CN106250353A - A kind of entropy weight computational methods and Multiobjective Decision Making Method - Google Patents

Abstract

The invention discloses a kind of entropy weight computational methods and device, the method includes: obtain the evaluation index being evaluated object, and the entropy of each evaluation index；Calculate the entropy weight of each evaluation index according to the following formula: Wherein, ω_ejRepresent the entropy weight of evaluation index j, H_jRepresent the entropy of evaluation index j,Representing the average of all entropy not being 1, m represents the quantity of evaluation index, j=1,2 ..., m.Thus, power all can be rationally composed when entropy is bigger than normal or less than normal, solve the entropy when all evaluation indexes that entropy assessment in prior art exists level off to 1 time, the minute differences of entropy can cause the change at double of corresponding entropy weight, and then the problem causing entropy weight unreasonable distribution.It addition, the embodiment of the present invention additionally provides a kind of Multiobjective Decision Making Method realized based on technique scheme and device, simple and efficient, it is especially suitable for running online.

Description

A kind of entropy weight computational methods and Multiobjective Decision Making Method

Technical field

The present invention relates to entropy assessment technical field, more particularly, it relates to a kind of entropy weight computational methods and multiobjective decision-making Method.

Background technology

Entropy (Entropy) itself is thermodynamics concept, and Shannon (C.E.Shannon) was introduced in theory of information later, composes Give the concept of entropy broad sense.According to the thought of entropy, it is to determine decision accuracy that people obtain the quality and quantity of information in decision-making One of key factor with reliability.Entropy assessment is to utilize entropy can be formed with the feature of the useful information amount that metric data is provided A kind of method of Objective Weight.In the case of given evaluation object collection and various evaluation index value all determine, from information angle From the point of view of Du, the weight using entropy assessment to give certain index represents this index and passes to the size of policymaker's quantity of information, i.e. one finger The difference degree being marked between each evaluation object is the biggest, and the quantity of information that it comprises is the biggest, and its entropy is the least, therefore this index The weight obtained just should be the biggest, and it is the biggest on the impact of assessment result certainly.

Assuming to be evaluated in the appraisement system of object at m item evaluation index, n, iotave evaluation matrix is D_nm, to this matrix It is standardized process and obtains Standard Process R_nm.According to the definition of entropy, determine the entropy of evaluation index j:

$H_j=-k\underset{i=1}{\overset{n}{\Σ}}{f}_{ij}\ln f_{ij}$

$f_{ij}=\frac{R_{ij}}{\underset{i=1}{\overset{n}{\Σ}}{R}_{ij}}$

$k=\frac{1}{\ln n}$

Wherein, H_jRepresent the entropy of evaluation index j, 0≤H_j≤ 1, for making lnf_ijMeaningful, it is assumed that to work as f_ijWhen=0, f_ijlnf_ij=0；I=1,2 ..., n；J=1,2 ..., m.

The entropy weight of entropy Calculation Estimation index j of Utilization assessment index j:

${\mathrm{\ω}}_{ej}=\frac{1-H_j}{\underset{j=1}{\overset{m}{\Σ}}(1-H_j)}$

Wherein, ω_ejRepresent the entropy weight of evaluation index j, 0≤ω_ej≤ 1, and

According to entropy weight calculating formula above, as the entropy H of all evaluation indexes_j→ 1 (j=1,2 ..., time m), entropy Minute differences will cause the change at double of corresponding entropy weight, such as: when evaluation index entropy vector be respectively (0.9999, 0.9998,0.9997) and time (0.9000,0.8000,0.7000), although the difference between entropy is different, but the entropy of both correspondences Weight vector is (0.1667,0.3333,0.5000), and this is the most irrational.And rational pass between entropy weight and entropy System should be: one is that different evaluation index entropy difference means that its quantity of information provided is essentially identical the most at most, then corresponding entropy Power also should be essentially identical；Two is that the quantity of information that different entropy vector representation provides is different, therefore should have different entropy weight to Amount.

In sum, in prior art entropy assessment exist when the entropy of all evaluation indexes level off to 1 time, entropy small Difference can cause the change at double of corresponding entropy weight, and then the problem causing entropy weight unreasonable distribution.

Summary of the invention

It is an object of the invention to provide a kind of entropy weight computational methods and device, to solve what entropy assessment in prior art existed When the entropy of all evaluation indexes level off to 1 time, the minute differences of entropy can cause the change at double of corresponding entropy weight, and then cause The problem of entropy weight unreasonable distribution；It addition, present invention also offers a kind of Multiobjective Decision Making Method and device.

To achieve these goals, the present invention provides following technical scheme:

A kind of entropy weight computational methods, including:

Obtain the evaluation index being evaluated object, and the entropy of each described evaluation index；

Calculate the entropy weight of each described evaluation index according to the following formula:

${\mathrm{\&omega;}}_{ej}=\left\{\begin{array}{cc}(1-\stackrel{\&OverBar;}{H}){\mathrm{\&omega;}}_{ej1}+\stackrel{\&OverBar;}{H}{\mathrm{\&omega;}}_{ej2}& H_j<1\\ 0& H_j=1\end{array}\right.$

${\mathrm{\&omega;}}_{ej1}=\frac{1-H_j}{\underset{j=1,H_j\&NotEqual;1}{\overset{m}{\&Sigma;}}(1-H_j)},{\mathrm{\&omega;}}_{ej2}=\frac{1/H_j}{\underset{j=1,H_j\&NotEqual;1}{\overset{m}{\&Sigma;}}(1/H_j)}$

Wherein, ω_ejRepresent the entropy weight of evaluation index j, H_jRepresent the entropy of evaluation index j,Represent all be not 1 entropy The average of value, m represents the quantity of described evaluation index, j=1,2 ..., m.

A kind of Multiobjective Decision Making Method, including:

Obtain and advance with the Pareto disaggregation that multi-objective Algorithm obtains, and described Pareto disaggregation is standardized place Reason, obtains Standard Process, and in this matrix, every a line represents a solution, total n solution, and every string represents a target, total m Individual target, each solution has m the desired value corresponding with m target；

Calculate the entropy of each target in described Standard Process, and utilize the entropy weight of the following equation each target of calculating:

${\mathrm{\&omega;}}_{ej}=\left\{\begin{array}{cc}(1-\stackrel{\&OverBar;}{H}){\mathrm{\&omega;}}_{ej1}+\stackrel{\&OverBar;}{H}{\mathrm{\&omega;}}_{ej2}& H_j<1\\ 0& H_j=1\end{array}\right.$

${\mathrm{\&omega;}}_{ej1}=\frac{1-H_j}{\underset{j=1,H_j\&NotEqual;1}{\overset{m}{\&Sigma;}}(1-H_j)},{\mathrm{\&omega;}}_{ej2}=\frac{1/H_j}{\underset{j=1,H_j\&NotEqual;1}{\overset{m}{\&Sigma;}}(1/H_j)}$

Wherein, ω_ejRepresent the entropy weight of target j, H_jRepresent the entropy of target j,For the average of all entropy not being 1, m The quantity of expression target, j=1,2 ..., m；

Entropy weight based on each target and desired value corresponding to each solution calculate the comprehensive assessment value of each solution, and choose Solution corresponding to big comprehensive assessment value is last solution.

