CN113988396A - NSGA-III algorithm-based process sequence multi-objective optimization method - Google Patents
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Abstract
The invention relates to a process sequencing multi-objective optimization method based on NSGA-III algorithm, which utilizes a process relation graph PRG to express priority relation among processes and generate a process constraint matrix, and an optimal solution is obtained through the NSGA-III algorithm under the constraint of the process constraint matrix by designing a multi-objective function taking the total process cost, the total process time and the total carbon emission as optimization objectives. The invention can solve the problem of processing the process constraint relationship and the problem of optimizing equipment resources in the process, and obtains a high-quality process route by solving through a genetic algorithm.
Description
Technical Field
The invention belongs to the technical field of process route optimization, and particularly relates to a process sequencing optimization method based on an NSGA-III algorithm.
Background
The reliable planning of the machining process route is an important guarantee for improving the resource utilization rate, reducing the process cost and the logistics cost, shortening the manufacturing period of parts and realizing the high-quality production target. The process sequencing optimization problem of the process planning is proved to be a combined optimization problem with NP-hard property due to the fact that multiple objectives such as cost, time and the like are involved. When the process sequencing is optimized, firstly, the process sequencing is required to meet the constraint of the process sequencing rule, and secondly, the manufacturing resource consumption, the manufacturing time, the logistics cost and the time of the process route design are reduced as much as possible.
The existing solution generally performs inter-process constraint processing through a sequential constraint matrix, then considers the cost of manufacturing resources, performs weighting and performs single-target optimization solution, and this way affects the overall optimization result due to the slight change of each objective function weight, resulting in low accuracy and robustness of the final result.
Disclosure of Invention
The invention aims to provide a process sequencing optimization method based on NSGA-III algorithm, aiming at solving the NP difficult problem formed by comprehensively considering factors such as cost, time, process constraint and the like in the field of process resource planning. The invention aims to solve the problems of procedure constraint relation processing and equipment resource optimization in the process, and a high-quality process route is obtained by solving through a genetic algorithm. The technical scheme is as follows:
a process ordering multi-objective optimization method based on NSGA-III algorithm utilizes a process relation graph PRG to express priority relations among processes and generate a process constraint matrix, and an optimal solution is obtained through the NSGA-III algorithm under the constraint of the process constraint matrix by designing a multi-objective function taking the total process cost, the total process time and the total carbon emission as optimization objectives, and the method comprises the following steps:
the first step is to use the process relation map PRG to express the priority relation among the processes and generate a process constraint matrix
The process ordering needs to meet a process ordering rule, a process relation graph PRG is generated after the requirements of the four types of priority relations are met according to the geometric characteristics of parts and the technical requirements of processing, the PRG is a directed acyclic graph, nodes are process numbers, edges represent the priority relation between the processes represented by the two connected nodes, and the following definitions are made for the relation of process nodes in the PRG:
1) precursor relationship: that is, if the step i can reach the step j through any one of the paths, the step i is called a step preceding the step j;
2) the successor relationship is: in contrast to the predecessor relationship, when process i can reach process j through any route, process j is called as the successor of process i;
3) the parallel relationship is as follows: if the process i and the process j are neither in a predecessor relationship nor in a successor relationship, the process i and the process j are called parallel processes;
a PRG graph is made based on the three types of relationships,
corresponding process constraint matrix PCM (pulse code modulation) generated based on PRG (pulse repetition generator) graphij：
Wherein, the PCMijRepresents the step PiAnd process PjThe priority relationship between the two, n is the total process number of the parts to be processed, PCMijTaken from the constraint relationship, as follows:
