CN113760506A - Method and device for solving multi-satellite cooperative earth observation task scheduling by improved genetic algorithm - Google Patents
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Abstract
The invention provides a method and a device for solving multi-satellite cooperative earth observation task scheduling by using an improved genetic algorithm, and relates to the technical field of satellite task scheduling. According to the method, a single-point multi-segment crossover operator and a target insertion operator are designed through an optimization method based on an improved genetic algorithm, and finally, a local search strategy is adopted to enhance the search capability and help the algorithm to jump out of local optimum. The optimization method provided by the invention can maximize the number of satellite observation target task points in the task time, reduce the task completion time, exert the satellite observation effect as much as possible, and effectively improve the observation benefit and the optimization efficiency of the multi-satellite multi-observation task.
Description
Technical Field
The invention relates to the technical field of satellite task scheduling, in particular to a method and a device for solving multi-satellite cooperative earth observation task scheduling by improving a genetic algorithm.
Background
With the continuous deepening of the satellite field technology, the aerospace technology is widely applied to a plurality of fields, plays a positive role in the aspects of economy, society and the like, and plays a vital role in a satellite-to-ground observation system in the fields of meteorology and traffic. Further expansion of user requirements is applied, and task planning is urgently needed to be carried out on the on-orbit satellite in order to obtain larger observation benefits and exert the cooperative observation capability among multi-satellite systems.
The multi-satellite cooperative earth observation task scheduling is that after an observation task is clarified, a satellite selects a proper time window and actual observation starting time for the observation task by calculating an executable time range of the observation task under the condition that the observation task is not conflicted with the time window and the task is not conflicted with the task. With the increase of the number of satellites and targets, the solution space of the planning and scheduling problem of the large-scale multi-satellite cooperative earth observation task is rapidly increased. The tasks executed in the planning process are determined through the algorithm, the number of the executable tasks can be increased by designing an effective algorithm, and the observation benefit and the optimization efficiency of the multi-satellite multi-observation task are improved.
However, the unreasonable task switching time of the task observation sequence may be insufficient or the number of observation tasks to be completed is small, so that the use efficiency of the satellite is reduced and the user's requirements cannot be met. Therefore, the task observation sequence is effectively planned, the number of observation tasks can be increased, and the service efficiency of the satellite is improved. The satellite earth observation task planning problem is proved to be an NP-hard problem, an accurate algorithm is only suitable for solving small-scale examples, and the method is more advantageous in solving an approximate optimal solution in a short time by using an intelligent algorithm in the face of gradually increasing user requirements. However, the classical genetic algorithm has the defects of low convergence rate, poor local search capability and the like, so that the solved multi-satellite cooperative earth observation task scheme has low observation income.
Disclosure of Invention
Technical problem to be solved
Aiming at the defects of the prior art, the invention provides a scheduling method and a scheduling device for solving a multi-satellite cooperative earth observation task by using an improved genetic algorithm, and solves the technical problem of low observation income of a multi-satellite cooperative earth observation task scheme in the prior art.
(II) technical scheme
In order to achieve the purpose, the invention is realized by the following technical scheme:
in a first aspect, the invention provides a method for improving a genetic algorithm to solve multi-satellite cooperative earth observation task scheduling, which comprises the following steps:
s1, acquiring a target task point set and a satellite resource set;
s2, constructing a multi-satellite cooperative earth observation task model by taking the number of the maximized satellite observation target task points in the task time as a target based on the target task point set and the satellite resource set;
s3, solving the multi-satellite cooperative earth observation task model by adopting a genetic algorithm with a single-point multi-segment crossover operator, and obtaining an optimal task sequence planning scheme for observing any one or more target task points by each satellite.
Preferably, the multi-satellite cooperative earth observation task model includes an objective function and constraint conditions, the objective function is shown in formula (2), and the constraint conditions are shown in formulas (3) to (6):
wherein:
xijhis a satellite saiAt h track to target task point tjNumber of observations of (2), xijh∈{0,1}；
Formula (3) shows that at most only one satellite observes any one target task point once;
formula (4) represents satellite saiFor the h-th circle of (1), for the target task point tiIs detected by the time window of visibility of,
in order to start the time of day,
is the end time;
equation (5) represents satellite saiFor the h-th circle of (1), for the target task point tiThe observation time window of (a) is,
in order to observe the start time of the measurement,
is the observation end time;
formula (6) represents satellite saiThe actual moment of completing the g-th time plus the task switching time is less than or equal to the actual starting time of the g + 1-th observation, wherein,
representing satellite saiThe time when the g-th observation is actually finished;
representing satellite saiCompleting the task switching time from the g th time to the g +1 th time;
represents the actual start time of the g +1 st observation.
