CN105739304A - Multi-UCAV on-line striking target allocation method of opposition-based genetic algorithm(GA) - Google Patents
Multi-UCAV on-line striking target allocation method of opposition-based genetic algorithm(GA) Download PDFInfo
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Abstract
The invention provides a multi-UCAV on-line striking target allocation method of an opposition-based genetic algorithm(GA). The multi-UCAV on-line striking target allocation method is characterized in that design variables capable of satisfying the constraint of the multi-UCAV on-line striking target allocation method of unequal dimension values can be designed, and the customized improvement of the standard genetic algorithm can be carried out by aiming at the multi-UCAV on-line striking target allocation problem, and then the opposition idea can be introduced into the genetic operation, and the diversity of the population can be increased. The method provided by the invention is advantageous in that the opposition idea processing mechanism and the genetic algorithm can be combined together, and the problems of the conventional algorithm such as locally optimal solution and long time during the solution process can be prevented, and therefore the provided method has the strong global convergence capability, and the problem of the prior art of the low efficiency during the solution of the multi-UCAV on-line striking target allocation can be solved.
Description
Technical field
The present invention relates to a kind of many UCAV based on antithetical ideas improved adaptive GA-IAGA to strike target online distribution method, belong to aircraft mission planning field.
Background technology
Towards increasingly sophisticated modern battlefield environment, battlefield task develops to the situation of multiplicity and complexity gradually, single rack UCAV (unmannedcombataerialvehicle, UCAV) almost cannot completing the combat duty specified, many UCAVs (UCAV) cooperation has become the inevitable choice of UCAV operational exertion.And many UCAV strikes target online, assignment problem is guarantee and the basis of many UCAV cooperation, its objective is to distribute target of attack for UCAV, it is the new and high technology grown up with modern information technologies, it it is one of the important content of many UCAV mission planning technical research, it it is a kind of typical torpedo_damaged warship (Weapon-TargetAssignment is called for short WTA) problem.How WTA is having different lethality with the weapon allocation of economic worth to designing different targets, in order to constitute the Strike system of global optimization.It is a critical function of modern UCAV cooperation, is also an important aid decision of modern automation command system.
The multiobject Weapon Target Assignment Problem of many weapons is a kind of np complete problem, its research contents is primarily directed to multiple striking target, the command and control system of attacker can effectively distribute firepower, quickly target is eliminated, make Least-cost that attacker pays and injuring of striking target maximum simultaneously.WTA is the optimum organization problem of a kind of resource, it is therefore an objective to seek preferably torpedo_damaged warship scheme, to improve fighting efficiency.The eighties in 20th century, general WTA problem has been done comparatively systematic research by PatrickAHosein and the MichaelAthans (1988) of the Massachusetts Institute of Technology.U.S. army national defence analysis and research have been devoted to the research of WTA problem always since the nineties in institute (InstituteforDefenseAnalysis, DA) 20th century.Genetic algorithm (GA) is used for solving WTA problem by Ravindra in nineteen ninety-five.
Genetic algorithm (GeneticAlgorithm, GA) is to be taught by the Holland of Univ Michigan-Ann Arbor USA to propose in 1975, is the biological evolution process of the simulation natural selection of Darwinian evolutionism and the genetic mechanisms method to search for optimal solution.The effective object of genetic algorithm is population, and each individuality in population is corresponding to a feasible solution of required solution problem.Individuality is at microcosmic level generally also referred to as chromosome, and chromosome represents a solution by certain forms (spread pattern such as bit string or symbol) coding.Genetic algorithm is by evolutional operations such as all individual applying intersections, variation and selections, making individual and population adaptive value update, and reach the purpose of trend optimum.But genetic algorithm is a kind of random search algorithm, produces excellent solution and generally requires the very long calculating time, the problem that requirement of real-time is significantly high is difficult to meet time-constrain by some.On the other hand, genetic algorithm is easily precocious when convergence rate is high, and algorithmic statement is in locally optimal solution, and what cause finally obtaining is not globally optimal solution, makes the Quality Down of solution.
Antithetical ideas has long history in philosophy, set theory, politics, sociology and physics, for instance " height " and " low ", " cold " and " heat " etc..And antithetical ideas is never employed in optimized algorithm always, by 2007, antithetical ideas is applied to differential evolution algorithm (DifferentialEvolution by doctor Rahnamayan, DE) in, and demonstrate based on antithetical ideas improve differential evolution algorithm in the quality two that convergence rate is conciliate, be better than DE.
