CN115759581A - Planning method, device and storage medium for observing dense group targets by multiple agile satellites - Google Patents
Planning method, device and storage medium for observing dense group targets by multiple agile satellites Download PDFInfo
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Abstract
The invention relates to a planning method, equipment and a storage medium for observing a dense group target by a multi-agile satellite, which are used for acquiring parameter information and observation group target information of all agile satellites and constructing a task planning evaluation function and constraint conditions of the multi-agile satellite; calculating the visibility of the agile satellite to an observation target, constructing a visible task sequence according to the visibility, and coding the visible task sequence based on a cuckoo algorithm; and outputting an observation scheme for observing the dense group targets by the multi-agile satellite by using a cuckoo algorithm. The invention can greatly improve the convergence rate of task planning and effectively inhibit the algorithm from being premature so as to realize the rapid and efficient planning of multi-agile satellites and dense group targets.
Description
Technical Field
The invention relates to the technical field of satellite observation, in particular to a planning method, equipment and a storage medium for observing dense group targets by a multi-agile satellite.
Background
Compared with the traditional non-agile observation satellite, the agile earth observation satellite can observe the target when the satellite does not reach the target point or has flown through the target point by adjusting the posture of the satellite, thereby greatly prolonging and expanding the visible window of the satellite on the target and enabling the agile satellite to have stronger earth observation capability.
The pitching and yawing freedom degrees of the agile satellite are increased, the solution space of the agile imaging satellite scheduling problem is enlarged due to a flexible observation mode brought by multiple degrees of freedom and high maneuverability of the agile satellite, the planning and scheduling problem of a single agile satellite is a time-dependent combined optimization problem with high complexity, the collaborative planning and scheduling of multiple agile satellites comprises a multi-satellite task allocation problem and a collaborative planning and scheduling problem, the two problems are coupled with each other to sharply increase the solution space, and the problems are more complicated particularly for complex observation tasks with large observation quantity and strong observation coupling degree, such as the observation of dense group targets by multiple agile satellites.
Currently, research on collaborative scheduling of multiple agile satellites is less. The existing scheduling problem aiming at multi-satellite collaborative planning mainly has three strategies; firstly, random distribution is carried out, and tasks are randomly distributed to a plurality of satellites, so that the superiority of a scheduling strategy is difficult to ensure; secondly, distributing tasks to the satellite with the earliest visible time window according to the earliest distribution of the time window, and distributing the tasks to the satellite with the earliest visible time window by Bianchessisi for planning and scheduling a COSMO-SkyMed constellation; richards and the like successively distribute the observation tasks to each satellite according to the sequence of the visible windows, so that although multi-satellite scheduling is adopted, the satellites are mutually independent, and the observation conflicts among the multiple satellites are not considered; and thirdly, the multi-satellite problem is taken as a single-satellite multi-orbit problem to be subjected to overall optimization processing without task allocation, and the method is only suitable for small-scale quantity of satellites and cannot solve planning and scheduling of large-scale quantity of agile satellites. Meanwhile, common collaborative optimization algorithms adopted at present, such as constraint planning, greedy, dynamic planning, tabu search algorithm, improved genetic algorithm, ant colony algorithm, genetic simulation annealing hybrid algorithm and the like, are easy to cause the algorithm to be early in maturity and fall into a local optimal solution when the ultra-large solution space problem, such as dense group target planning scheduling for multi-agile satellite observation, is solved, and the optimization efficiency is difficult to guarantee.
Disclosure of Invention
In view of the technical problems, the invention provides a planning method, a device and a storage medium for observing a dense group target by a multi-agile satellite, which can greatly improve the convergence rate and effectively inhibit the algorithm from being premature so as to realize the rapid and efficient planning of the multi-agile satellite and the dense group target.
The technical solution for realizing the purpose of the invention is as follows: a planning method for observing dense group targets by multiple agile satellites comprises the following steps:
s1, acquiring all agile satellite parameter information and observation group target information, and constructing a multi-agile satellite mission planning evaluation function and constraint conditions;
s2, calculating the visibility of the agile satellite to an observation target, constructing a visible task sequence according to the visibility, and coding the visible task sequence based on a cuckoo algorithm;
and S3, outputting an observation scheme for observing the dense group targets by the multi-agile satellite by using a cuckoo algorithm.