Preferably, the solving of the multi-satellite cooperative earth observation task model by using the improved genetic algorithm to obtain the optimal task sequence planning scheme for each satellite to observe any one or more target task points includes:
s301, initializing execution parameters of the improved genetic algorithm;
s302, initializing a population and generating an initial task sequence planning scheme set;
s303, carrying out constraint check and adjustment on the task sequence planning scheme in the initial task sequence planning scheme set;
s304, calculating the fitness value of the task sequence planning scheme in the initial task sequence planning scheme set according to a formula (2);
s305, selecting two different parent planning schemes RAAnd RBPerforming cross operation to obtain two offspring planning schemes RCAnd RD；
S306, planning scheme RCAnd RDPerforming a target insertion operation to obtain a solution REAnd RF，
S307, planning scheme REAnd RFPerforming local search operation to obtain a child planning scheme ReAnd RfAnd judging as the child planning scheme ReAnd RfFitness value greater than planning plan REAnd RFIf the fitness value is satisfied, replacing the parent scheme in the population; otherwise, not performing replacement;
s308, judging whether the maximum iteration times are reached, and if so, outputting the optimal chromosome; otherwise, go to step S305.
Preferably, the initializing the population and generating an initial task sequence planning scheme set include:
s302a, collecting target task points NtaRandomly arranging the target task points in NrForming a first dimension code of the chromosome;
s302b for permutation NrFrom the set NsaRandomly selecting a satellite for observation to form a second dimension code of the chromosome;
s302c, selecting the observed target task points according to the satellite numbers, and arranging the target task points in a non-descending order according to the earliest starting observation time of the time windows of the target task points, thereby obtaining a target task point sequence H observed by the satellitek；
S302d, according to the preset population size NpAnd repeating the steps S302 a-S302 c to obtain an initial task sequence planning scheme set.
Preferably, the selection of two different parent planning schemes RAAnd RBPerforming cross operation to obtain two offspring planning schemes RCAnd RDThe method specifically comprises the following steps:
s305a, selecting 2 chromosome parent R from population by using roulette methodAAnd parent RBFor cross-operation, according to parent RAAnd parent RBThe 2 nd line of the chromosome is divided into n segments;
s305b, judging the segmented parent chromosome RAkAnd RBkIf the same target task point does not exist in the first line, one target task point is randomly selected, and the segment parent chromosome R is segmentedAkAnd RBkThe gene position behind the target task point is exchanged to obtain a new offspring chromosome segment RCkAnd RDkIf the same target task point exists, segmenting the parent chromosome RAkAnd RBkThe gene position behind the target task point is exchanged to obtain a new offspring chromosome segment RCkAnd RDk；
S305c, repeating the step S305b according to the number n of the satellites, completing the single-point crossing operation of all the segments, and respectively combining the segments into complete offspring chromosomes according to the sequence of the satellite numbers.
Preferably, the pair of planning schemes RCAnd RDPerforming a target insertion operation to obtain a solution REAnd RFThe method comprises the following steps:
s306a, if the target task point set N is not observedvcIf not, go to step S306 b; otherwise, outputting the result;
s306b, collecting N from the satellitesaMedium random selection satellite sai；
S306c, judging the satellite saiIf the storage limit constraint is satisfied, if the constraint is violated, go to step S306 b; otherwise, go to step S306 d;
s306d, using formula (7) from NvcSelecting a target task point ti；
Equation (7) shows sa according to satelliteiSet N of unobserved target task pointsvcSelecting target task point t to be insertediTo satellite saiLast observed target task point t in current task sequenceuAnd non-inserted target task point tiThe difference between the task switching time and the actual observation starting time and the earliest observation starting time is minimized to be the selection criterion of the target task point;
s306, 306e checking the inserted target task point tiWhether constraint conditions are met or not, and if the constraint conditions are met, the target task point t is setiInserted into the current sequence of observation tasks and taken from the set NvcMid-delete target task point tiGo to step S306 d; otherwise, go to step S306 f;
s306f, collecting N from the satellitesaMedium deleted satellite saiGo to step S306 a.
Preferably, the local search operation includes: a single segment search operator and a multi-segment search operator, wherein,
the single segment search operator includes: from chromosomesRandomly selecting a segment, and inserting a target task point t at the feasible position of the segment selectioniRecording the current solution and comparing the current solution with the original solution every time of operation, and replacing the original solution if the current solution fitness function value is larger than the original solution fitness function value;
the multi-segment search operator includes: randomly selecting two segments from the chromosome, and respectively selecting a target task point t from the two segmentsi,tjAnd performing exchange insertion to a feasible position, recording the current solution and comparing the current solution with the original solution every time of performing operation, and replacing the original solution if the current solution fitness function value is greater than the original solution fitness function value.
