CN106295083A - Xuan is repaiied policy optimization method by a kind of wheel based on NSGA II algorithm - Google Patents
Xuan is repaiied policy optimization method by a kind of wheel based on NSGA II algorithm Download PDFInfo
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Abstract
The invention discloses a kind of wheel based on NSGA II algorithm and Xuan is repaiied policy optimization method, Xuan is repaiied strategy as object with wheel at present by the method, repair with wheel and Xuan the longest to service life that number of times is the shortest sets up Model for Multi-Objective Optimization for target, for wheel, Xuan is repaiied policy mandates Xuan and repair number of times this feature as far as possible less, change local density's computational methods of tradition NSGA II, have changed genetic operator, it is achieved that Xuan is repaiied effective optimization of strategy by wheel simultaneously.
Description
Technical field
The present invention relates to a kind of wheel based on the NSGA-II algorithm improved and Xuan is repaiied policy optimization method, belong to calculation
Method optimizes field.
Background technology
Since 21 century, China railways net develops rapidly, State Council of China annual data show 2012 and
Within 2013, China railways runs 98000,103000 kilometers altogether.Plan further according to China 13, whole nation ferrum during 13
Road construction object is newly built railway 2.9 ten thousand kilometers to 150,000 kilometers, wherein high ferro 1.1 ten thousand kilometers.Although China railways is rapidly sent out
Exhibition, China railways maintenance is still within fixing repairing the stage, and Xuan is repaiied and still relies on veteran wheel to enter service engineer by wheel
Line pipe is managed.
Xuan is repaiied, for wheel, the research enriched by Chinese scholars.Before 20th century, Chinese scholars relies primarily on
Many-body dynamics, the friction physical characteristic such as mechanics, car body dynamics of orbits carry out taking turns the analysis to wear characteristics and Xuan repaiies plan
Optimization slightly；After 20 worlds, along with the development of computer science and technology, sight is turned to data analysis etc. by minority scholar
Computer correlation technique, has scholar to utilize the methods such as emulation, numerical analysis to study wheel to the Xuan problem of repairing, but these grinds
Study carefully all by wheel to service life and Xuan repair number of times and treat in isolation, research quantity few, still rely primarily on take turns to correlative
Reason characteristic, produced abundant data during not making full use of train operation.
Take turns to multiple factors such as state and temperature, running route, humidity relevant, and China region is wide, various places physical features
Style and features, weather form are the most different, thus, it is difficult to be analyzed defining to China's Railway wheelset character from physical characteristic.
Data as quantity of information rich in natural resources the most, are contained wheel and directly affect residing environment to it by wheel, base of the present invention
In abundant data resource, for wheel, Xuan repaiied strategy and establish that to consider that Xuan is repaiied number of times by service life and wheel by wheel many simultaneously
Objective optimization model, meanwhile, during repairing Xuan according to wheel, Xuan repaiies number of times this requirement optimization NSGA-II as far as possible less, it is achieved
Effectively take turns and Xuan is repaiied policy optimization, promote that China changes to status maintenance from fixing repairing.
Summary of the invention
Present invention seek to address that the existing problem taken turns and Xuan is repaiied dependence experience.According to wheel to the time of use length as far as possible and wheel
Xuan is repaiied the feature that number of times is the fewest, for single take turns to characteristic be analyzed, propose optimize NSGA-II algorithm, have
Effect ground solves the wheel problem of repairing Xuan, add wheel to service life while decrease wheel to Xuan repair number of times.
The present invention solves above-mentioned technical problem by the following technical solutions:
Xuan is repaiied policy optimization method by a kind of wheel based on NSGA-II algorithm, first to take turns and Xuan the longest to the time of use
Repairing least number of times is target, and the wheel that Xuan repaiies according to specific needs, to determining constraints, sets up Model for Multi-Objective Optimization；Then adopt
Obtain wheel with NSGA-II Algorithm for Solving and Xuan is repaiied strategy.
Described bound for objective function is taken turns abrasion determined by the historical data during operation according to wheel
Curve and different wheel are to being determined.
