CN108090566A - Multiple target test preferred method based on connection in series-parallel genetic algorithm - Google Patents
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Abstract
The invention discloses a kind of multiple targets based on connection in series-parallel genetic algorithm to test preferred method, the preferred optimization aim of test and constraints of several electronic systems are determined as needed, perform genetic algorithm several times respectively first, new population is obtained in genetic algorithmic procedures every time, claim to filter out the individual addition elite solution set for meeting constraints, obtain individual by domination number in elite solution set, whether the individual in population belongs to elite solution set calculates fitness value in different ways；Then the optimal solution set of this genetic algorithm several times is merged, as the individual in initial population, then performs a genetic algorithm and obtain optimal solution set, each of which individual is a test preferred embodiment.The present invention is based on Pareto optimality, a kind of connection in series-parallel genetic algorithm is designed, obtains a variety of test preferred embodiments for meeting multiple targets, it is alternative so as to provide a variety of preferred embodiments of testing for policymaker, solution can be provided under different occasions.
Description
Technical field
The invention belongs to Fault Diagnosis for Electronic System technical fields, more specifically, are related to a kind of based on connection in series-parallel something lost
The multiple target test preferred method of propagation algorithm.
Background technology
In the troubleshooting issue for large scale electronic equipment system, how testing scheme is selected, make fault detect rate
(FDR, fault diagnose rate), false alarm rate (FAR, fault alarm rate) and the every expense of test (time,
Economic dispatch) etc. testabilities index meet constraints simultaneously and even tend to more preferable, be that academic or engineering field is constantly explored
The problem of.
For the above test optimal selection problem for considering multiple testability indexes simultaneously, multi-objective optimization question can be considered as.
Multi-objective optimization question is discussed how under certain constraints, is found and is met multiple targets and be attained by optimal solution.One
As in the case of, be contradiction between each sub-goal of multi-objective optimization question, the improvement of a sub-goal is it is possible that can cause
The reduced performance of another or another several sub-goals, that is, to make simultaneously multiple sub-goals be optimal together value be can not
Can, and can only be coordinated between which and compromise processing, make each sub-goal all being optimal as much as possible.
Multiple-objection optimization can use formula (1) to express, that is, need to find suitable x and cause all N number of object function f (x)
It is minimum：
Minimize F (x)=(f1(x),f2(x),…,fN(x)) (1)
Essential distinction with single-object problem is that the solution of multi-objective optimization question is simultaneously not exclusive, but there are one
The optimal solution set that group is made of numerous Pareto (Pareto) optimal solution, each element in set are known as Pareto optimal solutions
Or Pareto optimal.For the vectorial F (x determined by formula (1)i) and F (xj), if two phasors are unequal and F (xi) inner
All elements are all not more than F (xj) inner correspondence position element, then claim F (xi) dominate F (xj), xjReferred to as dominate solution, xiIt is referred to as non-
Dominate solution.The collection being made of all non-domination solutions is collectively referred to as Pareto optimality collection.
For multi-objective optimization question, presently the most universal method is that summation is weighted to multiple target, such as formula (2) institute
Show, and function g (x) regard single-object problem as.
Wherein, n=1,2 ..., N.
There are two the problem of this processing method：(1) weight factor subjectivity is strong, and designer is often less susceptible to select；
(2) optimum results are single, it is impossible to provide multiple selections.For Fault Diagnosis for Electronic System field, sometimes require to isolate as early as possible
Failure, the importance of testing cost is relatively secondary, and sometimes requires stringent control cost, not high to time requirement.It needs at this time
Optimization algorithm is wanted to be capable of providing multiple choices alternative to policymaker, solution can be provided under different occasions.
The content of the invention
It is an object of the invention to overcome the deficiencies of the prior art and provide a kind of multiple targets based on connection in series-parallel genetic algorithm
Preferred method is tested, based on Pareto optimality, a kind of connection in series-parallel genetic algorithm is designed, obtains a variety of tests for meeting multiple targets
Preferred embodiment.