According to an aspect of the present invention, in step S1, ns agile satellites and Nt observation targets are included, and the planning evaluation function is:
where Ti represents the time taken to observe the ith observation target.
According to an aspect of the invention, in the step S1, the constraint conditions at least include agile satellite attitude maneuver constraints, visibility window execution constraints, agile satellite imaging observation time constraints, solar altitude constraints, and task condition constraints.
According to an aspect of the present invention, in step S2, the visible task sequence is encoded based on a cuckoo algorithm, which is specifically represented as:
wherein the content of the first and second substances,
represents a group of observation schemes, toc, obtained by the ith nest in the t generation with respect to N observation targets N And representing the observation time of the Nth target obtained in the planning evaluation function.
According to an aspect of the present invention, in step S3, specifically, the method includes:
s301, updating the bird nest position of the cuckoo by adjusting the flight step length and the discovery probability of the Levy to optimize a task sequence;
step S302, dynamically adjusting the nests through a learning strategy, abandoning poor nests and generating new nests;
step S303, judging whether a termination condition is met, and outputting the best bird nest;
any egg in the nest of the cuckoo is represented as a task scheduling scheme, and any cuckoo has one egg and only one egg.
According to one aspect of the invention, in said step S301, when the ith cuckoo generates a new solution, the levy flight is performed as follows:
in the formula, alpha>0, representing the step control quantity, num representing the number of hosts, t representing the current algebra,
represents the ith nest of the t generation,
representing point-to-point multiplication, and L y (lambda) representing the L y flight path, where L v g -λ (1＜λ≤3)；
λ is a levy flight parameter, which is an average or expected value of event occurrences during a unit interval, and the calculation formula of the levy is:
where μ and v are normal distribution parameters, θ μ And theta v Is the standard deviation of the normal distribution and,
wherein the content of the first and second substances,
θ v Γ denotes the gamma function, the L vy flight provides random walk, and the random step size is derived from the L vy distribution function.
According to an aspect of the present invention, in the step S302, the position of the bird' S nest is moved to the global optimum position, the global optimum (solution) in the current iteration is always greater than or at least equal to the global optimum in the last iteration, F (x) g (t)) and F (x) g (t-1)) represent the optimal solutions for the bird's nest found in the current iteration and the last iteration, respectively, including:
defining a nest evolutionary degree factor of the learning cuckoo, wherein the formula is as follows:
bird nest evolutionary degree factor utilization algorithm historical information and reflection of levy flight search speed
Wherein, m is more than 0 d Less than or equal to 1, and the current best solution of the bird nest is close to the best solution along with the reduction of md;
defining a nest aggregation factor of the learning type cuckoo, wherein the formula is as follows:
wherein F (xg (t)) is expressed as the optimal solution, mt is expressed as the average value of the optimal positions of all the nests, and the formula is as follows:
f (xi (t)) is expressed as the optimal solution F (xg (t)) is better than the function value of each nest in the current iteration.
According to one aspect of the invention, the step size control quantity and the discovery probability are updated by:
α＝α 0 -α m (m d ,a am )+α s (s d ,a as )
p a ＝p 0 -p m (m d ,a pm )+p s (s d ,a ps )
wherein alpha is 0 For an initial step size and p0 for an initial bird egg discovery probability,
by learning the history information and responding as follows:
wherein l α As step size efficacy factor, l p To find the probabilistic effectiveness factor, α m (m d ,a am ) And alpha s (s d ,a as ) Respectively representing the influence effect of the nest evolution factor and the nest aggregation factor on the step length alpha, p m (m d ,a pm ) And p s (s d ,a ps ) Respectively representing the bird nest evolutionary degree factor and the bird nest aggregation degree factor to the discovery probability p a The effect of (c).