secondly, establishing a multi-objective optimization model for process sequencing
The process sequencing optimization establishes an objective function by taking the total machine tool cost, the total cutter cost, the total machine tool change cost, the total cutter replacement cost and the total carbon emission as optimization targets and takes a process constraint matrix PCMijAnd constructing a constraint relation for optimization solution, thereby constructing the following optimization objective function:
i total machine cost
Wherein Mcot is the total machine tool cost, n is the total number of steps, MTIDiIs the machine tool code, omega, used in procedure imIs the MTID of the machine tooliCost ofA coefficient;
ii total tool cost
Wherein Tcost is the total tool cost, n is the total number of passes, TLIDiIs the tool code, omega, used in Process itIs a tool TLIDiThe cost factor of (2);
iii change cost of main machine
Wherein MCcost is the total machine change cost, ωmcIs the change cost coefficient, MTID, of the machine tooli，MTIDi+1The machine tool codes, gamma, used in the process i and the process i +1, respectively1Is a machine tool change function;
iv Total tool Change cost
Where TCcost is the total tool change cost, ωtcIs the change cost coefficient, MTID, of the tooli，MTIDi+1The machine tool codes, Tl, used in step i and step i +1, respectivelyi，Tli+1The tool code, gamma, used in step i and step i +1, respectively2Is a tool change function;
v total carbon emission
Where MPCost is the total carbon emission, MTIDiIs the machine tool code, omega, used in procedure ico2Is the MTID of the machine tooliCarbon emission coefficient of (a);
third, chromosomal coding
In order to effectively map each element involved in optimization, a dyeing coding mechanism suitable for process optimization is adopted, and the coding mode comprises three sections: the first section is a procedure sequence coding sequence, and adopts sequencing coding, which represents the sequencing sequence of each procedure; the second section is the machine tool number used in the procedure, integer coding is adopted, and the value range is the code range of the alternative machine tool; the third section is the number of the cutter used in the procedure, integer coding is adopted, and the value range is the code range of the alternative cutter;
fourthly, solving based on NSGA-III algorithm, wherein the method comprises the following steps:
step i, setting algorithm parameters including evolution times, variation rate, cross rate and the like; initializing a reference point and a population N according to a chromosome coding mode and a mapping relation;
step ii for parent population PtGenerating a sub-population Q by selection, crossing, mutationt；
Step iii mixing PtAnd QtObtaining a new population RtOn a scale of 2N; to RtDecoding all the individuals in the optimization model and calculating the fitness of the individuals, wherein the fitness is each objective function in the optimization model: mcost, Tcost, MCcost, TCcost, MPcost;
step iv for RtNon-dominated sorting and partitioning into non-dominated solution sets (F) of different hierarchical levels1,F2,F3,…,FL,…,FW)；
Step v generates a new solution set StFrom F1Initially, move one non-dominated solution set to S at a timetUntil S first appearstIs greater than or equal to the number of N, let FLIs to satisfy this for the first timeA solution set of conditions, then
Step vi from StThe intermediate selection solution generates the next generation parent group Pt+1: if StIs equal to N, then Pt+1＝StOtherwise, first StSet of (1) (F)1,F2,F3,…FL-1) Put in Pt+1Then from FLSelecting other solutions according to a reference point-based selection mechanism;
and vii, if the termination condition is met, outputting a final solution, otherwise, repeating the step ii.
In the first step, the process ordering rules should include the following categories:
1) coarse-first-fine type priority relationship: in the processing process of the part, divided processing is required to be carried out according to stages, such as the sequence of rough processing, semi-finishing and finishing;
2) surface-first hole type priority relationship: when the surface characteristics and the hole characteristics corresponding to the surface characteristics are processed, in order to ensure the uniform stress requirement of the drill hole, the surface characteristics are processed firstly, and then the hole characteristics are processed;
3) reference look-ahead priority relationship: the datum is determined by the characteristics of the part and the technological requirements, and the datum characteristic is processed preferentially when the datum characteristic and the dependent characteristic are processed;
4) primary and secondary type priority relationship: the primary surface refers to the critical working surface on the part that is arranged to be machined after the secondary surface during the finishing stage in order to prevent the secondary performance machining from scratching the primary surface.