Preferably, entropy weight based on each target and desired value corresponding to each solution calculate the comprehensive assessment value of each solution, Including:

Calculate the comprehensive assessment value of each solution according to the following formula:

$u_i=\underset{j=1}{\overset{m}{\&Sigma;}}{\mathrm{\&omega;}}_{ej}{r}_{ij}$

Wherein, u_iRepresent the comprehensive assessment value solving i, r_ijRepresent the target solving i correspondence target j in described Standard Process Value.

Preferably, entropy weight based on each target and desired value corresponding to each solution calculate the comprehensive assessment value of each solution, Including:

Obtain the subjective weight given for each target of external world's input, and calculate combining of each target according to the following formula Conjunction weight:

${\mathrm{\&omega;}}_j=\frac{{\mathrm{\&omega;}}_{sj}\&CenterDot;{\mathrm{\&omega;}}_{ej}}{\underset{j=1}{\overset{m}{\&Sigma;}}{\mathrm{\&omega;}}_{sj}\&CenterDot;{\mathrm{\&omega;}}_{ej}}$

Wherein, ω_jRepresent the comprehensive weight of target j, ω_sjRepresent the subjective weight of target j；

Calculate the comprehensive assessment value of each solution according to the following formula:

$u_i=\underset{j=1}{\overset{m}{\&Sigma;}}{\mathrm{\&omega;}}_j{r}_{ij}$

Wherein, u_iRepresent the comprehensive assessment value solving i, r_ijRepresent the target solving i correspondence target j in described Standard Process Value.

Preferably, it is standardized described Pareto disaggregation processing, obtains Standard Process, including:

If described Multiobjective Decision Making Method specially maximizes optimization problem, the most according to the following formula to described Pareto Disaggregation is standardized processing:

$r_{ij}=\frac{x_{ij}-\underset{1\&le;i\&le;n}{\min }{x}_{ij}}{\underset{1\&le;i\&le;n}{max}{x}_{ij}-\underset{1\&le;i\&le;n}{\min }{x}_{ij}}$

If described Multiobjective Decision Making Method specially minimizes optimization problem, the most according to the following formula to described Pareto Disaggregation is standardized processing:

$r_{ij}=\frac{\underset{1\&le;i\&le;n}{max}{x}_{ij}-x_{ij}}{\underset{1\&le;i\&le;n}{max}{x}_{ij}-\underset{1\&le;i\&le;n}{min}{x}_{ij}}$

Wherein, r_ijRepresent the desired value solving i correspondence target j in Standard Process, x_ijRepresent that Pareto solution is concentrated and solve i pair Answer the desired value of target j, 1≤i≤n, 1≤j≤m.

A kind of entropy weight calculates device, including:

Data acquisition module, for obtaining the evaluation index being evaluated object, and the entropy of each described evaluation index；

Entropy weight computing module, for calculating the entropy weight of each described evaluation index according to the following formula:

${\mathrm{\&omega;}}_{ej}=\left\{\begin{array}{cc}(1-\stackrel{\&OverBar;}{H}){\mathrm{\&omega;}}_{ej1}+\stackrel{\&OverBar;}{H}{\mathrm{\&omega;}}_{ej2}& H_j<1\\ 0& H_j=1\end{array}\right.$

${\mathrm{\&omega;}}_{ej1}=\frac{1-H_j}{\underset{j=1,H_j\&NotEqual;1}{\overset{m}{\&Sigma;}}(1-H_j)},{\mathrm{\&omega;}}_{ej2}=\frac{1/H_j}{\underset{j=1,H_j\&NotEqual;1}{\overset{m}{\&Sigma;}}(1/H_j)}$

Wherein, ω_ejRepresent the entropy weight of evaluation index j, H_jRepresent the entropy of evaluation index j,Represent all be not 1 entropy The average of value, m represents the quantity of described evaluation index, j=1,2 ..., m.

A kind of multiobjective decision-making device, including:

Matrix disposal module, advances with, for obtaining, the Pareto disaggregation that multi-objective Algorithm obtains, and tires out described handkerchief Torr disaggregation is standardized processing, and obtains Standard Process, and in this matrix, every a line represents a solution, total n solution, every string Representing a target, total m target, each solution has m the desired value corresponding with m target；

Entropy weight acquisition module, for calculating the entropy of each target in described Standard Process, and utilizes following equation meter Calculate the entropy weight of each target:

${\mathrm{\&omega;}}_{ej}=\left\{\begin{array}{cc}(1-\stackrel{\&OverBar;}{H}){\mathrm{\&omega;}}_{ej1}+\stackrel{\&OverBar;}{H}{\mathrm{\&omega;}}_{ej2}& H_j<1\\ 0& H_j=1\end{array}\right.$

${\mathrm{\&omega;}}_{ej1}=\frac{1-H_j}{\underset{j=1,H_j\&NotEqual;1}{\overset{m}{\&Sigma;}}(1-H_j)},{\mathrm{\&omega;}}_{ej2}=\frac{1/H_j}{\underset{j=1,H_j\&NotEqual;1}{\overset{m}{\&Sigma;}}(1/H_j)}$

Wherein, ω_ejRepresent the entropy weight of target j, H_jRepresent the entropy of target j,For the average of all entropy not being 1, m The quantity of expression target, j=1,2 ..., m；

Last solution acquisition module, the desired value corresponding for entropy weight based on each target and each solution calculates each solution Comprehensive assessment value, and solution corresponding to comprehensive assessment value choosing maximum be last solution.

Preferably, last solution acquisition module includes:

First assessment unit, for calculating the comprehensive assessment value of each solution according to the following formula:

$u_i=\underset{j=1}{\overset{m}{\&Sigma;}}{\mathrm{\&omega;}}_{ej}{r}_{ij}$

Wherein, u_iRepresent the comprehensive assessment value solving i, r_ijRepresent the target solving i correspondence target j in described Standard Process Value.