In a second aspect, the present invention provides an improved genetic algorithm scheduling apparatus for solving multi-satellite cooperative earth observation tasks, including:
the data acquisition module is used for acquiring a target task point set and a satellite resource set;
the model construction module is used for constructing a multi-satellite cooperative earth observation task model by taking the number of the maximized satellite observation target task points in the task time as a target based on the target task point set and the satellite resource set;
and the model solving module is used for solving the multi-satellite cooperative earth observation task model by adopting a genetic algorithm with a single-point multi-segment crossover operator to obtain an optimal task sequence planning scheme for observing any one or more target task points by each satellite.
In a third aspect, the present invention provides a computer-readable storage medium storing a computer program for improving a genetic algorithm to solve multi-satellite cooperative earth observation task scheduling, wherein the computer program causes a computer to execute the method for improving the genetic algorithm to solve the multi-satellite cooperative earth observation task scheduling as described above.
In a third aspect, the present invention provides an electronic device comprising:
one or more processors;
a memory; and
one or more programs, wherein the one or more programs are stored in the memory and configured to be executed by the one or more processors, the programs comprising instructions for performing the improved genetic algorithm for solving the multi-star collaborative earth observation task scheduling method as described above.
(III) advantageous effects
The invention provides a method and a device for solving multi-satellite cooperative earth observation task scheduling by using an improved genetic algorithm. Compared with the prior art, the method has the following beneficial effects:
according to the method, a single-point multi-segment crossover operator and a target insertion operator are designed through an optimization method based on an improved genetic algorithm, and finally, a local search strategy is adopted to enhance the search capability and help the algorithm to jump out of local optimum. The optimization method provided by the invention can maximize the number of satellite observation target task points in the task time, reduce the task completion time, exert the satellite observation effect as much as possible, and effectively improve the observation benefit and the optimization efficiency of the multi-satellite multi-observation task.
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In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings used in the description of the embodiments or the prior art will be briefly described below, it is obvious that the drawings in the following description are only some embodiments of the present invention, and for those skilled in the art, other drawings can be obtained according to the drawings without creative efforts.
FIG. 1 is a block diagram of a method for solving a multi-satellite cooperative earth observation task scheduling by using an improved genetic algorithm according to an embodiment of the present invention;
FIG. 2 is a schematic chromosome coding diagram;
FIG. 3 is a schematic diagram of a population initialization process;
FIG. 4 is a schematic diagram of a single point multiple segment crossing of a chromosome, wherein (a) is a method of single point multiple segment crossing in the absence of the same target point; (b) the method is a single-point multi-segment crossing method when the same target point exists;
FIG. 5 is a schematic diagram of a local search operator, (a) is a schematic diagram of a single-segment search operator, and (b) is a multi-segment search operator.
Detailed Description
In order to make the objects, technical solutions and advantages of the embodiments of the present invention clearer, the technical solutions in the embodiments of the present invention are clearly and completely described, and it is obvious that the described embodiments are a part of the embodiments of the present invention, but not all of the embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
The embodiment of the application provides the scheduling method and the scheduling device for solving the multi-satellite collaborative earth observation task by the improved genetic algorithm, solves the technical problem that the solved multi-satellite collaborative earth observation task scheme in the prior art is low in observation income, realizes maximization of the number of satellite observation target task points in task time, reduces the task completion time, exerts the satellite observation utility as much as possible, and effectively improves the observation income and the optimization efficiency of the multi-satellite and earth observation task.
In order to solve the technical problems, the general idea of the embodiment of the application is as follows:
the multi-satellite cooperative earth observation task scheduling is that after an observation task is clarified, a satellite selects a proper time window and actual observation starting time for the observation task by calculating an executable time range of the observation task under the condition that the observation task is not conflicted with the time window and the task is not conflicted with the task. With the increase of the number of satellites and targets, the solution space of the planning and scheduling problem of the large-scale multi-satellite cooperative earth observation task is rapidly increased. The tasks executed in the planning process are determined through the algorithm, the number of the executable tasks can be increased by designing an effective algorithm, and the observation benefit and the optimization efficiency of the multi-satellite multi-observation task are improved. The embodiment of the invention provides a scheduling method and a device for solving a multi-satellite cooperative earth observation task by an improved genetic algorithm, which are used for optimizing the scheduling problem characteristics of a large-scale multi-satellite cooperative earth observation task, and specially design a population initialization mode, a crossing mode and an insertion mode, so that the number of satellite observation target task points is maximized within the task time, the task completion time is reduced, the utility of a satellite is exerted to the maximum extent, the observation income and the optimization efficiency of the multi-satellite cooperative earth observation task are effectively improved, and a high-quality multi-satellite cooperative earth observation scheduling scheme is obtained.
