CN112836293B - Automobile product design scheme selection method based on PSO information granulation - Google Patents
Automobile product design scheme selection method based on PSO information granulation Download PDFInfo
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Abstract
The invention relates to the technical field of strategy selection, in particular to a method for selecting an automobile product design scheme based on PSO information granulation. The method comprises the following steps: step S1, establishing an alternative solution set X and a solution evaluation language term set L 1 (ii) a Step S2, establishing a decision maker set E and a decision maker evaluation language term set L 2 (ii) a Step S3, establishing a language term evaluation matrix D of each decision maker for all alternatives 1w (ii) a Step S4, establishing a language term evaluation matrix D for the decision maker 2 (ii) a Step S5, granulating the language terms to obtain a language term evaluation matrix D 1w Numerical evaluation matrix ND of 1w And a language term evaluation matrix D 2 Numerical evaluation matrix ND of 2 (ii) a Step S6, evaluating the matrix ND according to the numeralization 1w And a numerical evaluation matrix ND 2 And acquiring the preference degree of each alternative scheme so as to acquire the optimal design scheme. The invention can better convert the preference information of language glossing into the numerical processing problem, thereby obtaining the optimal scheme.
Description
Technical Field
The invention relates to the technical field of strategy selection, in particular to a method for selecting an automobile product design scheme based on PSO information granulation.
Background
The automobile industry is one of the important post industries of national economy, and with the development of automobile products becoming more and more rapid, the life style of people is being changed slowly. As a new industry, the demand of automobiles is increasing, and the industrial revolution taking information technology as the leading factor also brings new development opportunities to the automobile industry. The traditional automobile and the new generation information technology are continuously fused, the continuous change of the consumer demand is adapted, and the mode of providing better service for users becomes a new mode of the development of the current automobile products. On one hand, the development process of automobile products is very tedious, and not only needs to know the requirements of users, but also needs to pay attention to quality management. On the other hand, with the development of the internet and big data, technologies such as artificial intelligence and voice recognition provide more ideas and suggestions for the design of automobile products. In order to develop the intelligent networked automobile which is popular with consumers, the automobile enterprises need to consider opinions from various departments, such as a research and development department, a production department, a financial department, a market department and the like, when carrying out research and development decisions, so as to select an automobile product design scheme which best meets the requirements of users and the strategic planning of enterprises.
Because opinions from various departments during the design of automotive products contain a large number of relatively ambiguous language terms, it is difficult to make a systematic analysis and decision.
Disclosure of Invention
The present invention provides a method for automotive product design selection based on PSO information pelletization that overcomes some or all of the deficiencies of the prior art.
The invention relates to a method for selecting an automobile product design scheme based on PSO information granulation, which comprises the following steps:
step S1, establishing a candidate set X and a scheme evaluation language term set L for evaluating the preference degree of the candidate 1 (ii) a Wherein X ═ { X ═ X t |t＝1,2,3,…,i,…,j,…,n}，x t Represents the tth alternative; wherein L is 1 ＝{l h |h＝-p,-p+1,…,0,…,p-1,p}，l h The h language term, language term l, representing the evaluation of the scheme -p To the linguistic term l p The indicated preference degree is increased;
step S2, establishing a decision maker set E and a decision maker evaluation language for evaluating the preference degree of the decision makerGlossary set L 2 (ii) a Wherein E ═ { E ═ E w |w＝1,2,3,…,s…,g,…,m}，e w Represents the w-th decision maker; wherein L is 2 ＝{l r |r＝-q,-q+1,…,0,…,q-1,q}，l r The r-th language term, language term l, representing the evaluation of a decision maker -q To the linguistic term l q The indicated preference degree is increased;
step S3, each decision maker evaluates the language term set L based on the scheme 1 The language term in (1) carries out language term evaluation on all the alternatives, and a language term evaluation matrix D of each decision maker on all the alternatives is established 1w ，D 1w Representing a matrix obtained after the w-th decision maker evaluates all the alternative schemes; wherein the content of the first and second substances,
indicating the w-th decision maker compared to alternative x j For alternative x i (ii) a linguistic term evaluation of (a); wherein the content of the first and second substances,
representing linguistic term evaluation
Chinese language term l h The probability of occupation; wherein, the first and the second end of the pipe are connected with each other,
and is provided with
Step S4, evaluating language term set L based on decision maker 2 The language terms in (1) evaluate the language terms of all decision-makers, and a language term evaluation matrix D for the decision-makers is established 2 (ii) a Wherein the content of the first and second substances,
representing the comparison to decision e g For decision maker e s (ii) a linguistic term evaluation of (a); wherein the content of the first and second substances,
representing linguistic term evaluation
Chinese language term l r The probability of occupation; wherein the content of the first and second substances,
and is provided with
Step S5, granulating the language terms to obtain a language term evaluation matrix D 1w Numerical evaluation matrix ND of 1w ，
Indicating the w-th decision maker compared to alternative x j For alternative x i Numerical evaluation of (1); and a language term evaluation matrix D 2 Numerical evaluation matrix ND of 2 ，
Representing the comparison to decision e g Block gamePolicy e s Numerical evaluation of (1);
step S6, evaluating the matrix ND according to the numeralization 1w And a numerical evaluation matrix ND 2 And acquiring the preference degree of each alternative scheme so as to acquire the optimal design scheme.