Preferably, last solution acquisition module includes:

Second assessment unit, calculates combining of each solution for the desired value that entropy weight based on each target and each solution are corresponding Close assessed value, including:

Obtain the subjective weight given for each target of external world's input, and calculate combining of each target according to the following formula Conjunction weight:

${\mathrm{\&omega;}}_j=\frac{{\mathrm{\&omega;}}_{sj}\&CenterDot;{\mathrm{\&omega;}}_{ej}}{\underset{j=1}{\overset{m}{\&Sigma;}}{\mathrm{\&omega;}}_{sj}\&CenterDot;{\mathrm{\&omega;}}_{ej}}$

Wherein, ω_jRepresent the comprehensive weight of target j, ω_sjRepresent the subjective weight of target j；

Calculate the comprehensive assessment value of each solution according to the following formula:

$u_i=\underset{j=1}{\overset{m}{\&Sigma;}}{\mathrm{\&omega;}}_j{r}_{ij}$

Wherein, u_iRepresent the comprehensive assessment value solving i, r_ijRepresent the target solving i correspondence target j in described Standard Process Value.

Preferably, matrix disposal module includes:

First processing unit, if being used for solving maximizing optimization problem for described multiobjective decision-making device, then according to Described Pareto disaggregation is standardized processing by following equation:

$r_{ij}=\frac{x_{ij}-\underset{1\&le;i\&le;n}{\min }{x}_{ij}}{\underset{1\&le;i\&le;n}{max}{x}_{ij}-\underset{1\&le;i\&le;n}{\min }{x}_{ij}}$

Second processing unit, if minimizing optimization problem for described multiobjective decision-making device for solution, then according to Described Pareto disaggregation is standardized processing by following equation:

$r_{ij}=\frac{\underset{1\&le;i\&le;n}{max}{x}_{ij}-x_{ij}}{\underset{1\&le;i\&le;n}{max}{x}_{ij}-\underset{1\&le;i\&le;n}{min}{x}_{ij}}$

Wherein, r_ijRepresent the desired value solving i correspondence target j in Standard Process, x_ijRepresent that Pareto solution is concentrated and solve i pair Answer the desired value of target j, 1≤i≤n, 1≤j≤m.

The invention provides a kind of entropy weight computational methods and device, wherein, the method includes: obtains and is evaluated commenting of object Valency index, and the entropy of each described evaluation index；Calculate the entropy weight of each described evaluation index according to the following formula:

${\mathrm{\&omega;}}_{ej}=\left\{\begin{array}{cc}(1-\stackrel{\&OverBar;}{H}){\mathrm{\&omega;}}_{ej1}+\stackrel{\&OverBar;}{H}{\mathrm{\&omega;}}_{ej2}& H_j<1\\ 0& H_j=1\end{array}\right.$

${\mathrm{\&omega;}}_{ej1}=\frac{1-H_j}{\underset{j=1,H_j\&NotEqual;1}{\overset{m}{\&Sigma;}}(1-H_j)},{\mathrm{\&omega;}}_{ej2}=\frac{1/H_j}{\underset{j=1,H_j\&NotEqual;1}{\overset{m}{\&Sigma;}}(1/H_j)}$

Wherein, ω_ejRepresent the entropy weight of evaluation index j, H_jRepresent the entropy of evaluation index j,Represent all be not 1 entropy The average of value, m represents the quantity of described evaluation index, j=1,2 ..., m.Technique scheme disclosed by the invention, in background Technology adds in the single tax power mode of the formula of original calculating entropy weight tax the power mode, i.e. ω of a kind of complementation_ej1With ω_ej2；And use the mean control two kinds of the entropy of each evaluation index to compose the ratio shared by power mode, i.e. use each evaluation index The average of entropy as weight so that the ω when entropy is close to 0_ej2Shared ratio reduces, when entropy is close to 1 ω_ej1Proportion reduces, and then the scheme calculating entropy weight in the present invention all can rationally be composed when entropy is bigger than normal or less than normal Power, solve the entropy when all evaluation indexes that entropy assessment in prior art exists level off to 1 time, the minute differences meeting of entropy Cause the change at double of corresponding entropy weight, and then the problem causing entropy weight unreasonable distribution.It addition, the embodiment of the present invention additionally provides A kind of Multiobjective Decision Making Method and device, wherein, the method includes: obtains and advances with the Pareto solution that multi-objective Algorithm obtains Collection, and be standardized described Pareto disaggregation processing, obtaining Standard Process, in this matrix, every a line represents a solution, Total n solution, every string represents a target, and total m target, each solution has m the desired value corresponding with m target； Calculate the entropy of each target in described Standard Process, and utilize the entropy weight of the following equation each target of calculating:

${\mathrm{\&omega;}}_{ej}=\left\{\begin{array}{cc}(1-\stackrel{\&OverBar;}{H}){\mathrm{\&omega;}}_{ej1}+\stackrel{\&OverBar;}{H}{\mathrm{\&omega;}}_{ej2}& H_j<1\\ 0& H_j=1\end{array}\right.$

${\mathrm{\&omega;}}_{ej1}=\frac{1-H_j}{\underset{j=1,H_j\&NotEqual;1}{\overset{m}{\&Sigma;}}(1-H_j)},{\mathrm{\&omega;}}_{ej2}=\frac{1/H_j}{\underset{j=1,H_j\&NotEqual;1}{\overset{m}{\&Sigma;}}(1/H_j)}$

Wherein, ω_ejRepresent the entropy weight of target j, H_jRepresent the entropy of target j,For the average of all entropy not being 1, m The quantity of expression target, j=1,2 ..., m；Entropy weight based on each target and desired value corresponding to each solution calculate each solution Comprehensive assessment value, and solution corresponding to comprehensive assessment value choosing maximum be last solution.The technique scheme letter that the present invention provides Single and efficient, it is especially suitable for running online.

Accompanying drawing explanation

In order to be illustrated more clearly that the embodiment of the present invention or technical scheme of the prior art, below will be to embodiment or existing In having technology to describe, the required accompanying drawing used is briefly described, it should be apparent that, the accompanying drawing in describing below is only this Inventive embodiment, for those of ordinary skill in the art, on the premise of not paying creative work, it is also possible to according to The accompanying drawing provided obtains other accompanying drawing.

The flow chart of a kind of entropy weight computational methods that Fig. 1 provides for the embodiment of the present invention；

Fig. 2 calculates the structural representation of device for a kind of entropy weight that the embodiment of the present invention provides；

The flow chart of a kind of Multiobjective Decision Making Method that Fig. 3 provides for the embodiment of the present invention；

The structural representation of a kind of multiobjective decision-making device that Fig. 4 provides for the embodiment of the present invention.