In order to better understand the technical solution, the technical solution will be described in detail with reference to the drawings and the specific embodiments.
The embodiment of the invention provides a method for solving multi-satellite cooperative earth observation task scheduling by using an improved genetic algorithm, which comprises the following steps of:
s1, acquiring a target task point set and a satellite resource set;
s2, constructing a multi-satellite cooperative earth observation task model by taking the number of the maximized satellite observation target task points in the task time as a target based on the target task point set and the satellite resource set;
s3, solving the multi-satellite cooperative earth observation task model by adopting a genetic algorithm with a single-point multi-segment crossover operator, and obtaining an optimal task sequence planning scheme for observing any one or more target task points by each satellite.
The optimization method provided by the embodiment of the invention can maximize the number of satellite observation target task points in the task time, reduce the task completion time, exert the satellite observation utility as much as possible, and effectively improve the observation benefits and the optimization efficiency of the multi-satellite multi-observation task.
The following describes in detail the specific implementation of the embodiments of the present invention:
in step S1, a target task point set and a satellite resource set are obtained, and the specific implementation process is as follows:
the computer acquires m target task points to form a target task point set Nta＝{t1,t2,...,ti,...,tmSet N of satellite resources consisting of N satellitessa＝{sa1,sa2,...,sai,...,san}。
In step S2, a multi-satellite cooperative earth observation task model is constructed based on the target task point set and the satellite resource set with the number of the maximized satellite observation target task points in the task time as a target, and the specific implementation manner is as follows:
satellite sa within task time windowiContains orbiConstructing a multi-satellite cooperative earth observation task model by each orbit circle as follows:
set of observation windows for targeted task points, i.e.
Wherein,
is a target task point tiObservation window of (1), target task point tiThe observation window has a value range of
Target task point tiThe total number of the observation windows is si。
The objective function of the multi-satellite cooperative earth observation task model maximizes the number of satellite observation target task points in the task time, namely:
wherein x isijhIs a satellite saiAt h track to target task point tjNumber of observations of (2), xijhE {0,1 }. And if the weights are different, multiplying the weights by corresponding weights.
The constraint conditions of the multi-satellite cooperative earth observation task model comprise:
equation (3) shows that for any one target mission point, at most only one satellite makes one observation.
Formula (4) represents satellite saiFor the h-th circle of (1), for the target task point tiIs detected by the time window of visibility of,
in order to start the time of day,
is the end time.
Equation (5) represents satellite saiFor the h-th circle of (1), for the target task point tiThe observation time window of (a) is,
in order to observe the start time of the measurement,
to observe the end time.
Formula (6) represents satellite saiThe actual completion time of the g th time plus the task switching time is less than or equal to the actual start time of the g +1 th observation. Wherein,
representing satellite saiThe time when the g-th observation is actually finished;
representing satellite saiCompleting the task switching time from the g th time to the g +1 th time；
Represents the actual start time of the g +1 st observation.
In step S3, an improved genetic algorithm is used to solve the multi-satellite cooperative earth observation task model, and an optimal task sequence planning scheme for observing any one or more target task points by each satellite is obtained, which is specifically implemented as follows:
s301, initializing execution parameters of the improved genetic algorithm, such as population size NpCross probability PcrAnd iteration times Itera, wherein the population size is set to be 100, the cross probability is set to be 0.7, and the iteration times are set to be 500 in the embodiment of the invention.
S302, initializing a population and generating an initial task sequence planning scheme set;
adopting the multi-satellite cooperative earth observation task model, and acquiring an initial task sequence planning scheme population of the multi-satellite earth observation according to the orbit circle, the sidesway angle, the observation starting time and the observation ending time of each satellite and the observation window of each target task point, wherein the method comprises the following steps:
according to the characteristics of the satellite scheduling scheme, a two-dimensional chromosome coding mode is designed, wherein a first-dimensional code represents a target task point sequence, and a second-dimensional code represents a selected satellite. The coding mode has the advantages that: (1) the chromosome can directly represent a solution for satellite scheduling; (2) the excellent genetic information of the parents can be simply transmitted to the offspring by adopting a cross method for the chromosome; (3) for each chromosome, the algorithm can directly derive its fitness value. The chromosome coding scheme is shown in FIG. 2:
the chromosomes shown in FIG. 2 represent: satellite sa1For target task point t6Target task point t3Target task point t5Target task point t4And target task point t7Carrying out observation; satellite sa2For target task point t8Target task point t9Target task point t1And target task point t2And (6) carrying out observation.
