CN113987936A - Equipment test resource overall allocation method based on chaotic genetic algorithm - Google Patents

Equipment test resource overall allocation method based on chaotic genetic algorithm Download PDF

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CN113987936A
CN113987936A CN202111256361.9A CN202111256361A CN113987936A CN 113987936 A CN113987936 A CN 113987936A CN 202111256361 A CN202111256361 A CN 202111256361A CN 113987936 A CN113987936 A CN 113987936A
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孙晓
赵颖
孙鹏
徐熙阳
孙磊
古先光
盛经雨
郭旭凯
冉讯
殷浚喆
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Abstract

The invention discloses a chaotic genetic algorithm-based equipment test resource overall allocation method, which comprises the steps of determining an allocation optimization set according to the current test task requirement and equipment resource condition, namely determining a task/step set participating in allocation and an available equipment set participating in allocation; constructing an objective function and a constraint condition; solving a mathematical model of the overall test resource allocation process by using a chaotic genetic algorithm; constructing a chaotic crossover operator based on Logistic chaotic mapping, and generating a new individual by utilizing the crossover probability of the chaotic genetic algorithm and the chaotic crossover operator; and forming a new generation of population by new individuals generated after chaotic crossing and chaotic variation until the chaotic genetic algorithm converges to obtain an optimal solution for overall allocation of the test resources, taking the optimal solution as a final allocation scheme for equipping the test resources, and outputting the allocation scheme. The invention changes the manual allocation mode of the existing equipment test resources and solves the problems of uneven equipment test resource allocation, low test efficiency and the like.

Description

Equipment test resource overall allocation method based on chaotic genetic algorithm
Technical Field
The invention relates to the field of equipment tests, in particular to a chaotic genetic algorithm-based equipment test resource overall allocation method.
Background
The equipment test is the examination and verification of equipment performance, production conditions, use conditions and the like according to specified requirements in the equipment development process, and aims to ensure that the performance and the quality of the equipment reach specified standards. For the completion of the equipment test activities, different test subjects need different test resources to guarantee. Different test resources are distributed in different places and units, the test resources are mutually isolated, and in the process of equipping test organization, the test resources are frequently required to be allocated and allocated comprehensively by test personnel in a manual allocation and manual overall planning mode in the direction of different test requirements. In the process, due to the fact that the test resources are various and complex to use, different test resources are various and complex to use, and various communication protocols and clock formats are used, common technical requirements for unified allocation, resource state monitoring, data acquisition, data transmission and the like exist. Especially for the situation that a large amount of test resources are needed, if a manual allocation and planning mode is still adopted, the common technical requirement problem cannot be solved, and even the problems of test resource waste, resource allocation inequality and the like are generated. Therefore, in order to improve the overall allocation capacity and the reasonable planning capacity of the equipment test resources, the existing test resources need to be fully integrated, and appropriate test resources are allocated to each test task and subject so as to achieve the optimal overall test effect.
Disclosure of Invention
Aiming at the problem that the common technical requirements of unified allocation, resource state monitoring, data acquisition, data transmission and the like are difficult to solve in the manual allocation and planning process of a large amount of test resources, the invention can abstract the problem into the problem of combination optimization. There are many solutions to the combinatorial optimization problem, in which the genetic algorithm is a more classical intelligent solution algorithm. However, the standard genetic algorithm has the problems of premature convergence, high randomness of new individuals, incapability of obtaining an accurate solution and the like in the solving process. Therefore, the chaos theory is introduced to improve the chaos theory, and the invention discloses a chaotic genetic algorithm-based equipment test resource overall allocation method. The method provides decision support for resource optimization in the equipment test process.
The invention discloses a chaotic genetic algorithm-based equipment test resource overall allocation method. The method constructs a mathematical model for overall allocation of test resources, solves the mathematical model by using a chaotic genetic algorithm to obtain an overall allocation scheme of the test resources, and supports test conditions to construct quantitative scientific decisions.
And the test resource overall allocation is to allocate the test resources according to the test task requirements and the current use condition of the equipment facility resources after receiving the test tasks, so that the efficiency of completing the test tasks is highest, and the load utilization of the equipment facilities is more balanced.
The invention discloses a chaotic genetic algorithm-based equipment test resource overall allocation method, which specifically comprises the following steps:
and S1, determining a deployment optimization set according to the current test task requirement and the equipment resource condition, namely determining a task/step set participating in deployment and an available equipment set participating in deployment.
And S2, establishing a mathematical model of the overall test resource allocation process, and constructing an objective function and constraint conditions.
S3, solving the mathematical model of the overall test resource allocation process by using the chaotic genetic algorithm, defining the chromosome coding mode of the overall test resource allocation as double-layer genetic coding, and establishing the mapping relation from the allocation problem solution space to the chromosome coding space by combining with an allocation optimization set.
And S4, initializing and generating a plurality of individuals to form an initial population based on the constraint conditions, wherein the number of the individuals is the population scale, and each individual is a coded chromosome.
S5, determining a target function in the mathematical model of the overall test resource allocation process as a fitness function, calculating the fitness of all individuals in the population, judging whether the cyclic algebra reaches a cyclic algebra threshold value, if so, outputting the optimization result of the chaotic genetic algorithm as the overall test resource allocation result, and if not, executing the step S6.
