CN108536921A - Constraint higher-dimension multiple target decomposition optimization for the design of naval vessel general matter - Google Patents
Constraint higher-dimension multiple target decomposition optimization for the design of naval vessel general matter Download PDFInfo
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Abstract
The invention discloses a kind of constraint higher-dimension multiple target decomposition optimizations for the design of naval vessel general matter, it is directed to the characteristics of according to naval vessel multiple target Cooperative Optimization, it establishes with financial charges, flight-deck area, natural rolling period, metacentric height, resistance is the ship design model of optimizing index, then it is that weight vectors distribute individual using matching strategy again, the selection selected the superior and eliminated the inferior using new individual comparison criterion and then Advanced group species, it is proposed that a kind of intelligent Constraint higher-dimension multiple target decomposition optimization optimizes solution to ship design model, so as to be effectively improved the performance of multiple requirement objectives in large ship design.
Description
Technical field
The present invention relates to Ship Design fields, and in particular to a kind of constraint higher-dimension for the design of naval vessel general matter
Multiple target decomposition optimization.
Background technology
It is also one of most important work that large ship general matter optimization design, which is most basic in naval architecture company,.Such as
What suitable general matter of selection will be directly related to safety, economy and the militancy etc. of large ship.Therefore, it designs
Good Optimized model and optimization method has highly important practical significance.Current naval vessel general matter Optimized model at most wraps
4 targets are included, the diverse requirements in actual environment cannot be met well.Simultaneously as having when large ship navigates by water changeable
Property and naval vessel self-technique have complexity, it is very that best naval vessel general matter is acquired using traditional design method
Difficult.Some researchers optimize naval vessel using evolution algorithm at present, however they are by large ship mostly
General matter multi-objective optimization design of power problem is converted into be solved without constraint single-object problem, can only obtain single solution,
The Pareto optimal solution sets being evenly distributed can not be acquired, to which various selection scheme cannot be provided.In addition, some researchs are to big
Type naval vessel general matter optimization design problem carries out multiple-objection optimization, but required disaggregation is unevenly distributed, convergence is bad, to
The design scheme haveing excellent performance cannot be provided.
Invention content
For these reasons, it is necessary to which multiple requirement objectives in large ship design can be effectively improved by providing one kind
The constraint higher-dimension multiple target decomposition optimization for the design of naval vessel general matter of performance.
The present invention provides a kind of constraint higher-dimension multiple target decomposition optimization for the design of naval vessel general matter, the use
Include the following steps in the constraint higher-dimension multiple target decomposition optimization of naval vessel general matter design：
S1, it naval vessel general matter is established according to the general matter, object function and constraints of model designs a model；
S2, design a model to naval vessel general matter the corresponding initial parameter of setting, according to initial parameter generate initial population and
Weight vectors include multiple individuals in the initial population；
S3, the Neighbourhood set for calculating each weight vectors；
Matching strategy is weight vectors distribution individual again for S4, design；
S5, according to the correspondence of weight vectors and individual, from the Neighbourhood set of weight vectors using differential variation and
Crossover operation generates new individual；
The individual next-generation population of comparison criterion selection of S6, design.
Preferably, in step S1,
The general matter includes：Flight-deck is long, flight-deck is wide, water line length, waterline breadth, absorbs water depth, moldeed depth, standard
Displacement, the speed of a ship or plane and Block Coefficient；
The object function includes：Financial charges, metacentric height, flight-deck area, resistance, natural rolling period；
The constraints includes：The wide restriction range of the restriction range of flight-deck length, flight-deck, designed waterline length
Restriction range, the restriction range of designwaterline breadth, the restriction range of designed draft, the restriction range of moldeed depth and standard displacement
Restriction range.
Preferably, the step S4 include it is following step by step：
S41, all weight vectors and individual are initialized；
Partial ordering relation between S42, calculating weight vectors and individual；
S43, according to the partial ordering relation between weight vectors and individual, to be not previously allocated the weight vectors distribution of individual
Body；
S44, until all weight vectors individual is all assigned.
