CN113657642A - Smelting workshop production and transportation collaborative optimization method and system based on hybrid algorithm - Google Patents
Smelting workshop production and transportation collaborative optimization method and system based on hybrid algorithm Download PDFInfo
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Abstract
The invention provides a method and a system for collaborative optimization of production and transportation of a smelting workshop based on a hybrid algorithm, and relates to the technical field of workshop scheduling optimization. Aiming at the problem of production and transportation collaborative optimization of a smelting workshop, the invention provides a hybrid algorithm based on a BRKGA algorithm and a TSA algorithm. Firstly, the aviation component numbers and the real number codes are in one-to-one correspondence based on a random key coding mode, the smelting and transportation sequence of the components is determined, and secondly, the mold box full loading principle is utilized, so that the span from 0 to the next stage of transportation of all the components is calculated, and Tc is calculated accordingly. Then, according to the basic idea of the BRKGA algorithm, a chromosome population is initialized randomly, iteration updating is continuously carried out on the chromosome population through population classification and crossing and variation operations based on the TSA algorithm, and finally an approximate optimal solution is obtained. The technical scheme enables the solving process to be more efficient and accurate, improves the production efficiency of enterprises and reduces the energy consumption of the enterprises.
Description
Technical Field
The invention relates to the technical field of optimization scheduling of a smelting workshop, in particular to a smelting workshop production and transportation collaborative optimization method and system based on a hybrid algorithm.
Background
The smelting workshop is a production workshop with a vacuum induction smelting furnace, and smelting objects of the smelting workshop are materials such as special steel and alloy which are needed urgently by high-end aerospace equipment such as aerospace and the like. The process of the smelting workshop can be summarized into links such as feeding, smelting, casting, cooling, transportation and the like, and how to provide a reasonable linkage optimization scheduling scheme of multi-stage operation aiming at the process can improve the production efficiency of enterprises and reduce the energy consumption of the enterprises, and has great significance for the enterprises.
At present, a linkage optimization scheduling scheme for multi-stage operation of a smelting plant generally sets an objective function according to actual production needs, and then optimizes the flow of the smelting plant by using a related algorithm (such as a genetic algorithm) so as to obtain a final optimization result, and executes production scheduling according to the optimization result.
However, when the linkage optimization scheduling scheme of the multi-stage operation of the smelting plant is solved, the applied algorithm has certain defects, so that the solution efficiency of the optimization scheduling result is low, or the solution result is not accurate enough. For example, the Bias Random Key Genetic Algorithm (BRKGA) can avoid premature convergence of the algorithm to a certain extent, but does not introduce a guidance function of an optimal solution, and affects the convergence speed of the algorithm to a certain extent; while the Tree-seed algorithm (TSA) can effectively control the search direction of the algorithm, the algorithm is difficult to avoid getting into the local optimal dilemma at the later stage of operation. Based on the above, the invention provides a mixed algorithm-based method for collaborative optimization scheduling of production and transportation of a smelting workshop, so as to solve the problems that the solution efficiency of the optimized scheduling result is low and inaccurate when the existing smelting workshop is subjected to multi-stage operation linkage optimization scheduling.
Disclosure of Invention
Technical problem to be solved
Aiming at the defects of the prior art, the invention provides a smelting workshop production and transportation collaborative optimization method and system based on a hybrid algorithm, and solves the problems of low optimal scheduling result solving efficiency and inaccuracy existing in the multi-stage operation linkage optimal scheduling technology of the existing smelting workshop.
(II) technical scheme
In order to achieve the purpose, the invention is realized by the following technical scheme:
in a first aspect, the invention firstly provides a smelting workshop production and transportation collaborative optimization method based on a hybrid algorithm, and the method comprises the following steps:
s1, initializing relevant parameters and populations of the BRKGA algorithm and the TSA algorithm; the related parameters comprise a population scale N, an elite individual proportion alpha, a variant individual proportion beta and a variant probability delta of the BRKGA algorithm, a tree species generation proportion gamma in an iterative operator based on the TSA algorithm is searched for a trend control parameter ST, and the maximum number Mit of new solutions generated in the operation process of the algorithm is calculated; the current batch number k of the aviation components is 1; start time of melting of kth lot of aviation Componentsk0; the number n of aviation components which need to be processed in a smelting workshop; the number m of the molds which can be placed in the cooling chamber loaded with the molds at most; size of mold for the first aerospace component slWherein, l is 1, 2.. and n; the crucible capacity V in the smelting furnace; cooling time per unit size plWherein, l is 1, 2.. and n; energy consumption c of the smelting plant in unit time;
s2, calculating fitness function values of all chromosomes in the current population, dividing alpha individuals with the best fitness function values into elite individual groups, and dividing the rest individuals into non-elite individual groups;
s3, reserving the elite individual and performing a cross operation based on a TSA algorithm on the non-elite individual group;
s4, calculating fitness function values of all chromosomes in the updated population, and executing variation operation based on the TSA algorithm on the updated non-elite individual groups with the updated fitness function values being in the number of beta N in the reciprocal;
s5, judging whether the number of new solutions generated by the current iteration is smaller than the maximum number Mit of new solutions, if so, turning to S2, otherwise, outputting the optimal solution XbestAnd the corresponding fitness function value fitbestAnd optimizing the scheduling scheme by corresponding coordination.