In the fourth step, the reference point-based selection method may be: first, find the population StIdeal point C of*＝(Mcost*,Tcost*,MCcost*,TCcost*,MPcost*) Wherein Mcost*,Tcost*,MCcost*,TCcost*,MPcost*Are respectively a population StThe minimum value of each objective function value in all solutions is obtained, and then the population and the reference point are normalized; by calculation ofStTo each reference line, and then connecting the solution with the reference point by the shortest distance, at FLIn the method, individuals are selected by adopting a niche protection method, and the method comprises the following steps: let the habitat number ρjIs F1To FL-1Finding out the number of individuals connected with the jth reference point in the layer to have the minimum habitat number rhoiReference point i of, then determines FLWhether or not the individual is connected to reference point i; if an individual is connected to the reference point i, an individual is selected to enter P according to the distance from the individual to the reference pointt+1(ii) a Otherwise, this iteration will not take this reference point into account, but repeat the above operations with another reference point having the smallest number of habitats, until Pt+1Is equal to the value of N.
The invention has the advantages that: and (3) fully considering all elements in the process, constructing a multi-objective function taking the total machine tool cost, the total cutter cost, the total machine tool change cost, the total cutter replacement cost and the total carbon emission as optimization targets, expressing various priority constraint relations of the processes by using an inter-process constraint matrix, and completing a solution model of process planning. Meanwhile, in order to integrate the constraint relation and improve the optimizing rate, the invention creates a multi-chromosome coding mechanism and solves the problem through an NSGA-III algorithm, wherein the NSGA-III algorithm adopts a selection mechanism based on a reference point on the basis of a non-dominated sorting genetic algorithm (NSGA-II) with elite strategies, and can effectively perform high-dimensional multi-objective optimization calculation.
Drawings
FIG. 1 PRG diagram
FIG. 2 chromosome coding scheme
FIG. 3 solves based on the NSGA-III algorithm
FIG. 4 Pareto front plot
Detailed Description
The invention expresses the priority relation among the processes based on a Process Relationship Graph (PRG) and generates a Process constraint matrix, and an optimal solution is solved through an NSGA-III algorithm under the constraint of the Process constraint matrix by designing a multi-objective function taking the total Process cost, the total Process time and the total carbon emission as optimization targets. The method comprises the following steps:
firstly, generating a procedure constraint matrix based on a procedure relation graph PRG
The process ordering needs to satisfy a certain process ordering rule, which is obtained by refining according to process ordering knowledge, such as priority relationships of coarse-to-fine, surface-to-hole, primary-to-secondary, and the like, which belong to mandatory priority relationships, and must be satisfied. Generally, the process sequence has the following main priority relations:
5) coarse-first-fine type priority relationship: in the processing process of the part, divided processing is required to be carried out according to stages, such as the sequence of rough processing, semi-finishing and finishing;
6) surface-first hole type priority relationship: when the surface characteristics and the hole characteristics corresponding to the surface characteristics are processed, in order to meet the requirements of uniform stress of a drill hole and the like, the surface characteristics are processed first and then the hole characteristics are processed;
7) reference look-ahead priority relationship: the datum is determined by the characteristics of the part and the technological requirements, and the datum characteristic is processed preferentially when the datum characteristic and the dependent characteristic are processed;
8) primary and secondary type priority relationship: the primary surface is an important working surface on the part and is a main factor determining the quality of the part, for example, in the finishing stage, the primary surface should be arranged to be machined after the secondary surface in order to prevent the secondary performance machining from scratching the primary surface.
According to the geometric characteristics of the parts and the technical requirements of processing, a process relation graph PRG can be generated after the four types of priority relation requirements are met, the PRG is a directed acyclic graph, the nodes are process numbers, and the edges represent the priority relation between the processes represented by the two connected nodes. According to the related concepts of the data structure and the model solving needs, the invention makes the following definitions of the relationships of the process nodes in the PRG:
4) precursor relationship: that is, if the step i can reach the step j through any one of the paths, the step i is called a step preceding the step j;
5) the successor relationship is: in contrast to the predecessor relationship, when process i can reach process j through any route, process j is called as the successor of process i;
6) the parallel relationship is as follows: if the process i and the process j are neither in a predecessor relationship nor in a successor relationship, the process i and the process j are referred to as parallel processes.