S6, defining the selection mode of the overall allocation of the test resources as the selection of roulette, selecting individuals with fitness higher than a certain threshold value in the population by using the mode, and eliminating the individuals with fitness lower than the certain threshold value.
And S7, constructing a chaotic crossover operator based on Logistic chaotic mapping, and generating a new individual by using the crossover probability of the chaotic genetic algorithm and the chaotic crossover operator.
And S8, constructing a chaotic mutation operator based on Logistic chaotic mapping, and generating a new individual by using the mutation probability of the chaotic genetic algorithm and the chaotic mutation operator.
And S9, forming a new generation of population by the new individuals generated after chaotic crossing and chaotic variation, repeating the steps S5 to S8 until the chaotic genetic algorithm is converged to obtain the optimal solution for overall allocation of the test resources, taking the optimal solution as the final allocation scheme of the equipment test resources, and outputting the allocation scheme. When the optimal fitness of the population individuals changes to be smaller than a certain threshold value along with the increase of the cyclic algebra, the chaotic genetic algorithm converges.
In step S1, a deployment optimization set is determined, which includes a task/step set and a set of available device resources. Firstly, for a task/step set, determining a test task participating in allocation according to the requirement of the test task, dividing the test task and the step into a completed task and a step and an unfinished task and a step according to the flow step of the test task and the progress condition of the current test task, and forming the unfinished task and the step into a set as a task/step set in a scheduling optimization set. Secondly, for the available equipment resource set, by analyzing the technical and tactical indexes and the abilities of the equipment resources, the equipment resources which can play roles in each task and operation step in the task/step set are selected, and a set is formed to be the available equipment resource set in the scheduling optimization set.
The step S2 specifically includes the following steps of describing the overall equipment test resource allocation problem: in the test process, n tasks to be tested and m available equipment resources exist, each test task comprises a plurality of flow steps and is carried out according to a certain sequence. Each step of each test task can only run on one equipment resource, the running time of the test task is fixed, and each equipment resource can be used for multiple steps of different test tasks. The allocation target of the mathematical model of the overall test resource allocation process is to arrange and determine the execution sequence of the steps of the test tasks on each equipment resource and determine the starting time of each task step; piFor the ith test task, i is 1, 2, 3, …, n, and n is total; mkFor the kth device resource, k is 1, 2, 3, …, m, and there are m device resources; o isijFor test task PiJ ═ 1, 2, 3, …, Ji，JiFor the number of steps of the ith test task,
Figure BDA0003324210930000031
q is the total number of steps; ST (ST)ikFor test task PiAt device resource MkThe start time of (c); FTikFor test task PiAt device resource MkThe completion time of (c); DTikFor test task PiAt device resource MkThe time of use of (1); x is the number ofilkMeans that when the device resource M iskUpper test task PiEarlier than test task PlWhen the user arrives and uses the system first, the value is 1, otherwise, the value is 0; a isihkWhen test task P meansiFirst in the device resource MhGo to device resource MkAnd executing the operation, wherein the value is 1, otherwise, the value is 0.
For the mathematical model of the overall test resource allocation process, the minimization of the maximum completion time of the test task is taken as an objective function, and the expression of the objective function is as follows:
min max(FTik)， (1)
the constraint conditions of the mathematical model are as follows:
FTlk-FTik+M(1-xilk)≥DTlk， (2)
FTik-DTih+M(1-aihk)≥FTih， (3)
FTik＝STik+DTik， (4)
xilk，aihk＝0，1， (5)
FTik≥0， (6)
i＝1，2，…，n，l＝1，2，…，n；h＝1，2，…，m，k＝1，2，…，m； (7)
the parameter M represents a control factor, which takes on a positive number different from zero.
Step S3, the solution space of the mathematical model of the overall test resource allocation process is converted into the coding space of the chromosome in the genetic algorithm by a double-layer genetic coding method, the chromosome is divided into two layers according to the available device resource set and the corresponding service time of each device resource, the first layer is the code of the step based on the test task, namely the step code, the second layer is the code based on the available device resource, namely the device code, the chromosome has 2Q coding bits, and the specific representation is as follows:
C＝[α1，...αj，...αQ|β1，...，βj，...βQ]，
wherein C represents a chromosome, the jth step code αjIn the test task set { P1，P2，...PnThe internal value of (i) corresponds to the test task number, if the ith test task PiIn the j-th occurrence in the step code, use step OijIndicating a test task PiThe total number of times that the test task number appears in the step code represents the total number of steps of the test task. J device code betajCorresponding to step OijThe selected equipment resource number has the value range of equipment resource set { M1，M2，...Mn}。
Step S6, a target function in the mathematical model of the overall test resource allocation process is used as a calculation function of fitness in the chaotic genetic algorithm, and a roulette selection method is used to select good individuals according to the fitness of chromosomes, and a ratio of the fitness of each chromosome to the sum of the fitness of population chromosomes is used as a probability that the chromosome is selected as a good individual, wherein the roulette selection method specifically comprises the following steps: firstly, calculating the fitness of each chromosome individual, secondly, representing the fitness of the whole population chromosome by using a pie chart, wherein each chromosome individual represents a region in the pie chart, the area of the corresponding region is in direct proportion to the fitness of the chromosome individual, assuming that a pointer points to the initial position in the pie chart along the radius of the pie chart, then rotating the pie chart, wherein the pointer does not move in the rotating process, and the chromosome individual region pointed by the pointer after the rotating process is stopped is the chromosome individual to be selected.