Preferably, the step S43 includes following supplementary condition：
When an individual is assigned under two weight vectors, carried out therewith by the weight vectors of individual choice preference
Matching.
Preferably, the step S6 include it is following step by step：
S61, the new individual generated according to step S5, repair new individual；
S62, the new individual after repairing is subjected to good and bad comparison with the old individual in step S2；
S63, according to comparison result, select winning individual to be exported as next-generation population.
Constraint higher-dimension multiple target decomposition optimization of the present invention for the design of naval vessel general matter is directed to basis
The characteristics of naval vessel multiple target Cooperative Optimization, establishes with financial charges, flight-deck area, natural rolling period, initial stability
High, resistance is the ship design model of optimizing index, is then that weight vectors distribute individual using matching strategy again, using new
The selection selected the superior and eliminated the inferior of individual comparison criterion so that Advanced group species, it is excellent to propose that a kind of intelligent Constraint higher-dimension multiple target is decomposed
Change method optimizes solution to ship design model, so as to be effectively improved multiple requirement objectives in large ship design
Performance.
Description of the drawings
The step of Fig. 1 is the constraint higher-dimension multiple target decomposition optimization of the present invention designed for naval vessel general matter
Flow chart.
Specific implementation mode
In order to make the purpose , technical scheme and advantage of the present invention be clearer, with reference to the accompanying drawings and embodiments, right
The present invention is further elaborated, it should be understood that and the specific embodiments described herein are merely illustrative of the present invention, and
It is not used in the restriction present invention.
The present invention provides a kind of constraint higher-dimension multiple target decomposition optimization for the design of naval vessel general matter, the use
Include the following steps in the constraint higher-dimension multiple target decomposition optimization of naval vessel general matter design：
For the constraint higher-dimension multiple target decomposition optimization of naval vessel general matter design, as shown in Figure 1, described be used for warship
The constraint higher-dimension multiple target decomposition optimization of ship general matter design includes the following steps：
S1, it naval vessel general matter is established according to the general matter, object function and constraints of model designs a model；
S2, design a model to naval vessel general matter the corresponding initial parameter of setting, according to initial parameter generate initial population and
Weight vectors include multiple individuals in the initial population；
S3, the Neighbourhood set for calculating each weight vectors；
Matching strategy is weight vectors distribution individual again for S4, design；
S5, according to the correspondence of weight vectors and individual, from the Neighbourhood set of weight vectors using differential variation and
Crossover operation generates new individual；
The individual next-generation population of comparison criterion selection of S6, design.
Design a model specifically, initially setting up large ship general matter, including the general matter of model, object function and
Constraints.
I.e. determining general matter has the long Ld of flight-deck, the wide Bd of flight-deck, water line length Lw, waterline breadth Bw, drinking water deep
T, moldeed depth D, standard displacement △, speed of a ship or plane V and Block Coefficient Cb.Determining object function has financial charges TcIt minimizes, is just steady
Property high GM most rationalizes, flight-deck area S is maximized, resistance P is minimized and natural rolling period TrIt maximizes, respectively such as
Formula (1) is to shown in formula (5).
Tc=0.26 (2000Ws 0.85+3500Wo+2400P0.8)+40000(△-Ws-Wo-Wm)
Ws=0.034Lw 1.7Bw 0.7D0.4Cb 0.5
Wo=1.0Lw 0.8Bw 0.6D0.3Cb 0.1
Wm=0.17P0.9 (1)
S=Ld×Bd (3)
Wherein, △0=58000t, V0=29kn, P0=200000hp.