By the method, the opinions of enterprise managers and decision makers of all departments can be comprehensively considered, and a decision model is constructed to determine the optimal automobile product design scheme aiming at several feasible automobile product design schemes, so that the product is closer to the real requirement of a user, and higher-quality service is provided for the user. Meanwhile, in the decision process, considering the ambiguity of different decision makers when using language terms to express preference information, a PSO information granulation method taking consistency and consensus degree as indexes is provided, the language values are converted into a calculable interval particle form, and finally the most appropriate scheme is selected by comprehensively considering the opinions of all departments.
Preferably, step S5 includes a scenario evaluation language term set L 1 And the decision maker evaluates the language term set L 2 In the form of zones as information particles, each linguistic term is expressed as a zone [0,1] of]A subset of (a). Therefore, the language information can be preferably converted into numerical information, and the subsequent operation is further facilitated.
Preferably, in step S5, the language term set L is evaluated for the decider 2 When granulation is carried out, the method comprises the following steps:
step S511, setting language term l r Corresponding subinterval is [ b ] r-1 ,b r ]And set b -q-1 ＝0， b q ＝1；
Step S512, calculating intercept point vector [ b ] based on PSO algorithm -q ,b -q+1 ,…,b 0 ,…b q-2 ,b q-1 ]And the decision maker evaluates the language term set L 2 Set of values W of L2 ，W L2 ＝{W Lr -q +1, …,0, …, q-1, q }; wherein, W Lr Denotes the language term l r By the term l in the language r Corresponding subintervals are all continuousUniformly sampling and obtaining;
in this step, the matrix ND is evaluated numerically 2 Multiplicative consistency indicator Q 2 As an optimization index and by a multiplicative consistency index Q 2 Mean value of
Establishing an optimization model as a fitness function, wherein
Representing a numerical evaluation matrix ND established based on values obtained by continuous uniformly distributed sampling in the kth operation 2 A multiplicative consistency indicator of;
the optimization model is established by the following steps of,
MaxQ 2 ＝cND 2
wherein, "cND 2 "indicates the numerical evaluation matrix ND obtained for each operation 2 A multiplicative consistency indicator of; constructed fitness function f 2 In order to realize the purpose,
step S513, constraint condition based on optimization model and fitness function f 2 Get the intercept point vector [ b ] -q ,b -q+1 ,…,b 0 ,…b q-2 ,b q-1 ]And a numerical evaluation matrix ND 2 (ii) a Wherein the content of the first and second substances,
through steps S511 to S513, the linguistic term matrix is preferably digitized, and the weight of each decision maker can be preferably obtained.
Preferably, the method further comprises calculating a set of decision maker values W, W ═ W w |w＝1,2,3,…,s…,g,…,m}，W w A weight representing the w-th decision maker; w w The calculation formula is as follows,
so that the weight for each decision maker can be preferably obtained.