Generating an initial task sequence planning scheme set of multi-star earth observation according to the following 4 steps:
s302a, collecting target task points NtaRandomly arranging the target task points in NrForming a first dimension code of the chromosome;
s302b for permutation NrFrom the set NsaRandomly selecting a satellite for observation to form a second dimension code of the chromosome;
s302c, selecting the observed target task points according to the satellite numbers, and arranging the target task points in a non-descending order according to the earliest starting observation time of the time windows of the target task points, thereby obtaining a target task point sequence H observed by the satellitek；
S302d, according to the preset population size NpAnd repeating the steps S302 a-S302 c to obtain an initial task sequence planning scheme set.
The population initialization process is shown in fig. 3.
The chromosomes shown in FIG. 3 represent: satellite sa1The target task point to be observed has t3、t4、t5、t6And t7Sa satellite2The target task point to be observed has t1、t2、t8And t9. After the time window is adjusted, the satellite sa1From the target task point t6Starting to observe, sequentially observing target task points t5Target task point t4Target task point t3And target task point t7(ii) a Satellite sa2From the target task point t8Starting to observe, sequentially observing target task points t1Target task point t9And target task point t2。
S303, carrying out constraint check and adjustment on the task sequence planning schemes in the initial task sequence planning scheme set, which specifically comprises the following steps:
the following three types of constraints are mainly used in the task scheduling planning model: a storage limit constraint, a duration constraint, and a task switch time constraint. Aiming at the three types of constraints, the observation task sequence is adjusted according to the following constraint strategies:
constraint strategy: and for the problem chromosome, firstly deleting the target task points violating the storage limit constraint in the task sequence, secondly deleting the target task points violating the working time constraint in the task sequence, and finally deleting the target task points violating the task conversion time constraint in the task sequence until the three types of constraint conditions are met.
The chromosome after the constraint checking and adjusting strategy is a feasible observation task planning scheme.
S304, calculating the fitness value of the task sequence planning scheme in the initial task sequence planning scheme set according to a formula (2);
s305, selecting two different parent planning schemes RAAnd RBPerforming cross operation to obtain two offspring planning schemes RCAnd RDThe method specifically comprises the following steps:
each segment of the chromosome after population initialization represents an observation task sequence planning scheme of one satellite. Aiming at the characteristics of a chromosome coding mode, a single-point multi-segment crossing mode is designed, and the method comprises the following steps:
s305a, selecting 2 chromosome parent R from population by using roulette methodAAnd parent RBFor cross-operation, according to parent RAAnd parent RBThe 2 nd line of the chromosome is divided into n segments;
s305b, cross parent RAAnd parent RBMiddle and same segment chromosome. The operation is as follows: judging the segmented parent chromosome RAkAnd RBkIf the same target task point does not exist in the first line, one target task point is randomly selected, and the segment parent chromosome R is segmentedAkAnd RBkThe gene position behind the target task point is exchanged to obtain a new offspring chromosome segment RCkAnd RDkAs shown in fig. 4 (a); if the same target task point exists, segmenting the parent chromosome RAkAnd RBkThe gene position behind the target task point is exchanged to obtain a new offspring chromosome segment RCkAnd RDkAs shown in FIG. 4 (b);
s305c, repeating the step S305b according to the number n of the satellites, completing the single-point crossing operation of all the segments, and respectively combining the segments into complete offspring chromosomes according to the sequence of the satellite numbers.
The single-point multi-segment intersection shown in fig. 4(a) shows: when the same target task point does not exist, the target task point is randomly selected, and the chromosome R is divided intoAMiddle satellite sa1Target task point t in task sequence3And target task point t7And chromosome RBMiddle satellite sa1Target task point t in task sequence2And carrying out exchange. The cross pattern shown in fig. 4(b) shows: when the same target task point exists, the same target task point is selected. Chromosome RAMiddle satellite sa1Target task point t in task sequence6Target task point t4And target task point t7And chromosome RBMiddle satellite sa1Target task point t in task sequence2And carrying out exchange.