Based on antithetical ideas improved adaptive GA-IAGA (Opposition-basedGA, OGA) many UCAV strike target distribution method online, antithetical ideas is incorporated into genetic algorithm, genetic algorithm is improved, the feature of the assignment problem that strikes target online according to many UCAV, design meets the individual UVR exposure mode of Target Assignment constraint.After intersection, mutation operation, decide whether the population at individual after cross and variation carries out opposition operation according to opposition probability, select according to fitness function value after opposition computing, select preferably population number of individual and be iterated operation as follow-on population at individual, to improve the ability of searching optimum of population diversity and algorithm.Based on antithetical ideas Revised genetic algorithum solve many UCAV strike target online assignment problem time, compare common genetic algorithm and there is stronger ability of searching optimum, improve algorithm performance.
Summary of the invention
The invention aims to solve many UCAV in modern weapons mission planning strike target online assignment problem, for problems such as solution procedure low, the length consuming time of optimal solution convergence, it is proposed to a kind of solve many UCAV based on antithetical ideas Revised genetic algorithum and strike target online assignment problem.The integrated antithetical ideas treatment mechanism of the method and genetic algorithm, avoid and traditional algorithm solution procedure is absorbed in locally optimal solution and consuming time oversize, and the method formed has stronger global convergence ability, alleviate prior art and strike target online the efficiency in assignment problem solving many UCAV.
In order to better illustrate technical scheme, introduce the model of present invention foundation and the method for employing in detail below:
1, many UCAV strike target distribution model online
Assuming in battlefield, we has UCAV, the M={M of m frame isomorphism or isomery1,M2,…,Mm, the numbering of many UCAV is followed successively by 1～m, it is necessary to n target T={T of online strike1,T2,…,Tn, m > n, target designation is followed successively by 1～n, if X={x1,x2,…,xnFor torpedo_damaged warship set, variable xiRepresent the UCAV numbering that i-th target is distributed.
Many UCAV distribution that strikes target online is optimum for target with the whole UCAV overall combat effectiveness formed into columns, and the loss of attack time, target Damage usefulness and the UCAV that the attack time that single UCAV strikes target, many UCAV strike target is the leading indicator evaluating fighting efficiency.
(1) single UCAV attack time is the shortest
Single UCAV attack time is the shortest, and namely first Target Assignment distributes nearest the striking target of attack distance for each UCAV, the attack time needed for then calculating from the position of current UCAV to the target of attack selected.
(2) many UCAV attack time is the shortest
Owing to only meeting in the index situation that single UCAV attack time is the shortest it is possible that the multi rack UCAV situation that one target of distance is close simultaneously, so needing to be thought of as many UCAV the shortest attack time of Target Assignment from the overall situation, making total voyage the shortest, namely total attack time is the shortest.
(3) target Damage usefulness
When target Damage usefulness Maximum Index is by performing task to UCAV, the target value destroyed is assessed, and comes the optimization of guiding target distribution and decision-making towards making the maximized direction of fighting efficiency carry out.This index makes UCAV trend towards attacking high pay-off target.
(4) loss of UCAV
The direction that UCAV loss objective injures cost by minimizing the cost guiding target distribution of UCAV target of attack towards reduction UCAV task carries out.This index makes UCAV trend towards flying at safety fairway, makes the Threat suffered by UCAV minimum.
In the present invention the shortest in index with many UCAV attack time, and assume that all UCAV are identical and each target has equal value, suppose that every frame UCAV can only attack at most a target simultaneously.Carrying out torpedo_damaged warship under above particular case, detailed process is as follows:
(1) unfriendly target is analyzed by the information according to the information of we and UCAV detection, unfriendly target is numbered and threat sequercing.
(2) cost value calculating is carried out according to our UCAV quantity, locus, firepower value and the quantity of unfriendly target, locus, threat value.
(3) using the UCAV-objective cross the shortest for the attack time result as torpedo_damaged warship.
Weapon Target Assignment Problem essence is an optimization problem, and the object function set up herein is that many UCAV attack time is the shortest, and its mathematical model is:
Wherein, xiRepresent the UCAV numbering that i-th target is distributed；t(xi, i) represent xthiIndividual UCAV completes i-th target and hits the required time.