Preferably, in step S5, the language term set L is evaluated for the scheme 1 When the granulation is carried out, the method comprises the following steps:
step S521, setting language term l h Corresponding subinterval is [ a ] h-1 ,a h ]And set a -p-1 ＝0， a p ＝1；
Step S522, calculating intercept point vector [ a ] based on PSO algorithm -p ,a -p+1 ,…,a 0 ,…a p-2 ,a p-1 ]And a scheme evaluation language term set L 1 Set of values W of L1 ， W L1 ＝W Lh -p, -p +1, …,0, …, p-1, p }; wherein, W Lh Denotes the language term l h By the term l in the language h Continuously and uniformly sampling and obtaining in the corresponding subinterval;
in the step of the method,
the optimization model is established as follows,
constructed suitablyResponse function f 1 In order to realize the purpose,
wherein the content of the first and second substances,
cND 1w represents a numerical evaluation matrix ND 1w The consistency index of (a); o is 2 Is corresponding to O 1 The index of the consistency ratio of (1), delta is a balance factor, and delta belongs to [0,1]]；
Step S523, constraint condition based on optimization model and fitness function f 1 Obtaining an intercept point vector [ a ] -p ,a -p+1 ,…,a 0 ,…a p-2 ,a p-1 ]And a numerical evaluation matrix ND 1w (ii) a Wherein the content of the first and second substances,
through steps S521-S523, the numeralization of the pattern language terms is preferably achieved.
Preferably, in step S6, a total evaluation matrix ND is acquired c ，
Wherein the content of the first and second substances,
so that all evaluation matrices will preferably be integrated according to the weight of each decision maker.
Preferably, in step S6, the total evaluation matrix ND is calculated based on the average operator c An acquisition scheme evaluation matrix X ' and X ' are calculated to obtain (X ' t ) 1×n ，
So that the evaluation value of each scheme can be preferably acquired.
Preferably, in step S6, the optimal solution is obtained by sorting based on the solution evaluation matrix X'. So that the optimum scheme can be preferably obtained.
Drawings
FIG. 1 is a schematic view of the method in example 1;
FIG. 2 is a fitness function f in example 1 2 The numerical iteration change map of (2);
FIG. 3 is a fitness function f in example 1 1 The value of (2) is iteratively changed.
Detailed Description
For a further understanding of the invention, reference should be made to the following detailed description taken in conjunction with the accompanying drawings and examples. It is to be understood that the examples are illustrative of the invention and not limiting.
Example 1
Referring to fig. 1, the present embodiment provides a method for selecting an automotive product design scheme based on PSO information granulation, which includes the following steps:
step S1, establishing a candidate set X and a scheme evaluation language term set L for evaluating the preference degree of the candidate 1 (ii) a Wherein X is { X ═ X t |t＝1,2,3,…,i,…,j,…,n}，x t Represents the tth alternative; wherein L is 1 ＝{l h |h＝-p,-p+1,…,0,…,p-1,p}，l h Denotes the h-th language term, language term l, which evaluates the scheme -p To the linguistic term l p The indicated preference degree is increased;
step S2, establishing a decision maker set E and a decision maker evaluation language term set L for evaluating the preference degree of the decision maker 2 (ii) a Wherein E ═ { E ═ E w |w＝1,2,3,…,s…,g,…,m}，e w Represents the w-th decision maker; wherein L is 2 ＝{l r |r＝-q,-q+1,…,0,…,q-1,q}，l r The r-th language term, language term l, representing the evaluation of a decision maker -q To the linguistic term l q The indicated preference degree is increased;
step S3, each decision maker evaluates the language term set L based on the scheme 1 The language term in (1) performs language term evaluation on all alternatives, and establishes eachLanguage term evaluation matrix D for all alternatives by decision maker 1w ，D 1w Representing a matrix obtained after the w-th decision maker evaluates all the alternative schemes; wherein, the first and the second end of the pipe are connected with each other,
indicating the w-th decision maker compared to alternative x j For alternative x i (ii) a linguistic term evaluation of (a); wherein, the first and the second end of the pipe are connected with each other,
representing linguistic term evaluation
Chinese language term l h The probability of occupation; wherein the content of the first and second substances,
and is provided with
Step S4, evaluating language term set L based on decision maker 2 The language term in (1) carries out language term evaluation on all decision-makers, and establishes a language term evaluation matrix D for the decision-makers 2 (ii) a Wherein the content of the first and second substances,
representing the comparison to decision e g For decision maker e s (ii) a linguistic term evaluation of (a); wherein, the first and the second end of the pipe are connected with each other,
representing linguistic term evaluation
Chinese language term l r The probability of occupation; wherein, the first and the second end of the pipe are connected with each other,
and is
Step S5, granulating the language terms to obtain a language term evaluation matrix D 1w Numerical evaluation matrix ND of 1w ，
Indicating the w-th decision maker compared to alternative x j For alternative x i Numerical evaluation of (1); and a language term evaluation matrix D 2 Numerical evaluation matrix ND of 2 ，
Representing the comparison to decision e g For decision maker e s Numerical evaluation of (1);
step S6, evaluating the matrix ND according to the numeralization 1w And a numerical evaluation matrix ND 2 And acquiring the preference degree of each alternative scheme so as to acquire the optimal design scheme.