Preferably, when the population is initialized in S1, generating an initial solution of the population according to the random key coding rule specifically includes:
randomly initializing a population of the BRKGA algorithm, wherein the population contains N chromosomes X ═ Xi1, 2., N }, wherein the genome of the ith chromosome is Xi＝{xij∈(0，1)|j＝1，2，...，n}；
A number set a of n parts is defined, a { j | j ═ 1,2i＝{Xi1，Xi2，...，XinH, mixing XijCorresponding to the part number j one by one;
all elements in the number set A are according to the corresponding XiArranging the components in ascending order from small to large, taking the numbering sequence of the components in the reordered numbering set A as the smelting and transporting sequence of the components, and recording the sequence as the smelting and transporting sequence of the components
The corresponding set of mold sizes is { s }1，s2，...，snSet of cooling times per unit size as { p }1，p2，...，pn}。
Preferably, the S2 calculates fitness function values of chromosomes in the current population, and divides the α individuals with the best fitness function values into elite individual groups, and the dividing the remaining individuals into non-elite individual groups includes:
s21, setting k ═1Let startk＝0，
Wherein k represents the current batch number; starting timekRepresents the melting start time of the kth batch;
s22, order
And let k be k +1, and then let
wherein ,
finishkrepresents the cooling end time of the k-th batch;
n represents the number of aviation components which need to be processed in the smelting workshop;
m represents the number of the molds which can be placed in the cooling chamber with the molds at most at the same time;
sldenotes the mold size of the first aircraft component, l ═ 1, 2.., n;
v represents the crucible capacity in the smelting furnace;
r represents the time required for the melting of the metallic material of all the aerospace components;
pldenotes the cooling time per unit size, l ═ 1, 2.., n;
t represents the time required for the last part to be transported to the next stage from time 0;
c represents the energy consumption of the smelting plant in unit time;
s23, determining whether (k-1) m ≦ n is true, if true, going to S22, otherwise, outputting Tc ═ finish ×, and using Tc ═ finish ≦ c as the fitness value of the chromosome, and calculating the fitness value fit ═ { fit ≦ of each chromosome in the current populationi|i＝1，2，...，N}；
S24, carrying out ascending order arrangement on chromosomes in the population according to fitness values, and marking the individuals corresponding to the chromosomes with the top alpha as elite individual groups
Individuals corresponding to the rest chromosomes are marked as non-elite individual groups
Preferably, the performing of the TSA algorithm-based crossover operation on the non-elite individual group in S3 includes:
s31, recording the current t-th generation non-elite individual group as
Setting i ═ α N + 1;
s32 for non-elite individuals
If the random number rand (0,1) is less than or equal to ST, then
Randomly generating a tree species
Otherwise according to
Randomly generating a tree species
Judging whether the solution of the tree species is superior to that of the original tree species or not, and replacing the solution of the original tree species if the solution of the tree species is superior to that of the original tree species;
wherein ST represents a search trend control parameter;
the j gene of any chromosome except the best chromosome in the group of elite individuals; alpha is alphaijIs a random scaling factor;
s33, repeating the operation gamma N times in the step S32;
wherein gamma represents the tree species generation proportion in the iterative operator based on the TSA algorithm; n represents the population scale of the BRKGA algorithm;
s34, if i > N, the loop is ended, otherwise, i is made to i +1 and S32 is proceeded to.
Preferably, the performing, in S4, a TSA algorithm-based mutation operation on the updated non-elite group of individuals having the updated fitness function value in the reciprocal β N includes:
s41, recording the beta N non-elite individuals of the current t-th generation reciprocal
Setting i ═ (1- β) N + 1;
s42 for Xnep(t) any non-elite individual
Setting j to 1;
s43, if the random number rand (0,1) is less than or equal to 0.5, making
Otherwise make
wherein ,
the j gene of the current best chromosome;
gene j of any one of the elite group of individuals except the best chromosome;
and S44, making j equal to j +1, judging whether j is less than or equal to n, if so, turning to S43, and if not, ending the loop.
In a second aspect, the present invention further provides a system for collaborative optimization of production and transportation of a smelting plant based on a hybrid algorithm, the system comprising:
a processing unit for performing the steps of:
s1, initializing relevant parameters of a BRKGA algorithm and a TSA algorithm, and a population; the related parameters comprise a population scale N, an elite individual proportion alpha, a variant individual proportion beta and a variant probability delta of the BRKGA algorithm, a tree species generation proportion gamma in an iterative operator based on the TSA algorithm is searched for a trend control parameter ST, and the maximum number Mit of new solutions generated in the operation process of the algorithm is calculated; the current batch number k of the aviation components is 1; batch kStart time to melt of an aircraft componentk0; the number n of aviation components which need to be processed in a smelting workshop; the number m of the molds which can be placed in the cooling chamber loaded with the molds at most; size of mold for the first aerospace component slWherein, l is 1, 2.. and n; the crucible capacity V in the smelting furnace; cooling time per unit size plWherein, l is 1, 2.. and n; energy consumption c of the smelting plant in unit time;
s2, calculating fitness function values of all chromosomes in the current population, dividing alpha individuals with the best fitness function values into elite individual groups, and dividing the rest individuals into non-elite individual groups;
s3, reserving the elite individual and performing a cross operation based on a TSA algorithm on the non-elite individual group;
s4, calculating fitness function values of all chromosomes in the updated population, and executing variation operation based on the TSA algorithm on the updated non-elite individual groups with the updated fitness function values being in the number of beta N in the reciprocal;
s5, judging whether the number of new solutions generated by the current iteration is smaller than the maximum number Mit of new solutions or not, if so, turning to S2, otherwise, ending the algorithm execution;
an output unit for outputting the optimal solution XbestAnd corresponding fitbestAnd a corresponding scheme.