And S7, constructing a chaotic crossover operator based on Logistic chaotic mapping, and generating a new individual by using the crossover probability of the chaotic genetic algorithm and the chaotic crossover operator.
In the step S7, two chromosomes are randomly selected, and the specific operations performed again include:
s71, it is determined whether to perform a crossover operation on the two chromosomes. Arbitrarily take the initial value s0E.g. 0,1, by Logistic chaotic mapping formula sr+1＝λsr(1-sr) Generating an iterative sequence, srIs the r-th element in the iterative sequence, r is a set value, and is represented by lambda, the value range of the element in the iterative sequence is (0,1), and s is usedr+1As a scale for controlling the crossing operation, the crossing probability p is set in advanceMaking a businessComparison if sr+1Greater than pMaking a businessIf so, performing cross operation on the two chromosomes, otherwise, not performing cross operation;
s72, determining the crossing position of the chromosome; the first layer of the chromosome is divided into a number of gene segments, each of which contains one or more loci. Simultaneously dividing the interval (0,1) into a plurality of subintervals, wherein each subinterval corresponds to a gene segment, and then utilizing Logistic chaotic mapping to arbitrarily take an initial value s0Generating an iterative sequence, wherein the value intervals of elements in the iterative sequence are also (0,1), and judging sr+1In the subinterval, the chromosome gene segment corresponding to the subinterval is the cross position of the chromosome.
S73, performing cross operation on the two chromosomes and performing local adjustment on the gene segments on the two chromosomes. The gene segments in the first layer cross positions of the two chromosomes are interchanged, and the gene segments on the crossed chromosomes are locally adjusted, so that the expressed task number and the step number of the gene segments accord with the constraint condition, and the second layer equipment codes of the chromosomes can be selected only in the available equipment set of the first layer equipment codes.
And S8, constructing a chaotic mutation operator based on Logistic chaotic mapping, and generating a new individual by using the mutation probability of the chaotic genetic algorithm and the chaotic mutation operator.
In the step S8, a chromosome is randomly selected, and the specific operations further performed include:
and S81, determining whether mutation operation is carried out on the randomly selected chromosome. Generating an iterative sequence by Logistic chaotic mapping when s in the iterative sequencer+1Greater than the mutation probability pBecomeWhen it comes toCarrying out mutation operation on the selected chromosome, otherwise, not carrying out mutation operation on the chromosome;
and S82, determining the variation position of the chromosome. The first layer of the chromosome is divided into Q segments, each segment contains a gene locus, and the interval (0,1) is divided into Q subintervals and corresponds to the chromosome gene loci one by one. By using the position-changing variation method, two initial values are arbitrarily selected
Figure BDA0003324210930000061
Generating two corresponding iterative sequences by Logistic chaotic mapping, judging that the value intervals of elements in the iterative sequences are (0,1)
Figure BDA0003324210930000062
And
Figure BDA0003324210930000063
in the sub-interval of the interval (0,1), the gene position corresponding to the sub-interval is the mutation position.
And S83, performing mutation operation on the randomly selected chromosome and locally adjusting the gene segments on the chromosome. Exchanging the gene positions of the two variation positions of the first layer of the chromosome, and locally adjusting the gene segments on the varied chromosome, so that the second layer of equipment codes of the chromosome can be selected only in the available equipment set of the first layer of step codes.
The invention has the beneficial effects that: the invention determines a deployment optimization set including a test task/step data set and an available equipment resource data set for the equipment test resource overall deployment process, and then constructs an optimization objective function and related variable constraint conditions. And Logistic chaotic mapping is introduced into a standard genetic algorithm to construct a chaotic crossover operator and a chaotic mutation operator so as to dynamically control the generation probability of a new individual, and finally, an optimal solution of the overall allocation problem of test resources is obtained, so that the defects that the standard genetic algorithm is too early to be converged and is easy to fall into local optimization are overcome, the manual allocation mode of the test resources of the existing equipment is changed, and the problems of uneven allocation of the test resources, low test efficiency and the like of the equipment are solved.
Drawings
FIG. 1 is a flow chart of the method for overall allocation of equipment test resources based on a chaotic genetic algorithm;
FIG. 2 is a diagram of an example of chromosomal codes of the present invention;
FIG. 3 is a flowchart of the test task 1 steps of the example;
FIG. 4 is a flowchart of an example test task 4 step;
FIG. 5 is an optimal value convergence curve of the chaotic genetic algorithm of the embodiment;
FIG. 6 is an optimal convergence curve of the standard genetic algorithm of the example.