For achieving the above object, the present invention is based on connection in series-parallel genetic algorithm multiple target test preferred method include with
Lower step：
S1：The preferred optimization aim of test and constraints of several electronic systems, note optimization mesh are determined as needed
Target quantity is N, and sets the optimization object function f of testing schemen(x), n=1,2 ..., N, optimization object function value is smaller,
Testing scheme is more excellent, and note constraints is A >=A*, A expression constraints parameters, A*Expression parameter threshold value；
S2：K genetic algorithm is performed, obtains K optimal solution set, the specific steps of genetic algorithm include：
S2.1：Initialize iterations t=1, elite solution setY individual x of generation at randomi, i=1,2 ...,
Y, the binary code that each individual is M for a length, M represent the test quantity of electronic system；Each individual represents a survey
Examination scheme when than the m-th data is 0 in binary code, represents unselected m-th of test in testing scheme corresponding to individual, when
When than the m-th data is 1 in binary code, represent to choose m-th of test, m=1,2 ..., M in testing scheme corresponding to individual；
S2.2：Meet the individual of constraints in being screened from current population, add in elite solution set E；
S2.3：According to N number of optimization object function fn(x), current elite solution set E each individual x are calculateddIt is corresponding N number of excellent
Change target function value fn(xd), d=1,2 ..., | E |, | E | represent individual amount in current elite solution set E, it is more individual two-by-two
Optimization object function value, obtain the number z that each individual is dominatedd；It searches in current elite solution set E by domination number most
Mostly with minimum individual, z is denoted as by domination number respectivelymaxAnd zmin；
S2.4：Calculate individual xiFitness value Fi：
S2.5：If t ＜ T, enter step S2.6, otherwise enter step S2.8：
S2.6：To making choice, intersecting, mutation operation in current population, next-generation population is generated；
S2.7：Make t=t+1, return to step S2.2；
S2.8：Each individual corresponding N number of optimization object function value in current elite solution set E is calculated, searches for current essence
Non-dominant individual in English solution set E forms the optimal solution set of this genetic algorithm；
S3：Merge the K optimal solution set that step S2 is obtained, obtain set U, if the individual amount in set U | U | etc.
In Y, then by set U as population V；If | U | less than Y, random generation generates Y- | U | individual forms kind together with set U
Group V；If | U | more than Y, each individual corresponding N number of optimization object function value, more individual two-by-two in set of computations U
Optimization object function value obtains the number that each individual is dominated, individual is pressed and is arranged by domination number ascending order, Y before taking
Body forms population V；Using population V as initial population, the genetic algorithm in a step S2 is performed, obtains optimal solution set, often
Individual is a test preferred embodiment.
Multiple target the present invention is based on connection in series-parallel genetic algorithm tests preferred method, determines several Departments of Electronics as needed
The preferred optimization aim of test and constraints of system, perform genetic algorithm several times respectively first, every in genetic algorithmic procedures
It is secondary to obtain new population, claim to filter out the individual addition elite solution set for meeting constraints, obtain individual in elite solution set
By domination number, whether the individual in population belongs to elite solution set calculates fitness value in different ways；So
Afterwards by this several times genetic algorithm optimal solution set merge, as the individual in initial population, then perform a genetic algorithm and obtain
To optimal solution set, each of which individual is a test preferred embodiment.The present invention is based on Pareto optimalities, design a kind of connection in series-parallel
Genetic algorithm obtains a variety of test preferred embodiments for meeting multiple targets, so as to provide a variety of test preferred embodiments for policymaker
Alternatively, solution can be provided under different occasions.
Description of the drawings
Fig. 1 is that the present invention is based on the specific embodiment flows of the multiple target of connection in series-parallel genetic algorithm test preferred method
Figure；
Fig. 2 is the flow chart of genetic algorithm in the present invention.
Specific embodiment
The specific embodiment of the present invention is described below in conjunction with the accompanying drawings, so as to those skilled in the art preferably
Understand the present invention.Requiring particular attention is that in the following description, when known function and the detailed description of design perhaps
When can desalinate the main contents of the present invention, these descriptions will be ignored herein.