Preferably, when initializing the population in S1, the processing unit generates an initial solution of the population according to the random key coding rule, including:
randomly initializing a population of the BRKGA algorithm, wherein the population contains N chromosomes X ═ Xi1, 2., N }, wherein the genome of the ith chromosome is Xi＝{xij∈(0，1)|j＝1，2，...，n}；
A number set a of n parts is defined, a { j | j ═ 1,2i＝{Xi1，Xi2，...，XinH, mixing XijCorresponding to the part number j one by one;
all elements in the number set A are according to the corresponding XiFrom small to largeIs arranged in ascending order, the part number sequence in the reordered number set A is taken as the smelting and transportation sequence of the part and is recorded as
The corresponding set of mold sizes is { s }1，s2，...，snSet of cooling times per unit size as { p }1，p2，...，pn}。
Preferably, the processing unit, when executing S2, calculates fitness function values of chromosomes in the current population, and divides α individuals having the best fitness function values into elite individual groups, and the dividing the remaining individuals into non-elite individual groups includes:
s21, setting k to 1, and startingk＝0，
Wherein k represents the current batch number; starting timekRepresents the melting start time of the kth batch;
s22, order
And let k be k +1, and then let
wherein ,
finishkrepresents the cooling end time of the k-th batch;
n represents the number of aviation components which need to be processed in the smelting workshop;
m represents the number of the molds which can be placed in the cooling chamber with the molds at most at the same time;
sldenotes the mold size of the first aircraft component, l ═ 1, 2.., n;
v represents the crucible capacity in the smelting furnace;
r represents the time required for the melting of the metallic material of all the aerospace components;
pldenotes the cooling time per unit size, l ═ 1, 2.., n;
t represents the time required for the last part to be transported to the next stage from time 0;
c represents the energy consumption of the smelting plant in unit time;
s23, determining whether (k-1) m ≦ n is true, if true, going to S22, otherwise, outputting Tc ═ finish ×, and using Tc ═ finish ≦ c as the fitness value of the chromosome, and calculating the fitness value fit ═ { fit ≦ of each chromosome in the current populationi|i＝1，2，...，N}；
S24, carrying out ascending order arrangement on chromosomes in the population according to fitness values, and marking the individuals corresponding to the chromosomes with the top alpha as elite individual groups
The remaining chromosomes are scored as non-elite cohorts
Preferably, when executing S3, the processing unit performing a TSA algorithm-based crossover operation on the non-elite individual group includes:
s31, recording the current t-th generation non-elite individual group as
Setting i ═ α N + 1;
s32 for non-elite individuals
If the random number rand (0,1) is less than or equal to ST, then
Randomly generating a tree species
Otherwise according to
Randomly generating a tree species
Judging whether the tree seed solution is superior to the original tree seed solution or not, and replacing the original tree seed solution if the tree seed solution is superior to the original tree seed solution;
wherein ST represents a search trend control parameter;
the j gene of any chromosome except the best chromosome in the group of elite individuals; alpha is alphaijIs a random scaling factor;
s33, repeating the operation gamma N times in the step S32;
wherein gamma represents the tree species generation proportion in the iterative operator based on the TSA algorithm; n represents the population scale of the BRKGA algorithm;
s34, if i > N, the loop is ended, otherwise, i is made to i +1 and S32 is proceeded to.
Preferably, when executing S4, the performing, by the processing unit, a mutation operation based on the TSA algorithm on the updated non-elite individual group whose updated fitness function value is in the inverse number β N includes:
s41, recording the beta N non-elite individuals of the current t-th generation reciprocal
Setting i ═ (1- β) N + 1;
s42 for Xnep(t) any non-elite individual
Setting j to 1;
s43, if the random number rand (0,1) is less than or equal to 0.5, making
Otherwise make
wherein ,
the j gene of the current best chromosome;
gene j of any one of the elite group of individuals except the best chromosome;
and S44, making j equal to j +1, judging whether j is less than or equal to n, if so, turning to S43, and if not, ending the loop.
(III) advantageous effects
The invention provides a smelting workshop production and transportation collaborative optimization method and system based on a hybrid algorithm. Compared with the prior art, the method has the following beneficial effects:
1. the technical scheme of the invention is that aviation component numbers and real number codes are in one-to-one correspondence based on a random key coding mode, the smelting and transportation sequence of components is determined, then the mold box full principle is utilized, the span time T from 0 to the next stage of transportation of all aerospace components is calculated, Tc is calculated, then chromosome populations are initialized randomly based on a mixed algorithm of a BRKGA algorithm and a TSA algorithm, and the chromosome populations are updated iteratively continuously through population classification and TSA-based intersection and variation operation, so that an approximately optimal solution is finally obtained. According to the technical scheme, the hybrid algorithm based on the BRKGA algorithm and the TSA algorithm can provide a better search advancing direction in the solving process, has better convergence, and also gives consideration to the advantages of population parallel distributed calculation, so that the solving process is more efficient and accurate, the production efficiency of enterprises is improved, and the energy consumption of the enterprises is reduced.
2. Compared with the cross operation of the traditional BRKGA algorithm, the cross operation based on the TSA algorithm is used in the technical scheme of the invention, so that the search direction in the solving process can be guided to move towards the current optimized direction, the solving speed is improved, and the solving accuracy is improved;
3. in the technical scheme of the invention, the mutation operation based on the TSA algorithm is used, the gene of the elite individual is introduced, and compared with the mutation operation of the traditional BRKGA algorithm, the search effectiveness is ensured to a certain extent.
