CN109684666A - Aircraft index parameter susceptibility optimization method based on genetic algorithm - Google Patents
Aircraft index parameter susceptibility optimization method based on genetic algorithm Download PDFInfo
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Abstract
This application provides a kind of aircraft index parameter susceptibility optimization method based on genetic algorithm, comprising: analysis one-parameter susceptibility；Analysis based on the one-parameter susceptibility is as a result, determine the decision variable and constraint condition of influence index parameter；Large sample optimizing is carried out to aircraft index parameter value, aircraft index parameter value is obtained and optimizes solution space；It is optimized with optimizing solution space to the index parameter susceptibility of aircraft.
Description
Technical field
This application involves aircraft technology fields, and it is excellent specifically to provide a kind of aircraft index parameter susceptibility based on genetic algorithm
Change method.
Background technique
Parameter sensitivity optimization refers to that specific input parameter determines difference to the sensitivity of output with this in analysis system
It inputs parameter and the impact factor of output is studied to judge that the importance of each input parameter provides foundation with sensitivity analysis
The stability of optimal solution when initial data is inaccurate or changes.
Currently, the optimization of fighter plane index parameter susceptibility is only capable of providing " carpet figure ", theoretical depth is inadequate, model is simply thick
Rough, the sensitivity analysis method taken in projects model task is inconsistent, and without rule, the result provided is often unilateral, section
The property learned is inadequate, and method is immature, not system.
Summary of the invention
At least one in order to solve the above-mentioned technical problem, the aircraft index ginseng based on genetic algorithm that this application provides a kind of
Number susceptibility optimization method, comprising: analysis one-parameter susceptibility；Analysis based on the one-parameter susceptibility is as a result, determine shadow
Ring the decision variable and constraint condition of index parameter；Large sample optimizing is carried out to aircraft index parameter value, obtains aircraft index ginseng
Numerical optimization solution space is optimized with optimizing solution space to the index parameter susceptibility of aircraft.
According at least one embodiment of the application, one-parameter susceptibility is analyzed, including finds sensitive spot selective goal ginseng
Number；The transformation in susceptibility section is carried out to sensitive spot；Calculate the index parameter of aircraft；Find the variation rule of aircraft index parameter
Rule；Carry out sensitivity analysis, obtains the conclusion of one-parameter sensitivity analysis.
According at least one embodiment of the application, sensitivity coefficient is calculated according to the following formula:
Wherein, E is sensitivity coefficient of the evaluation index A to factor F, and Δ F is the change rate of uncertain factor F, and Δ A is
The change rate of evaluation index A when uncertain factor F changes.
According at least one embodiment of the application, large sample optimizing is carried out to aircraft index parameter value, comprising: based on changing
Into genetic algorithm, every time to more than two decision variables carry out large sample optimizing, according to preliminary design of aircraft target setting
Optimization goal；Wherein, the Revised genetic algorithum are as follows: use optimum maintaining strategy, select to use optimal guarantor when Optimization goal
Deposit strategy.
It according at least one embodiment of the application, obtains aircraft index parameter value and optimizes solution space, comprising: setting decision
The variation range value of variable is as emulation input；Three kinds of design alternative, hybridization, variation genetic operators are as research tool；With excellent
Change design criteria of the obtained solution space as index parameter.
According at least one embodiment of the application, the index parameter includes the climb rate and flight of aircraft.
According at least one embodiment of the application, the decision variable is the influence factor for determining aircraft basic performance,
Wherein, the influence factor includes weight, motor power and aerodynamic force.
According at least one embodiment of the application, the constraint condition is the variation range of the decision variable 5%
Between~25%.
Aircraft index parameter susceptibility optimization method provided by the embodiments of the present application based on genetic algorithm, with the prior art
It compares, the result provided is comprehensive, has science, and genetic algorithm can efficiently search for a very broad problem sky
Between, thus it is many it is potential, important, may the Parameters Optimal Design considered of teacher of being not designed can be investigated by genetic algorithm and
It excavates.
Detailed description of the invention
Fig. 1 is one-parameter sensitivity analysis methods and results schematic diagram provided by the embodiments of the present application；
Fig. 2 is multi-parameter susceptibility optimization method optimal value trend chart provided by the embodiments of the present application；
Fig. 3 is multi-parameter susceptibility optimization method optimal solution space diagram provided by the embodiments of the present application.
