CN109325301A - Weapon Equipment System efficiency fast Optimization - Google Patents
Weapon Equipment System efficiency fast Optimization Download PDFInfo
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- CN109325301A CN109325301A CN201811160511.4A CN201811160511A CN109325301A CN 109325301 A CN109325301 A CN 109325301A CN 201811160511 A CN201811160511 A CN 201811160511A CN 109325301 A CN109325301 A CN 109325301A
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Abstract
The present invention provides a kind of Weapon Equipment System efficiency fast Optimizations, generate design space, specify composition, the task, scenario of Weapon Equipment System to be studied, refine efficiency influencing factor as situational variables, and determine the value constraint of situational variables；Efficiency influencing factor screening, if efficiency influencing factor number is more than the first setting value, screens efficiency influencing factor, removes efficiency influence value less than the efficiency influencing factor of the second setting value, obtains important factor in order, otherwise, saves efficiency influencing factor；Efficiency optimization is carried out to the important factor in order filtered out, optimized by optimization principles selection based on simulation model according to the emulation cycle of weaponry or is optimized based on meta-model, the optimization principles select the Multiple-population Genetic Algorithm of traditional genetic algorithm and mixed discrete optimization according to discrete variable.Using meta-model technical substitution simulation model, Validation of Simulation Models is utilized to optimum results, improves optimization efficiency, guarantees optimization precision.
Description
Technical field
The present invention relates to modeling and simulating field more particularly to a kind of Weapon Equipment System efficiency fast Optimizations.
Background technique
Modern war is the confrontation of both sides' Weapon Equipment System, where focus of the Weapon Equipment System as confrontation both sides,
Directly decide outcome of a war.The superiority and inferiority of Weapon Equipment System can be judged by Weapon Equipment System efficiency, and by pair
The fight capability of Weapon Equipment System can be improved in the optimization of Weapon Equipment System efficiency.
Weapon Equipment System is one complicated " system of system ", each system independent operating, but interdependence is associated with,
Various factors, which cooperatively forms, to be emerged in large numbers, and the fight capability of system entirety is influenced, and shows the general characteristic of complication system, i.e., non-
Linearly, opening, dynamic, diversity, emerging in large numbers property, adaptivity, self-organization etc., this is excellent to Weapon Equipment System efficiency
Change brings extreme difficulties.
Current Weapon Equipment System efficiency optimization method can be divided into two class of analytic method and emulation mode: analytic method category
In static optimization method, this method is optimized using the analytic modell analytical model between System Design index and System Effectiveness evaluation result
It solves, the analytic modell analytical model that this method is established is difficult to describe the complex characteristic of Weapon Equipment System；Simulation method refers to computer mould
Type is laboratory facilities, Weapon Equipment System progress l-G simulation test is substituted by constructing simulation model true to nature, according to simulation result
It optimizes.Emulation mode have many advantages, such as it is with a high credibility, with strong points, repeatable, therefore Weapon Equipment System imitate
It can be widely applied in optimization.
The complexity of Weapon Equipment System brings bigger difficulty to the solution of Weapon Equipment System efficiency optimization problem
It is consumed with calculating, each Optimized Iterative is both needed to operating simulation system, and simulation calculation consumption is excessively huge to result even in optimization
It is infeasible.Agent model (Surrogate model) of the meta-model (Metamodel) as a kind of simplification of simulation model leads to
The inputoutput data of over-fitting simulation model obtains mathematical model that is approximate, simplifying.L-G simulation test is carried out using meta-model,
Can be while guaranteeing precision, Reduction Computation expense significantly improves simulation efficiency.The present invention proposes a kind of Weapon Equipment System
Efficiency fast Optimization, it is theoretical in conjunction with meta-model under " the Weapon Equipment System measures of effectiveness based on emulation " research framework,
The rapid Optimum model based on Weapon Equipment System measures of effectiveness meta-model is constructed, realizes the quick excellent of Weapon Equipment System efficiency
Change.
Summary of the invention
The present invention proposes a kind of Weapon Equipment System efficiency fast Optimization, and its object is to be managed using Simulation Meta Model
By realizing the quick excellent of Weapon Equipment System efficiency based on the rapid Optimum model of Weapon Equipment System measures of effectiveness meta-model
Change, solves the problems, such as the difficulty and meter that complexity because of Weapon Equipment System is brought to the solution of Weapon Equipment System efficiency optimization
Calculate consumption.