Detailed description of the invention

Below in conjunction with the accompanying drawing in the embodiment of the present invention, the technical scheme in the embodiment of the present invention is carried out clear, complete Describe, it is clear that described embodiment is only a part of embodiment of the present invention rather than whole embodiments wholely.Based on Embodiment in the present invention, it is every other that those of ordinary skill in the art are obtained under not making creative work premise Embodiment, broadly falls into the scope of protection of the invention.

Refer to Fig. 1, it illustrates a kind of entropy weight computational methods that the embodiment of the present invention provides, following step can be included Rapid:

S11: obtain the evaluation index being evaluated object, and the entropy of each evaluation index.

It should be noted that the entropy being evaluated object, evaluation index and evaluation index obtained in this step can be with Corresponding contents involved in background technology is identical, and specifically, iotave evaluation matrix can be D_nm, wherein, every a line represents One is evaluated object, and total n is evaluated object, and every string represents an evaluation index, and total m item evaluation index is each It is evaluated the corresponding each evaluation index of object and is respectively provided with the value of correspondence, be each element in iotave evaluation matrix；Comment original Valency matrix is standardized process and obtains Standard Process R_nm, R_nmIn element and D_nmIn element implication identical, difference only exist In each element has been carried out standardization.Therefore, it can calculate each evaluation index according to the formula in background technology Entropy, to calculate the entropy weight of corresponding evaluation index based on this entropy.

S12: calculate the entropy weight of each evaluation index according to the following formula:

${\mathrm{\&omega;}}_{ej}=\left\{\begin{array}{cc}(1-\stackrel{\&OverBar;}{H}){\mathrm{\&omega;}}_{ej1}+\stackrel{\&OverBar;}{H}{\mathrm{\&omega;}}_{ej2}& H_j<1\\ 0& H_j=1\end{array}\right.$

${\mathrm{\&omega;}}_{ej1}=\frac{1-H_j}{\underset{j=1,H_j\&NotEqual;1}{\overset{m}{\&Sigma;}}(1-H_j)},{\mathrm{\&omega;}}_{ej2}=\frac{1/H_j}{\underset{j=1,H_j\&NotEqual;1}{\overset{m}{\&Sigma;}}(1/H_j)}$

Wherein, ω_ejRepresent the entropy weight of evaluation index j, H_jRepresent the entropy of evaluation index j,Represent all be not 1 entropy The average of value, m represents the quantity of evaluation index, j=1,2 ..., m.Owing to this formula is used for calculating entropy weight, therefore, in the accompanying drawings Above-mentioned formula is represented by this title of entropy weight computing formula.

It should be noted that the formula of above-mentioned calculating entropy weight meets the requirement that the biggest entropy weight of entropy is the least, meet simultaneously with Lower requirement:

0≤ω_ej≤1

$\underset{j=1}{\overset{m}{\&Sigma;}}{\mathrm{\&omega;}}_{ej}=1$

Due to ω_ej2At H_jThe difference that when → 0, entropy is small can cause entropy weight change at double, just with ω_ej1Formed mutually Benefit relation, uses the average of the entropy of each evaluation index simultaneouslyAs weight, the ω when entropy is close to 0 can be made_ej2Shared Ratio reduce, the ω when entropy is close to 1_ej1Proportion reduce, the formula of the most above-mentioned calculating entropy weight bigger than normal at entropy or Power all can be rationally composed, to avoid the entropy weight deformity when entropy is in interval two ends time less than normal.

Technique scheme disclosed by the invention, the single tax power side of the formula of the most original calculating entropy weight Tax the power mode, i.e. ω of a kind of complementation is added in formula_ej1And ω_ej2；And use the mean control two of the entropy of each evaluation index Plant the ratio shared by tax power mode, i.e. use the average of entropy of each evaluation index as weight so that when entropy is close to 0 ω_ej2Shared ratio reduces, the ω when entropy is close to 1_ej1Proportion reduces, and then makes to calculate in the present invention entropy weight Scheme all can rationally compose power when entropy is bigger than normal or less than normal, solve that entropy assessment in prior art exists when all evaluation indexes Entropy level off to 1 time, the minute differences of entropy can cause the change at double of corresponding entropy weight, and then cause entropy weight unreasonable distribution Problem.

It addition, for the reasonability verifying technique scheme that the embodiment of the present invention provides, use different entropy classes Type is tested, and the technique scheme (B) using the technical scheme (A) in background technology, the embodiment of the present invention to provide calculates Entropy weight, the results are shown in Table 1.

Table 1 background technology scheme and the contrast of the present invention program result of calculation

As shown in Table 1, when entropy vector is bigger than normal or less than normal, the technical scheme that the embodiment of the present invention provides gives more Moderate and rational entropy weight vector；Particularly, for entropy vector (1.0000,0.5000,0.1000), the embodiment of the present invention carries The technical scheme of confession has obtained having more the entropy weight vector of discrimination compared to the technical scheme in background technology.

Refer to Fig. 2, it illustrates the flow chart of a kind of Multiobjective Decision Making Method that the embodiment of the present invention provides, can wrap Include following steps:

S21: obtain and advance with the Pareto disaggregation that multi-objective Algorithm obtains, and Pareto disaggregation is standardized place Reason, obtains Standard Process, and in this matrix, every a line represents a solution, total n solution, and every string represents a target, total m Individual target, each solution has m the desired value corresponding with m target.

Wherein it is desired to explanation, evolution multi-objective Algorithm is utilized to obtain Pareto disaggregation X_nm, wherein, every a line represents One solution, total n solution, every string represents a target, has m target, and it is individual that each solution has the m corresponding with m target Desired value, the most corresponding Pareto disaggregation X of each desired value_nmIn an element；Respective party disclosed in this step and prior art Case principle is consistent, does not repeats them here.To Pareto disaggregation X_nmIt is standardized processing, obtains Standard Process R_nm, wherein, Each element and Pareto disaggregation X in Standard Process_nmIn the implication of each element consistent, differ only in each element Carry out standardization.