Specific embodiment
The application is described in further detail with reference to the accompanying drawings and examples.It is understood that this place is retouched
The specific embodiment stated is used only for explaining related application, rather than the restriction to this application.It also should be noted that in order to
Convenient for description, part relevant to the application is illustrated only in attached drawing.
It should be noted that in the absence of conflict, the features in the embodiments and the embodiments of the present application can phase
Mutually combination.The application is described in detail below with reference to the accompanying drawings and in conjunction with the embodiments.
Aircraft index parameter susceptibility optimization method provided by the embodiments of the present application based on genetic algorithm includes following step
It is rapid:
Step 1, one-parameter susceptibility is analyzed.
In the present embodiment, one-parameter sensitivity analysis method can use one-shot change method (OAT), obtain local sensitivity
Degree, specifically, analysis but Parameter sensitivity the following steps are included:
Find sensitive spot selective goal parameter；
The transformation in susceptibility section is carried out to sensitive spot；
Calculate the index parameter of aircraft；
Find the changing rule of aircraft index parameter；
Carry out sensitivity analysis, obtains the conclusion of one-parameter sensitivity analysis.
Wherein, sensitivity coefficient is to reflect that sensitivity coefficient is higher to the index of factor sensitivity, then sensitivity is got over
Height can be calculated by following formula:
Wherein, E is sensitivity coefficient of the evaluation index A to factor F, and Δ F is the change rate of uncertain factor F, and Δ A is
The change rate of evaluation index A when uncertain factor F changes.
Fig. 1 shows the analysis result of one-parameter sensitivity analysis method.
Step 2, based on the analysis of the one-parameter susceptibility as a result, determining decision variable and the constraint of influence index parameter
Condition.
Wherein, index parameter includes the climb rate and flight of aircraft, and decision variable is to determine aircraft basic performance
Influence factor, influence factor includes weight, motor power and aerodynamic force, and constraint condition is the variation range of decision variable
Between 5%~25%.
In one example, aeroplane performance index parameter is analyzed, determine and chooses sensible factor as decision variable,
It keeps other parameters be in neutral value when being analyzed constant, adjusts parameter to be analyzed every time, according to certain percentage increase and decrease
(for example, carrying out constraint transformation according to 5%~20%), objective function are towards aeroplane performance index parameter, including normal atmosphere
With pressure height, machine lifting resistance characteristic, motor power and oil consumption characteristic, body platform population parameter are let fly away, aircraft weight rises and falls
Braking property etc..
Step 3, large sample optimizing is carried out to aircraft index parameter value, obtains aircraft index parameter value and optimizes solution space.
In the present embodiment, Revised genetic algorithum can be based on by carrying out large sample optimizing to aircraft index parameter value, often
It is secondary that large sample optimizing is carried out to more than two decision variables, according to preliminary design of aircraft target setting Optimization goal.
Wherein, Revised genetic algorithum are as follows: use optimum maintaining strategy, select to use optimal save strategy plan when Optimization goal
Slightly.
The premature problem of genetic algorithm is that more one of distinct issues, Premature Convergence refer in current genetic algorithm
Genetic algorithm early stage, occurs super individual in population, the adaptive value of the individual substantially exceeds the average individual of current population
Adaptive value, so that the individual occupies absolute ratio in population quickly, the diversity of population is reduced rapidly, Swarm Evolution
Ability is lost substantially, so that the phenomenon that algorithm more early converges on locally optimal solution.
Taken in the present embodiment it is conditional most have a conversation strategy, i.e., it is conditional optimized individual is directly delivered to it is next
In generation, is at least equal to prior-generation, can effectively prevent Premature Convergence in this way.
Optimum maintaining strategy just refers to that the highest individual of fitness is not involved in crossing operation and mutation operator in current group,
But the low individual of generated fitness after the genetic manipulations such as intersect, make a variation is replaced in Ben Dai group with it.
The specific operation process of optimum maintaining strategy evolution Model is as follows:
Find out the highest individual of fitness in current group and the minimum individual of fitness；
If the fitness of optimized individual is more taller than the fitness value of total best individual so far in current group,
The then best individual so far new with the composition of current group optimized individual；
The worst individual in current group is replaced with best individual so far.
