the single neighborhood radius search is easy to fall into local minimum, so that all solution spaces cannot be searched, and the optimal solution cannot be found.
Therefore, the invention mainly aims to improve the solution process of the tabu search algorithm so as to improve the solution quality and efficiency of the tabu search algorithm.
(1) Aiming at a fixed neighborhood searching structure adopted by the original technical scheme, an improved neighborhood structure and an adjustable neighborhood structure are designed, and the searching capability of an algorithm on a solution space and the capability of avoiding a local optimal solution are enhanced by alternately using the two neighborhood structures.
(2) Aiming at the fact that the fixed taboo length is adopted in the prior art, the dynamic taboo length is designed by combining the improved neighborhood and the adjustment neighborhood. Namely, when the improved neighborhood is used, the short length of the tabu list can be adopted to realize the concentrated search of the previous search domain and enable the current solution to approach a certain local minimum solution as soon as possible; when using the adjusted neighborhood, a longer tabu list length is used to prompt the search process to quickly reach other search areas, thus giving the opportunity to find better solutions.
(3) And aiming at the single neighborhood radius search, selecting a variable search radius for searching, namely judging whether the whole solution space traverses after searching an optimal solution which is continuously unchanged for several generations, and if not, expanding the search radius until the whole solution space traverses. This avoids falling into local optima. And then searching an optimal solution in the whole solution space, reducing the search radius, and performing fine search in the range of the new optimal solution until a termination condition is met.
Therefore, the embodiment of the invention provides a method for processing the remote sensing satellite earth observation mission planning problem by adopting a dynamic variable neighborhood tabu search algorithm, as shown in fig. 3, the method shown in fig. 3 comprises the following steps:
s1, setting an initial tabu table T in the tabu search algorithm when the dynamic variable neighborhood tabu search algorithm is adopted to process the remote sensing satellite observation task planning problem*Length of initial tabu watchDynamic variable neighborhood tabu table lengthAnd the initial solution x0And let the optimal solution equal the initial solution x*＝x0；
S2, starting from the iteration number k equal to 1, according to the initial tabu table T*Dynamic change of tabu lengthAnd the initial solution x0Adopting an improved neighborhood construction method to execute an iteration process of a tabu search algorithm; after the iteration process meets the stop criterion of the tabu search algorithm, outputting the optimal solution x when the stop criterion is met*；
S3, according to the tabu list T*Dynamic change of tabu lengthOptimal solution x when the stop criterion is satisfied in step S2*Adopting an adjustment type neighborhood construction method to execute an iteration process of a tabu search algorithm;
s4, after the iterative process of the step S3 meets the stop criterion of the dynamic neighborhood change tabu search algorithm, the final output optimal solution x is processed*And obtaining a better solution to be output by adopting a local search algorithm as an optimal scheme for the remote sensing satellite observation task planning.
The method of the embodiment obviously improves the problem solving performance. Meanwhile, by alternately using the two types of neighborhood structures, the searching capability of the algorithm to the solution space and the capability of avoiding the local optimal solution are enhanced, and the solving quality and efficiency are improved.
For example, the aforementioned step S2 may include:
set taboo tableInitial tabu length Starting from the iteration number k equal to 1, randomly selecting an initial solution x in the feasible solution space0And calculating an objective function value f (x) of the initial solution0)；
Obtaining an initial solution x using an improved neighborhood approach0N of the neighborhood solution set1(x) For N (x) is formed as N1(x) The fitness function to solve is f [ n (x)]According to f [ n (x)]Screening out n*(x)，n*(x) Is a set N1(x) The solution which maximizes the fitness function value is obtained;
the selection rule of the solution is as follows:
if n*(x)∈T*And n is*(x) When scofflaw criterion is satisfied, order x*＝n*(x)，n*(x)→T*；
if n*(x)∈T*And n is*(x) If scofflaw criterion is not satisfied, order x*N '(x), where f [ n' (x)]＝opt{f[n(x)],n(x)∈(N1(x)-T*)}；
③ if:then let x*＝n*(x)，n*(x)→T*；
Judging whether the stop criterion is met, if yes, stopping outputting x*Else, for the current x*Continuously adopting an improved neighborhood construction method to generate disturbance, and continuously iterating;
in will be connected with n*(x) Tabu table T is put into corresponding neighborhood moving method*In the middle, the taboo length is set asAnd the tabu table T will be used after each subsequent iteration*In and n*(x) The length of the taboo of the corresponding neighborhood shift method is reduced by 1.