Drawings
In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings used in the description of the embodiments or the prior art will be briefly described below, it is obvious that the drawings in the following description are only some embodiments of the present invention, and for those skilled in the art, other drawings can be obtained according to the drawings without creative efforts.
Fig. 1 is a flow chart of a hybrid algorithm-based smelt shop production and transportation collaborative optimization method in an embodiment of the present invention.
Detailed Description
In order to make the objects, technical solutions and advantages of the embodiments of the present invention clearer, the technical solutions in the embodiments of the present invention are clearly and completely described, and it is obvious that the described embodiments are a part of the embodiments of the present invention, but not all of the embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
The embodiment of the application provides a method and a system for collaborative optimization of production and transportation of a smelting workshop based on a hybrid algorithm, solves the problems that the solving efficiency of the optimized scheduling result is low and inaccurate in the multi-stage operation linkage optimized scheduling technology of the existing smelting workshop, and achieves the purposes of quickly and accurately solving the optimized scheduling result, improving the production efficiency of enterprises and reducing the energy consumption of the enterprises.
In order to solve the technical problems, the general idea of the embodiment of the application is as follows:
aiming at the problem of production and transportation collaborative optimization of a smelting workshop, the invention provides a hybrid algorithm based on a BRKGA algorithm and a TSA algorithm. Firstly, the aviation component numbers and the real number codes are in one-to-one correspondence based on a random key coding mode, the smelting and transportation sequence of the components is determined, and secondly, the mold box full loading principle is utilized, so that the span from 0 to the next stage of transportation of all the components is calculated, and Tc is calculated accordingly. Then, according to the basic idea of the BRKGA algorithm, a chromosome population is initialized randomly, iteration updating is continuously carried out on the chromosome population through population classification and crossing and variation operations based on the TSA algorithm, and finally an approximate optimal solution is obtained.
In order to better understand the technical solution, the technical solution will be described in detail with reference to the drawings and the specific embodiments.
The smelting workshop is a production workshop with a vacuum induction smelting furnace, and smelting objects of the vacuum induction smelting furnace are generally special steel, alloy and other materials which are urgently needed by high-end aerospace equipment such as aerospace and the like. The flow of the smelting workshop is roughly divided into the links of feeding, smelting, casting, cooling and transporting and the like. Adding corresponding metal raw materials through a feed inlet of a smelting furnace; immediately starting to melt the metal in a vacuum environment so that it changes from a solid state to a liquid state; then pouring the liquid metal liquid into the die in sequence; finally cooling and forming in the die and transporting to the next station for reprocessing. In terms of feeding, the existing feeding mode comprises a disposable feeding mode and a continuous feeding mode, and the technical scheme aims at continuous feeding, namely, metal materials can be added into a crucible of a furnace body on the premise of not influencing the vacuum environment in the furnace in the smelting process so as to maintain the continuity of the production process. In addition, the mould system carrying the moulds can simultaneously bear a plurality of moulds with different sizes, the pouring mode is sequential pouring, after all the moulds in the mould system are poured, the batch of moulds can be cooled and then transported to a station for next reprocessing, and the mould system can carry out mould pouring operation again after cooling. The technical scheme aims to provide an optimal product production scheduling scheme of the smelting workshop and a logistics transportation scheme in a product factory aiming at the problem of production and transportation cooperative scheduling of the smelting workshop. In the present technical solution, it is assumed that:
(1) the smelting workshop needs to process n aviation components, and the size of a mould of each aviation component is sl1,2, n, wherein the crucible capacity in the smelting furnace is V;
(2) the melting time of the metal materials of all the aerospace components is r, and the cooling time of the unit size is pl1, 2. The pouring time is shorter than the melting time and the cooling time, so that each pouring is carried outThe time of note is ignored;
(3) the cooling chamber with the molds can be used for placing m molds at most simultaneously, metal materials are melted in the crucible and poured into the molds in sequence, the molds are cooled and formed in the cooling chamber, then the molds are transported to the next section for processing, and the cooling chamber is returned to continue operation until all parts are finished.
(4) Assuming that the time interval from time 0 to the last component transport to the next stage is T and the smelt shop energy consumption per unit time is c, the goal is to find an optimal component smelting and transport scheme to minimize the energy consumption Tc.