The present invention realizes Weapon Equipment System efficiency rapid Optimum using following scheme:
Step 1 is to generate design space first
Composition, task, the scenario etc. of the clear Weapon Equipment System to be studied refine efficiency influencing factor as analysis
Variable, and determine the value constraint of variable；
Step 2 carries out efficiency influencing factor screening
If efficiency influencing factor is excessive, possible some of them factor influences very little to efficiency, even without influence, if
It is not screened out, will increase the workload of efficiency optimization；This step is skipped if factor is relatively few, without Factor Selection,
Factor Selection is carried out essentially according to the process of sensitivity analysis, finally obtains the important factor in order of System Effectiveness；
Step 3 carries out efficiency optimization to the important factor in order filtered out
It is determined to optimize or be based on meta-model based on simulation model according to the emulation cycle of Weapon Equipment System analogue system
Optimization, the selection of optimization method consider to whether there is discrete variable in optimizing factors, select Multiple-population Genetic Algorithm and mix from
The Multiple-population Genetic Algorithm for dissipating optimization, if weapon system model is relatively easy, it may be considered that optimized using traditional genetic algorithm,
Optimum results finally are provided, wherein the optimization based on meta-model needs to test optimum results substitution simulation model.
Preferably, in step 1, efficiency influencing factor includes the war skill index of each weapon system in Weapon Equipment System, force
Device equipment fielding position and weaponry arrangement plan, with x=(x1,x2,…,xn)T∈RnIndicate all efficiency influencing factors, Rn
The real vector space is tieed up for n, n is efficiency influencing factor quantity；
Preferably, in step 2, used efficiency influencing factor screening is comprised the steps of:
Step (1) determines efficiency influencing factor amount threshold Num, can determine as the case may be；
Whether the efficiency influencing factor quantity n formed in step (2), judgment step 1 is greater than Num, if n > Num, uses
Sensitivity Analysis Method screens efficiency influencing factor, and the efficiency influencing factor after screening is denoted as x '=(x '1, x '2...,
x′m)T∈Rm, RmThe real vector space is tieed up for m, m is efficiency influencing factor quantity.
Preferably, in step 3, efficiency optimization is carried out to the important factor in order filtered out and is comprised the steps of:
Step (1) determines Weapon Equipment System analogue system runing time threshold value Tmax, can determine as the case may be；
Step (2) judges whether Weapon Equipment System analogue system running time T is greater than Tmax, if T > Tmax, uses
Optimized based on meta-model, if T≤Tmax, is directly based upon analogue system optimization；
Preferably, it in step (2), is comprised the steps of: based on meta-model optimization
Step (1) obtains test sample
Experimental design is carried out to simulation model, counting system contribution degree obtains test sample, test sample is divided into training
Sample and test sample；
Step (2), construction meta-model
Suitable meta-model is chosen according to model feature, the meta-model of fitting precision, benefit are met based on training sample construction
The precision of prediction that meta-model is examined with test sample, when precision of prediction is met the requirements, then it is assumed that meta-model construction complete, Ke Yidai
For simulation model, otherwise reselects meta-model or increase training sample point, reconfigure meta-model, wanted until meeting precision
It asks；
Step (3) is optimized based on meta-model
Optimization algorithm is chosen, the meta-model based on construction optimizes, and obtained prioritization scheme is substituted into simulation model, such as
The optimum results of fruit meta-model and the simulation result of simulation model meet required precision, then it is assumed that optimum results are credible, otherwise weigh
New optimizing；Wherein, required precision is to calculate the simulation result relative error of the optimum results and simulation model of meta-model, opposite to miss
Difference is less than 1 × 10-3Then think to meet required precision；
Compared with prior art, the present invention its advantages and beneficial effects is:
1. invention provides a kind of Weapon Equipment System efficiency fast Optimizations；
2. scheme of the present invention is under " the Weapon Equipment System efficiency optimization based on emulation " frame foundation, in conjunction with first mould
Type is theoretical, constructs the rapid Optimum model based on Weapon Equipment System measures of effectiveness meta-model, realizes Weapon Equipment System efficiency
Rapid Optimum；
3. be based on fast Optimization provided by scheme of the present invention, using meta-model technical substitution simulation model,
The precision of optimization ensure that while improving optimization efficiency using Validation of Simulation Models to optimum results.
Detailed description of the invention
Upon reading the detailed description of non-limiting embodiments with reference to the following drawings, other feature of the invention,
Objects and advantages will become more apparent upon:
Fig. 1 is the Weapon Equipment System efficiency rapid Optimum flow chart of the embodiment of the present invention；
Fig. 2 is the Surface to air missile armament system schematic diagram that embodiment is related to；
Fig. 3 is embodiment sensitivity analysis process；
Fig. 4 is the flow chart for constructing meta-model；
Fig. 5 is optimization process and optimum results.