2, antithetical ideas
Assume that x ∈ [a, b], a, b are real number, then the reversely number of x is defined as
Same principle, the definition of inverse algorithms can expand to high dimension vector.
Assume P=(x1,x2..., xD) it is vector, here an x of D dimension space1,x2,……,xD∈ R and xi∈[a,i]bi,Opposite vectorDefined by the reversely number of each component in vector completely
Utilizing the principle of opposite vector, inverse algorithms definition is as follows.
Assume P=(x1,x2..., xD) it is a vector of D dimension space, f () is that fitness function is for calculating the fitness of individuality.Definition according to reversely number,It is vector P=(x1,x2..., xD) opposite vector.If the fitness of opposite vectorSo vectorCan substitute for P.So in order to the individuality making fitness more excellent proceeds procreation, vectorNeed to be evaluated with vector P simultaneously.
3, based on antithetical ideas Revised genetic algorithum
The coded system that genetic algorithm generally adopts is binary coding, but in Weapon Target Assignment Problem, binary coding can not represent the matching relationship of weaponry target intuitively, the present invention adopts decimal scale to be encoded, namely each chromosome is made up of according to target tactic UCAV numbering, and each gene representation distributes to UCAV numbering (all UCAV being numbered in advance) of corresponding target.Such as a chromosome is: [4,5,3,1,2,6,8,7] represent and the UCAV being numbered 4 is distributed to the 1st strike target, and is numbered the UCAV of 7 and distributes to the 8th and strike target.
Obviously, decimally coded representation chromosome can more clearly represent the relations of distribution of weaponry target, current invention assumes that a frame UCAV can only distribute to a target, so occurring that the phenomenon that chromogene repeats is infeasible, thus forbid producing the chromosome of gene redundancy when initializing population, and intersection below or mutation operator adopt special interleaved mode (PMX), variation mode (DM) is to avoid occurring in new individuality the phenomenon that chromogene repeats, in the process of whole iteration, so avoid the occurrence of individual UVR exposure be unsatisfactory for the situation of constraint.
The present invention is achieved through the following technical solutions.
A kind of many UCAV based on antithetical ideas improved adaptive GA-IAGA strike target distribution method online, comprise the following steps that
Step 1, initialization of population, namely strike target online the particular constraints of the given design variable of assignment problem according to many UCAV, namely each dimension numerical value of design variable is not mutually equal, giving the value that all initial population individualities one meet design variable constraint at random, each individuality in initial population is that many UCAV strike target a feasible solution of assignment problem online.
Step 2, whether inspection current iteration number of times meets convergence criterion, and algorithm has multiple convergence criterion, for instance reach maximum iteration time, maximum model call number and optimal solution deviation etc..Whether inspection current iteration number of times meets the concrete grammar of convergence criterion: utilize certain one or more condition in formula (4), (5) and (6), if meeting convergence criterion, then current iteration optimal solution is that current many UCAV strike target the globally optimal solution of assignment problem or suboptimal solution online, exporting currently most result, iteration terminates；
gen≤gen_max(4)
nfe≤NFE_max(5)
Wherein, gen is current genetic algebra, and gen_max is maximum genetic algebra, and nfe is "current" model call number, and NFE_max is maximum model call number, and ε is for being manually set convergence error.
Step 3, the adaptive value of each individuality of population is calculated according to fitness function, adopt roulette selection strategy to select from current population and treat intersection operator, to wait intersect operator according to crossover probability and according to specific crossover operator (PMX) carry out intersect operation, then according to mutation probability and specific mutation operator (DM), the individuality after intersecting being carried out mutation operation, the individuality after intersection, mutation genetic operation remains able to meet the particular constraints of design variable.
Step 4, according to the random number between 0～1 randomly generated, it is judged that this random number, whether less than the opposition probability designed, if met, proceeds to step 5；Otherwise proceed to step 7.
Step 5, does opposition computing according to probability to the individuality in colony, does the individuality after opposition computing and still meets the particular constraints of design variable.The individual opposition that performs in the colony of satisfied opposition probability is operated, P=(x1,x2..., xD) concrete grammar that carries out opposition computing is: utilize formula (7) to calculate the opposition value of each dimension individual；Perform the individuality after opposition operation and still meet the particular constraints of design variable, namely do not have the individuality that each dimension variable value of design variable repeats.