Fig. 1 is that the present invention is based on the specific embodiment flows of the multiple target of connection in series-parallel genetic algorithm test preferred method
Figure.As shown in Figure 1, the specific steps the present invention is based on the multiple target test preferred method of connection in series-parallel genetic algorithm include：
S101：Determine the preferred target of test and constraints：
The preferred optimization aim of test and constraints of several electronic systems are determined as needed, remember optimization aim
Quantity is N, and sets the optimization object function f of testing schemen(x), n=1,2 ..., N, optimization object function value is smaller, test
Scheme is more excellent, and note constraints is A >=A*, A expression constraints parameters, A*Expression parameter threshold value.
In Fault Diagnosis for Electronic System field, testing preferred optimization aim includes test funds minimum, testing time
Minimizing overhead, Fault Isolation degree maximize, fault detect rate maximizes etc., it is clear that when optimization aim is to be the bigger the better, example
Such as Fault Isolation degree and fault detect rate, optimization object function could be provided as falling for Fault Isolation degree or fault detect rate
Number.The parameter of constraints can generally select Fault Isolation degree, fault detect rate, false alarm rate etc..Test preferred target and
Constraints is all to determine according to actual needs.
S102：Perform K genetic algorithm：
K genetic algorithm is performed, obtains K optimal solution set.The size of K is set according to actual needs, and the time will
Ask urgent, K can take smaller value, and higher value can be taken when high to required precision.For carrying out the preferred heredity of multiple target test
Algorithm is the key algorithm of the present invention.Fig. 2 is the flow chart of genetic algorithm in the present invention.As shown in Fig. 2, genetic algorithm is specific
Step includes：
S201：Initialization data：
Initialize iterations t=1, elite solution setY individual x of generation at randomi, i=1,2 ..., Y, often
The binary code that individual is M for a length, M represent the test quantity of electronic system.Each individual represents a test side
Case when than the m-th data is 0 in binary code, represents unselected m-th of test in testing scheme corresponding to individual, when two into
When than the m-th data is 1 in code processed, represent to choose m-th of test, m=1,2 ..., M in testing scheme corresponding to individual.
S202：Individual is screened according to constraints：
Meet the individual of constraints in being screened from current population, add in elite solution set E.
S203：Individual is calculated by domination number：
According to N number of optimization object function fn(x), current elite solution set E each individual x are calculateddCorresponding N number of optimization mesh
Offer of tender numerical value fn(xd), d=1,2 ..., | E |, | E | represent individual amount in current elite solution set E, it is more individual excellent two-by-two
Change target function value, obtain the number z that each individual is dominatedd.Search in current elite solution set E by domination number at most and
Minimum individual is denoted as z respectively by domination numbermaxAnd zmin。
S204：Calculate the fitness value of individual：
It will be apparent that currently there are two types of the individuals in population：Belong to elite solution set E or be not belonging to set E, adapted to calculating
Angle value, which needs to distinguish, to be calculated.In the present invention, individual fitness value is calculated according to the following formula：
Wherein, AiRepresent individual xiConstraints parameter value.
Obviously, the individual in elite solution set E is belonged to, it is more few more excellent by domination number, the individual of set E is not belonging to, about
Beam conditional parameter is more big more excellent, therefore the present invention, and individual fitness value is bigger, and individual is more excellent.
S205：Judge whether iterations t is less than default maximum iteration T, if so, entering step S206, otherwise
Enter step S208.
S206：The next-generation population of generation：
To making choice, intersecting, mutation operation in current population, next-generation population is generated.
S207：Make t=t+1, return to step S202.
S208：Obtain optimal solution set：
Each individual corresponding N number of optimization object function value in current elite solution set E is calculated, searches for current elite disaggregation
The non-dominant individual in E is closed, that is, forms the optimal solution set of this genetic algorithm.
S103：It is comprehensive to solve：
Merge the K optimal solution set that step S102 is obtained, obtain set U, if the individual amount in set U | U | etc.