S306, planning scheme RCAnd RDPerforming a target insertion operation to obtain a solution REAnd RFThe method specifically comprises the following steps:
chromosomes which finish cross operation in a single-point multi-segment cross mode do not necessarily meet the constraint conditions of a planning and scheduling model of a multi-star collaborative earth observation task, each chromosome in a population needs to be subjected to constraint inspection, and target task points which do not meet the constraints are placed in an unobserved task point set Nvc. If the chromosomes after constraint inspection may not complete the observation tasks of all the target task points, the target task points of which the observation tasks are not completed need to be inserted into the task sequence. In the task sequence planning scheme, each target task point is observed by a satellite independently, and in order to accelerate the global search process, a target insertion operator is designed, and the method comprises the following steps:
s306a, if the target task point set N is not observedvcIf not, go to step S306 b; otherwise, outputting the result;
s306b, collecting N from the satellitesaMedium random selection satellite sai；
S306c, judging the satellite saiIf the storage limit constraint is satisfied, if the constraint is violated, go to step S306 b; otherwise, go to step S306 d;
s306d, using formula (7) from NvcSelecting a target task point ti；
Equation (7) shows sa according to satelliteiSet N of unobserved target task pointsvcSelecting target task point t to be insertediTo satellite saiLast observed target task point t in current task sequenceuAnd non-inserted target task point tiThe task switching time between and the difference between the actual start observation time and the earliest start observation time are minimized as the selection criteria of the target task point.
S306, 306e checking the inserted target task point tiWhether constraint conditions are met or not, and if the constraint conditions are met, the target task point t is setiInserted into the current sequence of observation tasks and taken from the set NvcMid-delete target task point tiGo to step S306 d; otherwise, go to step S306 f;
s306f, collecting N from the satellitesaMedium deleted satellite saiGo to step S306 a.
S307, planning scheme REAnd RFPerforming local search operation to obtain a child planning scheme ReAnd RfAnd judging as the child planning scheme ReAnd RfFitness value greater than planning plan REAnd RFIf the fitness value is satisfied, replacing the parent scheme in the population; otherwise, not performing the replacement, specifically:
the local search operator performs local search on the task sequence planning scheme generated by the intersection mode and the target insertion operator, so as to obtain an optimized task sequence planning scheme, and as shown in fig. 5, two local search operators are designed, namely a single-segment search operator and a multi-segment search operator. The local search operator is defined as follows:
single segment search operator:randomly selecting a segment from the chromosome, and inserting the target task point t at the feasible position selected in the segmentiIf the current solution fitness function value is greater than the original solution fitness function value, the original solution is replaced, as shown in fig. 5 (a).
Multi-segment search operator: randomly selecting two segments from the chromosome, and respectively selecting a target task point t from the two segmentsi,tjAnd (c) performing switching insertion to a feasible position, recording the current solution and comparing the current solution with the original solution every time operation is performed, and replacing the original solution if the current solution fitness function value is greater than the original solution fitness function value, as shown in fig. 5 (b).
S308, judging whether the maximum iteration times are reached, and if so, outputting the optimal chromosome; otherwise, go to step S305.
The embodiment of the invention also provides a device for solving the multi-satellite cooperative earth observation task scheduling by the improved genetic algorithm, which comprises the following steps:
the data acquisition module is used for acquiring a target task point set and a satellite resource set;
the model construction module is used for constructing a multi-satellite cooperative earth observation task model by taking the number of the maximized satellite observation target task points in the task time as a target based on the target task point set and the satellite resource set;
and the model solving module is used for solving the multi-satellite cooperative earth observation task model by adopting a genetic algorithm with a single-point multi-segment crossover operator to obtain an optimal task sequence planning scheme for observing any one or more target task points by each satellite.
It can be understood that the improved genetic algorithm solution multi-satellite cooperative earth observation task scheduling device provided in the embodiment of the present invention corresponds to the improved genetic algorithm solution multi-satellite cooperative earth observation task scheduling method, and the explanation, examples, and beneficial effects of the relevant contents thereof may refer to the corresponding contents in the improved genetic algorithm solution multi-satellite cooperative earth observation task scheduling method, and are not described herein again.
The embodiment of the present invention further provides a computer readable storage medium, which stores a computer program for improving a genetic algorithm to solve the scheduling of the multi-satellite cooperative earth observation task, wherein the computer program enables a computer to execute the method for improving the genetic algorithm to solve the scheduling of the multi-satellite cooperative earth observation task as described above.
An embodiment of the present invention further provides an electronic device, including:
one or more processors;
a memory; and
one or more programs, wherein the one or more programs are stored in the memory and configured to be executed by the one or more processors, the programs comprising instructions for performing the improved genetic algorithm for solving the multi-star collaborative earth observation task scheduling method as described above.