According to an aspect of the invention, there is provided an apparatus comprising: one or more processors, one or more memories, and one or more computer programs; wherein, a processor is connected with a memory, the one or more computer programs are stored in the memory, when the device runs, the processor executes the one or more computer programs stored in the memory, so that the device executes the method for planning the observation of dense group targets by the multiple agile satellites according to any one of the above technical solutions.
According to an aspect of the present invention, there is provided a computer readable storage medium for storing computer instructions, which when executed by a processor, implement a method for planning multiple agile satellites observing dense group targets as described in any one of the above technical solutions.
According to the concept of the invention, a planning method, equipment and a computer readable storage medium for observing a dense group target by a plurality of agile satellites are provided, a learning-type cuckoo search algorithm is provided for the first time to solve the problem of overlarge solution space for planning the plurality of agile satellites and the dense group target, a planning and scheduling model for observing the dense group target by a plurality of agile satellite systems is established, each agile satellite and each observation target in the plurality of agile satellites systems can be cooperatively optimized, and the step length and the nest discovery probability of the cuckoo algorithm are adaptively adjusted by learning the relation between the cuckoo parameters and the calculation convergence speed.
Drawings
FIG. 1 schematically represents a flow diagram of a method for planning multiple agile satellite observations of a dense group of targets, according to one embodiment of the invention;
FIG. 2 schematically represents a flow diagram of a method for planning multiple agile satellite observations of a dense group of targets, according to one embodiment of the invention;
FIG. 3 is a schematic representation of a distribution of 100 dense cluster targets according to one embodiment of the present invention;
FIG. 4 is a schematic representation of a 100 dense cluster target distribution according to one embodiment of the present invention;
FIG. 5 is a schematic representation of the scheduling results of 50 dense group targets after the present invention has been employed, according to one embodiment of the present invention;
FIG. 6 is a schematic representation of the scheduling results of a 100 dense group targets planning using the present invention, according to one embodiment of the present invention.
Detailed Description
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 embodiments will be briefly described below. It is obvious that the drawings in the following description are only some embodiments of the invention, and that for a person skilled in the art, other drawings can be derived from them without inventive effort.
The present invention is described in detail below with reference to the drawings and the specific embodiments, which are not repeated herein, but the embodiments of the present invention are not limited to the following embodiments.
As shown in fig. 1 to 6, a method for planning a multi-agile satellite observation dense group target of the present invention includes the following steps:
s1, acquiring parameter information and observation group target information of all agile satellites, and constructing a multi-agile satellite task planning evaluation function and constraint conditions;
s2, calculating the visibility of the agile satellite to an observation target, constructing a visible task sequence according to the visibility, and encoding the visible task sequence based on a cuckoo algorithm;
and S3, outputting an observation scheme for observing the dense group targets by the multi-agile satellite by using a cuckoo algorithm.
In the embodiment, a planning and scheduling model for observing the dense group targets by the multi-agile satellite system is established, each agile satellite and each observation target in the multi-agile satellite system can be cooperatively optimized, the step length and the discovery probability of the Lnevy flight can be adaptively adjusted by learning the current state and the historical state of the bird nest, the depth and the breadth of searching can be balanced to accelerate the convergence speed and jump out a local optimal solution, so that the global optimal solution can be quickly searched in a larger solution space, the convergence speed of task planning can be greatly improved, the algorithm is effectively inhibited from being premature, and the quick and efficient planning of the multi-agile satellite and the dense group targets can be realized.
In an embodiment of the present invention, preferably, in the step S1, ns agile satellites and Nt observation targets are included, and the planning evaluation function is:
where Ti represents the time it takes to observe the ith observed object.
In one embodiment of the present invention, preferably, in the step S1, the constraint conditions at least include an agile satellite attitude maneuver constraint, a visibility window execution constraint, an agile satellite imaging observation time constraint, a solar altitude angle constraint, and a mission condition constraint.
Wherein, the agile satellite attitude maneuver constraint is expressed as the actual observation starting time between two observed targets i and j if the same satellite observes 2 adjacent targets
And with
The interval is larger than or equal to the sum of the maneuvering time and the imaging time for observing the two targets, and is expressed as follows:
for observing attitude maneuver time required by target i by agile satellite
Which means that the measured value, in seconds,
the time required to observe the object i.