Example 1:
in a first aspect, the present invention firstly proposes a method for collaborative optimization of production and transportation of a smelting plant based on a hybrid algorithm, and with reference to fig. 1, the method comprises:
s1, initializing relevant parameters and populations of the BRKGA algorithm and the TSA algorithm; the related parameters comprise a population scale N, an elite individual proportion alpha, a variant individual proportion beta and a variant probability delta of the BRKGA algorithm, a tree species generation proportion gamma in an iterative operator based on the TSA algorithm is searched for a trend control parameter ST, and the maximum number Mit of new solutions generated in the operation process of the algorithm is calculated; the current batch number k of the aviation components is 1; start time of melting of kth lot of aviation Componentsk0; the number n of aviation components which need to be processed in a smelting workshop; the number m of the molds which can be placed in the cooling chamber loaded with the molds at most; size of mold for the first aerospace component slWherein, l is 1, 2.. and n; the crucible capacity V in the smelting furnace; cooling time per unit size plWherein, l is 1, 2.. and n; energy consumption c of the smelting plant in unit time;
s2, calculating fitness function values of all chromosomes in the current population, dividing alpha individuals with the best fitness function values into elite individual groups, and dividing the rest individuals into non-elite individual groups;
s3, reserving the elite individual and performing a cross operation based on a TSA algorithm on the non-elite individual group;
s4, calculating fitness function values of all chromosomes in the updated population, and executing variation operation based on the TSA algorithm on the updated non-elite individual groups with the updated fitness function values being in the number of beta N in the reciprocal;
s5, judging whether the number of new solutions generated by the current iteration is smaller than the maximum number Mit of new solutions, if so, turning to S2, otherwise, outputting the optimal solution XbestAnd the corresponding fitness function value fitbest。
It can be seen that the technical scheme of the invention firstly corresponds aviation component numbers and real number codes one by one based on a random key coding mode, determines the smelting and transportation sequence of the components, then calculates the span time T from 0 to the next stage of transportation of all aerospace components by using the die box full principle, and calculates Tc accordingly, then randomly initializes chromosome population based on a mixed algorithm of a BRKGA algorithm and a TSA algorithm, and continuously carries out iterative update on the chromosome population through population classification and TSA-based intersection and variation operation, and finally obtains an approximate optimal solution. According to the technical scheme, the hybrid algorithm based on the BRKGA algorithm and the TSA algorithm can provide a better search advancing direction in the solving process, has better convergence, and also gives consideration to the advantages of population parallel distributed calculation, so that the solving process is more efficient and accurate, the production efficiency of enterprises is improved, and the energy consumption of the enterprises is reduced.
In the method according to the embodiment of the present invention, in order to determine the melting and transportation sequence of the component, a preferred processing manner is to correspond the aviation component number and the real number code one to one based on a random key coding manner, and at this time, when a seed group is initialized in S1, an initial solution of the seed group is generated according to a random key coding rule, which specifically includes:
randomly initializing a population of the BRKGA algorithm, wherein the population contains N chromosomes X ═ Xi1, 2., N }, wherein the genome of the ith chromosome is Xi＝{xij∈(0,1)|j＝1,2,...,n}；
A number set a of n parts is defined, a { j | j ═ 1,2i＝{Xi1,Xi2,...,XinH, mixing XijCorresponding to the part number j one by one;
all elements in the number set A are according to the corresponding XiArranging the components in ascending order from small to large, taking the numbering sequence of the components in the reordered numbering set A as the smelting and transporting sequence of the components, and recording the sequence as the smelting and transporting sequence of the components
The corresponding set of mold sizes is { s }1,s2,...,snSet of cooling times per unit size as { p }1,p2,...,pn}。
In practice, in order to accurately divide the elite individual group and the non-elite individual group and find the optimal component melting and transportation plan on the premise of minimizing energy consumption, a preferred processing method is that, in S2, fitness function values of chromosomes in the current population are calculated, and the α individual with the best fitness function value is divided into the elite individual group, and when the remaining individuals are divided into the non-elite individual group, the method includes:
s21, setting k to 1, and startingk＝0，
Wherein k represents the current batch number; starting timekRepresents the melting start time of the kth batch;
s22, order
And let k be k +1, and then let
wherein ,
finishkrepresents the cooling end time of the k-th batch;
n represents the number of aviation components which need to be processed in the smelting workshop;
m represents the number of the molds which can be placed in the cooling chamber with the molds at most at the same time;
sldenotes the mold size of the first aircraft component, l ═ 1, 2.., n;
v represents the crucible capacity in the smelting furnace;
r represents the time required for the melting of the metallic material of all the aerospace components;
pldenotes the cooling time per unit size, l ═ 1, 2.., n;
t represents the time required for the last part to be transported to the next stage from time 0;
c represents the energy consumption of the smelting plant in unit time;
s23, determining whether (k-1) m ≦ n is true, if true, going to S22, otherwise, outputting Tc ═ finish ×, and using Tc ═ finish ≦ c as the fitness value of the chromosome, and calculating the fitness value fit ═ { fit ≦ of each chromosome in the current populationi|i＝1，2，...，N}；
S24, carrying out ascending order arrangement on chromosomes in the population according to fitness values, and marking the individuals corresponding to the chromosomes with the top alpha as elite individual groups
Individuals corresponding to the rest chromosomes are marked as non-elite individual groups
In addition, in order to guide the search direction to advance toward the current optimization direction in the solving process, improve the solving speed, and improve the solving accuracy, the crossing operation based on the TSA algorithm is used, in this case, a preferable processing manner is that the performing of the crossing operation based on the TSA algorithm on the non-elite individual group in S3 includes:
s31, recording the current t-th generation non-elite individual group as
Setting i ═ α N + 1;
s32 for non-elite individuals
If the random number rand (0,1) is less than or equal to ST, then
Randomly generating a tree species
Otherwise according to
Randomly generating a tree species
Judging whether the tree seed solution is superior to the original tree seed solution or not, and replacing the original tree seed solution if the tree seed solution is superior to the original tree seed solution;
wherein ST represents a search trend control parameter;
the j gene of any chromosome except the best chromosome in the group of elite individuals; alpha is alphaijIs a random scaling factor;
s33, repeating the operation gamma N times in the step S32;
wherein gamma represents the tree species generation proportion in the iterative operator based on the TSA algorithm; n represents the population scale of the BRKGA algorithm;
s34, if i > N, the loop is ended, otherwise, i is made to i +1 and S32 is proceeded to.