In another alternative implementation, the step S2 of the method shown in fig. 1 includes:
initial tabu table T*And the initial solution x0Length of initial tabu watchThe length of the initial tabu table can be adjusted according to the scale of the problem, and the tabu length is dynamically changed intoStarting from the iteration number k equal to 1; randomly adopting new task insertion or task replacement method to x0Generation of random perturbation neighborhood solution space N1(x) In N at1(x) To obtain a new better solution j1Judging whether to accept a new better solution j1(ii) a Repeating the iteration process until a stopping rule is met, and obtaining a better solution x when the stopping rule is met*；
Step S3 includes:
using reassignment tasks on better solutions x when the stopping criterion is met in step S2*Generating random disturbance to generate neighborhood solution space N2(x) In N at2(x) To obtain a new better solution j2Judging whether to accept a new better solution j2(ii) a The iterative process is repeated until a stopping rule is satisfied.
In the present embodiment, each of the preferred solutions may include: executing the satellite identification, the observation starting time point and the observation ending time point of each task; a start time point and an end time point of each satellite interacting with at least one ground station.
Of course, the method may further include the following step S5:
and S5, sending the task instruction corresponding to each satellite in the optimal scheme to the satellite, so that the remote sensor of the satellite executes according to the planned observation task.
The stopping criterion of the dynamic variable neighborhood tabu search algorithm is as follows:
(1) determining the step number termination criterion: taking the current optimal solution recorded when the step number reaches a threshold value as the optimal solution output by the dynamic variable neighborhood tabu search algorithm;
(2) frequency control principle: when the frequency of a certain solution sequence or the optimal solution of the objective function value in the iterative process exceeds a given threshold value, the calculation is stopped;
(3) target control principle: and within the given step number, the current optimal value is not changed any more, or the changed difference value is within a preset range, and the calculation is terminated.
In order to better understand the content of the embodiment of the invention, firstly, an improved neighborhood and an adjustable neighborhood are designed in advance aiming at the problem of remote sensing satellite earth observation task planning as follows:
(1) neighborhood structure design
The neighborhood structure is one of the basic elements of the tabu search algorithm, and the iterative improvement process of the tabu search is realized by continuously searching for better solutions in the neighborhood of the current solution. Aiming at the characteristics of the remote sensing satellite earth observation task planning problem, the following three different neighborhood structures are designed:
inserting new task neighborhood (namely an improved neighborhood)
The function of inserting the new task neighborhood is to insert a new observation task which is not arranged originally into the current solution i, so that the range of the task to be captured and the range of the satellite resource of the task to be inserted can be further limited for convenient control. The neighborhood is designed mainly for the characteristic that the satellite resources may still be idle to complete more tasks within a certain period of time.
② replacing task neighborhood (namely an improved neighborhood)
The effect of the replacement task neighborhood is enough to replace a certain unscheduled observation task in the previous solution i with a certain unscheduled observation task for convenient control, and the scope of the observation tasks which can be replaced with each other and the scope of the satellite resource where the replaced task is located can be further limited. The design of the neighborhood mainly aims at the following problem characteristics: in a certain time window, the satellite resources can observe different observation targets by adopting different postures, but the satellite resources cannot simultaneously observe at one time and only one of the satellite resources can be selected. It is possible to improve the quality of the solution if a high priority task can be substituted for a low priority task.
③ redistribution task neighborhood (namely an adjusting neighborhood)
The role of the reassignment task neighborhood is to exchange the observation tasks of two different satellites in the current solution i, thereby realizing the reassignment of the satellite resources for executing the observation tasks; or the observation tasks executed by a certain satellite are adjusted from one visible time window to another visible time window, so that the redistribution of the single-satellite internal observation task time window is realized.
In the three neighborhood structures, the first two are improved neighborhood search, and the optimization goal can be improved by arranging more tasks or replacing low-priority tasks with high-priority tasks. The third is an adjustment type neighborhood search, which is essentially only the adjustment of the position of the scheduled task and has no effect on optimizing the objective function value.