Described NSGA-II algorithm includes step:
Step (1): generation initial population is as parent population, and calculates the adaptive value of each particle in parent population；Parent
Each particle in population represents one group of Xuan and repaiies strategy；By abrasion model each group of Xuan repaiied strategy carry out Xuan repair number of times and
The calculating of use time, repaiies least number of times as object function using wheel, Xuan the longest to the use time, and Xuan repaiies according to specific needs
Wheel is to determining constraints；
Step (2): repair number of times according to Xuan and parent population is divided into different Xuan repaiies the classification of number of times level；With the probability of 50%
Repair selection particle the classification of least number of times from Xuan, and constituted novel species with equiprobability other particles of cross selection in residue classification
Group；And the new population obtained is intersected, making a variation obtains progeny population；
Step (3): merged with parent population by progeny population, carries out quick non-dominated ranking and utilizes gaussian kernel function meter
Calculate its crowding distance；The computational methods of concrete gaussian kernel function are as follows:
Wherein, dcIt is the value that sets of user, dijIt it is the distance of j point-to-point i；
Step (4): according to the result of step (3) quickly non-dominated ranking and utilize that gaussian kernel function calculates crowded away from
From generating parent population of future generation；
Step (5) judges whether to arrive maximum iteration time, the most then go to step (8)；Otherwise, return step (3),
Iterations adds 1；
Step (6): terminating, the Pareto forward position of the parent population of new generation of generation is required disaggregation, thus is taken turns
Xuan is repaiied strategy.
Parent kind heap sort is specially by described step (2) and repaiies number of times according to Xuan whole population is divided into A, B, C tri-class,
Then the probability with 0.5 is from A classification and new from B itself, C itself, A and B, B and C, A and C selection formation respectively with equiprobability
Population.
The method calculating the parent population after merging and progeny population in described step (3) can also use Euclidean distance,
Chebyshev's distance, manhatton distance or Minkowski Distance calculate to realize expanding to complete by the distance calculating of original monolayer
The distance of office calculates.
Described step (4) is specially and first chooses Pareto ground floor, the then second layer, by that analogy；Arrive kth layer
Time, if kth layer all adds will be greater than open ended Population Size, then preferentially choose crowding distance according to the crowding distance of population
Big particle adds in parent population of future generation.
The beneficial effect that the present invention reaches: 1. for wheel, Xuan is repaiied problem, considers wheel right to service life and wheel simultaneously
Xuan repaiies number of times two aspect, takes full advantage of the mass data taken turns producing during operation, efficiently solves wheel at present right
Xuan repaiies the present situation of optimisation strategy disappearance；2. in the NSGA-II of the improvement that the present invention proposes, the improved method of crowding distance can be
Expand the scope of feasible zone to a certain extent, and expansion effectively is correlated with disaggregation；3. the NSGA-II that the present invention proposes passes through pin
Disaggregation required for problem is expanded by the improvement of the genetic operator of this problem effectively, and solution is maintained at as far as possible required
In the region wanted.
Accompanying drawing explanation
Fig. 1 is for selecting operating process；
Fig. 2 is the NSGA-II flow chart improved.
Detailed description of the invention
In order to make the purpose of the present invention, technical scheme and advantage clearer, below in conjunction with accompanying drawing, the present invention is entered
Row further describes.Specific embodiment described herein only in order to explain the present invention, is not intended to limit the present invention.
Due to the most traditional domestic foreign steamer Xuan repaiied mainly with wheel to physical characteristic, utilize many-body dynamics, car body
The methods such as dynamics of orbits and friction mechanics, numerical analysis, analogue simulation are main, it may appear that optimization object is the most single, and does not has
The problem such as targetedly.This algorithm relies on substantial amounts of wheel data, repaiies number of times at and the Xuan the longest to the use time with wheel set up
Minimum for the Model for Multi-Objective Optimization of target on the basis of, during algorithm after setting up model performs, mass data can quilt
It is used for emulating, and then tries to achieve required wheel Xuan is repaiied strategy, and simulation process needs substantial amounts of historical data eventually
Determine take turns to wear profile.It is the longest that two object functions of multi-objective Model are designated as service life, and Xuan repaiies least number of times, and its
Relevant constraint then to be taken turns being limited by specifically, and object function is as follows:
Wherein, Y is a year number, and F is that Xuan repaiies number of times, and D is that wheel footpath to be maintained between 1150～1250, and T is flange thickness,
Being maintained between 28 and 34, the value before the Xuan that Xuan repaiies each time repaiies is less than the value after Xuan repaiies.
And, the wheel of different model to the condition difference of overall dimensions, meanwhile, be not same take turns to abrasion
Curve abrasion parameter also can difference, thus concrete model needs according to concrete wheel setting up.