Meanwhile, in order to ensure the effectiveness of the search to a certain extent, the TSA algorithm-based mutation operation is used to introduce the genes of the elite individuals, and in this case, a preferred processing manner is that, in S4, the TSA algorithm-based mutation operation is performed on the updated non-elite individual group whose fitness function value is in the reciprocal β N, and includes:
s41, recording the beta N non-elite individuals of the current t-th generation reciprocal
Setting i ═ (1- β) N + 1;
s42 for Xnep(t) any non-elite individual
Setting j to 1;
s43, if the random number rand (0,1) is less than or equal to 0.5, making
Otherwise make
wherein ,
the j gene of the current best chromosome;
gene j of any one of the elite group of individuals except the best chromosome;
and S44, making j equal to j +1, judging whether j is less than or equal to n, if so, turning to S43, and if not, ending the loop.
The following describes a specific implementation process of an embodiment of the present invention with reference to the detailed explanation of specific steps S1-S7.
S1, initializing relevant parameters of a BRKGA algorithm and a TSA algorithm, and a population; the related parameters comprise a population scale N, an elite individual proportion alpha, a variant individual proportion beta and a variant probability delta of the BRKGA algorithm, a tree species generation proportion gamma in an iterative operator based on the TSA algorithm is searched for a trend control parameter ST, and the maximum number Mit of new solutions generated in the operation process of the algorithm is calculated; the current batch number k of the aviation components is 1; start time of melting of kth lot of aviation Componentsk0; the number n of aviation components which need to be processed in a smelting workshop; the number m of the molds which can be placed in the cooling chamber loaded with the molds at most; size of mold for the first aerospace component slWherein, l is 1, 2.. and n; the crucible capacity V in the smelting furnace; cooling time per unit size plWherein, l is 1, 2.. and n; energy consumption of the smelting plant in unit time c.
S11, setting a population size N, an elite individual proportion alpha, a variant individual proportion beta and a variant probability delta of a Bias Random Key Genetic Algorithm (BRKGA);setting a Tree species generation proportion gamma in an iterative operator based on a Tree-seed algorithm (TSA), and searching a trend control parameter ST; setting an algorithm operation iteration termination condition Mit, namely the number of new solutions generated in the algorithm operation process does not exceed Mit; the current batch number k of the aviation components is 1; start time of melting of kth lot of aviation Componentsk0; the number n of aviation components which need to be processed in a smelting workshop; the number m of the molds which can be placed in the cooling chamber loaded with the molds at most; size of mold for the first aerospace component slWherein, l is 1, 2.. and n; the crucible capacity V in the smelting furnace; cooling time per unit size plWherein, l is 1, 2.. and n; energy consumption of the smelting plant in unit time c.
And S12, initializing the population. Generating an initial solution of the population according to a random key coding rule to generate N chromosomes X ═ { X ═ Xi1, 2., N }, wherein the genome of the ith chromosome is Xi＝{xij∈(0，1)|j＝1，2，...，n}。
Randomly initializing a population of the BRKGA algorithm, wherein the population contains N chromosomes X ═ Xi1, 2., N }, wherein the genome of the ith chromosome is Xi＝{xijE (0,1) | j ═ 1, 2. For any chromosome Xi＝{Xi1，Xi2，...，XinA numbering set a of n parts is defined as { j | j ═ 1,2i＝{Xi1，Xi2，...，XinH, mixing XijCorresponding one-to-one to the part number j. Assuming that a number set a of n aerospace components is {1, 2.., n }, X is setijAnd the number j of the aerospace part corresponds to one. Then all elements in the number set A are according to the corresponding XiArranging the components in ascending order from small to large, taking the numbering sequence of the components in the reordered numbering set A as the smelting and transporting sequence of the components, and recording the sequence as the smelting and transporting sequence of the components
The corresponding set of mold sizes is { s }1，s2，...，sn}, singleBit-sized set of cooling times is { p }1，p2，...，pn}。
S2, calculating fitness function values of all chromosomes in the current population, dividing alpha individuals with the best fitness function values into elite individual groups, and dividing the rest individuals into non-elite individual groups.
Calculating fitness function value fit of each chromosome in the current population as { fit ═ fiti|i＝1，2，...，N}。
S21, setting k to 1, and startingk＝0，
S22, order
And let k be k +1, and then let
Wherein k represents the current batch number;
startkrepresents the melting start time of the kth batch;
finishkrepresents the cooling end time of the k-th batch;
n represents the number of aviation components which need to be processed in the smelting workshop;
m represents the number of the molds which can be placed in the cooling chamber with the molds at most at the same time;
sldenotes the mold size of the first aircraft component, l ═ 1, 2.., n;
v represents the crucible capacity in the smelting furnace;
r represents the time required for the melting of the metallic material of all the aerospace components;
pldenotes the cooling time per unit size, l ═ 1, 2.., n;
t represents the time required for the last part to be transported to the next stage from time 0;
c represents the energy consumption of the smelting plant in unit time;
s23, judging whether (k-1) m is less than or equal to n, if yes, turning to S22, and if not, turning toThe Tc ═ finish ═ c is output and taken as the fitness value of the chromosome, and the fitness function value fit ═ fit { [ fit ] of each chromosome in the current population is calculatedi1, 2. Wherein the minimum fitness value is denoted as fitbestThe chromosome corresponding to the fitness value is marked as Xbest＝{xbj∈(0，1)|j＝1，2，...，n}。
S24, carrying out ascending order arrangement on chromosomes in the population according to fitness values, and recording the chromosomes in the top alpha as an elite individual group
The remaining chromosomes are scored as non-elite cohorts
S3, reserving the elite individual and executing the crossing operation based on the TSA algorithm on the non-elite individual group.