Detailed Description
For a better understanding of the present disclosure, an example is given here. FIG. 1 is a flow chart of the method for overall allocation of equipment test resources based on a chaotic genetic algorithm; FIG. 2 is a diagram of an example of chromosomal codes of the present invention; FIG. 3 is a flowchart of the test task 1 steps of the example; FIG. 4 is a flowchart of an example test task 4 step; FIG. 5 is an optimal value convergence curve of the chaotic genetic algorithm of the embodiment; FIG. 6 is an optimal convergence curve of the standard genetic algorithm of the example.
The invention discloses a chaotic genetic algorithm-based equipment test resource overall allocation method, wherein the flow of the whole method is shown in figure 1, and the method specifically comprises the following steps:
and S1, determining a deployment optimization set according to the current test task requirement and the equipment resource condition, namely determining a task/step set participating in deployment and an available equipment set participating in deployment.
And S2, establishing a mathematical model of the overall test resource allocation process, and constructing an objective function and constraint conditions.
S3, solving the mathematical model of the overall test resource allocation process by using the chaotic genetic algorithm, defining the chromosome coding mode of the overall test resource allocation as double-layer genetic coding, and establishing the mapping relation from the allocation problem solution space to the chromosome coding space by combining with an allocation optimization set.
And S4, initializing and generating a plurality of individuals to form an initial population based on the constraint conditions, wherein the number of the individuals is the population scale, and each individual is a coded chromosome.
S5, determining a target function in the mathematical model of the overall test resource allocation process as a fitness function, calculating the fitness of all individuals in the population, judging whether the cyclic algebra reaches a cyclic algebra threshold value, if so, outputting the optimization result of the chaotic genetic algorithm as the overall test resource allocation result, and if not, executing the step S6.
S6, defining the selection mode of the overall allocation of the test resources as the selection of roulette, selecting individuals with fitness higher than a certain threshold value in the population by using the mode, and eliminating the individuals with fitness lower than the certain threshold value.
And S7, constructing a chaotic crossover operator based on Logistic chaotic mapping, and generating a new individual by using the crossover probability of the chaotic genetic algorithm and the chaotic crossover operator.
And S8, constructing a chaotic mutation operator based on Logistic chaotic mapping, and generating a new individual by using the mutation probability of the chaotic genetic algorithm and the chaotic mutation operator.
And S9, forming a new generation of population by the new individuals generated after chaotic crossing and chaotic variation, repeating the steps S5 to S8 until the chaotic genetic algorithm is converged to obtain the optimal solution for overall allocation of the test resources, taking the optimal solution as the final allocation scheme of the equipment test resources, and outputting the allocation scheme. When the optimal fitness of the population individuals changes to be smaller than a certain threshold value along with the increase of the cyclic algebra, the chaotic genetic algorithm converges.
In step S1, a deployment optimization set is determined, which includes a task/step set and a set of available device resources. Firstly, for a task/step set, determining a test task participating in allocation according to the requirement of the test task, dividing the test task and the step into a completed task and a step and an unfinished task and a step according to the flow step of the test task and the progress condition of the current test task, and forming the unfinished task and the step into a set as a task/step set in a scheduling optimization set. Secondly, for the available equipment resource set, by analyzing the technical and tactical indexes and the abilities of the equipment resources, the equipment resources which can play roles in each task and operation step in the task/step set are selected, and a set is formed to be the available equipment resource set in the scheduling optimization set.
For example, a deployment problem has 2 to-be-completed test tasks (each including 3 process steps), and 4 available devices. Using table OijSteps j, M of test task ikRepresenting the kth device resource. The tasks/steps that have been completed at a certain deployment time are: all steps of task 1 and step 1, i.e. O, of task 211,O12,O13,O21. So the outstanding tasks/step set in the deployment optimization set is { O }22,O23,O31,O32,O33Is defined as { { M {, L, M {, L-M {, L, M {, L, M {, L, M2},{M1,M4},{M3},{M2},{M3,M4}}。
The step S2 specifically includes the following steps of describing the overall equipment test resource allocation problem: in the test process, n tasks to be tested and m available equipment resources exist, each test task comprises a plurality of flow steps and is carried out according to a certain sequence. Each step of each test task can only run on one equipment resource, the running time of the test task is fixed, and each equipment resource can be used for multiple steps of different test tasks. The allocation target of the mathematical model of the overall test resource allocation process is to arrange and determine the execution sequence of the steps of the test tasks on each equipment resource and determine the starting time of each task step so as to meet certain performance. And constructing a mathematical model for overall planning and allocation of equipment test resources based on the description.
Before the mathematical model is built, the symbols involved are defined: piFor the ith test task, i is 1, 2, 3, …, n, and n is total; mkFor the kth device resource, k is 1, 2, 3, …, m, and there are m device resources; o isijFor test task PiJ ═ 1, 2, 3, …, Ji，JiFor the number of steps of the ith test task,
Figure BDA0003324210930000091
q is the total number of steps; ST (ST)ikFor test task PiAt device resource MkThe start time of (c); FTikFor test task PiAt device resource MkThe completion time of (c); DTikFor test task PiAt device resource MkThe time of use of (1); x is the number ofilkMeans that when the device resource M iskUpper test task PiEarlier than test task PlWhen the user arrives and uses the system first, the value is 1, otherwise, the value is 0; a isihkWhen test task P meansiFirst in the device resource MhGo to device resource MkAnd executing the operation, wherein the value is 1, otherwise, the value is 0.