The visible window execution constraint is expressed as that the satellite only works in the visible execution window with the observation target point,
to observe the start time of the observation of the k-th orbit by the target i and the satellite j,
observing the target i and the satellite j at the observation end time of the kth orbit circle, observing the target i and the satellite j at the visible time window of the kth orbit circle,
for the start time of visibility for target i and satellite j,
for target i and satellite j visible end times, the expression is:
the agile satellite load single-track longest working time constraint is as follows:
j represents the jth satellite; cj is the number of orbital turns of the satellite j in the planning period, kj is the maximum time duration that the satellite j can work in a single orbital turn,
the observation time for target i; an agile satellite imaging observation time constraint represented as:
the solar altitude angle is restricted, when the imaging visible light camera images the ground, the imaging visible light camera should be larger than the minimum solar altitude angle required by the ground observation, and the expression is as follows:
η Qi ≥η min ，
wherein eta min Is the minimum solar altitude.
And (3) task condition constraint, in order to ensure observation quality, only one task can be executed by one satellite at one moment, and a task condition constraint expression is as follows:
in an embodiment of the present invention, preferably, in the step S2, the visible task sequence is encoded based on a cuckoo algorithm, which is specifically represented as:
in a cuckoo-based multi-agile satellite observation dense group target method, each of cuckoo's bird nestsEach egg represents a task scheduling solution, each cuckoo can only lay one egg (and therefore represents a solution),
represents a group of observation schemes, toc, obtained by the ith nest in the t generation with respect to N observation targets N And representing the observation time of the Nth target obtained in the planning evaluation function.
In an embodiment of the present invention, preferably, in the step S3, specifically including:
s301, updating the bird nest position of the cuckoo by adjusting the flight step length and the discovery probability of the Levy to optimize a task sequence;
step S302, dynamically adjusting the bird nest through a learning strategy, discarding the poor bird nest and generating a new bird nest;
step S303, judging whether a termination condition is met, and outputting the best bird nest;
any egg in the nest of the cuckoo is represented as a task scheduling scheme, and any cuckoo has one egg and only one egg.
In this embodiment, the basic idea of the cuckoo optimization algorithm is based on the nest parasitic behavior of cuckoos and the levy flight behavior of birds, and includes three elements of selecting the best, adopting local random movement, and randomly selecting through global levy flight. The L vy flight is a typical non-Gaussian random walk mechanism, the flight second moment of the L vy flight diverges, so that the L vy motion process often generates extremely large jump under the condition of small aggregation, the change of the flight path follows the heavy tail distribution, and the L vy flight can effectively avoid trapping into a local optimal solution. Meanwhile, because the number of available host bird nests is fixed, the host discovers foreign eggs with a discovery probability pa [0,1] so as to simulate the bionic process of a real cuckoo and adjust the overall optimization rhythm.
In one embodiment of the present invention, preferably, in the step S301, when the ith cuckoo generates a new solution, the levy flight is performed as follows:
in the formula, alpha>0, representing the step control quantity, num representing the number of hosts, t representing the current algebra,
represents the ith nest of the tth generation,
represents point-to-point multiplication, and L vy (lambda) represents a L vy flight path, wherein L vy is between g and L -λ (1＜λ≤3)；
λ is a levy flight parameter, which is an average or expected value of event occurrences during a unit interval, and the calculation formula of the levy is:
where μ and v are normal distribution parameters, θ μ And theta v Is the standard deviation of a normal distribution and,
wherein the content of the first and second substances,
θ v Γ denotes the gamma function, the L vy flight provides random walk, and the random step size is derived from the L vy distribution function.