The present invention is directed to existing wheel and Xuan is repaiied problem, it is proposed that the NSGA-II algorithm of optimization.On the one hand, due to traditional
NSGA-II only focuses on the range averaging of the point adjacent thereto on Pareto level residing for this point when calculating the crowding distance solved
Value, it is impossible to enough effective overall distribution characters that this point is described.Thus the present invention is in NSGA-II calculates the step of crowding distance
The traditional computational methods changed, have employed gaussian kernel function and substitute crowding distance.The computational methods of gaussian kernel function
As follows:
Wherein, dcIt is the threshold value that sets of user, dijBeing the distance of j point-to-point i, i, j represent the particle in population；
Calculating in the past is limited to one layer, and the present invention calculates distance and is put in all of individuality, other ratio
Such as Euclidean distance, Chebyshev's distance, manhatton distance, Minkowski Distance etc. can also realize original monolayer distance
Calculate and expand to the distance of the overall situation and calculate, utilize the gaussian kernel function present invention to calculate the global density characteristic of arbitrarily solution, and with
This is as the second characteristic during NSGA-II non-dominated ranking.Meanwhile, during non-dominated ranking, all solution can be divided
Being segmented into the 1st, 2,3 ... layer etc., wherein ground floor is Pareto forward position.In traditional NSGA-II, all solution can be arranged according to it
Property be divided, thus, in NSGA-II algorithm proposed by the invention, each solution has two characteristics, the layer that NSGA-II calculates
The crowding distance that secondary and gaussian kernel function calculates.On this basis, in the non-dominated ranking of the present invention, it is in front end level
Solve and can first be selected in parent of future generation, big preferentially the entering compared with other solutions of crowding distance that the gaussian kernel function on same layer is calculated
Select parent.The method, as one of the prioritization scheme of the present invention, has been weighed each from overall angle and has been solved crowding distance, made
Whole efficient solution set is effectively expanded, it is to avoid recessed area is optimum.
On the other hand, owing to Xuan is repaiied policy mandates Xuan by wheel, to repair number of times the fewest, and service life to the greatest extent may be used by simultaneous wheels
Can be long, Xuan repaiies each time is all to sacrifice wheel footpath to reply flange thickness, and reaches the limit of wheel just scrap once take turns footpath, thus subtracts
Go up Xuan less to repair number of times and can reduce the wheel footpath lost because Xuan repaiies to a certain extent, so wheel footpath i.e. have more space by
Expend in the general wear of train operation.Additionally, our multi-objective Model set up is taken turns to service life be also main
Want one of Consideration, thus, required solution must be one and obtain in taking as an elective course number of times and service life and preferably do effect
Combination solves.So the present invention is directed to the particularity of this application scenarios, NSGA-II genetic operator is improved.First, the present invention
To the feasible solution classification obtained, the first kind can will be set to, because Xuan repaiies number of times under normal circumstances less than 10 times with initial setting
Being 3～5 times (repairing the limit), we should fall within 10 as far as possible at required solution, and the most too much takes as an elective course number of times every time
Also bringing along certain economic loss, find Xuan to repair the classification that number of times is few, during selecting, the probability with 50% selects
Select Xuan and repair the feasible solution that number of times is few, and with equiprobability cross selection in residue classification, as a example by Fig. 1, population is divided into parent
A, B, C tri-class, A, B, C repair number of times according to Xuan and classify, thus was selecting in A class i.e. takes as an elective course the number of times part less than 10
Journey takes 50%, simultaneously in order to ensure the multiformity solved, do not omits the outstanding result in inferior solution, also combine that remaining is all kinds of,
Will remaining equal proportion between any two intersect, to keep the multiformity of population.Then when carrying out selecting operation, classification A will be with
The probability of 50% is chosen, and remaining part has B itself, C itself, A Yu B, A Yu C, B Yu C to select with equiprobability the most respectively, this
Sample obtains new parent population.The method is as the optimization method of genetic operator of the present invention, it is possible to required for effectively expanding
Feasible solution.