For the mathematical model of the overall test resource allocation process, the minimization of the maximum completion time of the test task is taken as an objective function, and the expression of the objective function is as follows:
min max(FTik)， (1)
the constraint conditions of the mathematical model are as follows:
FTlk-FTik+M(1-xilk)≥DTlk， (2)
FTik-DTih+M(1-aihk)≥FTih， (3)
FTik＝STik+DTik， (4)
xilk，aihk＝0，1，(5)
FTik≥0， (6)
i＝1，2，…，n，l＝1，2，…，n；h＝1，2，…，m，k＝1，2，…，m； (7)
the parameter M represents a control factor, which takes on a positive number different from zero. Equation (2) represents the constraint of the sequence of the process steps on the device resource, device resource MkOnly in the preceding test task PiThe next test task P can be executed after the execution is finishedl(ii) a Equation (3) represents the constraint of the sequence of test task steps, test task PiAt the position ofSpare resource MhCan arrive at the device M after the upper execution is finishedkExecuting; equation (4) represents test task PiAt device resource MkThe completion time of (c) is equal to the start time plus the usage time of the step; formula (5) represents xilkAnd aihkCan only have a value of 0 or 1; equation (6) represents test task PiAt device resource MkCompletion time on is not negative; the formula (7) represents the value ranges of i, l, h and k.
Step S3, the solution space of the mathematical model of the overall test resource allocation process is converted into the coding space of the chromosome in the genetic algorithm by a double-layer genetic coding method, the chromosome is divided into two layers according to the available device resource set and the corresponding service time of each device resource, the first layer is the code of the step based on the test task, namely the step code, the second layer is the code based on the available device resource, namely the device code, the chromosome has 2Q coding bits, and the specific representation is as follows:
C＝[α1，...αj，...αQ|β1，...，βj，...βQ]，
wherein C represents a chromosome, the jth step code αjIn the test task set { P1，P2，...PnThe internal value of (i) corresponds to the test task number, if the ith test task PiIn the j-th occurrence in the step code, use step OijIndicating a test task PiThe total number of times that the test task number appears in the step code represents the total number of steps of the test task. J device code betajCorresponding to step OijThe selected equipment resource number has the value range of equipment resource set { M1，M2，...Mn}。
As shown in fig. 2, in the overall scheduling problem of 2 × 4 test resources, there are 2 test tasks to be scheduled and 4 available device resources, each task includes 3 steps, and the chromosomes are encoded on the basis of the available device resource sets and the corresponding usage time sets in each step. The first 6 positions are step codes, the first gene position 2 of which is the 1 st occurrence and represents the 1 st step O of test task 221The third gene position 1 is the 2 nd occurrence of the 2 nd step O representing task 112. The last 6 bits are the device code, the first gene 3 of which represents the 1 st step O of task 221Performed on the device resource 3, the third gene site 2 of the device code represents the 2 nd step o of task 112On the device resource 2.
Step S6, a target function in the mathematical model of the overall test resource allocation process is used as a calculation function of fitness in the chaotic genetic algorithm, and a roulette selection method is used to select good individuals according to the fitness of chromosomes, and a ratio of the fitness of each chromosome to the sum of the fitness of population chromosomes is used as a probability that the chromosome is selected as a good individual, wherein the roulette selection method specifically comprises the following steps: firstly, calculating the fitness of each chromosome individual, secondly, representing the fitness of the whole population chromosome by using a pie chart, wherein each chromosome individual represents a region in the pie chart, the area of the corresponding region is in direct proportion to the fitness of the chromosome individual, assuming that a pointer points to the initial position in the pie chart along the radius of the pie chart, then rotating the pie chart, wherein the pointer does not move in the rotating process, and the chromosome individual region pointed by the pointer after the rotating process is stopped is the chromosome individual to be selected.
In the step S7, two chromosomes are randomly selected, and the specific operations performed again include:
s71, it is determined whether to perform a crossover operation on the two chromosomes. Arbitrarily take the initial value s0E (0,1) and using Logistic chaotic mapping formula sr+1＝λsr(1-sr) Generating an iterative sequence, srIs the r-th element in the iterative sequence, r is a set value, and is represented by lambda, the value range of the element in the iterative sequence is (0,1), and s is usedr+1As a scale for controlling the crossing operation, the crossing probability p is set in advanceMaking a businessComparison if sr+1Greater than pMaking a businessIf so, performing cross operation on the two chromosomes, otherwise, not performing cross operation;
s72, determining the crossing position of the chromosome; will dyeThe first layer of the body is divided into a number of gene segments, each gene segment comprising one or more loci. Simultaneously dividing the interval (0,1) into a plurality of subintervals, wherein each subinterval corresponds to a gene segment, and then utilizing Logistic chaotic mapping to arbitrarily take an initial value s0Generating an iterative sequence, wherein the value intervals of elements in the iterative sequence are also (0,1), and judging sr+1In the subinterval, the chromosome gene segment corresponding to the subinterval is the cross position of the chromosome.