Although the adjustable neighborhood does not directly affect the optimization objective, the quality of a solution can be improved by adjusting the observation scheme, and the improved neighborhood is used as a basis for a variable neighborhood search strategy.
(2) Dynamic changing of tabu length
The tabu length size is a key parameter that affects the performance of the tabu search algorithm. The taboo length is the maximum number of times the taboo object is not allowed to be selected without regard to the scofflaw criteria. Too long a tabu length increases the calculation amount and affects the operation efficiency of the algorithm, while too short a tabu length easily falls into a circular search state.
Aiming at the problem, the dynamic taboo length is designed by combining the improved neighborhood and the adjustment neighborhoodI.e. at the start of an iterationThe method is small, and when the improved neighborhood is used, the concentrated search of the previous search domain is realized by adopting a shorter tabu list length, and the current solution is enabled to approach a certain local minimum solution as soon as possible; when using a modified neighborhood, as the value of k increases,and is increased. A longer tabu list length is used to force the search process to quickly reach other search areas, giving the opportunity to find better solutions.
In addition, in the present embodiment, the inserted new task or the replaced task may be understood as: a user who receives in advance waits for at least one satellite to execute an observation/downloading task;
reassignment tasks can be understood as: and adjusting the observation task executed by a certain satellite from one visible time window to another visible time window.
The process for solving the remote sensing earth observation task planning problem by determining the dynamic variable neighborhood tabu search algorithm through the improved scheme is as follows:
step 1: initializing algorithm parameters and setting tabu tableInitial tabu lengthDynamic changing of tabu lengthAnd randomly selecting an initial solution x from the feasible solution space0Let the optimal solution x*＝x0；
Step 2: and judging whether the algorithm termination condition is met. If yes, finishing the algorithm and outputting an optimization result; otherwise, executing step 3;
and step 3: generation of current better solution x according to improved neighborhood method*N of the neighborhood solution set1(x) To pairAccording to f [ n (x)]Screening out n*(x) The screening method comprises the following steps:
if n*(x)∈T*And n is*(x) When scofflaw criterion is satisfied, order x*＝n*(x)，n*(x)→T*；
if n*(x)∈T*And n is*(x) If scofflaw criterion is not satisfied, order x*N '(x), where f [ n' (x)]＝opt{f[n(x)],n(x)∈(N1(x)-T*)}；
③ if:then let x*＝n*(x)，n*(x)→T*；
And 4, step 4: the length of the taboo isUpdating T*And removing the failed object;
and 5: and judging whether the termination condition is met, if so, turning to the step 6, and otherwise, turning to the step 2.
Step 6: forming the current better solution according to the adjusted neighborhoodx*N of the neighborhood solution set2(x) To pairAccording to f [ n (x)]Screening out n*(x) The screening method comprises the following steps:
if n*(x)∈T*And n is*(x) When scofflaw criterion is satisfied, order x*＝n*(x)，n*(x)→T*；
if n*(x)∈T*And n is*(x) If scofflaw criterion is not satisfied, order x*N '(x), where f [ n' (x)]＝opt{f[n(x)],n(x)∈(N1(x)-T*)}；
③ if:then let x*＝n*(x)，n*(x)→T*；
And 7: the length of the taboo isUpdating T*Removing the failed object;
and 8: and judging whether the termination condition is met, if so, turning to the step 9, and otherwise, turning to the step 6.
And step 9: and (4) adopting a local search algorithm for the optimal solution finally output in the steps until the optimal solution generated by the algorithm is finished is used as the optimal solution for solving the remote sensing satellite earth observation task plan.
Currently, in practical applications, after the method finds the optimal solution, the following steps of sending the task instruction corresponding to each satellite in the optimal solution to the satellite can be executed, so that the remote sensor of the satellite executes according to a planned observation task.
1. And (5) designing a neighborhood structure. The invention designs two improved neighborhood structures and an adjusting neighborhood structure aiming at the results that the tabu search algorithm directly adopts a fixed neighborhood search structure to carry out tabu search has unsatisfactory effect and the search speed is slow.