By the method in the embodiment, the opinions of enterprise managers and decision makers of all departments can be comprehensively considered, and a decision model is constructed to determine the optimal automobile product design scheme aiming at a plurality of feasible automobile product design schemes, so that the product is closer to the real requirement of a user, and better service is provided for the user. Meanwhile, in the decision making process, considering the ambiguity of different decision makers when using language terms to express preference information, a PSO information granulation method taking consistency and consensus degree as indexes is provided, the language value is converted into a calculable interval particle form, and finally the opinion of each department is comprehensively considered to select the most appropriate scheme.
In the invention, the language term set is a limited language expression set with the number of elements not being 1, and the PSO algorithm is a particle swarm algorithm.
This embodiment is an embodiment of the present invention, and the present invention is explained with a specific application.
In this embodiment, the alternative set X includes 4 elements, each of which is X 1 、x 2 、x 3 And x 4 I.e., n ═ 4; x is the number of 1 、x 2 、x 3 And x 4 The different vehicle designs in fig. 4 are shown.
In this example, the scheme evaluation language term set L 1 The 5 elements involved are "Very Low (VL)", "low (L)", "same (M)", "high (H)" and "Very High (VH)", respectively, i.e. p ═ 2. "Very Low (VL)", "low (L)", "same (M)", "high (H)" and "Very High (VH)" are feasibility assessment terms that may be used when comparing two different protocols.
In this embodiment, the decision maker set E comprises 4 elements E 1 、e 2 、e 3 And e 4 I.e., m is 4; e.g. of a cylinder 1 、e 2 、e 3 And e 4 Representing different departments of a company, such as the "product department", "research and development department", "finance department", and "marketing department".
In this example, L 2 The inclusion of 7 elements is "very unimportant (WI)", "Less Important (LI)", "unimportant (NI)", "Equally Important (EI)", "important (I)", "more important (VI)" "very important (SI)", i.e. q ═ 3, respectively; "very important (WI)", "Less Important (LI)", "Not Important (NI)", "equally important (NI)", "not important (ii)" andEI) "," important (I) "," more important (VI) "," very important (SI) "are important evaluation terms that are available when comparing opinions of different departments.
As shown in tables 1-4, the results of all the evaluation of the solutions for different decision makers, namely D 1w (ii) a As shown in Table 5, the results of the evaluation of the importance of the company managers to the different departments, namely D, were obtained 2 。
TABLE 1 department of products (e) 1 ) Language term evaluation matrix D obtained after all schemes are evaluated 1w
TABLE 2 research and development part (e) 2 ) Language term evaluation matrix D obtained after all schemes are evaluated 12
TABLE 3 finance department (e) 3 ) Language term evaluation matrix D obtained after all schemes are evaluated 13
TABLE 4 market sector (e) 4 ) Language term evaluation matrix D obtained after all schemes are evaluated 14
TABLE 5 language term evaluation matrix D obtained after evaluation of all departments by the company manager 2
As can be seen from tables 1 to 5, it is difficult to perform a systematic and effective evaluation on the language-glossed evaluation matrix during the selection of the actual automobile product design. This makes it difficult to efficiently process the language decision information of each department during the design of the actual product, i.e., to select the optimal evaluation from the language-glossed evaluation decisions.