S73, performing cross operation on the two chromosomes and performing local adjustment on the gene segments on the two chromosomes. The gene segments in the first layer cross positions of the two chromosomes are interchanged, and the gene segments on the crossed chromosomes are locally adjusted, so that the expressed task number and the step number of the gene segments accord with the constraint condition, and the second layer equipment codes of the chromosomes can be selected only in the available equipment set of the first layer equipment codes.
And S8, constructing a chaotic mutation operator based on Logistic chaotic mapping, and generating a new individual by using the mutation probability of the chaotic genetic algorithm and the chaotic mutation operator.
In the step S8, a chromosome is randomly selected, and the specific operations further performed include:
and S81, determining whether mutation operation is carried out on the randomly selected chromosome. Generating an iterative sequence by Logistic chaotic mapping when s in the iterative sequencer+1Greater than the mutation probability pBecomeCarrying out mutation operation on the randomly selected chromosome, otherwise, not carrying out mutation operation on the chromosome;
and S82, determining the variation position of the chromosome. The first layer of the chromosome is divided into Q segments, each segment contains a gene locus, and the interval (0,1) is divided into Q subintervals and corresponds to the chromosome gene loci one by one. By using the position-changing variation method, two initial values are arbitrarily selected
Figure BDA0003324210930000121
Generating two corresponding iterative sequences by Logistic chaotic mapping, judging that the value intervals of elements in the iterative sequences are (0,1)
Figure BDA0003324210930000122
And
Figure BDA0003324210930000123
in the sub-interval of the interval (0,1), the gene position corresponding to the sub-interval is the mutation position.
And S83, performing mutation operation on the randomly selected chromosome and locally adjusting the gene segments on the chromosome. Exchanging the gene positions of the two variation positions of the first layer of the chromosome, and locally adjusting the gene segments on the varied chromosome, so that the second layer of equipment codes of the chromosome can be selected only in the available equipment set of the first layer of step codes.
Taking the overall planning and allocation problem of 6 to-be-completed test tasks, 10 equipment resources and 6 steps of each test task in the test process of a certain device as an example to carry out verification analysis. The following gives the flow of steps for two test tasks, task 1 and task 4, as in figures 3 and 4, with similar other tasks. Task 1 in fig. 3 is from a test site a, and includes four fields, i.e., one field, two fields, three fields, and four fields, and six test steps, i.e., static inspection, function test, performance test, shooting test, ballistic test, and destruction, are required. Task 4 in fig. 4 is from a B test site, includes A, B two fields in total, and needs six test steps of static inspection, function test, performance test, development test, electromagnetic test, and simulation evaluation. The equipment resources involved in the allocation problem include 10 SG internal parameter test equipment, a structural characteristic quantity test system, a comprehensive electronic management test system, a test IP network, a power supply system, a simulation test system, an electromagnetic test system and the like, and are respectively numbered. The task/step completed at a certain deployment moment has O11，O12，O21，O22，O31，O32，O41，O51，O52，O61，O62The remaining steps form an incomplete task/step set in the deployment optimization set, and the corresponding set of available device resources can be converted into a table 1 task-step-device correspondence table, where the rows represent tasks, the columns represent steps, the first row represents the task/step setThe fifth column 3, 7 indicates that step 5 of task 1 can be performed on device 3 and device 7. The usage time correspondence table (table 2) shows the usage time of each task step on the corresponding device, the same row shows the tasks, and the columns show the steps, the number of each cell of which corresponds to one of each cell of table 1, for example, the fifth column 10 in the first row, 16 shows that the usage time of step 5 of task 1 on device 3 and device 7 is 10 and 16, respectively.
TABLE 1 task-step-device correspondence Table
Figure BDA0003324210930000131
TABLE 2 usage time table
Figure BDA0003324210930000132
And (3) realizing a chaotic genetic algorithm in a Matlab environment and solving the problem of overall planning and allocation of the test resources. Setting algorithm parameters: the population size is 100, the crossover probability is 0.6, the mutation probability is 0.1, and the maximum number of iterations is 100. Running the algorithm can prepare a Gantt chart and an optimal value convergence curve, wherein the optimal value convergence curve is as shown in figure 5. The optimal value convergence curve represents the variation trend of the average value of the optimal individual target value and the population individual target value in the algorithm iteration process. It can be seen that the algorithm starts to converge from about generation 20 and the optimal solution tends to stabilize.
The convergence curve of the optimal value obtained by solving the above deployment problem by using the standard genetic algorithm is shown in fig. 6, and can be obtained by comparing fig. 5 with fig. 6: 1) the chaotic genetic algorithm avoids the problem of premature convergence of a standard genetic algorithm; 2) the maximum completion time of the optimization target obtained by the chaotic genetic algorithm is shorter, and the optimization result is better.
The optimal result of the chaotic genetic algorithm is then compared with the standard genetic algorithm result to obtain table 3. The smaller the maximum completion time of the optimization target is, the better the optimization target is, so the optimization performance of the visible chaotic genetic algorithm is greatly improved compared with that of the standard genetic algorithm, and the optimization target has certain superiority.