2. A dynamic tabu length is used. The invention combines the improved neighborhood and the adjustment neighborhood, and adopts the dynamic tabu length in the searching process. Namely, when the improved neighborhood is used, the short length of the tabu table is adopted; when using an adjusted neighborhood, a longer tabu list length is used.
3. And selecting a variable search radius for searching, namely judging whether the whole solution space is traversed after searching an optimal solution which is unchanged for a plurality of continuous generations, and if not, expanding the search radius until the whole space is traversed. And then searching an optimal solution in the whole solution space, reducing the search radius, and performing fine search in the range of the new optimal solution until a termination condition is met.
The method enhances the searching capability of the algorithm to the solution space; the algorithm has better capability of avoiding the local optimal solution and is not easy to fall into the local optimal solution; the length-lengthened tabu length is more flexible than the fixed tabu length, so that the operation efficiency of the algorithm can be improved, and the algorithm is not easy to fall into a circular search state; the algorithm has better solving quality.
In another aspect, the present invention further provides a device for processing a remote sensing satellite earth observation mission plan, including: the processor is connected with the receiver;
the receiver receives at least one observation task executed by the satellite remote sensor;
the processor adopts a dynamic variable neighborhood tabu search algorithm to process the planning of the earth observation task of the remote sensing satellite, and specifically comprises the following steps: setting an initial tabu table T in a dynamic variable neighborhood tabu search algorithm*Length of initial tabu watchDynamic changing of tabu lengthAnd the initial solution x0And let the optimal solution equal the initial solution x*＝x0；
According to the initial tabu table T*Dynamic change of tabu lengthAnd the initial solution x0Starting from the iteration time k being 1, executing the iteration process of the dynamic variable neighborhood tabu search algorithm by adopting an improved neighborhood method; after the iteration process meets the stopping criterion of the dynamic variable neighborhood tabu search algorithm, outputting the optimal solution x meeting the stopping criterion*；
According to the tabu table T*Dynamic change of tabu lengthBased on the above-mentioned optimal solution x when the stopping criterion is satisfied*Adopting an adjustment type neighborhood method to execute an iteration process of a tabu search algorithm;
after the iterative process executed based on the adjustment type neighborhood method meets the stopping criterion of the tabu search algorithm, the final output optimal solution x is subjected to*And obtaining a better solution to be output by adopting a local search algorithm as an optimal scheme for the remote sensing satellite earth observation task planning.
Specifically, the above apparatus may further include: a transmitter coupled to the processor; the transmitter transmits the mission instructions for each satellite to the satellite so that the remote sensors of the satellite perform according to the planned observation mission.
For example, the processor described above may be specifically adapted to perform the content of any of the method embodiments described above, e.g. as
The execution of the foregoing step S2 may include:
set taboo tableInitial tabu length Starting from the iteration number k equal to 1, randomly selecting an initial solution x in the feasible solution space0And calculating an objective function value f (x) of the initial solution0)；
Obtaining an initial solution x using an improved neighborhood approach0N of the neighborhood solution set1(x) For N (x) is formed as N1(x) The fitness function to solve is f [ n (x)]According to f [ n (x)]Screening out n*(x)，n*(x) Is a set N1(x) The solution which maximizes the fitness function value is obtained;
the selection rule of the solution is as follows:
if n*(x)∈T*And n is*(x) When scofflaw criterion is satisfied, order x*＝n*(x)，n*(x)→T*；
if n*(x)∈T*And n is*(x) If scofflaw criterion is not satisfied, order x*N '(x), where f [ n' (x)]＝opt{f[n(x)],n(x)∈(N1(x)-T*)}；
③ if:then let x*＝n*(x)，n*(x)→T*；
Judging whether the stop criterion is met, if yes, stopping outputting x*Else, for the current x*Continuously adopting an improved neighborhood construction method to generate disturbance, and continuously iterating;
in will be connected with n*(x) Tabu table T is put into corresponding neighborhood moving method*In the middle, the taboo length is set asAnd the tabu table T will be used after each subsequent iteration*In and n*(x) The length of the taboo of the corresponding neighborhood shift method is reduced by 1.