In Y, then by set U as population V；If | U | less than Y, random generation generates Y- | U | individual forms kind together with set U
Group V；If | U | more than Y, each individual corresponding N number of optimization object function value, more individual two-by-two in set of computations U
Optimization object function value obtains the number that each individual is dominated, individual is pressed and is arranged by domination number ascending order, Y before taking
Body forms population V.Using population V as initial population, the genetic algorithm in a step S102 is performed, obtains optimal solution set,
Each individual is a test preferred embodiment.
In order to which technical solution of the present invention is better described, using a specific example, the present invention is described in detail.Table 1
It is the test dependence matrix of the present embodiment.
Table 1
As shown in table 1, the electronic system of the present embodiment shares 50 kinds of malfunctions, and 20 are alternatively tested, and first in table 1
Column data is the normalization incidence of each failure, remaining binary data represents accordingly to test whether that corresponding event can be tested
Barrier, 1 represents to test, and 0 expression cannot be tested.
In the present embodiment, optimization aim is minimum for test funds and testing time, and constraints uses Percent Isolated,
Percent Isolated is asked to reach 100%.
Table 2 is every test funds in need and the normalized value of time.
| t1 | t2 | t3 | t4 | t5 | t6 | t7 | t8 | t9 | t10 | t11 | t12 | t13 | t14 | t15 | t16 | t17 | t18 | t19 | t20 | |
| Funds | 11 | 2 | 10 | 9 | 9 | 5 | 5 | 2 | 5 | 2 | 4 | 5 | 11 | 3 | 8 | 5 | 3 | 1 | 9 | 4 |
| Time | 7 | 6 | 3 | 8 | 8 | 6 | 8 | 5 | 8 | 1 | 8 | 2 | 2 | 7 | 2 | 7 | 8 | 2 | 7 | 6 |
Table 2
The expression formula difference of two optimization object functions of test funds and testing time is as follows：
Wherein, xmRepresent m-th of element in individual x, cm、τmThe funds needed for m-th of test and time are represented respectively.
Constraints, the i.e. calculation formula of Percent Isolated FIR are：
Wherein, wvRepresent the incidence of v failures, bv(x) represent under the testing scheme corresponding to individual x whether is v failures
It is isolated, calculation formula is：
Wherein, dvm、dv′mIt represents to test whether that v failures and v ' failures can be tested for m-th respectively.
Genetic algorithm twice is run first, and initial population individual amount is 100.The adaptive value degree of the present embodiment individual according to
The following formula calculates：
Table 4 is the optimal solution set that first time genetic algorithm obtains.Table 5 is the optimal solution set that second of genetic algorithm obtains.
| Sequence number | t1 | t2 | t3 | t4 | t5 | t6 | t7 | t8 | t9 | t10 | t11 | t12 | t13 | t14 | t15 | t16 | t17 | t18 | t19 | t20 |
| 1 | 0 | 0 | 1 | 0 | 1 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 1 | 1 | 1 | 0 | 0 | 1 | 0 | 0 |
| 2 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 1 | 1 | 1 | 0 | 0 | 1 | 1 | 0 | 1 |
| 3 | 0 | 1 | 0 | 0 | 0 | 1 | 0 | 1 | 0 | 1 | 1 | 1 | 0 | 1 | 0 | 0 | 0 | 0 | 1 | 0 |
| 4 | 0 | 1 | 0 | 0 | 0 | 1 | 0 | 1 | 1 | 1 | 1 | 0 | 0 | 1 | 0 | 0 | 0 | 1 | 0 | 0 |
| 5 | 0 | 1 | 0 | 0 | 1 | 1 | 0 | 0 | 0 | 1 | 0 | 1 | 0 | 1 | 0 | 0 | 0 | 1 | 1 | 0 |