By the method in the embodiment, the decision of each department and the importance of each department opinion can be comprehensively considered, the fuzzified evaluation of language terminology can be converted into a numerical matrix through a PSO algorithm, and an optimal scheme can be further obtained through a series of processing on numerical values.
In this embodiment, the language term set L is evaluated by the distributed language preference relationship construction scheme 1 And the decision maker evaluates the language term set L 2 Therefore, the information in the language glossing evaluation can be retained to the maximum extent.
In this embodiment, step S5 includes a scheme evaluation language term set L 1 And the decision maker evaluates the language term set L 2 In the form of message granules, each language term is expressed as an interval [0, 1%]A subset of (a). Therefore, the language information can be preferably converted into numerical information, and the subsequent operation is facilitated.
In the embodiment, in consideration of the fact that the language information itself is not operable, in order to obtain an intuitive result, the language terms are firstly converted into numerical values, and an information granulation process is introduced, so that the data processing can be preferably facilitated by mapping the language terms into the form of information particles convenient for calculation. In this embodiment, the interval can be selected as the form of the information particle, that is, each language term corresponds to a sub-interval of [0,1] interval; by this, the processing of the language term sets L1 and L2 can be converted into the processing of the upper and lower limits of the interval for each language term, that is, the problem of the granulation of the language terms can be converted into the problem of the allocation of the intercept points of the interval, thereby preferably facilitating the data processing.
In this embodiment, step S5In (1), evaluating language term set L for the judger 2 When granulation is carried out, the method comprises the following steps:
step S511, setting language term l r Corresponding sub-interval is [ b ] r-1 ,b r ]And set b -q-1 ＝0， b q ＝1；
Step S512, calculating intercept point vector [ b ] based on PSO algorithm -q ,b -q+1 ,…,b 0 ,…b q-2 ,b q-1 ]And the decision maker evaluates the language term set L 2 Set of values W of L2 ，W L2 ＝{W Lr -q +1, …,0, …, q-1, q }; wherein, W Lr Denotes the language term l r By the term l in the language r Continuously and uniformly sampling and obtaining in the corresponding subinterval;
in this step, the matrix ND is evaluated numerically 2 Index Q of multiplicative consistency 2 As an optimization index and by a multiplicative consistency index Q 2 Mean value of
Establishing an optimization model as a fitness function, wherein
Representing a numerical evaluation matrix ND established based on values obtained by continuous uniformly distributed sampling in the kth operation 2 A multiplicative consistency indicator of;
the optimization model is established as follows,
MaxQ 2 ＝cND 2
wherein, "cND 2 "indicates the numerical evaluation matrix ND obtained for each operation 2 A multiplicative consistency indicator of;
constructed fitness function f 2 In order to realize the purpose,
step S513, constraint condition based on optimization model and fitness function f 2 Get the intercept point vector [ b ] -q ,b -q+1 ,…,b 0 ,…b q-2 ,b q-1 ]And a numerical evaluation matrix ND 2 Wherein; wherein the content of the first and second substances,
through steps S511 to S513, the linguistic term matrix is preferably digitized, and the weight of each decision maker can be preferably obtained.
In this embodiment, the distribution problem of the interval intercept points is actually an optimization process, and the numeralization can be preferably realized by constructing an optimization model and solving the optimization model. The model established in the embodiment can better realize the solution of the model by optimizing the consistency index.
In this embodiment, the parameters of the PSO algorithm can be set as: the number of particles is 100, the maximum number of iterations is 500, the inertia factor is 0.7, the learning factors c1 and c2 are both 2, and the sampling number K is 500.
Through solving the optimization model, the fitness function f in the embodiment can be obtained 2 Has an optimal value of 0.985 and a fitness function f 2 The graph of the iterative change of the numerical values of (a) is shown in fig. 2.