Alternatively, the processor executes the following content included in the aforementioned step S2:
initial tabu table T*And the initial solution x0Length of initial tabu watchThe length of the initial tabu table can be adjusted according to the scale of the problem, and the tabu length is dynamically changed intoStarting from the iteration number k equal to 1; randomly adopting new task insertion or task replacement method to x0Generation of random perturbation neighborhood solution space N1(x) In N at1(x) To obtain a new better solution j1Judging whether to accept a new better solution j1(ii) a Repeating the iteration process until a stopping rule is met, and obtaining a better solution x when the stopping rule is met*；
Step S3 includes:
using reassignment tasks on better solutions x when the stopping criterion is met in step S2*Generating random disturbance to generate neighborhood solution space N2(x) In N at2(x) To obtain a new better solution j2Judging whether to accept a new better solution j2(ii) a The iterative process is repeated until a stopping rule is satisfied.
Further, the stopping criteria of the dynamic variant neighborhood tabu search algorithm in the present embodiment may include:
determining the step number termination criterion: taking the current optimal solution recorded when the step number reaches a threshold value as the optimal solution output by the dynamic variable neighborhood tabu search algorithm;
or,
frequency control principle: when the frequency of a certain solution sequence or the optimal solution of the objective function value in the iterative process exceeds a given threshold value, the calculation is stopped;
or,
target control principle: and within the given step number, the current optimal value is not changed any more, or the changed difference value is within a preset range, and the calculation is terminated.
Wherein each preferred solution comprises:
executing the satellite identification, the observation starting time point and the observation ending time point of each task;
the start time point, the end time point and/or the new or alternative tasks inserted for each satellite's interaction with at least one ground station are: a user who receives in advance waits for at least one satellite to execute an observation/downloading task;
the reassignment tasks are: and adjusting the observation task executed by a certain satellite from one visible time window to another visible time window.
It is obvious to those skilled in the art that, for convenience and simplicity of description, the foregoing division of the functional modules is merely used as an example, and in practical applications, the above function distribution may be performed by different functional modules according to needs, that is, the internal structure of the device is divided into different functional modules to perform all or part of the above described functions. For the specific working process of the device described above, reference may be made to the corresponding process in the foregoing method embodiment, which is not described herein again.
Finally, it should be noted that: the above-mentioned embodiments are only used for illustrating the technical solution of the present invention, and not for limiting the same; although the present invention has been described in detail with reference to the foregoing embodiments, it will be understood by those of ordinary skill in the art that: the technical solutions described in the foregoing embodiments may still be modified, or some or all of the technical features may be equivalently replaced; and the modifications or the substitutions do not make the essence of the corresponding technical solutions depart from the scope of the technical solutions of the embodiments of the present invention.
Claims (8)
1. A method for planning an observation task of a remote sensing satellite is characterized in that the method adopts a dynamic variable neighborhood tabu search algorithm to process the problem of planning the observation task of the remote sensing satellite, and the method comprises the following steps:
s1, setting an initial tabu table T in the tabu search algorithm when the dynamic variable neighborhood tabu search algorithm is adopted to process the remote sensing satellite observation task planning problem*Length of initial tabu watchDynamic variable neighborhood tabu table lengthAnd the initial solution x0And let the optimal solution equal the initial solution x*＝x0；
S2, starting from the iteration number k equal to 1, according to the initial tabu table T*Dynamic change of tabu lengthAnd the initial solution x0Adopting an improved neighborhood construction method to execute an iteration process of a tabu search algorithm; after the iteration process meets the stop criterion of the tabu search algorithm, outputting the optimal solution x when the stop criterion is met*；
S3, according to the tabu list T*Dynamic change of tabu lengthOptimal solution x when the stop criterion is satisfied in step S2*Adopting an adjustment type neighborhood construction method to execute an iteration process of a tabu search algorithm;
s4, after the iterative process of the step S3 meets the stop criterion of the dynamic neighborhood change tabu search algorithm, the final output optimal solution x is processed*Acquiring a better solution to be output by adopting a local search algorithm as an optimal scheme for the remote sensing satellite observation task planning;
step S2 includes:
set taboo tableInitial tabu length Starting from the iteration number k-1, randomly selecting an initial solution in the feasible solution spacex0And calculating an objective function value f (x) of the initial solution0)；
Obtaining an initial solution x using an improved neighborhood approach0N of the neighborhood solution set1(x) For N (x) is formed as N1(x) The fitness function to solve is f [ n (x)]According to f [ n (x)]Screening out n*(x)，n*(x) Is a set N1(x) The solution which maximizes the fitness function value is obtained;
the selection rule of the solution is as follows:
if n*(x)∈T*And n is*(x) When scofflaw criterion is satisfied, order x*＝n*(x)，n*(x)→T*；
if n*(x)∈T*And n is*(x) If scofflaw criterion is not satisfied, order x*N '(x), where f [ n' (x)]＝opt{f[n(x)],n(x)∈(N1(x)-T*)}；
③ if:then let x*＝n*(x)，n*(x)→T*；
Judging whether the stop criterion is met, if yes, stopping outputting x*Else, for the current x*Continuously adopting an improved neighborhood construction method to generate disturbance, and continuously iterating;
in will be connected with n*(x) Tabu table T is put into corresponding neighborhood moving method*In the middle, the taboo length is set asAnd the tabu table T will be used after each subsequent iteration*In and n*(x) The length of the taboo of the corresponding neighborhood shift method is reduced by 1.