| 6 | 0 | 1 | 1 | 0 | 0 | 1 | 0 | 0 | 0 | 1 | 0 | 1 | 0 | 1 | 0 | 0 | 0 | 1 | 1 | 0 |
| 7 | 1 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 1 | 1 | 1 | 0 | 0 | 1 | 0 | 0 |
Table 4
| Sequence number | t1 | t2 | t3 | t4 | t5 | t6 | t7 | t8 | t9 | t10 | t11 | t12 | t13 | t14 | t15 | t16 | t17 | t18 | t19 | t20 |
| 1 | 0 | 1 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 1 | 1 | 1 | 0 | 1 | 0 | 0 | 0 | 1 | 0 | 1 |
| 2 | 0 | 1 | 1 | 0 | 0 | 1 | 0 | 0 | 0 | 1 | 0 | 1 | 0 | 1 | 0 | 0 | 0 | 1 | 0 | 1 |
| 3 | 0 | 1 | 1 | 0 | 0 | 1 | 0 | 0 | 0 | 1 | 0 | 1 | 0 | 1 | 0 | 0 | 1 | 1 | 0 | 0 |
| 4 | 0 | 1 | 1 | 0 | 0 | 1 | 0 | 0 | 0 | 1 | 0 | 1 | 1 | 1 | 1 | 0 | 0 | 1 | 0 | 0 |
Table 5
As shown in table 4 and table 5, a kind of testing scheme of each correspondence of each row in table, each row in each testing scheme
The corresponding measuring point of 0 or 1,0 expression is taken not to be selected, in table 4, the 2nd row the 1st is classified as 0, represents that test 1 is not in the 2nd testing scheme
It is selected.And the first row the 3rd is classified as 1, represents that the 3rd test is selected in scheme one.It is not difficult to verify, table 4 and institute in table 5
The FIR for having scheme is 1, meets minimum index request.
Table 6 is test funds and the time of all testing schemes in table 4.Table 7 is the test warp of all testing schemes in table 5
Take and the time.
| Funds | Time |
| 0.49 | 0.27 |
| 0.33 | 0.41 |
| 0.32 | 0.42 |
| 0.24 | 0.43 |
| 0.36 | 0.39 |
| 0.37 | 0.34 |
| 0.51 | 0.26 |
Table 6
| Funds | Time |
| 0.26 | 0.38 |
| 0.32 | 0.33 |
| 0.31 | 0.35 |
| 0.47 | 0.31 |
Table 7
From table 6 and table 7 as can be seen that each testing scheme is not dominated in its table by other testing schemes.
By 89 testing schemes of 11 testing schemes included in table 4 and table 5 and random generation, comprehensive solve is formed
Initial population.Table 8 is the comprehensive optimal solution set for solving and obtaining.
| Sequence number | t1 | t2 | t3 | t4 | t5 | t6 | t7 | t8 | t9 | t10 | t11 | t12 | t13 | t14 | t15 | t16 | t17 | t18 | t19 | t20 |
| 1 | 0 | 0 | 1 | 0 | 1 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 1 | 1 | 1 | 0 | 0 | 1 | 0 | 0 |
| 2 | 0 | 1 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 1 | 1 | 1 | 0 | 1 | 0 | 0 | 0 | 1 | 0 | 1 |
| 3 | 0 | 1 | 0 | 0 | 0 | 1 | 0 | 1 | 1 | 1 | 1 | 0 | 0 | 1 | 0 | 0 | 0 | 1 | 0 | 0 |
| 4 | 0 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 1 | 1 | 1 | 1 | 0 | 0 | 0 | 1 | 0 | 0 |
| 5 | 0 | 1 | 1 | 0 | 0 | 1 | 0 | 0 | 0 | 1 | 0 | 1 | 0 | 1 | 0 | 0 | 0 | 1 | 0 | 1 |
| 6 | 0 | 1 | 1 | 0 | 0 | 1 | 0 | 0 | 0 | 1 | 0 | 1 | 0 | 1 | 0 | 0 | 1 | 1 | 0 | 0 |
| 7 | 1 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 1 | 1 | 1 | 0 | 0 | 1 | 0 | 0 |
Table 8
Table 9 is test funds and the time of all testing schemes in table 8.
| Funds | Time |
| 0.24 | 0.43 |
| 0.26 | 0.38 |
| 0.31 | 0.35 |
| 0.32 | 0.33 |
| 0.38 | 0.31 |
| 0.49 | 0.27 |
| 0.51 | 0.26 |
Table 9
As can be seen from Table 9, compared with other 6 kinds of schemes, its test funds of the scheme that the first row provides are minimum, only
0.24, but its testing time highest, it is 0.43, suitable for the more stringent occasion of cost control.Conversely, last column is fitted
For requiring the testing time urgent situation.The scheme of intermediate several rows is the scheme of compromise, no matter that scheme, be all to meet
Index request.