A PRG graph can be made based on three types of relationships, as shown in fig. 1, for a total of 12 processes,
corresponding process constraint matrix PCM can be generated based on PRG graphij：
Wherein, the PCMijRepresents the step PiAnd process PjThe priority relationship between the parts is shown in the specification, and n is the total number of the processing steps of the parts to be processed. PCM (pulse code modulation)ijTaken from the constraint relationship, as follows:
establishing multi-objective optimization model for process sequencing
The process sequencing optimization establishes an objective function by taking the total machine tool cost, the total cutter cost, the total machine tool change cost, the total cutter replacement cost and the total carbon emission as optimization targets and takes a process constraint matrix PCMijAnd constructing a constraint relation to perform optimization solution.
The following optimization objective function is thus constructed:
i total machine cost
Wherein Mcot is the total machine tool cost, n is the total number of steps, MTIDiIs the machine tool code, omega, used in procedure imIs the MTID of the machine tooliThe cost factor of (c).
ii total tool cost
Wherein Tcost is the total tool cost, n is the total number of passes, TLIDiIs the tool code, omega, used in Process itIs a tool TLIDiThe cost factor of (c).
iii change cost of main machine
Wherein MCcost is the total machine change cost, ωmcIs the change cost coefficient, MTID, of the machine tooli，MTIDi+1The machine tool codes, gamma, used in the process i and the process i +1, respectively1Is a machine tool change function.
iv Total tool Change cost
Where TCcost is the total tool change cost, ωtcIs the change cost coefficient, MTID, of the tooli，MTIDi+1The machine tool codes, Tl, used in step i and step i +1, respectivelyi，Tli+1The tool code, gamma, used in step i and step i +1, respectively2Is the tool change function.
v total carbon emission
Where MPCost is the total carbon emission, MTIDiIs the machine tool code used in step i. Omegaco2Is the MTID of the machine tooliCarbon emission coefficient of (a).
③ chromosome coding
The process sequencing optimization belongs to a multi-objective optimization problem, and when the process sequencing optimization is solved by using a genetic algorithm, chromosome coding is firstly carried out on the process sequencing optimization, and then the problem is solved by an evolution mechanism.
In order to effectively map each element involved in optimization, the invention provides a dyeing coding mechanism suitable for process optimization, as shown in fig. 2. The coding mode consists of three sections: the first section is a procedure sequence coding sequence, and adopts sequencing coding, which represents the sequencing sequence of each procedure; the second section is the machine tool number used in the procedure, integer coding is adopted, and the value range is the code range of the alternative machine tool; the third section is the number of the cutter used in the procedure, integer coding is adopted, and the value range is the code range of the alternative cutter. As noted in the figure, the 3 rd in the process sequence is process P4The number of the machine tool selected is 2, and the number of the cutter selected is 4.
Solving based on NSGA-III algorithm
In the face of a multi-objective model for process sequencing optimization, in order to effectively solve, the invention adopts a third generation non-dominated sequencing genetic algorithm (NSGA-III), and the algorithm adopts a selection mechanism based on a reference point to perform sequencing and select an excellent solution on the basis of a non-dominated sequencing genetic algorithm (NSGA-II) with an elite strategy. The NSGA-III is developed aiming at the conditions that the high-dimensional multi-objective optimization computation complexity is high and Pareto solutions are difficult to select, and the process sequencing optimization problem corresponds to the optimization category, so that the NSGA-III is adopted to solve the model.
The NSGA-III solution process is shown in fig. 3 and mainly includes the following steps:
step i, setting algorithm parameters including evolution times, variation rate, cross rate and the like; and initializing a reference point and a population N according to the chromosome coding mode and the mapping relation.
Step ii for parent population PtGenerating a sub-population Q by selection, crossing, mutationt。
Step iii mixingPtAnd QtObtaining a new population RtThe scale was 2N. To RtDecoding all the individuals in the optimization model and calculating the fitness of the individuals, wherein the fitness is each objective function in the optimization model: mcost, Tcost, MCcost, TCcost, MPcost.