In one embodiment of the present invention, preferably, in the step S302, in the learning cuckoo algorithm, the position of the bird nest is moved to the global optimum position, the global optimum value (solution) in the current iteration is always greater than or at least equal to the global optimum value in the last iteration, and F (x) is g (t)) and F (x) g (t-1)) respectively represent the best solutions for the bird's nest found in the current iteration and the last iteration, including:
defining a nest evolutionary degree factor of the learning type cuckoo, wherein the formula is as follows:
the bird's nest evolutionary degree factor utilizes the historical information of the algorithm and reflects the search speed of levy flight,
wherein, m is more than 0 d Less than or equal to 1, and the current best solution of the bird nest is close to the best solution along with the reduction of md;
defining a nest aggregation factor of the learning type cuckoo, wherein the formula is as follows:
the aggregation factor represents the aggregation degree of all current nests, the diversity of the nests is reflected, and the larger the aggregation factor is, the smaller the diversity of the nests is.
Wherein F (xg (t)) is expressed as the optimal solution, mt is expressed as the average value of the optimal positions of all the nests, and the formula is as follows:
f (xi (t)) is expressed as the optimal solution F (xg (t)) is better than the function value of each nest in the current iteration.
In one embodiment of the present invention, preferably, the step size control amount and the discovery probability are updated by the following formula:
α＝α 0 -α m (m d ,a am )+α s (s d ,a as )
p a ＝p 0 -p m (m d ,a pm )+p s (s d ,a ps )
wherein alpha is 0 Is the initial step size and p 0 In order to determine the probability that an initial avian egg will be found,
by learning the history information and responding as follows:
wherein l α As a step size effectiveness factor,/ p To find the probabilistic effectiveness factor, α m (m d ,a am ) And alpha s (s d ,a as ) Respectively representing the influence effect of the nest evolution factor and the nest aggregation factor on the step length alpha, p m (m d ,a pm ) And p s (s d ,a ps ) Respectively representing the nest evolutionary degree factor and the nest aggregation degree factor to the discovery probability p a The effect of (c).
In order to increase the diversity of the solution, the learning cuckoo calculation weakens the action effect of the evolutionary factor at the initial stage of the algorithm and strengthens the action effect of the aggregation factor, and in order to improve the convergence speed of the optimization process, the influence of the response of the aggregation factor should be gradually strengthened, and the action influence of the evolutionary factor should be gradually reduced.
In one embodiment of the present invention, preferably, in step S303, the termination condition may be that a set maximum number of iterations is reached or that an iteration error is less than ∈ dd (ε dd May be set according to task requirements), when the termination condition is not satisfied, return to step S301.
As shown in fig. 2, in the planning method for observing a dense group target by using a multi-agile satellite according to the present invention, after parameter information of the multi-agile satellite and target information of an observation group are obtained, a multi-agile satellite task planning evaluation function and constraint conditions are determined, visibility of the agile satellite to the target is calculated, a visible task sequence is encoded, learning type cuckoo algorithm parameters are initialized, nests are obtained at random, the positions of the nests of the cuckoo are updated through L é vy flight to optimize the task sequence, the nests are dynamically adjusted through a learning strategy, poor nests are discarded and new nests are generated, and whether termination conditions are met is judged, and if yes, the best nest is output; and if not, returning to the step of randomly acquiring the bird nest and updating the position of the bird nest of the cuckoo through L é vy flight to optimize the task sequence.
The specific embodiment is as follows:
in the following table, table 1 shows the satellite parameters of an observation system consisting of 12 agile optical satellites. Each satellite has a mobility of 60 °.
TABLE 1
Fig. 5 and 6 are simulation comparisons of 50 dense cluster targets and 100 dense cluster targets using the planning algorithm and the classical modified cuckoo algorithm (ICS), the quantum-behaved particle swarm algorithm (QPSO), and the Genetic Algorithm (GA), respectively.
As can be seen from the simulation results:
(1) The invention has faster convergence rate. Aiming at 50 dense group targets, the target can be converged after 896 iterations to reach a better convergence position, and other methods all need more than 2500 generations; for 100 dense group targets, the invention can converge in 2374 generations and reach a better convergence position, and other methods all need 2800 generations or more.
(2) The invention can obtain a better planning scheme. Aiming at 50 dense group targets, the scheduling scheme takes 32.61 hours to observe all targets, and the corresponding ICS, QPSO and GA methods respectively take 36.0 hours, 46.96 hours and 51.64 hours. For 100 dense group targets, the planning and scheduling scheme takes 69.57 hours to observe all targets, and the corresponding ICS, QPSO and GA methods respectively take 77.70 hours, 81.54 hours and 104.469 hours.