In this embodiment, the obtained language term set L 2 Has a intercept point vector of [0.15,0.28,0.34,0.50,0.58, 0.84%]I.e. a set of language terms L 2 Of these, the interval corresponding to the very insignificant (WI) is [0,0.15 ], the interval corresponding to the less significant (LI) is [0.15,0.28 ], the interval corresponding to the insignificant (NI) is [0.28,0.34),the intervals corresponding to Equally Important (EI) are [0.34,0.50 ], the intervals corresponding to important (I) are [0.50,0.58 ], the intervals corresponding to more important (VI) are [0.58,0.84 ], and the intervals corresponding to very important (SI) are [0.84,1]。
At the same time, under the optimal solution, the evaluation matrix ND is digitized 2 The following were used:
in this embodiment, the numerical evaluation matrix ND is acquired 2 Then, the decision value set W can be calculated preferably, where W is { W ═ W w |w＝1,2,3,…,s…,g,…,m}，W w A weight representing the w-th decision maker; w w The calculation formula is as follows,
in this embodiment, the obtained decision maker value set W is: w ═ 0.209,0.359,0.276,0.155 }.
In step S5 of the present embodiment, the language term set L is evaluated for the pattern 1 When granulation is carried out, the method comprises the following steps:
step S521, setting language term l h Corresponding subinterval is [ a ] h-1 ,a h ]And set a -p-1 ＝0， a p ＝1；
Step S522, calculating intercept point vector [ a ] based on PSO algorithm -p ,a -p+1 ,…,a 0 ,…a p-2 ,a p-1 ]And a scheme evaluation language term set L 1 Set of values W of L1 ， W L1 ＝W Lh -p, -p +1, …,0, …, p-1, p }; wherein, W Lh Denotes the language term l h By the linguistic term l h Continuously and uniformly sampling and obtaining in the corresponding subinterval;
in the step of the method,
the optimization model is established as follows,
constructed fitness function f 1 In order to realize the purpose,
wherein the content of the first and second substances,
cND 1w represents a numerical evaluation matrix ND 1w The consistency index of (a); o is 2 Is corresponding to O 1 The index of the consistency ratio of (1), delta is a balance factor, and delta belongs to [0,1]]；
Step S523, constraint condition based on optimization model and fitness function f 1 Obtaining an intercept point vector [ a ] -p ,a -p+1 ,…,a 0 ,…a p-2 ,a p-1 ]And a numerical evaluation matrix ND 1w (ii) a Wherein the content of the first and second substances,
through steps S521-S523, the numeralization of the case language terms is preferably achieved.
In this embodiment, the parameter δ is a parameter that balances the overall degree of identity and consistency, and when δ is smaller, it indicates that the optimization result is more concerned with the overall degree of identity; and conversely, the optimization result is more concerned about the consistency of the decision-making individuals. In the present embodiment, δ is set to 0.25, so that a higher degree of consensus can be preferably achieved.
Through solving the optimization model, the fitness function f in the embodiment can be obtained 1 Has an optimal value of 0.922 and a fitness function f 1 The graph of the iterative change of the numerical values of (a) is shown in fig. 3.
In this embodiment, the language term set L is obtained 1 Has an intercept point vector of [0.39,0.58,0.60,0.61 ]]I.e. the set of language terms L 1 Of the ranges, Very Low (VL) corresponds to [0,0.39 ], low (L) corresponds to [0.39,0.58 ], the same (M) corresponds to [0.58,0.60 ], high (H) corresponds to [0.60,0.61), and Very High (VH) corresponds to [0.61,1]。
At the same time, under the optimal solution, the evaluation matrix ND is digitized 11 To ND 12 The following were used:
in step S6 of the present embodiment, a total evaluation matrix ND is acquired c ，
Wherein the content of the first and second substances,
so that all evaluation matrices will preferably be integrated according to the weight of each decision maker.
In this embodiment, the obtained total evaluation matrix ND c Comprises the following steps:
in this embodiment, in step S6, the total evaluation matrix ND is determined based on the average operator c Calculating to obtain a scheme evaluation matrix X ', X ═ X' t ) 1×n ，
So that the evaluation value of each scheme can be preferably acquired.
In this embodiment, the obtained scheme evaluation matrix X' is {0.589,0.603,0.433,0.376 }.
In this embodiment, in step S6, the optimal solution may be obtained by sorting based on the solution evaluation matrix X'. So that the optimum scheme can be preferably obtained.