S22: calculate the entropy of each target in Standard Process, and utilize the entropy weight of the following equation each target of calculating:

${\mathrm{\&omega;}}_{ej}=\left\{\begin{array}{cc}(1-\stackrel{\&OverBar;}{H}){\mathrm{\&omega;}}_{ej1}+\stackrel{\&OverBar;}{H}{\mathrm{\&omega;}}_{ej2}& H_j<1\\ 0& H_j=1\end{array}\right.$

${\mathrm{\&omega;}}_{ej1}=\frac{1-H_j}{\underset{j=1,H_j\&NotEqual;1}{\overset{m}{\&Sigma;}}(1-H_j)},{\mathrm{\&omega;}}_{ej2}=\frac{1/H_j}{\underset{j=1,H_j\&NotEqual;1}{\overset{m}{\&Sigma;}}(1/H_j)}$

Wherein, ω_ejRepresent the entropy weight of target j, H_jRepresent the entropy of target j,For the average of all entropy not being 1, m The quantity of expression target, j=1,2 ..., m.Owing to this formula is used for calculating entropy weight, therefore, entropy weight computing formula is used in the accompanying drawings This title represents above-mentioned formula.

Wherein, the entropy calculating each evaluation index in Standard Process in the entropy of each target and background technology is calculated Computing formula used is identical, and the implication differing only in parameter is different, specifically, determines each target according to the following formula Entropy:

$H_j=-k\underset{i=1}{\overset{n}{\&Sigma;}}{f}_{ij}\ln f_{ij}$

$f_{ij}=\frac{R_{ij}}{\underset{i=1}{\overset{n}{\&Sigma;}}{R}_{ij}}$

$k=\frac{1}{\ln n}$

Wherein, H_jRepresent the entropy of target j, 0≤H_j≤ 1, for making lnf_ijMeaningful, it is assumed that to work as f_ijWhen=0, f_ijln f_ij =0；I=1,2 ..., n；J=1,2 ..., m.

S23: entropy weight based on each target and desired value corresponding to each solution calculate the comprehensive assessment value of each solution, and select The solution taking maximum comprehensive assessment value corresponding is last solution.

Entropy weight based on each target and desired value corresponding to each solution calculate the comprehensive assessment value of each solution, and then determine Go out last solution, i.e. concentrated by Pareto solution and select last solution, thus complete Multiobjective Decision Making Method.

It should be noted that existing Multiobjective Decision Making Method concentrates the process choosing last solution complicated in Pareto solution, And needing the participation of policymaker and parameter to arrange so that last solution selection course is difficult to on-line operation.Disclosure Technique scheme in, by the embodiment of the present invention provide above-mentioned entropy weight computational methods be applied in Multiobjective Decision Making Method, First Pareto disaggregation is standardized processing and being calculated the entropy of each target, then uses the embodiment of the present invention to provide Above-mentioned entropy weight computational methods calculate the entropy weight of each target, finally entropy weight and standardized desired value according to each target obtain respectively The comprehensive assessment value of individual solution, select the maximum solution of comprehensive assessment value as last solution, thus fast and effectively by Pareto disaggregation In get last solution, i.e. the technique scheme that the present invention provides is simple and efficiently, and, above-mentioned skill disclosed by the invention Art scheme can select last solution for policymaker automatically according to normalized matrix, it is not necessary to policymaker participates in, and is especially suitable for transporting online OK.

A kind of Multiobjective Decision Making Method that the embodiment of the present invention provides, entropy weight based on each target and each solution are corresponding Desired value calculates the comprehensive assessment value of each solution, may include that

Calculate the comprehensive assessment value of each solution according to the following formula:

$u_i=\underset{j=1}{\overset{m}{\&Sigma;}}{\mathrm{\&omega;}}_{ej}{r}_{ij}$

Wherein, u_iRepresent the comprehensive assessment value solving i, r_ijRepresent the desired value solving i correspondence target j in Standard Process.

When policymaker is not required to be the setting that each target carries out subjective weight, can directly determine the entropy weight of each target Be its weight, and then utilize the comprehensive assessment value of each solution of above-mentioned calculating, thus rapidly and efficiently get last solution.

A kind of Multiobjective Decision Making Method that the embodiment of the present invention provides, entropy weight based on each target and each solution are corresponding Desired value calculates the comprehensive assessment value of each solution, may include that

Obtain the subjective weight given for each target of external world's input, and calculate combining of each target according to the following formula Conjunction weight:

${\mathrm{\&omega;}}_j=\frac{{\mathrm{\&omega;}}_{sj}\&CenterDot;{\mathrm{\&omega;}}_{ej}}{\underset{j=1}{\overset{m}{\&Sigma;}}{\mathrm{\&omega;}}_{sj}\&CenterDot;{\mathrm{\&omega;}}_{ej}}$

Wherein, ω_jRepresent the comprehensive weight of target j, ω_sjRepresent the subjective weight of target j；

Calculate the comprehensive assessment value of each solution according to the following formula:

$u_i=\underset{j=1}{\overset{m}{\&Sigma;}}{\mathrm{\&omega;}}_j{r}_{ij}$

Wherein, u_iRepresent the comprehensive assessment value solving i, r_ijRepresent the desired value solving i correspondence target j in Standard Process.

When policymaker needs the setting that each target carries out subjective weight, each target of its input can be obtained Subjective weight, utilizes such scheme to calculate the comprehensive weight of each target, and then calculates combining of each solution based on this comprehensive weight Close assessed value, it is seen then that technical scheme disclosed by the invention can allow policymaker to set the subjective weight to each target, then Decision making process then need not policymaker's operation, therefore can conveniently realize online operation, improve the efficiency of decision-making further.

A kind of Multiobjective Decision Making Method that the embodiment of the present invention provides, is standardized Pareto disaggregation processing, obtains Standard Process, may include that

If Multiobjective Decision Making Method specially maximizes optimization problem, the most according to the following formula Pareto disaggregation is carried out Standardization:

$r_{ij}=\frac{x_{ij}-\underset{1\&le;i\&le;n}{\min }{x}_{ij}}{\underset{1\&le;i\&le;n}{max}{x}_{ij}-\underset{1\&le;i\&le;n}{\min }{x}_{ij}}$

If Multiobjective Decision Making Method specially minimizes optimization problem, the most according to the following formula Pareto disaggregation is carried out Standardization:

$r_{ij}=\frac{\underset{1\&le;i\&le;n}{max}{x}_{ij}-x_{ij}}{\underset{1\&le;i\&le;n}{max}{x}_{ij}-\underset{1\&le;i\&le;n}{min}{x}_{ij}}$

Wherein, r_ijRepresent the desired value solving i correspondence target j in Standard Process, x_ijRepresent that Pareto solution is concentrated and solve i pair Answer the desired value of target j, 1≤i≤n, 1≤j≤m.

It should be noted that the implication phase of largest optimization problem and minimum optimization problem corresponding title with prior art With, do not repeat them here；It addition, aforesaid way is the most only standardized one of mode of process, other can carry out standard The technical scheme that change processes is the most all within protection scope of the present invention.