Determine constraints：The long Ld ∈ [280m, 350m] of flight-deck, the wide Bd ∈ [60m, 80m] of flight-deck, design water
Line length Lw ∈ [250m, 300m], designwaterline breadth Bw ∈ [35m, 50m], designed draft T ∈ [8m, 12m], moldeed depth D ∈ [25m,
35m] and standard displacement △ ∈ [60000t, 80000t].2.5<GM<4.5, T/Lw>0.035, Tr>13, Ld<1.128Lw Bd
<1.84Bw。
Shown in the large ship general matter mathematical optimization models finally established such as formula (6).
Then it designs a model to naval vessel general matter and sets corresponding initial parameter, that is, the sampling on each target direction is set
Number H, population scaleMaximum evolution iterations Gmax, zoom factor F intersect factor CR；And according to above-mentioned first
Beginning parameter generates the initial population that scale is N, including individual, i.e., generates beginning population X at random1,X2,…Xi,…XN, Xi=
{ Ld, Bd, Lw, Bw, T, D, Δ, Vk, Cb }, calculate population in all individuals target function value F (Xi)=(Tr, GM, S, Tr,
P)；Construct reference point Z*=(z1,z2,…,z5), zi=min (fi(X) | X ∈ Ω), i=1,2 ... 5；
N number of equally distributed weight vectors λ is generated according to above-mentioned initial parameter1,λ2,L,λN；
According to the weight vectors of above-mentioned generation, the T weight vectors set B apart from each weight vectors arest neighbors is calculated
(i), that is, the Euclidean distance between weight vectors is calculated, determines weight vectors Neighbourhood set B (i)={ i1,i2,L,iT, { i1,i2,L,
iTRepresent distance weighting vector λiThe index of T nearest weight vectors；
Then it is weight vectors λ1,λ2,L,λNDistribution individual, it is null matrix, Ψ to initialize set Ψ (N rows N row) first
(i, j)=0 indicates weight vectors λiIndividual is not distributed, Ψ (i, j)=1 indicates λiIt is assigned with individual；Initialize set Rλ(1 row
N is arranged) it is zero array, Rλ[i]=0 indicates weight vectors λiIt is optional；Initialize set RX(1 row N row) are zero array, RX[i]
=0 indicates individual XiIt is optional；Initialization S is empty set.
Calculate partial ordering set φλAnd φX.Wherein, φλThe i-th row element represent weight vectors λiIt is calculated by formula (7)
To the △ about all individualsλ, and in ascending order.φXJth row element represent individual XjBy formula (8) be calculated about
The △ of all weight vectorsX, and in ascending order；
△λ(λ, X)=gte(X|λ,z*) (7)
Randomly choose RλThe weight vectors λ of [i]=0i；And select weight vectors λiIt is lower that there is minimum △λIndividual, and
Weight vectors λiMeet Ψ (i, j)=0, later by Ψ (i, j)=1.
Judge RX[j]=0 is then by XjDistribute to λi, S=S ∪ Xj, Rλ[i]=1, RX[j]=1；Otherwise RX[j]=1,
This illustrates XjIt has been allocated to another weight vectors λk, and λiAnd λkMost preference individual Xj, so XjSelection has smaller △X
Weight vectors λi, and by Rλ[i]=1, RX[k]=0；
If RλAll elements in array are 1, and S is exported；Otherwise R is randomly choosed againλThe weight vectors of [i]=0
λi。
From each B (i), i=1,2 ..., two individual X are randomly selected in Nr1、Xr2With XiBy differential variation operation and
Crossover operation generates experiment individual Y；
Y'=Xi+F×(Xr1-Xr2)
If the one-dimensional component of certain of Y exceeds domain, repair operation is carried out to the component, allows it again in domain,
And calculate target function value F (Y)=(f of newborn individual1(Y),f2(Y),L,fm(Y))；Update reference point：If
Then enable
Individual comparison phase：If Y is better than Xi, Xi=Y, FV are enabledi=F (Y)；
Y is better than
Wherein, G1And G2Respectively represent individual Y and XiConstraint violation degree.
Wherein, t is evolution iterations, GmaxFor maximum evolution iterations.