According to an aspect of the invention, there is provided an apparatus comprising: one or more processors, one or more memories, and one or more computer programs; wherein, a processor is connected with the memory, the one or more computer programs are stored in the memory, and when the device runs, the processor executes the one or more computer programs stored in the memory, so that the device executes the method for planning the multi-agile satellite observation dense group targets according to any one of the above technical solutions.
According to an aspect of the present invention, there is provided a computer readable storage medium for storing computer instructions, which when executed by a processor, implement a method for planning multiple agile satellites observing dense group targets as described in any one of the above technical solutions.
In summary, the invention provides a planning method, a device and a computer readable storage medium for observing a dense group target by a multi-agile satellite, firstly provides a learning-type cuckoo search algorithm to solve the problem of an ultra-large solution space for planning the multi-agile satellite and the dense group target, establishes a planning and scheduling model for observing the dense group target by a multi-agile satellite system, can perform cooperative optimization on each agile satellite and each observation target in the multi-agile satellite system, and adaptively adjusts the step length and the bird nest discovery probability of the cuckoo algorithm by learning the relationship between cuckoo parameters and calculation convergence speed.
Furthermore, it should be noted that the present invention may be provided as a method, apparatus or computer program product. Accordingly, embodiments of the present invention may take the form of an entirely hardware embodiment, an entirely software embodiment or an embodiment combining software and hardware aspects. Furthermore, embodiments of the present invention may take the form of a computer program product embodied on one or more computer-usable storage media having computer-usable program code embodied in the media.
Embodiments of the present invention are described with reference to flowchart illustrations and/or block diagrams of methods, terminal devices (systems), and computer program products according to embodiments of the invention. It will be understood that each flow and/or block of the flowchart illustrations and/or block diagrams, and combinations of flows and/or blocks in the flowchart illustrations and/or block diagrams, can be implemented by computer program instructions. These computer program instructions may be provided to a processor of a general purpose computer, embedded processor, or other programmable data processing terminal to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing terminal, create means for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be stored in a computer-readable memory that can direct a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the computer-readable memory produce an article of manufacture including instruction means which implement the function specified in the flowchart flow or flows and/or block diagram block or blocks. These computer program instructions may also be loaded onto a computer or other programmable data processing terminal to cause a series of operational steps to be performed on the computer or other programmable terminal to produce a computer implemented process such that the instructions which execute on the computer or other programmable terminal provide steps for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
It should also be noted that, in this document, the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or terminal 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 terminal apparatus. Without further limitation, an element defined by the phrases "comprising one of \ 8230; \8230;" does not exclude the presence of additional like elements in a process, method, article, or terminal device that comprises the element.
Finally, it should be noted that while the above describes a preferred embodiment of the invention, it will be appreciated by those skilled in the art that, once the basic inventive concepts have been learned, numerous changes and modifications may be made without departing from the principles of the invention, which shall be deemed to be within the scope of the invention. Therefore, it is intended that the appended claims be interpreted as including preferred embodiments and all such alterations and modifications as fall within the scope of the embodiments of the invention.
Claims (10)
1. A planning method for observing dense group targets by multiple agile satellites comprises the following steps:
s1, acquiring all agile satellite parameter information and observation group target information, and constructing a multi-agile satellite mission planning evaluation function and constraint conditions;
s2, calculating the visibility of the agile satellite to an observation target, constructing a visible task sequence according to the visibility, and encoding the visible task sequence based on a cuckoo algorithm;
and S3, outputting an observation scheme for observing the dense group targets by the multi-agile satellite by using a cuckoo algorithm.
2. The method of claim 1, wherein in step S1, including Ns agile satellites, nt observation targets, the planning merit function F is:
where Ti represents the time taken to observe the ith observation target.
3. The method according to claim 1, characterized in that in said step S1, said constraints comprise at least agile satellite attitude maneuver constraints, visibility window execution constraints, agile satellite imaging observation time constraints, solar altitude constraints, mission condition constraints.