In summary, compared with the prior art, the method has the following beneficial effects:
the invention provides a scheduling method and a device for solving a multi-satellite collaborative earth observation task by an improved genetic algorithm. The optimization method provided by the embodiment of the invention can maximize the number of satellite observation target task points in the task time, reduce the task completion time, exert the satellite observation utility as much as possible, and effectively improve the observation benefits and the optimization efficiency of the multi-satellite multi-observation task.
It is noted that, herein, relational terms such as first and second, and the like may be used solely to distinguish one entity or action from another entity or action without necessarily requiring or implying any actual such relationship or order between such entities or actions. Also, the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or apparatus. Without further limitation, an element defined by the phrase "comprising an … …" does not exclude the presence of other identical elements in a process, method, article, or apparatus that comprises the element.
The above examples are only intended to illustrate the technical solution of the present invention, but not to limit it; 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 technical features may be equivalently replaced; and such modifications or substitutions do not depart from the spirit and scope of the corresponding technical solutions of the embodiments of the present invention.
Claims (10)
1. A method for solving multi-satellite cooperative earth observation task scheduling by using an improved genetic algorithm is characterized by comprising the following steps:
s1, acquiring a target task point set and a satellite resource set;
s2, constructing a multi-satellite cooperative earth observation task model by taking the number of the maximized satellite observation target task points in the task time as a target based on the target task point set and the satellite resource set;
s3, solving the multi-satellite cooperative earth observation task model by adopting a genetic algorithm with a single-point multi-segment crossover operator, and obtaining an optimal task sequence planning scheme for observing any one or more target task points by each satellite.
2. The improved genetic algorithm solution multi-satellite cooperative earth observation task scheduling method as claimed in claim 1, wherein the multi-satellite cooperative earth observation task model comprises an objective function and constraint conditions, the objective function is shown in formula (2), and the constraint conditions are shown in formulas (3) to (6):
wherein:
xijhis a satellite saiAt h track to target task point tjNumber of observations of (2), xijh∈{0,1}；
Formula (3) shows that at most only one satellite observes any one target task point once;
formula (4) represents satellite saiFor the h-th circle of (1), for the target task point tiIs detected by the time window of visibility of,
in order to start the time of day,
is the end time;
equation (5) represents satellite saiFor the h-th circle of (1), for the target task point tiThe observation time window of (a) is,
in order to observe the start time of the measurement,
is the observation end time;
formula (6) represents satellite saiThe actual moment of completing the g-th time plus the task switching time is less than or equal to the actual starting time of the g + 1-th observation, wherein,
representing satellite saiThe time when the g-th observation is actually finished;
representing satellite saiCompleting the task switching time from the g th time to the g +1 th time;
represents the actual start time of the g +1 st observation.
3. The method for scheduling the multi-satellite cooperative earth observation task by the improved genetic algorithm according to claim 2, wherein the step of solving the multi-satellite cooperative earth observation task model by the improved genetic algorithm to obtain the optimal task sequence planning scheme for observing any one or more target task points by each satellite comprises the steps of:
s301, initializing execution parameters of the improved genetic algorithm;
s302, initializing a population and generating an initial task sequence planning scheme set;
s303, carrying out constraint check and adjustment on the task sequence planning scheme in the initial task sequence planning scheme set;
s304, calculating the fitness value of the task sequence planning scheme in the initial task sequence planning scheme set according to a formula (2);
s305, selecting two different parent planning schemes RAAnd RBPerforming cross operation to obtain two offspring planning schemes RCAnd RD；
S306, planning scheme RCAnd RDPerforming a target insertion operation to obtain a solution REAnd RF，
S307, planning scheme REAnd RFPerforming local search operation to obtain a child planning scheme ReAnd RfAnd judging as the child planning scheme ReAnd RfFitness value greater than planning plan REAnd RFIf the fitness value is satisfied, replacing the parent scheme in the population; otherwise, not performing replacement;
s308, judging whether the maximum iteration times are reached, and if so, outputting the optimal chromosome; otherwise, go to step S305.
4. The method for improving the scheduling of the genetic algorithm for solving the multi-satellite cooperative earth observation task according to claim 3, wherein the initializing the population and generating the initial task sequence planning scheme set comprise:
s302a, collecting target task points NtaRandomly arranging the target task points in NrForming a first dimension code of the chromosome;
s302b for permutation NrFrom the set NsaRandomly selecting a satellite for observation to form a second dimension code of the chromosome;
s302c, selecting the observed target task points according to the satellite numbers, and arranging the target task points in a non-descending order according to the earliest starting observation time of the time windows of the target task points, thereby obtaining a target task point sequence H observed by the satellitek；
S302d, according to the preset population size NpAnd repeating the steps S302 a-S302 c to obtain an initial task sequence planning scheme set.