In this example, the scheme evaluation matrix X' is paired with the scheme X 1 、x 2 、x 3 And x 4 Sorting may be performed to obtain, x 2 >x 1 >x 3 >x 4 . Therefore, should choose scheme x 2 As a left-right design scheme.
By the method in the embodiment, when the alternative automobile design scheme is aimed at, the opinions of all departments of a company can be comprehensively considered, and a scheme which is closest to the market demand and can be accepted by consumers most can be selected.
In consideration of the self-limited rationality of each department decision maker and the uncertainty of selecting the design scheme of the automobile product, the preference relationship of each department decision maker is expressed based on the distributed language preference relationship in the embodiment. Therefore, under the condition that the weight of each decision maker is unknown, the relative importance of each decision maker can be expressed by distributed language information expression, a corresponding relative importance matrix is further established, the absolute importance of each decision maker can be obtained by processing the relative importance matrix, and the weight of each decision maker can be better obtained by normalizing the absolute importance; then, based on the situation of each decision maker which is characterized by the distributed language information, a relevant granulation model can be preferably established based on the consistency of language preference and/or group preference consensus, and further, the language term problem can be preferably converted into a numerical processing problem, so that an optimal design scheme can be preferably obtained.
The present invention and its embodiments have been described above schematically, without limitation, and what is shown in the drawings is only one of the embodiments of the present invention, and the actual structure is not limited thereto. Therefore, if the person skilled in the art receives the teaching, without departing from the spirit of the invention, the person skilled in the art shall not inventively design the similar structural modes and embodiments to the technical solution, but shall fall within the scope of the invention.
Claims (4)
1. A method for selecting an automobile product design scheme based on PSO information granulation comprises the following steps:
step S1, establishing a candidate set X and a scheme evaluation language term set L for evaluating the preference degree of the candidate 1 (ii) a Wherein X ═ { X ═ X t |t＝1,2,3,…,i,…,j,…,n}，x t Represents the tth alternative; wherein L is 1 ＝{l h |h＝-p,-p+1,…,0,…,p-1,p}，l h The h language term, language term l, representing the evaluation of the scheme -p To the linguistic term l p The indicated preference degree is increased;
step S2, establishing a decision maker set E and a decision maker evaluation language term set L for evaluating the preference degree of the decision maker 2 (ii) a Wherein E ═ { E ═ E w |w＝1,2,3,…,s…,g,…,m}，e w Represents the w-th decision maker; wherein L is 2 ＝{l r |r＝-q,-q+1,…,0,…,q-1,q}，l r The r-th language term, language term l, representing the evaluation of a decision maker -q To the linguistic term l q The indicated preference degree is increased;
step S3, each decision maker evaluates the language term set L based on the scheme 1 The language term in (1) carries out language term evaluation on all the alternatives, and a language term evaluation matrix D of each decision maker on all the alternatives is established 1w ，D 1w Representing a matrix obtained after the w-th decision maker evaluates all the alternative schemes; wherein the content of the first and second substances,
indicating the w-th decision maker compared to alternative x j For alternative x i (ii) a linguistic term evaluation of (a); wherein the content of the first and second substances,
representing linguistic term evaluation
Chinese language term l h The probability of occupation; wherein the content of the first and second substances,
and is
Step S4, evaluating language term set L based on decision maker 2 The language term in (1) carries out language term evaluation on all decision-makers, and establishes a language term evaluation matrix D for the decision-makers 2 (ii) a Wherein, the first and the second end of the pipe are connected with each other,
representing the comparison to decision e g For decision maker e s (ii) a linguistic term evaluation of (a); wherein the content of the first and second substances,
representing linguistic term evaluation
Chinese language term l r The probability of occupation; wherein the content of the first and second substances,
and is