Wherein D is the dimension of individual P, xi∈[ai,bi].The opposition individuality of individual P is
Step 6, calculates, according to fitness function, the adaptive value that newly-generated opposition is individual, original seed group is selected the bigger individuality identical with former population number of adaptive value as current population with newly-generated individuality according to adaptive value size.
Step 7, proceeds iterative cycles using current population as a new generation population, proceeds to step 2.
The present invention has following beneficial effect:
Present invention achieves the assignment problem that strikes target online of many UCAV in modern weapons task grouping to solve, ensure the Optimality understood, avoid and traditional algorithm solution procedure is absorbed in locally optimal solution and too long of problem consuming time, and the method formed has stronger global convergence ability.Antithetical ideas treatment mechanism and genetic algorithm are combined, defines and there is the method for designing processing global optimization ability, solve prior art and strike target online the efficiency in assignment problem solving many UCAV.
Accompanying drawing explanation
Fig. 1 is the algorithm data handling process in detailed description of the invention；
Fig. 2 is current location and the relations of distribution of the UCAV in embodiment one and target；
Fig. 3 is the allocation result of the UCAV in embodiment two and target.
Detailed description of the invention
In order to the purpose of the present invention and advantage are better described, below by simulation calculation contrast test, in conjunction with form, accompanying drawing, the present invention will be further described, and by with traditional optimization results contrast, be verified the combination property of the present invention analyzing.
The effectiveness of extracting method in order to verify, is respectively adopted and solves many UCAV in modern weapons task grouping strike target online assignment problem based on antithetical ideas Revised genetic algorithum (being abbreviated as OGA), traditional genetic algorithm (being abbreviated as GA) customization.8 frame UCAV, two examples attacking 4 targets are selected to be illustrated.Wherein OGA and GA is in testing, and the scale of population all takes 50, and maximum iteration time takes 50, crossover probability Pc=0.9, mutation probability Pm=0.05, reverse probability Po=0.9.
Embodiment one
Assuming that a flight formation has 8 frame UCAV, attack 4 known target, each UCAV can only attack at most a target.When well-known theory optimum allocation result, use genetic algorithm respectively and calculate the averaging model call number required for optimum allocation result based on antithetical ideas Revised genetic algorithum.The changing coordinates of all UCAV and target such as table 1, schematic diagram is as in figure 2 it is shown, the speed of UCAV is set to 1.The numbering of unmanned vehicle is followed successively by 1～8 from left to right, and the numbering of target is followed successively by 1～4.Obviously, weaponry target optimum allocation result is 4 → 1,5 → 2,6 → 3,7 → 4, and namely optimum individual is (3456).
The current location of UCAV and target in table 1 embodiment one
It is as follows that employing is embodied as step based on the antithetical ideas Revised genetic algorithum process online target assignment problem of many UCAV:
Step 1, determines design variable dimension and span thereof according to UCAV and destination number, 8 frame UCAV attack 4 known target, and problem dimension is 4.Strike target online the particular constraints of the given design variable of assignment problem according to many UCAV, giving the value that all initial population individualities one meet design variable constraint at random, each individuality in initial population is that many UCAV strike target a feasible solution of assignment problem online.Such as [1,2,3,4], [3,5,7,8] and [6,2,5,7] are exactly the individuality in initial population.
Step 2, whether inspection current iteration number of times meets convergence criterion, if meeting convergence criterion (error of currently most solution and globally optimal solution is limited to 10e-6), then current iteration optimal solution is that current many UCAV strike target the globally optimal solution of assignment problem or suboptimal solution online, and iteration terminates.
Step 3, calculates the adaptive value of each individuality of population according to fitness function, adopts roulette selection strategy to select from current population and treats intersection operator, will wait to intersect operator according to crossover probability Pc=0.9 and carry out intersecting operation according to specific crossover operator (PMX), then according to mutation probability Pm=0.05 and specific mutation operator (DM) to intersect after individuality carry out mutation operation, through intersection, mutation genetic operation after individuality remain able to meet the particular constraints of design variable.
Step 4, according to the random number between 0～1 randomly generated, it is judged that this random number, whether less than the opposition probability designed, if met, proceeds to step 5；Otherwise proceed to step 7.