5. The method for scheduling multi-satellite cooperative earth observation tasks through improved genetic algorithm according to claim 3, wherein two different parent planning schemes R are selectedAAnd RBPerforming cross operation to obtain two offspring planning schemes RCAnd RDThe method specifically comprises the following steps:
s305a, selecting 2 chromosome parent R from population by using roulette methodAAnd parent RBFor cross-operation, according to parent RAAnd parent RBThe 2 nd line of the chromosome is divided into n segments;
s305b, judging the segmented parent chromosome RAkAnd RBkIf the same target task point does not exist in the first line, one target task point is randomly selected, and the segment parent chromosome R is segmentedAkAnd RBkThe gene position behind the target task point is exchanged to obtain a new offspring chromosome segment RCkAnd RDkIf the same target task point exists, segmenting the parent chromosome RAkAnd RBkThe gene position behind the target task point is exchanged to obtain a new offspring chromosome segment RCkAnd RDk；
S305c, repeating the step S305b according to the number n of the satellites, completing the single-point crossing operation of all the segments, and respectively combining the segments into complete offspring chromosomes according to the sequence of the satellite numbers.
6. The method for improving genetic algorithm solution of multi-satellite cooperative earth observation task scheduling as claimed in claim 3, wherein the pair planning scheme RCAnd RDPerforming a target insertion operation to obtain a solution REAnd RFThe method comprises the following steps:
s306a, if the target task point set N is not observedvcIf not, go to step S306 b; otherwise, outputting the result;
s306b, collecting N from the satellitesaMedium random selection satellite sai；
S306c, judging the satellite saiIf the storage limit constraint is satisfied, if the constraint is violated, go to step S306 b; otherwise, go to step S306 d;
s306d, using formula (7) from NvcSelecting a target task point ti；
Equation (7) shows sa according to satelliteiSet N of unobserved target task pointsvcSelecting target task point t to be insertediTo satellite saiLast observed target task point t in current task sequenceuAnd non-inserted target task point tiThe difference between the task switching time and the actual observation starting time and the earliest observation starting time is minimized to be the selection criterion of the target task point;
s306, 306e checking the inserted target task point tiWhether constraint conditions are met or not, and if the constraint conditions are met, the target task point t is setiInserted into the current sequence of observation tasks and taken from the set NvcMid-delete target task point tiGo to step S306 d; otherwise, go to step S306 f;
s306f, collecting N from the satellitesaMedium deleted satellite saiGo to step S306 a.
7. The method for improving the scheduling of genetic algorithm solution multi-satellite cooperative earth observation tasks according to claim 3, wherein the local search operation comprises: a single segment search operator and a multi-segment search operator, wherein,
the single segment search operator includes: randomly selecting a segment from the chromosome, and inserting the target task point t at the feasible position selected in the segmentiRecording the current solution and comparing the current solution with the original solution every time of operation, and replacing the original solution if the current solution fitness function value is larger than the original solution fitness function value;
the multi-segment search operator includes: randomly selecting two segments from the chromosome, and respectively selecting a target task point t from the two segmentsi,tjAnd performing exchange insertion to a feasible position, recording the current solution and comparing the current solution with the original solution every time of performing operation, and replacing the original solution if the current solution fitness function value is greater than the original solution fitness function value.
8. An improved genetic algorithm solving multi-satellite cooperative earth observation task scheduling device is characterized by comprising the following steps:
the data acquisition module is used for acquiring a target task point set and a satellite resource set;
the model construction module is used for constructing a multi-satellite cooperative earth observation task model by taking the number of the maximized satellite observation target task points in the task time as a target based on the target task point set and the satellite resource set;
and the model solving module is used for solving the multi-satellite cooperative earth observation task model by adopting a genetic algorithm with a single-point multi-segment crossover operator to obtain an optimal task sequence planning scheme for observing any one or more target task points by each satellite.
9. A computer-readable storage medium storing a computer program for improving a genetic algorithm to solve the multi-satellite cooperative earth observation task scheduling, wherein the computer program causes a computer to execute the method for improving the genetic algorithm to solve the multi-satellite cooperative earth observation task scheduling according to any one of claims 1 to 7.
10. An electronic device, comprising:
one or more processors;
a memory; and
one or more programs, wherein the one or more programs are stored in the memory and configured to be executed by the one or more processors, the programs comprising instructions for performing the improved genetic algorithm solution multi-star collaborative earth observation task scheduling method of any of claims 1-7.
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