Step S5, granulating the language terms to obtain a language term evaluation matrix D 1w Numerical evaluation matrix ND of 1w ，
Indicating the w-th decision maker compared to alternative x j For alternative x i Numerical evaluation of (1); and a language term evaluation matrix D 2 Numerical evaluation matrix ND of 2 ，
Representing the comparison to decision e g For decision maker e s Numerical evaluation of (1);
step S6, evaluating the matrix ND according to the numeralization 1w And a numerical evaluation matrix ND 2 Acquiring the preference degree of each alternative scheme to further acquire an optimal design scheme;
step S5 includes scheme evaluation language term set L 1 And the decision maker evaluates the language term set L 2 In the form of message granules, each language term is expressed as an interval [0, 1%]A subset of (a);
in step S5, the language term set L is evaluated for the decider 2 When granulation is carried out, the method comprises the following steps:
step S511, setting language term l r Corresponding sub-interval is [ b ] r-1 ,b r ]And set b -q-1 ＝0，b q ＝1；
Step S512, calculating intercept point vector [ b ] based on PSO algorithm -q ,b -q+1 ,…,b 0 ,…b q-2 ,b q-1 ]And the decision maker evaluates the language term set L 2 Set of values W of L2 ，W L2 ＝{W Lr -q +1, …,0, …, q-1, q }; wherein, W Lr Denotes the language term l r By the term l in the language r Continuously and uniformly sampling in the corresponding subintervals;
in this step, the matrix ND is evaluated numerically 2 Index Q of multiplicative consistency 2 As an optimization index and by a multiplicative consistency index Q 2 Mean value of
Establishing an optimization model as a fitness function, wherein
Expressing a numerical evaluation matrix ND established based on values obtained by continuous uniform distribution sampling in the kth operation 2 A multiplicative consistency indicator of;
the optimization model is established as follows,
MaxQ 2 ＝cND 2
wherein, "cND 2 "indicates the numerical evaluation matrix ND obtained for each operation 2 A multiplicative consistency indicator of;
constructed fitness function f 2 In order to realize the purpose,
step S513, constraint condition based on optimization model and fitness function f 2 Get the intercept point vector [ b ] -q ,b -q+1 ,…,b 0 ,…b q-2 ,b q-1 ]And a numerical evaluation matrix ND 2 (ii) a Wherein the content of the first and second substances,
further comprising the calculation of a decision maker weight matrix W, { W ═ W w |w＝1,2,3,…,s…,g,…,m}，W w A weight representing the w-th decision maker; w w The calculation formula is as follows,
in step S5, the language term set L is evaluated for the scheme 1 When granulation is carried out, the method comprises the following steps:
step S521, setting language term l h Corresponding subinterval is [ a ] h-1 ,a h ]And set a -p-1 ＝0，a p ＝1；
Step S522, calculating intercept point vector [ a ] based on PSO algorithm -p ,a -p+1 ,…,a 0 ,…a p-2 ,a p-1 ]And a scheme evaluation language term set L 1 Set of values W of L1 ，W L1 ＝{W Lh -p, -p +1, …,0, …, p-1, p }; wherein, W Lh Denotes the language term l h By the linguistic term l h Continuously and uniformly sampling and obtaining in the corresponding subinterval;
in the step of the method,
the optimization model is established as follows,
constructed fitness function f 1 In order to realize the purpose of the method,
wherein the content of the first and second substances,
cND 1w represents a numerical evaluation matrix ND 1w The consistency index of (a); o is 2 Is corresponding to O 1 The index of the consistency ratio of (1), delta is a balance factor, and delta belongs to [0,1]]；
Step S523, constraint condition based on optimization model and fitness function f 1 Obtaining an intercept point vector [ a ] -p ,a -p+1 ,…,a 0 ,…a p-2 ,a p-1 ]And a numerical evaluation matrix ND 1w (ii) a Wherein, the first and the second end of the pipe are connected with each other,
2. the method of claim 1, wherein the PSO information based automotive product design selection process comprises: in step S6, a total evaluation matrix ND is acquired c ，
Wherein, the first and the second end of the pipe are connected with each other,
3. the method of claim 2, wherein the PSO information based automotive product design selection process comprises: in step S6, the total evaluation matrix ND is subjected to averaging based on the average operator c An acquisition scheme evaluation matrix X ' and X ' are calculated to obtain (X ' t ) 1×n ，
4. The method of claim 3, wherein the PSO information based vehicle product design selection process comprises: in step S6, the optimal solution can be obtained by sorting based on the solution evaluation matrix X'.
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