The optimal solution solved will be integrated respectively and the optimal solution of genetic algorithm compares twice before, be not difficult to find out, it is comprehensive
The optimal solution that obtained optimal solution has all dominated preceding genetic algorithm twice is solved, i.e., the testing scheme that comprehensive solution obtains is more excellent.
As it can be seen that the present invention can provide a variety of testing scheme selections for electronic system, and obtained testing scheme is than single genetic algorithm
What is obtained is more excellent.
Although the illustrative specific embodiment of the present invention is described above, in order to the technology of the art
Personnel understand the present invention, it should be apparent that the invention is not restricted to the scope of specific embodiment, to the common skill of the art
For art personnel, if various change appended claim limit and definite the spirit and scope of the present invention in, these
Variation is it will be apparent that all utilize the innovation and creation of present inventive concept in the row of protection.
Claims (1)
1. a kind of multiple target test preferred method based on connection in series-parallel genetic algorithm, which is characterized in that comprise the following steps：
S1：The preferred optimization aim of test and constraints of several electronic systems are determined as needed, remember optimization aim
Quantity is N, and sets the optimization object function f of testing schemen(x), n=1,2 ..., N, optimization object function value is smaller, test
Scheme is more excellent, and note constraints is A >=A*, A expression constraints parameters, A*Expression parameter threshold value；
S2：K genetic algorithm is performed, obtains K optimal solution set, the specific steps of genetic algorithm include：
S2.1：Initialize iterations t=1, elite solution setY individual x of generation at randomi, i=1,2 ..., Y, often
The binary code that individual is M for a length, M represent the test quantity of electronic system.Each individual represents a test side
Case when than the m-th data is 0 in binary code, represents unselected m-th of test in testing scheme corresponding to individual, when two into
When than the m-th data is 1 in code processed, represent to choose m-th of test, m=1,2 ..., M in testing scheme corresponding to individual；
S2.2：Meet the individual of constraints in being screened from current population, add in elite solution set E；
S2.3：According to N number of optimization object function fn(x), current elite solution set E each individual x are calculateddCorresponding N number of optimization mesh
Offer of tender numerical value fn(xd), d=1,2 ..., | E |, | E | represent individual amount in current elite solution set E, it is more individual excellent two-by-two
Change target function value, obtain the number z that each individual is dominatedd；Search in current elite solution set E by domination number at most and
Minimum individual is denoted as z respectively by domination numbermaxAnd zmin；
S2.4：Calculate individual xiFitness value Fi：
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S2.5：If t ＜ T, enter step S2.6, otherwise enter step S2.8：
S2.6：To making choice, intersecting, mutation operation in current population, next-generation population is generated；
S2.7：Make t=t+1, return to step S2.2；
S2.8：Each individual corresponding N number of optimization object function value in current elite solution set E is calculated, searches for current elite solution
Non-dominant individual in set E forms the optimal solution set of this genetic algorithm；
S3：Merge K optimal solution set being obtained of step S2, obtain set U, if the individual amount in set U | U | equal to Y,
Then by set U as population V；If | U | less than Y, random generation generates Y- | U | individual forms population V together with set U；
If | U | more than Y, each individual corresponding N number of optimization object function value, two-by-two more individual optimization in set of computations U
Target function value obtains the number that each individual is dominated, individual is pressed and is arranged by domination number ascending order, Y individual structure before taking
Into population V；Using population V as initial population, the genetic algorithm in a step S2 is performed, obtains optimal solution set, each of which
Body is a test preferred embodiment.
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