In some embodiments, obtain aircraft index parameter value optimization solution space the following steps are included:
The variation range value of decision variable is set as emulation input；
Three kinds of design alternative, hybridization, variation genetic operators are as research tool；
To optimize obtained solution space as the design criteria of index parameter.
Wherein, three kinds of design alternative, hybridization, variation genetic operators are as research tool method particularly includes: determine coding
Method；Determine coding/decoding method；Determine fitness transformation rule；Design gene；Determine operating parameter.
Fig. 2 shows multi-parameter susceptibility optimization method optimal value trend charts, and it is excellent that Fig. 3 shows multi-parameter susceptibility
The optimal solution space of change method.
Step 4, it is optimized with optimizing solution space to the index parameter susceptibility of aircraft.
Wherein, according to using fitness function to be set in specific design according to aircraft as the function of detection individual adaptation degree
Count the requirement of specific targets parameter area, be designed tradeoff, and with the relevant specialities comprehensive design such as weight, engine, aerodynamic force
Tradeoff and coordination are taken according to airplane designs restrictive conditions such as increase and decrease double recipe case, engine cost, the demands of aerodynamic arrangement
House.
So far, it has been combined preferred embodiment shown in the drawings and describes the technical solution of the application, still, this field
Technical staff is it is easily understood that the protection scope of the application is expressly not limited to these specific embodiments.Without departing from this
Under the premise of the principle of application, those skilled in the art can make equivalent change or replacement to the relevant technologies feature, these
Technical solution after change or replacement is fallen within the protection scope of the application.
Claims (8)
1. a kind of aircraft index parameter susceptibility optimization method based on genetic algorithm characterized by comprising
Analyze one-parameter susceptibility；
Analysis based on the one-parameter susceptibility is as a result, determine the decision variable and constraint condition of influence index parameter；
Large sample optimizing is carried out to aircraft index parameter value, aircraft index parameter value is obtained and optimizes solution space；
It is optimized with optimizing solution space to the index parameter susceptibility of aircraft.
2. the aircraft index parameter susceptibility optimization method according to claim 1 based on genetic algorithm, which is characterized in that
One-parameter susceptibility is analyzed, including
Find sensitive spot selective goal parameter；
The transformation in susceptibility section is carried out to sensitive spot；
Calculate the index parameter of aircraft；
Find the changing rule of aircraft index parameter；
Carry out sensitivity analysis, obtains the conclusion of one-parameter sensitivity analysis.
3. the Parameter sensitivity optimization method of aeroplane performance index according to claim 2, which is characterized in that according to the following formula
Calculate sensitivity coefficient:
Wherein, E is sensitivity coefficient of the evaluation index A to factor F, and Δ F is the change rate of uncertain factor F, and Δ A is not true
Determine the change rate of evaluation index A when factor F variation.
4. the aircraft index parameter susceptibility optimization method according to claim 1 based on genetic algorithm, which is characterized in that
Large sample optimizing is carried out to aircraft index parameter value, comprising:
Based on Revised genetic algorithum, large sample optimizing is carried out to more than two decision variables every time, is totally set according to aircraft
Count target setting Optimization goal；
Wherein, the Revised genetic algorithum are as follows: use optimum maintaining strategy, select to use optimal save strategy plan when Optimization goal
Slightly.
5. the aircraft index parameter susceptibility optimization method according to claim 4 based on genetic algorithm, which is characterized in that
It obtains aircraft index parameter value and optimizes solution space, comprising:
The variation range value of decision variable is set as emulation input；
Three kinds of design alternative, hybridization, variation genetic operators are as research tool；
To optimize obtained solution space as the design criteria of index parameter.
6. the aircraft index parameter susceptibility optimization method according to claim 1 based on genetic algorithm, which is characterized in that
The index parameter includes the climb rate and flight of aircraft.
7. the aircraft index parameter susceptibility optimization method according to claim 1 based on genetic algorithm, which is characterized in that
The decision variable is the influence factor for determining aircraft basic performance, wherein the influence factor includes weight, motor power
And aerodynamic force.
8. the aircraft index parameter susceptibility optimization method according to claim 7 based on genetic algorithm, which is characterized in that
The constraint condition is the variation range of the decision variable between 5%~25%.
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| CN110376892A (en) * | 2019-07-16 | 2019-10-25 | 东华大学 | A kind of aircraft automatic calibrating method |
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