It addition, for the advantage verifying a kind of Multiobjective Decision Making Method that the embodiment of the present invention provides, carry out following test. Wherein, table 2 give this example use Pareto disaggregation:

With document " Multi-objective optimization and decision making for power dispatch of a large-scale integrated energy system with distributed DHCs Embedded " in as a example by the Pareto disaggregation of 39 solution 5 targets that draws of multiobjective decision-making, use technical scheme and suitable Sequence structure Evaluation Method (Preference Ranking Organization Method for Enrichment Evaluation, PROMETHEE) respectively this disaggregation is carried out decision-making, and the trap queuing result obtained is contrasted, such as table Shown in 2, wherein technical solution of the present invention correspondence sequence 1, sequential organization Evaluation Method correspondence sequence 2.Visible, two kinds of methods give Roughly the same decision scheme, all selecting the 19th to solve is last solution, this demonstrates technical solution of the present invention in multiobjective decision-making The effectiveness of middle selection last solution, and in implementation process, technical solution of the present invention is simpler relative to sequential organization Evaluation Method soon Speed.And document " Multi-objective optimization and decision making for power dispatch Of a large-scale integrated energy system with distributed DHCs embedded " use The method of evidential reasoning selects last solution, but due to the complexity of the method, five solutions that only have chosen overstriking in table 2 are entered Go assessment, and owing to target 1 and target 2 are used to the target that optimizes, decision making process can be not involved in, only use rear three mesh Mark carries out evidential reasoning and rear three targets with the addition of subjective weights omega_sj=[0.2,0.4,0.4].In order to push away with evidence Reason method is made comparisons, and technical solution of the present invention the most only uses these five rear three targets solved to carry out decision-making, and the result obtained is such as Shown in table 3, wherein, Evidential reasoning algorithm correspondence sequence 3.

Visible, three kinds of methods give identical sequencing schemes, but assess compared to Evidential reasoning algorithm and sequential organization Method, technical scheme disclosed by the invention has directly utilized the good and bad information that desired value itself comprises, the most efficiently, has been highly suitable for Line runs.

Table 2 two schemes is to 39 sequencing schemes solving 5 target disaggregation

3 three kinds of schemes of table are to 5 sequencing schemes solving 3 target disaggregation

Corresponding with said method, the embodiment of the present invention additionally provides a kind of entropy weight and calculates device, as it is shown on figure 3, permissible Including:

Data acquisition module 11, for obtaining the evaluation index being evaluated object, and the entropy of each evaluation index；

Entropy weight computing module 12, for calculating the entropy weight of each evaluation index according to the following formula:

${\mathrm{\&omega;}}_{ej}=\left\{\begin{array}{cc}(1-\stackrel{\&OverBar;}{H}){\mathrm{\&omega;}}_{ej1}+\stackrel{\&OverBar;}{H}{\mathrm{\&omega;}}_{ej2}& H_j<1\\ 0& H_j=1\end{array}\right.$

${\mathrm{\&omega;}}_{ej1}=\frac{1-H_j}{\underset{j=1,H_j\&NotEqual;1}{\overset{m}{\&Sigma;}}(1-H_j)},{\mathrm{\&omega;}}_{ej2}=\frac{1/H_j}{\underset{j=1,H_j\&NotEqual;1}{\overset{m}{\&Sigma;}}(1/H_j)}$

Wherein, ω_ejRepresent the entropy weight of evaluation index j, H_jRepresent the entropy of evaluation index j,Represent all be not 1 entropy The average of value, m represents the quantity of evaluation index, j=1,2 ..., m.

A kind of entropy weight that the embodiment of the present invention provides calculates the explanation of relevant portion in device and refers to the embodiment of the present invention In a kind of entropy weight computational methods provided, the detailed description of corresponding part, does not repeats them here.

Corresponding with said method, the embodiment of the present invention additionally provides a kind of multiobjective decision-making device, as shown in Figure 4, and can To include:

Matrix disposal module 21, advances with, for obtaining, the Pareto disaggregation that multi-objective Algorithm obtains, and to Pareto Disaggregation is standardized processing, and obtains Standard Process, and in this matrix, every a line represents a solution, total n solution, every string generation One target of table, total m target, each solution has m the desired value corresponding with m target；

Entropy weight acquisition module 22, for calculating the entropy of each target in Standard Process, and utilizes following equation to calculate The entropy weight of each target:

${\mathrm{\&omega;}}_{ej}=\left\{\begin{array}{cc}(1-\stackrel{\&OverBar;}{H}){\mathrm{\&omega;}}_{ej1}+\stackrel{\&OverBar;}{H}{\mathrm{\&omega;}}_{ej2}& H_j<1\\ 0& H_j=1\end{array}\right.$

${\mathrm{\&omega;}}_{ej1}=\frac{1-H_j}{\underset{j=1,H_j\&NotEqual;1}{\overset{m}{\&Sigma;}}(1-H_j)},{\mathrm{\&omega;}}_{ej2}=\frac{1/H_j}{\underset{j=1,H_j\&NotEqual;1}{\overset{m}{\&Sigma;}}(1/H_j)}$

Wherein, ω_ejRepresent the entropy weight of target j, H_jRepresent the entropy of target j,For the average of all entropy not being 1, m The quantity of expression target, j=1,2 ..., m；

Last solution acquisition module 23, the desired value corresponding for entropy weight based on each target and each solution calculates each solution Comprehensive assessment value, and solution corresponding to comprehensive assessment value choosing maximum be last solution.

A kind of multiobjective decision-making device that the embodiment of the present invention provides, last solution acquisition module may include that

First assessment unit, for calculating the comprehensive assessment value of each solution according to the following formula:

$u_i=\underset{j=1}{\overset{m}{\&Sigma;}}{\mathrm{\&omega;}}_{ej}{r}_{ij}$

Wherein, u_iRepresent the comprehensive assessment value solving i, r_ijRepresent the desired value solving i correspondence target j in Standard Process.

A kind of multiobjective decision-making device that the embodiment of the present invention provides, last solution acquisition module may include that

Second assessment unit, calculates combining of each solution for the desired value that entropy weight based on each target and each solution are corresponding Close assessed value, including:

Obtain the subjective weight given for each target of external world's input, and calculate combining of each target according to the following formula Conjunction weight:

${\mathrm{\&omega;}}_j=\frac{{\mathrm{\&omega;}}_{sj}\&CenterDot;{\mathrm{\&omega;}}_{ej}}{\underset{j=1}{\overset{m}{\&Sigma;}}{\mathrm{\&omega;}}_{sj}\&CenterDot;{\mathrm{\&omega;}}_{ej}}$

Wherein, ω_jRepresent the comprehensive weight of target j, ω_sjRepresent the subjective weight of target j；

Calculate the comprehensive assessment value of each solution according to the following formula:

$u_i=\underset{j=1}{\overset{m}{\&Sigma;}}{\mathrm{\&omega;}}_j{r}_{ij}$

Wherein, u_iRepresent the comprehensive assessment value solving i, r_ijRepresent the desired value solving i correspondence target j in Standard Process.