2. The method according to claim 1, wherein step S2 includes:
initial tabu table T*And the initial solution x0Dynamic change of tabu lengthStarting iteration times k equal to 0; randomly adopting new task insertion or task replacement method to x0Generation of random perturbation neighborhood solution space N1(x) In N at1(x) To obtain a new better solution j1Judging whether to accept a new better solution j1(ii) a Repeating the iteration process until a stopping rule is met, and obtaining a better solution x when the stopping rule is met*；
Step S3 includes:
using reassignment tasks on better solutions x when the stopping criterion is met in step S2*Generating random disturbance to generate neighborhood solution space N2(x) In N at2(x) To obtain a new better solution j2Judging whether to accept a new better solution j2(ii) a The iterative process is repeated until a stopping rule is satisfied.
3. The method according to claim 1 or 2, wherein the stopping criterion of the dynamic variable neighborhood tabu search algorithm comprises:
determining the step number termination criterion: taking the current optimal solution recorded when the step number reaches a threshold value as the optimal solution output by the dynamic variable neighborhood tabu search algorithm;
or,
frequency control principle: when the frequency of a certain solution sequence or the optimal solution of the objective function value in the iterative process exceeds a given threshold value, the calculation is stopped;
or,
target control principle: and within the given step number, the current optimal value is not changed any more, or the changed difference value is within a preset range, and the calculation is terminated.
4. The method according to claim 1 or 2,
each preferred solution includes:
executing the satellite identification, the observation starting time point and the observation ending time point of each task;
the starting time point and the ending time point of the interaction between each satellite and at least one ground station;
and/or the presence of a gas in the gas,
the improved neighborhood construction method is characterized in that a new task is inserted into a current solution sequence or one or more tasks are replaced, and a new solution space is constructed; the new or replacement tasks inserted are: a user who receives in advance waits for at least one satellite to execute an observation/downloading task;
the adjusting neighborhood construction method is that the current solution sequence reassignment task is that a certain observation task in a certain satellite solution sequence is adjusted from one visible time window to another visible time window of the satellite;
and/or the presence of a gas in the gas,
the method further comprises the following steps:
and sending the task instruction corresponding to each satellite in the optimal scheme to the satellite, so that a remote sensor of the satellite executes according to a planned observation task.