Step 5, does opposition computing according to probability to the individuality in colony, does the individuality after opposition computing and still meets the particular constraints of design variable.
Step 6, calculates, according to fitness function, the adaptive value that newly-generated opposition is individual, original seed group is selected the bigger individuality identical with former population number of adaptive value as current population with newly-generated individuality according to adaptive value size.
Step 7, proceeds iterative cycles using current population as a new generation population, proceeds to step 2.
The inventive method and customization GA being contrasted, above-mentioned model has all been carried out 100 times and has solved by two kinds of methods, and its statistical result is shown in Table 2, and solves the statistical information such as the meansigma methods of middle model call number, minima and median including 100 times.
The inventive method and customization GA result of calculation in table 2 embodiment one
Contrasting according to result of calculation, the inventive method and the customization every suboptimization of GA can both obtain globally optimal solution, but, needed for the inventive method, averaging model call number is considerably less than customization GA, and this illustrates that the inventive method is better than customization GA.
Embodiment two
In real situation, to show that the optimum allocation of weaponry target is very consuming time by Theoretical Calculation, it is difficult to meet the requirement of battlefield decision-making real-time, so when theoretical optimum allocation result is unknown, set maximum model call number as 2000 times, use the inventive method and customization GA to solve the online target assignment problem of many UCAV respectively, compare both result of calculation.Assuming that a flight formation has 8 frame UCAV, attack 4 known target, each UCAV can only attack at most a target, UCAV and such as table 3, target current location.
The current location of UCAV and target in table 3 embodiment two
It is as follows that employing is embodied as step based on the antithetical ideas Revised genetic algorithum process online target assignment problem of many UCAV:
Step 1, determines design variable dimension and span thereof according to UCAV and destination number, 8 frame UCAV attack 4 known target, and problem dimension is 4.Strike target online the particular constraints of the given design variable of assignment problem according to many UCAV, giving the value that all initial population individualities one meet design variable constraint at random, each individuality in initial population is that many UCAV strike target a feasible solution of assignment problem online.
Step 2, whether inspection current iteration number of times meets convergence criterion, if meeting convergence criterion (maximum model call number is 2000), then current iteration optimal solution is that current many UCAV strike target the globally optimal solution of assignment problem or suboptimal solution online, and iteration terminates.
Step 3, the adaptive value of each individuality of population is calculated according to fitness function, adopt roulette selection strategy to select from current population and treat intersection operator, to wait intersect operator according to crossover probability and according to specific crossover operator (PMX) carry out intersect operation, then according to mutation probability and specific mutation operator (DM), the individuality after intersecting being carried out mutation operation, the individuality after intersection, mutation genetic operation remains able to meet the particular constraints of design variable.
Step 4, according to the random number between 0～1 randomly generated, it is judged that this random number, whether less than the opposition probability designed, if met, proceeds to step 5；Otherwise proceed to step 7.
Step 5, does opposition computing according to probability to the individuality in colony, does the individuality after opposition computing and still meets the particular constraints of design variable.
Step 6, calculates, according to fitness function, the adaptive value that newly-generated opposition is individual, original seed group is selected the bigger individuality identical with former population number of adaptive value as current population with newly-generated individuality according to adaptive value size.
Step 7, proceeds iterative cycles using current population as a new generation population, proceeds to step 2.
Above-mentioned model has all been carried out 100 times and has solved by two kinds of methods, its statistical result is shown in Table 4, try to achieve the number of times of currently most solution in solving including 100 times, worst individuality and target function value thereof in solving for 100 times, solve middle optimum individual and target function value thereof 100 times.In solving for 100 times, the currently most solution that the inventive method is tried to achieve with customization GA is identical, as shown in Figure 3.And it is 71 times that the inventive method tries to achieve the number of times of currently most solution, and customizes GA and only have and obtain currently most solution 60 times.Meanwhile, the inventive method is obtained worst individuality and is better than customization GA.
The inventive method and customization GA result of calculation in table 4 embodiment two
Above-described specific descriptions; the purpose of invention, technical scheme and beneficial effect have been further described; it is it should be understood that; the foregoing is only specific embodiments of the invention; for explaining the present invention, the protection domain being not intended to limit the present invention, all within the spirit and principles in the present invention; any amendment of being made, equivalent replacement, improvement etc., should be included within protection scope of the present invention.