Step iv for RtNon-dominated sorting and partitioning into non-dominated solution sets (F) of different hierarchical levels1,F2,F3,…,FL,…,FW)。
Step v generates a new solution set St. After the sorting of the previous step, from F1Initially, move one non-dominated solution set to S at a timetUntil S first appearstIs greater than or equal to the number of N, provided that FLIs a solution set that satisfies this condition for the first time, then
Step vi from StThe intermediate selection solution generates the next generation parent group Pt+1. If StIs equal to N, then Pt+1＝StOtherwise, first StSet of (1) (F)1,F2,F3,…FL-1) Put in Pt+1Then from FLThe remaining solutions are selected according to a reference point-based selection mechanism.
The selection method based on the reference point comprises the following steps: first, find the population StIdeal point C of*＝(Mcost*,Tcost*,MCcost*,TCcost*,MPcost*) Wherein Mcost*,Tcost*,MCcost*,TCcost*,MPcost*Are respectively a population StThe minimum of each objective function value in all solutions, then the population and the reference point are normalized. By calculating StTo each reference line and then connecting the solution with the reference point with the shortest distance, thus at FLIn which a new niche protection method is used to select individuals. Number of habitats ρjIs F1To FL-1The number of individuals in a layer connected to the jth reference point. The smallThe habitat technology is to improve the distribution of NSGA-III, so it is first necessary to find the minimum habitat number ρiReference point i of, then determines FLWith or without the individual connected to reference point i. If an individual is connected to the reference point i, an individual is selected to enter P according to the distance from the individual to the reference pointt+1. Otherwise, this iteration will not take this reference point into account, but repeat the above operations with another reference point having the smallest number of habitats, until Pt+1Is equal to the value of N.
And vii, if the termination condition is met, outputting a final solution, otherwise, repeating the step ii.
Computational analysis is performed below in conjunction with the specific data expansion.
Firstly, generating a process constraint matrix based on a process relation graph PRG
Selecting the PRG chart as shown in FIG. 1, performing 12 processes, and generating a process constraint matrix PCM according to the PRG chart
Among them, the process 1 is prior to the processes of all other processes, the process 2 is prior to the processes of the processes 6, 7 and 12, the process 3 is prior to the process 4, the process 5 is prior to the process 9, the process 6 is prior to the processes of the processes 7 and 12, the process 7 is prior to the process of the process 12, the process 8 is prior to the processes of the processes 10 and 11, and the process 10 is prior to the process of the process 11.
Establishing a multi-objective optimization model for process sequencing
The machine tool-related information and cost coefficients available for 12 steps in the step relation map of fig. 1 are shown in table 1, the tool-related information and cost coefficients are shown in table 2, and the process-related information and candidate machine tool/tool information are shown in table 3.
TABLE 1 alternative machine tool information
TABLE 2 alternative tool information
TABLE 3 procedure Contents and execution information
The information in the table can be matched according to conditions in the algorithm solving stage and input into a multi-objective optimization function of the total machine tool cost, the total cutter cost, the total machine tool change cost, the total cutter replacement cost and the total carbon emission, so that the related information of the corresponding population is obtained. Here, it is assumed that the change cost coefficient ω of the machine toolmcAnd the change cost coefficient omega of the tooltcThe input value is a fixed value, which is 1.3 and 0.7 in sequence, and the dynamic value can be input according to the actual situation in the practical application.
③ chromosome coding
Based on the dyeing coding mechanism suitable for process optimization, the process sequencing code consists of 3 sections, wherein the first section is a process sequence code and consists of 1-12 numbers according to the sequencing code, and different sequencing sequences represent different process sequences; the second section is the machine tool code used in the procedure, and integer coding is adopted, and the value of the integer coding is determined by an alternative machine tool; the third section is the tool code used in the procedure, integer coding is adopted, and the value of the integer coding is determined by the alternative tool.
Algorithm solving
Five objective functions of total machine tool cost, total cutter cost, total machine tool change cost, total cutter change cost and total carbon emission are used as optimization targets to conduct Pareto optimization under the NSGA-III algorithm, and meanwhile, a comparison experiment is set through the NSGA-II algorithm.