TABLE 3 comparison of results of chaotic genetic algorithm with Standard genetic algorithm
Figure BDA0003324210930000141
The above description is only an example of the present application and is not intended to limit the present application. Various modifications and changes may occur to those skilled in the art. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present application should be included in the scope of the claims of the present application.

Claims (7)

1. A chaotic genetic algorithm-based equipment test resource overall allocation method is characterized by comprising the following steps:
s1, determining a deployment optimization set according to the current test task requirement and the equipment resource condition, namely determining a task/step set participating in deployment and an available equipment set participating in deployment;
s2, establishing a mathematical model of the overall test resource allocation process, and constructing a target function and constraint conditions;
s3, solving a mathematical model of the overall test resource allocation process by using a chaotic genetic algorithm, defining a chromosome coding mode of the overall test resource allocation as double-layer genetic coding, and establishing a mapping relation from an allocation problem solution space to a chromosome coding space by combining an allocation optimization set;
s4, initializing and generating a plurality of individuals to form an initial population based on constraint conditions, wherein the number of the individuals is the population scale, and each individual is a coded chromosome;
s5, determining a target function in a mathematical model of the overall test resource allocation process as a fitness function, calculating the fitness of all individuals in the population, judging whether a cycle algebra reaches a cycle algebra threshold value, if so, outputting an optimization result of the chaotic genetic algorithm as an overall test resource allocation result, and if not, executing a step S6;
s6, defining a selection mode of overall allocation of test resources as roulette selection, selecting individuals with fitness higher than a certain threshold value in a population by using the mode, and eliminating the individuals with the fitness lower than the certain threshold value;
s7, constructing a chaotic crossover operator based on Logistic chaotic mapping, and generating a new individual by utilizing the crossover probability of the chaotic genetic algorithm and the chaotic crossover operator;
s8, constructing a chaotic mutation operator based on Logistic chaotic mapping, and generating a new individual by using the mutation probability of the chaotic genetic algorithm and the chaotic mutation operator;
s9, forming a new generation of population by new individuals generated after chaotic crossing and chaotic variation, repeating the steps S5 to S8 until the chaotic genetic algorithm is converged to obtain an optimal solution for overall allocation of test resources, taking the optimal solution as a final allocation scheme for equipment test resources, and outputting the allocation scheme; when the optimal fitness of the population individuals changes to be smaller than a certain threshold value along with the increase of the cyclic algebra, the chaotic genetic algorithm converges.
2. The method for orchestrating resources for testing equipment based on chaotic genetic algorithm according to claim 1,
step S1, determining a deployment optimization set, which includes a task/step set and an available device resource set; firstly, for a task/step set, determining a test task participating in allocation according to the requirement of the test task, dividing the test task and the step into a completed task and a step and an unfinished task and a step according to the flow step of the test task and the progress condition of the current test task, and forming the unfinished task and the step into a set as a task/step set in a scheduling optimization set; secondly, for the available equipment resource set, by analyzing the technical and tactical indexes and the abilities of the equipment resources, the equipment resources which can play roles in each task and operation step in the task/step set are selected, and a set is formed to be the available equipment resource set in the scheduling optimization set.
3. The method for orchestrating resources for testing equipment based on chaotic genetic algorithm according to claim 1,
the step S2 specifically includes the following steps of describing the overall equipment test resource allocation problem: in the test process, n tasks to be tested and m available equipment resources exist, each test task comprises a plurality of flow steps and is carried out according to a certain sequence; each step of each test task can only run on one equipment resource, the running time of the test task is fixed, and each equipment resource can be used for multiple steps of different test tasks; the allocation target of the mathematical model of the overall test resource allocation process is to arrange and determine the execution sequence of the steps of the test tasks on each equipment resource and determine the starting time of each task step; piFor the ith test task, i is 1, 2, 3, …, n, and n is total; mkFor the kth device resource, k is 1, 2, 3, …, m, and there are m device resources; o isijFor test task PiJ ═ 1, 2, 3, …, Ji，JiFor the number of steps of the ith test task,
Figure FDA0003324210920000021
q is the total number of steps; ST (ST)ikFor test task PiAt device resource MkThe start time of (c); FTikFor test task PiAt device resource MkThe completion time of (c); DTikFor test task PiAt device resource MkThe time of use of (1); x is the number ofilkMeans that when the device resource M iskUpper test task PiEarlier than test task PlWhen the user arrives and uses the system first, the value is 1, otherwise, the value is 0; a isihkWhen test task P meansiFirst in the device resource MhGo to device resource MkExecuting, wherein the value is 1, otherwise, the value is 0;
for the mathematical model of the overall test resource allocation process, the minimization of the maximum completion time of the test task is taken as an objective function, and the expression of the objective function is as follows:
min max(FTik)，
the constraint conditions of the mathematical model are as follows:
FTlk-FTik+M(1-xilk)≥DTlk，
FTik-DTih+M(1-aihk)≥FTih，
FTik＝STik+DTik，
xilk，aihk＝0，1，
FTik≥0，
i＝1，2，…，n，l＝1，2，…，n；h＝1，2，…，m，k＝1，2，…，m；
the parameter M represents a control factor, which takes on a positive number different from zero.