Claims (3)
1. many UCAV of the genetic algorithm based on antithetical ideas strike target distribution method online, it is characterised in that comprise the following steps that
Step 1, initialization of population, the particular constraints of the design variable that the distribution that namely strikes target online according to many UCAV is given, giving the value that all initial population individualities one meet design variable constraint at random, each individuality in initial population is many UCAV feasible solution striking target in distribution method online；
Step 2, whether inspection current iteration number of times meets convergence criterion, if meeting convergence criterion, then current iteration optimal solution is that current many UCAV strike target the globally optimal solution of assignment problem or suboptimal solution online, and iteration terminates；
Step 3, the adaptive value of each individuality of population is calculated according to fitness function, adopt roulette selection strategy to select from current population and treat intersection operator, to wait intersect operator according to crossover probability and according to crossover operator PMX carry out intersect operation, then according to mutation probability and mutation operator DM, the individuality after intersecting being carried out mutation operation, the individuality after intersection, mutation genetic operation remains able to meet the particular constraints of design variable；
Step 4, according to the random number between 0～1 randomly generated, it is judged that this random number, whether less than the opposition probability designed, if met, proceeds to step 5；Otherwise proceed to step 7；
Step 5, does opposition computing according to probability to the individuality in colony, does the individuality after opposition computing and still meets the particular constraints of design variable；
Step 6, calculates, according to fitness function, the adaptive value that newly-generated opposition is individual, original seed group is selected the bigger individuality identical with former population number of adaptive value as current population with newly-generated individuality according to adaptive value；
Step 7, proceeds iterative cycles using current population as a new generation population, proceeds to step 2.
2. many UCAV of a kind of genetic algorithm based on antithetical ideas according to claim 1 strike target distribution method online, it is characterized in that: whether inspection current iteration number of times described in step 2 meets the concrete grammar of convergence criterion is: utilize certain one or more condition in formula (4), (5) and (6), if meeting convergence criterion, then current iteration optimal solution is that current many UCAV strike target the globally optimal solution of assignment problem or suboptimal solution online, exporting currently most result, iteration terminates；
gen≤gen_max(4)
nfe≤NFE_max(5)
Wherein, gen is current genetic algebra, and gen_max is maximum genetic algebra, and nfe is "current" model call number, and NFE_max is maximum model call number, and ε is for being manually set convergence error.
3. many UCAV of a kind of genetic algorithm based on antithetical ideas according to claim 1 strike target distribution method online, it is characterised in that: described in step 5, individual in the colony of satisfied opposition probability is performed opposition operation, P=(x1,x2..., xD) concrete grammar that carries out opposition computing is: utilize formula (7) to calculate the opposition value of each dimension individual；Perform the individuality after opposition operation and still meet the particular constraints of design variable, namely do not have the individuality that each dimension variable value of design variable repeats；
Wherein D is the dimension of individual P, xi∈[ai,bi]；The opposition individuality of individual P is
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| CN107330560A (en) * | 2017-07-04 | 2017-11-07 | 北京理工大学 | A kind of multitask coordinated distribution method of isomery aircraft for considering temporal constraint |
| CN107330560B (en) * | 2017-07-04 | 2020-05-12 | 北京理工大学 | Heterogeneous aircraft multi-task cooperative allocation method considering time sequence constraint |
| CN108416441A (en) * | 2018-05-10 | 2018-08-17 | 华中科技大学 | A kind of naval vessel opposite bank strike Algorithm of Firepower Allocation based on genetic algorithm |
| CN108416441B (en) * | 2018-05-10 | 2020-05-19 | 华中科技大学 | Ship-to-shore impact firepower distribution method based on genetic algorithm |
| CN110096822A (en) * | 2019-05-08 | 2019-08-06 | 北京理工大学 | Multi-platform cooperative dynamic task allocation method under a kind of condition of uncertainty |
| CN110377048A (en) * | 2019-06-26 | 2019-10-25 | 沈阳航空航天大学 | A kind of unmanned aerial vehicle group defensive disposition method based on genetic algorithm |
| CN114330715A (en) * | 2021-12-27 | 2022-04-12 | 哈尔滨工业大学 | Intelligent ammunition co-evolution task allocation method |
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