The population scale was set to 375, the crossover probability was 0.9, the mutation probability was 0.1, and the maximum number of iterations was 300, and after 10 comparison experiments, the mean of the results was shown in table 4, and the Pareto frontier graph was shown in fig. 4. Through comparison experiments, the NSGA-III is obviously superior to the NSGA-II in solving speed, and the obtained total machine tool cost mean value, the total cutter cost mean value and the total carbon emission mean value are superior to the NSGA-II. The low cost and the low emission requirement can be cooperatively considered through the established multi-objective function, and the sustainable development and the economic benefit of enterprises are effectively realized.
TABLE 4 results of the experiment
Compared with the prior art, the invention has the beneficial effects that: 1. according to the invention, various constraint relations among processes can be fully represented through the PRG graph, and the quantification processing and numerical calculation of the priority and constraint relations are realized by converting the PRG graph into a process constraint matrix PCM; 2. establishing a multi-objective function taking the total machine tool cost, the total cutter cost, the total machine tool change cost, the total cutter replacement cost and the total carbon emission as optimization targets, and thus covering various change elements in machining to complete global optimization; 3. in order to effectively express process information and realize mapping solution of genetic algorithm, the invention provides a dyeing coding mechanism suitable for process optimization, three-section coding is carried out on process input information to form element information and corresponding relations of a process sequence, a machine tool number and a cutter number, the multi-chromosome coding form can coordinate constraint relations at a process sequencing level so as to avoid generating a solution space for destroying a priority relation, and range limitation of available machine tools and available cutters of corresponding processes is received at a machine tool number and cutter number level so as to ensure feasibility of an integer set and effectively transmit resource information for a heuristic algorithm; 4. finally, a comparison experiment proves that NSGA-III has a better solving effect in the case of the invention, and each objective function of the procedure sequencing combination scheme is considered more comprehensively, so that the development requirements of enterprises can be better met.
Claims (3)
1. A process sequencing multi-objective optimization method based on NSGA-III algorithm comprises the following steps:
the first step is to use the process relation map PRG to express the priority relation among the processes and generate a process constraint matrix
The process ordering needs to meet a process ordering rule, a process relation graph PRG is generated after the requirements of the four types of priority relations are met according to the geometric characteristics of parts and the technical requirements of processing, the PRG is a directed acyclic graph, nodes are process numbers, edges represent the priority relation between the processes represented by the two connected nodes, and the following definitions are made for the relation of process nodes in the PRG:
1) precursor relationship: that is, if the step i can reach the step j through any one of the paths, the step i is called a step preceding the step j;
2) the successor relationship is: in contrast to the predecessor relationship, when process i can reach process j through any route, process j is called as the successor of process i;
3) the parallel relationship is as follows: if the process i and the process j are neither in a predecessor relationship nor in a successor relationship, the process i and the process j are called parallel processes;
a PRG graph is made based on the three types of relationships,
corresponding process constraint matrix PCM (pulse code modulation) generated based on PRG (pulse repetition generator) graphij：
Wherein, the PCMijRepresents the step PiAnd process PjThe priority relationship between the two, n is the total process number of the parts to be processed, PCMijTaken from the constraint relationship, as follows:
secondly, establishing a multi-objective optimization model for process sequencing
The process sequencing optimization establishes an objective function by taking the total machine tool cost, the total cutter cost, the total machine tool change cost, the total cutter replacement cost and the total carbon emission as optimization targets and takes a process constraint matrix PCMijAnd constructing a constraint relation for optimization solution, thereby constructing the following optimization objective function:
i total machine cost
Wherein Mcot is the total machine tool cost, n is the total number of steps, MTIDiIs the machine tool code, omega, used in procedure imIs the MTID of the machine tooliThe cost factor of (2);
ii total tool cost
Wherein Tcost is the total tool cost, n is the total number of passes, TLIDiIs the tool code, omega, used in Process itIs a tool TLIDiThe cost factor of (2);
iii change cost of main machine
Wherein MCcost is the total machine change cost, ωmcIs the change cost coefficient, MTID, of the machine tooli，MTIDi+1The machine tool codes, gamma, used in the process i and the process i +1, respectively1Is a machine tool change function;