4. The method for orchestrating resources for testing equipment based on chaotic genetic algorithm according to claim 1,
step S3, the solution space of the mathematical model of the overall test resource allocation process is converted into the coding space of the chromosome in the genetic algorithm by a double-layer genetic coding method, the chromosome is divided into two layers according to the available device resource set and the corresponding service time of each device resource, the first layer is the code of the step based on the test task, namely the step code, the second layer is the code based on the available device resource, namely the device code, the chromosome has 2Q coding bits, and the specific representation is as follows:
C＝[α1，...αj，...αQ|β1，...，βj，...βQ]，
wherein C represents a chromosome, the jth step code αjIn the test task set { P1，P2，...PnThe internal value of (i) corresponds to the test task number, if the ith test task PiIn the j-th occurrence in the step code, use step OijIndicating a test task PiThe j step, the total times of the test task number appearing in the step code represents the total step number of the test task; j device code betajCorresponding to step OijSelected byThe value range of the device resource number is the device resource set { M }1，M2，...Mm}。
5. The method for orchestrating resources for testing equipment based on chaotic genetic algorithm according to claim 1,
step S6, a target function in the mathematical model of the overall test resource allocation process is used as a calculation function of fitness in the chaotic genetic algorithm, and a roulette selection method is used to select good individuals according to the fitness of chromosomes, and a ratio of the fitness of each chromosome to the sum of the fitness of population chromosomes is used as a probability that the chromosome is selected as a good individual, wherein the roulette selection method specifically comprises the following steps: firstly, calculating the fitness of each chromosome individual, secondly, representing the fitness of the whole population chromosome by using a pie chart, wherein each chromosome individual represents a region in the pie chart, the area of the corresponding region is in direct proportion to the fitness of the chromosome individual, assuming that a pointer points to the initial position in the pie chart along the radius of the pie chart, then rotating the pie chart, wherein the pointer does not move in the rotating process, and the chromosome individual region pointed by the pointer after the rotating process is stopped is the chromosome individual to be selected.
6. The method for orchestrating resources for testing equipment based on chaotic genetic algorithm according to claim 1,
in the step S7, two chromosomes are randomly selected, and the specific operations performed again include:
s71, determining whether to perform a crossover operation on the two chromosomes; arbitrarily take the initial value s0E (0,1) and using Logistic chaotic mapping formula sr+1＝λsr(1-sr) Generating an iterative sequence, srIs the r-th element in the iterative sequence, r is a set value, and is represented by lambda, the value range of the element in the iterative sequence is (0,1), and s is usedr+1As a scale for controlling the crossing operation, the crossing probability p is set in advanceMaking a businessComparison if sr+1Is greater thanpMaking a businessIf so, performing cross operation on the two chromosomes, otherwise, not performing cross operation;
s72, determining the crossing position of the chromosome; dividing a first layer of a chromosome into a plurality of gene segments, each gene segment comprising one or more gene loci; simultaneously dividing the interval (0,1) into a plurality of subintervals, wherein each subinterval corresponds to a gene segment, and then utilizing Logistic chaotic mapping to arbitrarily take an initial value s0Generating an iterative sequence, wherein the value intervals of elements in the iterative sequence are also (0,1), and judging sr+1The corresponding chromosome gene segment of the subinterval is the cross position of the chromosome;
s73, performing cross operation on the two chromosomes and performing local adjustment on gene segments on the two chromosomes; the gene segments in the first layer cross positions of the two chromosomes are interchanged, and the gene segments on the crossed chromosomes are locally adjusted, so that the expressed task number and the step number of the gene segments accord with the constraint condition, and the second layer equipment codes of the chromosomes can be selected only in the available equipment set of the first layer equipment codes.
7. The method for orchestrating resources for testing equipment based on chaotic genetic algorithm according to claim 1,
in the step S8, a chromosome is randomly selected, and the specific operations further performed include:
s81, determining whether mutation operation is carried out on the randomly selected chromosome; generating an iterative sequence by Logistic chaotic mapping when s in the iterative sequencer+1Greater than the mutation probability pBecomeCarrying out mutation operation on the randomly selected chromosome, otherwise, not carrying out mutation operation on the chromosome;
s82, determining the variation position of the chromosome; equally dividing the first layer of the chromosome into Q sections, wherein each section comprises a gene locus, equally dividing the interval (0,1) into Q subintervals at the same time, and enabling the Q subintervals to correspond to the gene loci of the chromosome one by one; by using the position-changing variation method, two initial values are arbitrarily selected
Figure FDA0003324210920000051
Generating two corresponding iterative sequences by Logistic chaotic mapping, judging that the value intervals of elements in the iterative sequences are (0,1)
Figure FDA0003324210920000052
And
Figure FDA0003324210920000053
the subinterval (0,1) has a gene locus corresponding to the subinterval as a mutation position;
s83, carrying out mutation operation on the randomly selected chromosome and carrying out local adjustment on the gene segment on the chromosome; exchanging the gene positions of the two variation positions of the first layer of the chromosome, and locally adjusting the gene segments on the varied chromosome, so that the second layer of equipment codes of the chromosome can be selected only in the available equipment set of the first layer of step codes.
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