5. An apparatus for processing a remote sensing satellite observation mission plan, comprising:
the processor is connected with the receiver;
the receiver receives at least one observation task executed by the satellite remote sensor;
the processor adopts a dynamic variable neighborhood tabu search algorithm to process the planning of the observation task of the remote sensing satellite, and specifically comprises the following steps: when a dynamic neighborhood change tabu search algorithm is adopted to process the remote sensing satellite observation task planning problem, an initial tabu table T in the tabu search algorithm is set*Length of initial tabu watchDynamic variable neighborhood tabu table lengthAnd the initial solution x0And let the optimal solution equal the initial solution x*＝x0；
From iterationStarting with the number k equal to 1, according to the initial tabu table T*Dynamic change of tabu lengthAnd the initial solution x0Adopting an improved neighborhood construction method to execute an iteration process of a tabu search algorithm; after the iteration process meets the stop criterion of the tabu search algorithm, outputting the optimal solution x when the stop criterion is met*；
According to the tabu table T*Dynamic change of tabu lengthOptimal solution x when the stop criterion is satisfied in step S2*Adopting an adjustment type neighborhood construction method to execute an iteration process of a tabu search algorithm;
after the iteration process of the last step meets the stopping criterion of the dynamic variable neighborhood tabu search algorithm, the final output optimal solution x is subjected to*Acquiring a better solution to be output by adopting a local search algorithm as an optimal scheme for the remote sensing satellite observation task planning;
further comprising: a transmitter coupled to the processor;
the transmitter sends the task instruction of each satellite to the satellite so that a remote sensor of the satellite executes the task according to the planned observation task;
wherein each preferred solution comprises:
executing the satellite identification, the observation starting time point and the observation ending time point of each task;
a start time point and an end time point of each satellite interacting with at least one ground station.
And/or the presence of a gas in the gas,
the improved neighborhood construction method specifically executed by the processor is that a new task is inserted into the current solution sequence or one or more tasks are replaced, so that a new solution space is constructed; the new or replacement tasks inserted are: a user who receives in advance waits for at least one satellite to execute an observation/downloading task;
and/or the adjusting neighborhood construction method specifically executed by the processor is that the current solution sequence reassignment task is to adjust a certain observation task in a certain satellite solution sequence from one visible time window to another visible time window of the satellite.
6. The apparatus of claim 5, wherein the stopping criteria of the dynamic variable neighborhood tabu search algorithm comprises:
determining the step number termination criterion: taking the current optimal solution recorded when the step number reaches a threshold value as the optimal solution output by a tabu search algorithm;
or,
frequency control principle: when the frequency of a certain solution sequence or the optimal solution of the objective function value in the iterative process exceeds a given threshold value, the calculation is stopped;
or,
target control principle: and within the given step number, the current optimal value is not changed any more, or the changed difference value is within a preset range, and the calculation is terminated.
7. The apparatus of claim 5, wherein the processor is specifically configured to:
set taboo tableInitial tabu length Starting from the iteration number k equal to 1, randomly selecting an initial solution x in the feasible solution space0And calculating an objective function value f (x) of the initial solution0)；
Obtaining an initial solution x using an improved neighborhood approach0N of the neighborhood solution set1(x) For N (x) is formed as N1(x) The fitness function to solve is f [ n (x)]According to f [ n (x)]ScreeningGo out n*(x)，n*(x) Is a set N1(x) The solution which maximizes the fitness function value is obtained;
the selection rule of the solution is as follows:
if n*(x)∈T*And n is*(x) When scofflaw criterion is satisfied, order x*＝n*(x)，n*(x)→T*；
if n*(x)∈T*And n is*(x) If scofflaw criterion is not satisfied, order x*N '(x), where f [ n' (x)]＝opt{f[n(x)],n(x)∈(N1(x)-T*)}；
③ if:then let x*＝n*(x)，n*(x)→T*；
Judging whether the stop criterion is met, if yes, stopping outputting x*Else, for the current x*Continuously adopting an improved neighborhood construction method to generate disturbance, and continuously iterating;
in will be connected with n*(x) Tabu table T is put into corresponding neighborhood moving method*In the middle, the taboo length is set asAnd the tabu table T will be used after each subsequent iteration*In and n*(x) The length of the taboo of the corresponding neighborhood shift method is reduced by 1.
8. The apparatus of claim 7, wherein the processor is further configured to:
initial tabu table T*And the initial solution x0Dynamic change of tabu lengthThe iteration number k is 1; randomly adopting new task insertion or task replacement method to x0Generation of random perturbation neighborhood solution space N1(x) In N at1(x) To obtain a new better solution j1Judging whether to accept a new better solution j1(ii) a Repeating the iteration process until a stopping rule is met, and obtaining a better solution x when the stopping rule is met*；
And the number of the first and second groups,
better solution x when stopping criterion is satisfied by using redistribution task pair*Generating random disturbance to generate neighborhood solution space N2(x) In N at2(x) To obtain a new better solution j2Judging whether to accept a new better solution j2(ii) a The iterative process is repeated until a stopping rule is satisfied.
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