Wherein in Fig. 5, Best fitness indicates best features value, Mean fitness
Indicate mean eigenvalue.
Specific embodiment
The present invention is described in detail combined with specific embodiments below.Following embodiment will be helpful to the technology of this field
Personnel further understand the present invention, but the invention is not limited in any way.It should be pointed out that the ordinary skill of this field
For personnel, without departing from the inventive concept of the premise, several changes and improvements can also be made.These belong to the present invention
Protection scope.
Fig. 1 is the Weapon Equipment System efficiency rapid Optimum flow chart of one embodiment of the invention.The present embodiment is anti-with certain
For empty equipment architecture efficiency optimization, as shown in Fig. 2, the Surface to air missile armament system is by two sets of medium antiaricraft missile weapon system groups
At by monitoring warning system, command and control system, firepower intercepting system, communication system and relevant Support system group
At, wherein monitoring warning system include many types of air search radar, be responsible for aerial target carry out monitoring and to detection information into
Row preliminary treatment；Command and control system is divided into two-stage, is responsible for generating situation of battlefield, provides for commander decision assistant, to affiliated
Each combat unit distribution combat duty etc..
According to the composition of the Weapon Equipment System to be studied, task, scenario etc., efficiency influencing factor is refined as analysis
Variable, and determine the value constraint of variable, the efficiency influencing factor of refinement is denoted as y(k), including y(1)Maximum intercepts oblique distance, y(2)It covers
Protect frontal width, y(3)Maximum shooting depth, y(4)Maximum effective fire depth, y(5)Single Shot Probability Of Kill, y(6)Two hair accumulations are killed
Hurt probability, y(7)Firepower port number, y(8)Combat readiness time, y(9)System reaction time, y(10)Duration of run, y(11)In frame guided missile
Quantity, y(12)The missile loading time；
Sensitivity analysis is carried out to efficiency influencing factor, as shown in figure 3, using sequence branches method on 12 efficiency influence because
Element carries out sensitivity analysis, selects four main efficiency influencing factors by 10 testing sieves and kills for maximum interception oblique distance, single-shot
Hurt probability, firepower port number and system reaction time；
The meta-model of efficiency influencing factor and efficiency value is established, constructs first mould as shown in figure 4, testing using Latin hypercube
Type training sample set and test sample collection.System Effectiveness assessment is carried out according to obtained training sample set, obtains assessment result collection,
The random Kriging meta-model of training sample set Yu System Effectiveness assessment result collection is established using random Kriging modeling method,
Precision using test sample collection test meta-model increases Latin hypercube test number (TN) again if being unsatisfactory for required precision
Meta-model is constructed, until meta-model precision is met the requirements；
Optimization algorithm is chosen, the meta-model based on construction optimizes, and select genetic algorithm to carry out optimization in the present embodiment,
Obtained prioritization scheme is substituted into simulation model, if the optimum results of meta-model and the simulation result of simulation model meet precision
It is required that, then it is assumed that optimum results are credible, otherwise optimizing again；Wherein, required precision is to calculate the optimum results of meta-model and imitate
The simulation result relative error of true mode, relative error is less than 1 × 10-3Then think to meet required precision, optimization process and final
Optimum results as shown in figure 5, optimal value is (1- efficiency value) in figure, by optimization, System Effectiveness is by initial 0.61 optimization
It is 0.97, the significant increase efficiency of system.
One skilled in the art will appreciate that in addition to realizing system provided by the invention in a manner of pure computer readable program code
It, completely can be by the way that method and step be carried out programming in logic come so that provided by the invention other than system, device and its modules
System, device and its modules are declined with logic gate, switch, specific integrated circuit, programmable logic controller (PLC) and insertion
The form of controller etc. realizes identical program.So system provided by the invention, device and its modules may be considered that
It is a kind of hardware component, and the knot that the module for realizing various programs for including in it can also be considered as in hardware component
Structure；It can also will be considered as realizing the module of various functions either the software program of implementation method can be Hardware Subdivision again
Structure in part.
Specific embodiments of the present invention are described above.It is to be appreciated that the invention is not limited to above-mentioned
Particular implementation, those skilled in the art can make a variety of changes or modify within the scope of the claims, this not shadow
Ring substantive content of the invention.In the absence of conflict, the feature in embodiments herein and embodiment can any phase
Mutually combination.