A kind of multiobjective decision-making device that the embodiment of the present invention provides, matrix disposal module includes:

First processing unit, if being used for solving to maximize optimization problem, then according to following for multiobjective decision-making device Pareto disaggregation is standardized processing by formula:

$r_{ij}=\frac{x_{ij}-\underset{1\&le;i\&le;n}{\min }{x}_{ij}}{\underset{1\&le;i\&le;n}{max}{x}_{ij}-\underset{1\&le;i\&le;n}{\min }{x}_{ij}}$

Second processing unit, if minimizing optimization problem, then according to following for multiobjective decision-making device for solution Pareto disaggregation is standardized processing by formula:

$r_{ij}=\frac{\underset{1\&le;i\&le;n}{max}{x}_{ij}-x_{ij}}{\underset{1\&le;i\&le;n}{max}{x}_{ij}-\underset{1\&le;i\&le;n}{min}{x}_{ij}}$

Wherein, r_ijRepresent the desired value solving i correspondence target j in Standard Process, x_ijRepresent that Pareto solution is concentrated and solve i pair Answer the desired value of target j, 1≤i≤n, 1≤j≤m.

In a kind of multiobjective decision-making device that the embodiment of the present invention provides, the explanation of relevant portion refers to present invention enforcement In a kind of Multiobjective Decision Making Method that example provides, the detailed description of corresponding part, does not repeats them here.

Described above to the disclosed embodiments, makes those skilled in the art be capable of or uses the present invention.To this The multiple amendment of a little embodiments will be apparent from for a person skilled in the art, and generic principles defined herein can With without departing from the spirit or scope of the present invention, realize in other embodiments.Therefore, the present invention will not be limited It is formed on the embodiments shown herein, and is to fit to consistent with principles disclosed herein and features of novelty the widest Scope.

Claims (10)

1. entropy weight computational methods, it is characterised in that including:

Obtain the evaluation index being evaluated object, and the entropy of each described evaluation index；

Calculate the entropy weight of each described evaluation index according to the following formula:

${\mathrm{\&omega;}}_{ej}=\left\{\begin{array}{cc}(1-\stackrel{\&OverBar;}{H}){\mathrm{\&omega;}}_{ej1}+\stackrel{\&OverBar;}{H}{\mathrm{\&omega;}}_{ej2}& H_j<1\\ 0& H_j=1\end{array}\right.$

${\mathrm{\&omega;}}_{ej1}=\frac{1-H_j}{\underset{j=1,H_j\&NotEqual;1}{\overset{m}{\&Sigma;}}(1-H_j)},{\mathrm{\&omega;}}_{ej2}=\frac{1/H_j}{\underset{j=1,H_j\&NotEqual;1}{\overset{m}{\&Sigma;}}(1/H_j)}$

Wherein, ω_ejRepresent the entropy weight of evaluation index j, H_jRepresent the entropy of evaluation index j,Represent all be not 1 entropy Average, m represents the quantity of described evaluation index, j=1,2 ..., m.

2. a Multiobjective Decision Making Method, it is characterised in that including:

Obtain and advance with the Pareto disaggregation that multi-objective Algorithm obtains, and be standardized described Pareto disaggregation processing, Obtaining Standard Process, in this matrix, every a line represents a solution, total n solution, and every string represents a target, total m Target, each solution has m the desired value corresponding with m target；

Calculate the entropy of each target in described Standard Process, and utilize the entropy weight of the following equation each target of calculating:

${\mathrm{\&omega;}}_{ej}=\left\{\begin{array}{cc}(1-\stackrel{\&OverBar;}{H}){\mathrm{\&omega;}}_{ej1}+\stackrel{\&OverBar;}{H}{\mathrm{\&omega;}}_{ej2}& H_j<1\\ 0& H_j=1\end{array}\right.$

${\mathrm{\&omega;}}_{ej1}=\frac{1-H_j}{\underset{j=1,H_j\&NotEqual;1}{\overset{m}{\&Sigma;}}(1-H_j)},{\mathrm{\&omega;}}_{ej2}=\frac{1/H_j}{\underset{j=1,H_j\&NotEqual;1}{\overset{m}{\&Sigma;}}(1/H_j)}$

Wherein, ω_ejRepresent the entropy weight of target j, H_jRepresent the entropy of target j,For the average of all entropy not being 1, m represents The quantity of target, j=1,2 ..., m；

Entropy weight based on each target and desired value corresponding to each solution calculate the comprehensive assessment value of each solution, and choose maximum Solution corresponding to comprehensive assessment value is last solution.

Method the most according to claim 2, it is characterised in that entropy weight based on each target and target corresponding to each solution Value calculates the comprehensive assessment value of each solution, including:

Calculate the comprehensive assessment value of each solution according to the following formula:

$u_i=\underset{j=1}{\overset{m}{\&Sigma;}}{\mathrm{\&omega;}}_{ej}{r}_{ij}$

Wherein, u_iRepresent the comprehensive assessment value solving i, r_ijRepresent the desired value solving i correspondence target j in described Standard Process.

Method the most according to claim 2, it is characterised in that entropy weight based on each target and target corresponding to each solution Value calculates the comprehensive assessment value of each solution, including:

Obtain the subjective weight given for each target of external world's input, and calculate the synthetic weights of each target according to the following formula Weight:

${\mathrm{\&omega;}}_j=\frac{{\mathrm{\&omega;}}_{sj}\&CenterDot;{\mathrm{\&omega;}}_{ej}}{\underset{j=1}{\overset{m}{\&Sigma;}}{\mathrm{\&omega;}}_{sj}\&CenterDot;{\mathrm{\&omega;}}_{ej}}$

Wherein, ω_jRepresent the comprehensive weight of target j, ω_sjRepresent the subjective weight of target j；

Calculate the comprehensive assessment value of each solution according to the following formula:

$u_i=\underset{j=1}{\overset{m}{\&Sigma;}}{\mathrm{\&omega;}}_j{r}_{ij}$

Wherein, u_iRepresent the comprehensive assessment value solving i, r_ijRepresent the desired value solving i correspondence target j in described Standard Process.