Direct Retention for Elite Individual groups
For any non-elite individual
And performing population iteration by adopting a cross operation based on the TSA algorithm.
Specifically, the crossover operation based on the TSA algorithm includes:
s31 assume that the current t-th generation non-elite individual group is recorded as
Set i ═ α N + 1.
S32 for non-elite individuals
If the random number rand (0,1) is less than or equal to ST, then
Random generatorBecome a tree species
Otherwise, according to
Randomly generating a tree species
Wherein ST represents a search trend control parameter;
the j gene of any chromosome except the best chromosome in the group of elite individuals; alpha is alphaijIs a random scaling factor. And judging whether the tree seed solution is superior to the original tree solution or not, and replacing the original tree solution if the tree seed solution is superior to the original tree solution.
And S33, repeating the operation gamma N times of the step S32.
Wherein gamma represents the tree species generation proportion in the iterative operator based on the TSA algorithm; n denotes the population size of the BRKGA algorithm.
S34, if i > N, the loop is ended, otherwise, i is made to i +1 and S32 is proceeded to.
S4, calculating fitness function values of all chromosomes in the updated population, and executing variation operation based on the TSA algorithm on the updated non-elite individual group with the updated fitness function values being in the number of beta N in the reciprocal.
Recalculating the fitness function values of all chromosomes in the updated population, sorting the population in a descending order according to the fitness function values based on the updated fitness function values of all chromosomes, and performing variation operation based on a TSA algorithm on the reciprocal beta N chromosomes, which specifically comprises the following steps:
s41, recording the current t-th generation reciprocal beta N non-elite individual
Set i ═ (1-. beta.) N + 1.
S42 for Xnep(t) any non-elite individual
Set j to 1.
S43, if the random number rand (0,1) is less than or equal to 0.5, making
Otherwise make
wherein ,
the j-th gene of the currently best chromosome,
is the j-th gene of any individual in the current group of elite individuals except the best chromosome.
And S44, making j equal to j +1, judging whether j is less than or equal to n, if so, turning to S43, and if not, ending the loop.
S5, judging whether the number of new solutions generated by the current iteration is smaller than the maximum number Mit of new solutions or not, and if so, turning to S2; otherwise, outputting the optimal solution XbestAnd the corresponding fitness function value fitbest。
At this time, the output optimal solution XbestAnd corresponding to the optimal serial number of the production and transportation sequence of the products, the smelting workshop can produce and transport the products according to the serial number sequence. And fitbestCorresponding to the energy consumption generated when the production scheduling is performed according to the serial numbers of the production and transportation sequence of the products, namely the minimum energy consumption Tc.
Thus, the whole process of the smelting workshop production and transportation collaborative optimization method based on the hybrid algorithm is completed.
Example 2:
in a second aspect, the present invention further provides a system for collaborative optimization of production and transportation of a smelting plant based on a hybrid algorithm, the system comprising:
a processing unit for performing the steps of:
s1, initializing relevant parameters of a BRKGA algorithm and a TSA algorithm, and a population; the related parameters comprise a population scale N, an elite individual proportion alpha, a variant individual proportion beta and a variant probability delta of the BRKGA algorithm, a tree species generation proportion gamma in an iterative operator based on the TSA algorithm is searched for a trend control parameter ST, and the maximum number Mit of new solutions generated in the operation process of the algorithm is calculated; the current batch number k of the aviation components is 1; start time of melting of kth lot of aviation Componentsk0; the number n of aviation components which need to be processed in a smelting workshop; the number m of the molds which can be placed in the cooling chamber loaded with the molds at most; size of mold for the first aerospace component slWherein, l is 1, 2.. and n; the crucible capacity V in the smelting furnace; cooling time per unit size plWherein, l is 1, 2.. and n; energy consumption c of the smelting plant in unit time;
s2, calculating fitness function values of all chromosomes in the current population, dividing alpha individuals with the best fitness function values into elite individual groups, and dividing the rest individuals into non-elite individual groups;
s3, reserving the elite individual and performing a cross operation based on a TSA algorithm on the non-elite individual group;
s4, calculating fitness function values of all chromosomes in the updated population, and executing variation operation based on the TSA algorithm on the updated non-elite individual groups with the updated fitness function values being in the number of beta N in the reciprocal;
s5, judging whether the number of new solutions generated by the current iteration is smaller than the maximum number Mit of new solutions or not, if so, turning to S2, otherwise, ending the algorithm execution;
an output unit for outputting the optimal solution XbestAnd corresponding fitbestAnd a corresponding scheme.