iv Total tool Change cost
Where TCcost is the total tool change cost, ωtcIs the change cost coefficient, MTID, of the tooli，MTIDi+1The machine tool codes, Tl, used in step i and step i +1, respectivelyi，Tli+1The tool code, gamma, used in step i and step i +1, respectively2Is a tool change function;
v total carbon emission
Where MPCost is the total carbon emission, MTIDiIs the machine tool code, omega, used in procedure ico2Is the MTID of the machine tooliCarbon emission coefficient of (a);
third, chromosomal coding
In order to effectively map each element involved in optimization, a dyeing coding mechanism suitable for process optimization is adopted, and the coding mode comprises three sections: the first section is a procedure sequence coding sequence, and adopts sequencing coding, which represents the sequencing sequence of each procedure; the second section is the machine tool number used in the procedure, integer coding is adopted, and the value range is the code range of the alternative machine tool; the third section is the number of the cutter used in the procedure, integer coding is adopted, and the value range is the code range of the alternative cutter;
fourthly, solving based on NSGA-III algorithm, wherein the method comprises the following steps:
step i, setting algorithm parameters including evolution times, variation rate, cross rate and the like; initializing a reference point and a population N according to a chromosome coding mode and a mapping relation;
step ii for parent population PtGenerating a sub-population Q by selection, crossing, mutationt；
Step iii mixing PtAnd QtObtaining a new population RtOn a scale of 2N; to RtDecoding all the individuals in the optimization model and calculating the fitness of the individuals, wherein the fitness is each objective function in the optimization model: mcost, Tcost, MCcost, TCcost, MPcost;
step iv for RtNon-dominated sorting and partitioning into non-dominated solution sets (F) of different hierarchical levels1,F2,F3,…,FL,…,FW)；
Step v generates a new solution set StFrom F1Initially, move one non-dominated solution set to S at a timetUntil S first appearstIs greater than or equal to the number of N, let FLIs a solution set that satisfies this condition for the first time, then
Step vi from StThe intermediate selection solution generates the next generation parent group Pt+1: if StIs equal to N, then Pt+1＝StOtherwise, first StSet of (1) (F)1,F2,F3,…FL-1) Put in Pt+1Then from FLSelecting other solutions according to a reference point-based selection mechanism;
and vii, if the termination condition is met, outputting a final solution, otherwise, repeating the step ii.
2. The process sequence optimization method according to claim 1, wherein in the first step, the process sequence rules include the following categories:
1) coarse-first-fine type priority relationship: in the processing process of the part, divided processing is required to be carried out according to stages, such as the sequence of rough processing, semi-finishing and finishing;
2) surface-first hole type priority relationship: when the surface characteristics and the hole characteristics corresponding to the surface characteristics are processed, in order to ensure the uniform stress requirement of the drill hole, the surface characteristics are processed firstly, and then the hole characteristics are processed;
3) reference look-ahead priority relationship: the datum is determined by the characteristics of the part and the technological requirements, and the datum characteristic is processed preferentially when the datum characteristic and the dependent characteristic are processed;
4) primary and secondary type priority relationship: the primary surface refers to the critical working surface on the part that is arranged to be machined after the secondary surface during the finishing stage in order to prevent the secondary performance machining from scratching the primary surface.
3. The process sequence optimization method according to claim 1, wherein in the fourth step, the reference point-based selection method is: first, find the population StIdeal point C of*＝(Mcost*,Tcost*,MCcost*,TCcost*,MPcost*) Wherein Mcost*,Tcost*,MCcost*,TCcost*,MPcost*Are respectively a population StThe minimum value of each objective function value in all solutions is obtained, and then the population and the reference point are normalized; by calculating StTo each reference line, and then connecting the solution with the reference point by the shortest distance, at FLIn the method, individuals are selected by adopting a niche protection method, and the method comprises the following steps: let the habitat number ρjIs F1To FL-1Finding out the number of individuals connected with the jth reference point in the layer to have the minimum habitat number rhoiReference point i of, then determines FLWhether or not the individual is connected to reference point i; if an individual is connected to the reference point i, an individual is selected to enter P according to the distance from the individual to the reference pointt+1(ii) a Otherwise, this iteration will not take this reference point into account, but repeat the above operations with another reference point having the smallest number of habitats, until Pt+1Is equal to the value of N.
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