The multiple-objection optimization mould that Xuan is repaiied by service life and wheel number of times is taken turns as in figure 2 it is shown, the present invention is directed to optimize simultaneously
Mainly comprising the following steps of type:
(1) initialize: generate initial population as parent population；Each particle in parent population represents one group of Xuan and repaiies
Strategy；By abrasion model, each group of Xuan repaiies strategy to carry out Xuan and repair number of times and the calculating of the time of use, with use the time the longest,
Xuan repaiies least number of times as object function, relevant wheel footpath, the limits value of flange thickness and Xuan is repaiied front wheel felloe thickness little
Rear rim thickness is repaiied as restrictive condition in Xuan；
(2) adaptive value of each particle in parent population is calculated；
(3) according to shown in Fig. 1 according to adaptive value, i.e. repair number of times according to Xuan and whole population be divided into A, B, C tri-class, then with
The probability of 0.5 is from A classification, and selects particle to form novel species from B itself, C itself, A and B, B and C, A and C respectively with equiprobability
Group；
(4) utilize in (3) and select the new population obtained to carry out intersecting, making a variation, obtain progeny population；
(5) progeny population is merged with parent population, carry out first attribute of population after non-dominated ranking is merged；
(6) the crowding distance of population, second attribute of population after being merged after utilizing gaussian kernel function calculating to merge；
(7) consider the first attribute of feasible zone and the second attribute (first choose Pareto ground floor, then second
Layer, by that analogy, when arriving kth layer, will be greater than open ended parent if all adding, now according to the second attribute crowding distance
Carry out particle to choose), select parent of future generation, iterations adds 1；
(8) judge whether to arrive maximum iteration time, the most then go to (9), otherwise, go to (3)；
(9) terminating, the Pareto forward position of the parent population of new generation of generation is designated as required disaggregation；Pareto forward position is one to be
Row use time and Xuan repair the combination of number of times, can choose the solution oneself wanted solving to concentrate (time is the longest or takes as an elective course number of times
Minimum), Xuan repair strategy be according to selected service life and Xuan repair that number of times determines (by phase each time when program is run
Close strategy record in an array), the disaggregation obtained and strategy are one to one, and the solution determined just can determine that Xuan repaiies plan
Slightly.
Claims (6)
1. Xuan is repaiied policy optimization method by a wheel based on NSGA-II algorithm, it is characterised in that first with wheel to the time of use
It is target that the longest and Xuan repaiies least number of times, and the wheel that Xuan repaiies according to specific needs, to determining constraints, sets up multiple-objection optimization mould
Type；Then use NSGA-II Algorithm for Solving to obtain wheel and Xuan is repaiied strategy.
Xuan is repaiied policy optimization method by the most according to claim 1 wheel, it is characterised in that the constraint bar of described object function
Wear profile and difference are taken turns being determined taking turns determined by the historical data during operation by part according to wheel.
Xuan is repaiied policy optimization method by the most according to claim 1 wheel, it is characterised in that described NSGA-II algorithm includes
Step:
Step (1): generation initial population is as parent population, and calculates the adaptive value of each particle in parent population；Parent population
In each particle represent one group of Xuan and repair strategy；By abrasion model, each group of Xuan repaiies strategy to carry out Xuan and repair number of times and use
The calculating of time, repaiies least number of times as object function using wheel, Xuan the longest to the use time, and the wheel that Xuan repaiies according to specific needs is right
Determine constraints；
Step (2): repair number of times according to Xuan and parent population is divided into different Xuan repaiies the classification of number of times level；Probability with 50% is from Xuan
Repair selection particle in the classification of least number of times, and constituted new population with equiprobability other particles of cross selection in residue classification；
And the new population obtained is intersected, making a variation obtains progeny population；
Step (3): merged with parent population by progeny population, carries out quick non-dominated ranking and utilizes gaussian kernel function to calculate it
Crowding distance；The computational methods of concrete gaussian kernel function are as follows:
Wherein, dcIt is the value that sets of user, dijIt it is the distance of j point-to-point i；
Step (4): give birth to according to the result of step (3) quickly non-dominated ranking and the crowding distance that utilizes gaussian kernel function to calculate
Become parent population of future generation；
Step (5): judge whether to arrive maximum iteration time, the most then go to step (8)；Otherwise, step (3), iteration are returned
Number of times adds 1；
Step (6): terminate, the Pareto forward position of the parent population of new generation of generation is required disaggregation, thus obtains wheel to Xuan
Repair strategy.
Xuan is repaiied policy optimization method by the most according to claim 3 wheel, it is characterised in that to parent in described step (2)
Kind of heap sort is specially repaiies number of times according to Xuan whole population is divided into A, B, C tri-class, then with 0.5 probability from A classification, and
Select to form new population from B itself, C itself, A and B, B and C, A and C respectively with equiprobability.
Xuan is repaiied policy optimization method by the most according to claim 3 wheel, it is characterised in that calculates in described step (3) and closes
Parent population after and can also use Euclidean distance, Chebyshev's distance, manhatton distance or Min with the method for progeny population
Can Paderewski distance calculate to realize calculating original monolayer apart from calculating the distance expanding to the overall situation.
Xuan is repaiied policy optimization method by the most according to claim 3 wheel, it is characterised in that headed by described step (4) is concrete
First choose Pareto ground floor, the then second layer, by that analogy；When arriving kth layer, will be greater than holding if kth layer all adds
The Population Size received, then preferentially choose the big particle of crowding distance according to the crowding distance of population and add in parent population of future generation.
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