Method the most according to claim 2, it is characterised in that be standardized described Pareto disaggregation processing, obtain Standard Process, including:

If described Multiobjective Decision Making Method specially maximizes optimization problem, the most according to the following formula to described Pareto disaggregation It is standardized processing:

$r_{ij}=\frac{x_{ij}-\underset{1\&le;i\&le;n}{\min }{x}_{ij}}{\underset{1\&le;i\&le;n}{\max }{x}_{ij}-\underset{1\&le;i\&le;n}{\min }{x}_{ij}}$

If described Multiobjective Decision Making Method specially minimizes optimization problem, the most according to the following formula to described Pareto disaggregation It is standardized processing:

$r_{ij}=\frac{\underset{1\&le;i\&le;n}{\max }{x}_{ij}-x_{ij}}{\underset{1\&le;i\&le;n}{\max }{x}_{ij}-\underset{1\&le;i\&le;n}{\min }{x}_{ij}}$

Wherein, r_ijRepresent the desired value solving i correspondence target j in Standard Process, x_ijRepresent that Pareto solution is concentrated and solve i correspondence target The desired value of j, 1≤i≤n, 1≤j≤m.

6. an entropy weight calculates device, it is characterised in that including:

Data acquisition module, for obtaining the evaluation index being evaluated object, and the entropy of each described evaluation index；

Entropy weight computing module, for calculating the entropy weight of each described evaluation index according to the following formula:

${\mathrm{\&omega;}}_{ej}=\left\{\begin{array}{cc}(1-\stackrel{\&OverBar;}{H}){\mathrm{\&omega;}}_{ej1}+\stackrel{\&OverBar;}{H}{\mathrm{\&omega;}}_{ej2}& H_j<1\\ 0& H_j=1\end{array}\right.$

${\mathrm{\&omega;}}_{ej1}=\frac{1-H_j}{\underset{j=1,H_j\&NotEqual;1}{\overset{m}{\&Sigma;}}(1-H_j)},{\mathrm{\&omega;}}_{ej2}=\frac{1/H_j}{\underset{j=1,H_j\&NotEqual;1}{\overset{m}{\&Sigma;}}(1/H_j)}$

Wherein, ω_ejRepresent the entropy weight of evaluation index j, H_jRepresent the entropy of evaluation index j,Represent all be not 1 entropy Average, m represents the quantity of described evaluation index, j=1,2 ..., m.

7. a multiobjective decision-making device, it is characterised in that including:

Matrix disposal module, advances with, for obtaining, the Pareto disaggregation that multi-objective Algorithm obtains, and to described Pareto solution Collection is standardized processing, and obtains Standard Process, and in this matrix, every a line represents a solution, total n solution, and every string represents One target, total m target, each solution has m the desired value corresponding with m target；

Entropy weight acquisition module, for calculating the entropy of each target in described Standard Process, and it is every to utilize following equation to calculate The entropy weight of individual target:

${\mathrm{\&omega;}}_{ej}=\left\{\begin{array}{cc}(1-\stackrel{\&OverBar;}{H}){\mathrm{\&omega;}}_{ej1}+\stackrel{\&OverBar;}{H}{\mathrm{\&omega;}}_{ej2}& H_j<1\\ 0& H_j=1\end{array}\right.$

${\mathrm{\&omega;}}_{ej1}=\frac{1-H_j}{\underset{j=1,H_j\&NotEqual;1}{\overset{m}{\&Sigma;}}(1-H_j)},{\mathrm{\&omega;}}_{ej2}=\frac{1/H_j}{\underset{j=1,H_j\&NotEqual;1}{\overset{m}{\&Sigma;}}(1/H_j)}$

Wherein, ω_ejRepresent the entropy weight of target j, H_jRepresent the entropy of target j,For the average of all entropy not being 1, m represents The quantity of target, j=1,2 ..., m；

Last solution acquisition module, the desired value corresponding for entropy weight based on each target and each solution calculates the comprehensive of each solution Assessed value, and solution corresponding to comprehensive assessment value choosing maximum be last solution.

Device the most according to claim 7, it is characterised in that last solution acquisition module includes:

First assessment unit, for calculating the comprehensive assessment value of each solution according to the following formula:

$u_i=\underset{j=1}{\overset{m}{\&Sigma;}}{\mathrm{\&omega;}}_{ej}{r}_{ij}$

Wherein, u_iRepresent the comprehensive assessment value solving i, r_ijRepresent the desired value solving i correspondence target j in described Standard Process.

Device the most according to claim 7, it is characterised in that last solution acquisition module includes:

Second assessment unit, the desired value corresponding for entropy weight based on each target and each solution calculates comprehensively commenting of each solution Valuation, including:

Obtain the subjective weight given for each target of external world's input, and calculate the synthetic weights of each target according to the following formula Weight:

${\mathrm{\&omega;}}_j=\frac{{\mathrm{\&omega;}}_{sj}\&CenterDot;{\mathrm{\&omega;}}_{ej}}{\underset{j=1}{\overset{m}{\&Sigma;}}{\mathrm{\&omega;}}_{sj}\&CenterDot;{\mathrm{\&omega;}}_{ej}}$

Wherein, ω_jRepresent the comprehensive weight of target j, ω_sjRepresent the subjective weight of target j；

Calculate the comprehensive assessment value of each solution according to the following formula:

$u_i=\underset{j=1}{\overset{m}{\&Sigma;}}{\mathrm{\&omega;}}_j{r}_{ij}$

Wherein, u_iRepresent the comprehensive assessment value solving i, r_ijRepresent the desired value solving i correspondence target j in described Standard Process.

Device the most according to claim 7, it is characterised in that matrix disposal module includes:

First processing unit, if being used for solving to maximize optimization problem, then according to following for described multiobjective decision-making device Described Pareto disaggregation is standardized processing by formula:

$r_{ij}=\frac{x_{ij}-\underset{1\&le;i\&le;n}{\min }{x}_{ij}}{\underset{1\&le;i\&le;n}{\max }{x}_{ij}-\underset{1\&le;i\&le;n}{\min }{x}_{ij}}$

Second processing unit, if minimizing optimization problem, then according to following for described multiobjective decision-making device for solution Described Pareto disaggregation is standardized processing by formula:

$r_{ij}=\frac{\underset{1\&le;i\&le;n}{\max }{x}_{ij}-x_{ij}}{\underset{1\&le;i\&le;n}{\max }{x}_{ij}-\underset{1\&le;i\&le;n}{\min }{x}_{ij}}$

Wherein, r_ijRepresent the desired value solving i correspondence target j in Standard Process, x_ijRepresent that Pareto solution is concentrated and solve i correspondence target The desired value of j, 1≤i≤n, 1≤j≤m.