Claims (7)
1. a kind of Weapon Equipment System efficiency fast Optimization, which comprises the following steps:
Step A: generating design space, specifies composition, the task, scenario of Weapon Equipment System to be studied, and refining efficiency influences
Factor determines the value constraint of situational variables as situational variables；
Step B: efficiency influencing factor screening, if efficiency influencing factor number is more than the first setting value, to efficiency influencing factor
It is screened, removes efficiency influence value less than the efficiency influencing factor of the second setting value, be denoted as important factor in order, otherwise, then
Save efficiency influencing factor；
Step C: carrying out efficiency optimization to the important factor in order filtered out, according to the selection of the emulation cycle of weaponry based on imitative
True mode optimization is optimized based on meta-model.
2. Weapon Equipment System efficiency fast Optimization according to claim 1, which is characterized in that the efficiency influences
Factor includes war skill index, weaponry deployed position and the weaponry side of allocating of each weapon system in Weapon Equipment System
Case, with x=(x1, x2,…,xn)T∈RnIndicate all efficiency influencing factors, RnFor n tie up the real vector space, n be efficiency influence because
Prime number amount；Subscript T indicates the transposition of set x, xnIndicate n-th of efficiency influencing factor.
3. Weapon Equipment System efficiency fast Optimization according to claim 1, which is characterized in that the step B packet
It includes:
Step B1: efficiency influencing factor amount threshold Num is determined；
Step B2: determining whether efficiency influencing factor quantity n is greater than Num, if n > Num, using Sensitivity Analysis Method to effect
Energy influence factor is screened, and the efficiency influencing factor after screening is denoted as x '=(x '1, x '2..., x 'm)T∈Rm, RmIt is tieed up for m real
Vector space, m are efficiency influencing factor quantity；Subscript T indicates the transposition of set x, xm' indicate m-th of efficiency influencing factor.
4. Weapon Equipment System efficiency fast Optimization according to claim 1, which is characterized in that in the step C
In: optimized by optimization principles selection based on simulation model according to the emulation cycle of weaponry or is optimized based on meta-model,
The optimization principles are to select the heredity on multiple populations of Multiple-population Genetic Algorithm and/or mixed discrete optimization to calculate according to discrete variable
Method.
5. Weapon Equipment System efficiency fast Optimization according to claim 4, which is characterized in that in the step C
In: optimized with traditional genetic algorithm, provide optimum results, wherein optimum results are substituted into simulation model by the optimization based on meta-model
It tests.
6. Weapon Equipment System efficiency fast Optimization according to claim 1, which is characterized in that the step C packet
It includes:
Step C1: Weapon Equipment System analogue system runing time threshold value Tmax is determined；
Step C2: judging whether Weapon Equipment System analogue system running time T is greater than Tmax, if T > Tmax, uses and is based on
Meta-model optimization is optimized if T≤Tmax based on analogue system.
7. Weapon Equipment System efficiency fast Optimization according to claim 6, which is characterized in that meta-model optimization packet
It includes:
Step D1: experimental design is carried out to simulation model, counting system contribution degree obtains test sample, test sample is divided into
Training sample and test sample；
Step D2: choosing suitable meta-model according to model feature, and the meta-model of fitting precision is met based on training sample construction,
The precision of prediction that meta-model is examined using test sample, when precision of prediction is met the requirements, then it is assumed that meta-model construction complete, instead of
Otherwise simulation model then reselects meta-model or increases training sample point, meta-model is reconfigured, until meeting precision
It is required that；
Step D3: choosing optimization algorithm, and the meta-model based on construction optimizes, and obtained prioritization scheme is substituted into emulation mould
Type, if the optimum results of meta-model and the simulation result of simulation model meet required precision, then it is assumed that optimum results are credible, no
Then, then optimizing again；Wherein, required precision is that the optimum results of calculating meta-model are opposite with the simulation result of simulation model accidentally
Difference, relative error is less than 1 × 10-3Then think to meet required precision.
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| CN111879348A (en) * | 2020-07-10 | 2020-11-03 | 哈尔滨工业大学 | Efficiency analysis method for ground test system of performance of inertial instrument |
| CN112819303A (en) * | 2021-01-22 | 2021-05-18 | 中国人民解放军国防科技大学 | PCE agent model-based aircraft tracking efficiency evaluation method and system |
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Inventor after: Qian Xiaochao Inventor after: Lu Zhifeng Inventor after: Guo Tao Inventor after: Lai Peng Inventor after: Yu Haiming Inventor after: Zhou Jinpeng Inventor after: Chen Wei Inventor before: Qian Xiaochao Inventor before: Lu Zhifeng Inventor before: Lai Peng Inventor before: Yu Haiming Inventor before: Zhou Jinpeng Inventor before: Chen Wei |
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Application publication date: 20190212 |