If t=Gmax, algorithm stops and exports the Pareto optimal solutions in population as a result, otherwise, t=t+1,
Then matching strategy is weight vectors distribution individual to secondary design again again, until Pareto optimal solutions export.
Constraint higher-dimension multiple target decomposition optimization of the present invention for the design of naval vessel general matter is directed to basis
The characteristics of naval vessel multiple target Cooperative Optimization, establishes with financial charges, flight-deck area, natural rolling period, initial stability
High, resistance is the ship design model of optimizing index, is then that weight vectors distribute individual using matching strategy again, using new
The selection selected the superior and eliminated the inferior of individual comparison criterion so that Advanced group species, it is excellent to propose that a kind of intelligent Constraint higher-dimension multiple target is decomposed
Change method optimizes solution to ship design model, so as to be effectively improved multiple requirement objectives in large ship design
Performance.
The foregoing is merely presently preferred embodiments of the present invention, is not intended to limit the invention, it is all the present invention spirit and
Within principle, any modification, equivalent replacement, improvement and so on should all be included in the protection scope of the present invention.
Claims (5)
1. a kind of constraint higher-dimension multiple target decomposition optimization for the design of naval vessel general matter, which is characterized in that the use
Include the following steps in the constraint higher-dimension multiple target decomposition optimization of naval vessel general matter design：
S1, it naval vessel general matter is established according to the general matter, object function and constraints of model designs a model；
S2, design a model to naval vessel general matter the corresponding initial parameter of setting, and initial population and weight are generated according to initial parameter
Vector includes multiple individuals in the initial population；
S3, the Neighbourhood set for calculating each weight vectors；
Matching strategy is weight vectors distribution individual again for S4, design；
S5, according to weight vectors with individual correspondence, from the Neighbourhood set of weight vectors utilize differential variation and intersection
Operation generates new individual；
The individual next-generation population of comparison criterion selection of S6, design.
2. it is used for the constraint higher-dimension multiple target decomposition optimization of naval vessel general matter design according to claim 1, it is special
Sign is, in step S1,
The general matter includes：Flight-deck is long, flight-deck is wide, water line length, waterline breadth, absorbs water depth, moldeed depth, standard draining
Amount, the speed of a ship or plane and Block Coefficient；
The object function includes：Financial charges, metacentric height, flight-deck area, resistance, natural rolling period；
The constraints includes：The wide restriction range of the restriction range of flight-deck length, flight-deck, the pact of designed waterline length
The pact of beam range, the restriction range of designwaterline breadth, the restriction range of designed draft, the restriction range of moldeed depth and standard displacement
Beam range.
3. it is used for the constraint higher-dimension multiple target decomposition optimization of naval vessel general matter design according to claim 1, it is special
Sign is, the step S4 include it is following step by step：
S41, all weight vectors and individual are initialized；
Partial ordering relation between S42, calculating weight vectors and individual；
S43, according to the partial ordering relation between weight vectors and individual, the weight vectors to be not previously allocated individual distribute individual；
S44, until all weight vectors individual is all assigned.
4. it is used for the constraint higher-dimension multiple target decomposition optimization of naval vessel general matter design according to claim 3, it is special
Sign is that the step S43 includes following supplementary condition：
When an individual is assigned under two weight vectors, carried out therewith by the weight vectors of individual choice preference
Match.
5. it is used for the constraint higher-dimension multiple target decomposition optimization of naval vessel general matter design according to claim 1, it is special
Sign is, the step S6 include it is following step by step：
S61, the new individual generated according to step S5, repair new individual；
S62, the new individual after repairing is subjected to good and bad comparison with the old individual in step S2；
S63, according to comparison result, select winning individual to be exported as next-generation population.
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| CN113269432A (en) * | 2021-05-20 | 2021-08-17 | 北京航空航天大学 | Service bearing capacity evaluation method based on element aggregation network |
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