4. The method according to claim 1, characterized in that in step S2, the visible task sequence is encoded based on a cuckoo algorithm, specifically expressed as:
wherein the content of the first and second substances,
represents a group of observation schemes, toc, obtained by the ith nest in the t generation with respect to N observation targets N And representing the observation time of the Nth target obtained in the planning evaluation function.
5. The method according to claim 1, wherein in step S3, specifically comprising:
s301, updating the bird nest position of the cuckoo by adjusting the flight step length and the discovery probability of the Levy to optimize a task sequence;
step S302, dynamically adjusting the nests through a learning strategy, abandoning poor nests and generating new nests;
step S303, judging whether a termination condition is met, and outputting the best bird nest;
any egg in the nest of the cuckoo is represented as a task scheduling scheme, and any cuckoo has one egg and only one egg.
6. The method according to claim 5, characterized in that in said step S301, when the ith cuckoo generates a new solution, the L vy flight is performed as follows:
in the formula, alpha>0, representing the step control quantity, num representing the number of hosts, t representing the current generation number,
represents the ith nest of the tth generation,
representing point-to-point multiplication, and L y (lambda) representing the L y flight path, where L v g -λ (1＜λ≤3)；
λ is a levy flight parameter, which is an average or expected value of event occurrences during a unit interval, and the calculation formula of the levy is:
where μ and v are normal distribution parameters, θ μ And theta v Is the standard deviation of the normal distribution and,
wherein, the first and the second end of the pipe are connected with each other,
θ v Γ denotes the gamma function, the L vy flight provides random walk, and the random step size is derived from the L vy distribution function.
7. The method according to claim 6, wherein in step S302, the position of the bird nest is moved to a global optimum position, the global optimum (solution) in the current iteration is always greater than or at least equal to the global optimum in the previous iteration, and F (x) g (t)) and F (x) g (t-1)) represent the optimal solutions for the bird's nest found in the current iteration and the last iteration, respectively, including:
defining a nest evolutionary degree factor of the learning type cuckoo, wherein the formula is as follows:
bird nest evolutionary degree factor utilization algorithm historical information and levy flight search speed reflection
Wherein, 0 < m d Less than or equal to 1, and the current best solution of the bird nest is close to the best solution along with the reduction of md;
defining a nest aggregation factor of the learning type cuckoo, wherein the formula is as follows:
wherein F (xg (t)) is expressed as the optimal solution, mt is expressed as the average value of the optimal positions of all the nests, and the formula is as follows:
expressed as the optimal solution F (xg (t)) outperforms the function value for each nest in the current iteration.
8. The method of claim 7, wherein the step size control quantity and the discovery probability are updated by:
α＝α 0 -α m (m d ,a am )+α s (s d ,a as )
p a ＝p 0 -p m (m d ,a pm )+p s (s d ,a ps )
wherein alpha is 0 Is the initial step size and p 0 In order to determine the probability that an initial avian egg will be found,
by learning the history information and responding as follows:
wherein l α As a step size effectiveness factor,/ p To find the probability effectiveness factor, α m (m d ,a am ) And alpha s (s d ,a as ) Respectively representing the influence effect of the nest evolution factor and the nest aggregation factor on the step length alpha, p m (m d ,a pm ) And p s (s d ,a ps ) Respectively representing the bird nest evolutionary degree factor and the bird nest aggregation degree factor to the discovery probability p a The effect of (c).
9. An apparatus, comprising: one or more processors, one or more memories, and one or more computer programs; wherein a processor is connected to the memory, the one or more computer programs being stored in the memory, the processor executing the one or more computer programs stored in the memory when the apparatus is running, to cause the apparatus to perform a method of planning a multi-agile satellite observation dense group target as claimed in any one of claims 1 to 8.
10. A computer-readable storage medium storing computer instructions which, when executed by a processor, implement a method for planning multiple agile satellites for observing dense group targets according to any one of claims 1 to 8.
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| CN117574779B (en) * | 2024-01-12 | 2024-03-26 | 吉林大学 | Groundwater monitoring network optimization method for improving quantum particle swarm |
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