7. The system of claim 6, wherein the processing unit, in initializing the population in execution of S1, generates an initial solution for the population according to the random key encoding rule, comprising:
randomly initializing a population of the BRKGA algorithm, wherein the population contains N chromosomes X ═ Xi|i＝1，2，...，N}，Wherein the genome of the ith chromosome is Xi＝{xij∈(0，1)|j＝1，2，...，n}；
A number set a of n parts is defined, a { j | j ═ 1,2i＝{Xi1，Xi2，...，XinH, mixing XijCorresponding to the part number j one by one;
all elements in the number set A are according to the corresponding XiArranging the components in ascending order from small to large, taking the numbering sequence of the components in the reordered numbering set A as the smelting and transporting sequence of the components, and recording the sequence as the smelting and transporting sequence of the components
The corresponding set of mold sizes is { s }1，s2，...，snSet of cooling times per unit size as { p }1，p2，...，pn}。
8. The system of claim 6, wherein the processing unit when executing S2, calculates fitness function values for individual chromosomes within the current population, and divides the α individuals with the best fitness function values into a group of elite individuals, and the division of the remaining individuals into a group of non-elite individuals comprises:
s21, setting k to 1, and startingk＝0，
Wherein k represents the current batch number; starting timekRepresents the melting start time of the kth batch;
s22, order
And let k be k +1, and then let
wherein ,
finishkrepresents the cooling end time of the k-th batch;
n represents the number of aviation components which need to be processed in the smelting workshop;
m represents the number of the molds which can be placed in the cooling chamber with the molds at most at the same time;
sldenotes the mold size of the first aircraft component, l ═ 1, 2.., n;
v represents the crucible capacity in the smelting furnace;
r represents the time required for the melting of the metallic material of all the aerospace components;
pldenotes the cooling time per unit size, l ═ 1, 2.., n;
t represents the time required for the last part to be transported to the next stage from time 0;
c represents the energy consumption of the smelting plant in unit time;
s23, determining whether (k-1) m ≦ n is true, if true, going to S22, otherwise, outputting Tc ═ finish ×, and using Tc ═ finish ≦ c as the fitness value of the chromosome, and calculating the fitness value fit ═ { fit ≦ of each chromosome in the current populationi|i＝1，2，...，N}；
S24, carrying out ascending order arrangement on chromosomes in the population according to fitness values, and marking the individuals corresponding to the chromosomes with the top alpha as elite individual groups
The remaining chromosomes are scored as non-elite cohorts
9. The system of claim 8, wherein the processing unit when executing S3, performing a TSA algorithm-based crossover operation on the group of non-elite individuals comprises:
s31, recording the current t-th generation non-elite individual group as
Setting i ═ α N + 1;
s32 for non-elite individuals
If the random number rand (0,1) is less than or equal to ST,then according to
Randomly generating a tree species
Otherwise according to
Randomly generating a tree species
Judging whether the tree seed solution is superior to the original tree seed solution or not, and replacing the original tree seed solution if the tree seed solution is superior to the original tree seed solution;
wherein ST represents a search trend control parameter;
the j gene of any chromosome except the best chromosome in the group of elite individuals; alpha is alphaijIs a random scaling factor;
s33, repeating the operation gamma N times in the step S32;
wherein gamma represents the tree species generation proportion in the iterative operator based on the TSA algorithm; n represents the population scale of the BRKGA algorithm;
s34, if i > N, the loop is ended, otherwise, i is made to i +1 and S32 is proceeded to.
10. The system of claim 6, wherein the processing unit when executing S4, performing a TSA algorithm-based mutation operation on the updated group of non-elite individuals having updated fitness function values in the inverse β N number comprises:
s41, recording the beta N non-elite individuals of the current t-th generation reciprocal
Setting i ═ (1- β) N + 1;
s42 for Xnep(t) any non-elite individual
Setting j to 1;
s43, if the random number rand (0,1) is less than or equal to 0.5, making
Otherwise make
wherein ,
the j gene of the current best chromosome;
gene j of any one of the elite group of individuals except the best chromosome;
and S44, making j equal to j +1, judging whether j is less than or equal to n, if so, turning to S43, and if not, ending the loop.
It can be understood that the system for collaborative optimization of production and transportation of a smelting plant based on a hybrid algorithm provided in the embodiment of the present invention corresponds to the method for collaborative optimization of production and transportation of a smelting plant based on a hybrid algorithm, and the explanation, examples, and beneficial effects of the relevant contents thereof may refer to the corresponding contents in the method for collaborative optimization of production and transportation of a smelting plant based on a hybrid algorithm, and are not described herein again.
In summary, compared with the prior art, the method has the following beneficial effects:
1. the technical scheme of the invention is that aviation component numbers and real number codes are in one-to-one correspondence based on a random key coding mode, the smelting and transportation sequence of components is determined, then the mold box full principle is utilized, the span time T from 0 to the next stage of transportation of all aerospace components is calculated, Tc is calculated, then chromosome populations are initialized randomly based on a mixed algorithm of a BRKGA algorithm and a TSA algorithm, and the chromosome populations are updated iteratively continuously through population classification and TSA-based intersection and variation operation, so that an approximately optimal solution is finally obtained. According to the technical scheme, the hybrid algorithm based on the BRKGA algorithm and the TSA algorithm can provide a better search advancing direction in the solving process, has better convergence, and also gives consideration to the advantages of population parallel distributed calculation, so that the solving process is more efficient and accurate, the production efficiency of enterprises is improved, and the energy consumption of the enterprises is reduced.
2. Compared with the cross operation of the traditional BRKGA algorithm, the cross operation based on the TSA algorithm is used in the technical scheme of the invention, so that the search direction in the solving process can be guided to move towards the current optimized direction, the solving speed is improved, and the solving accuracy is improved;
3. in the technical scheme of the invention, the mutation operation based on the TSA algorithm is used, the gene of the elite individual is introduced, and compared with the mutation operation of the traditional BRKGA algorithm, the search effectiveness is ensured to a certain extent.
It is noted that, herein, relational terms such as first and second, and the like may be used solely to distinguish one entity or action from another entity or action without necessarily requiring or implying any actual such relationship or order between such entities or actions. Also, the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or apparatus. Without further limitation, an element defined by the phrase "comprising an … …" does not exclude the presence of other identical elements in a process, method, article, or apparatus that comprises the element.
The above examples are only intended to illustrate the technical solution of the present invention, but not to limit it; although the present invention has been described in detail with reference to the foregoing embodiments, it will be understood by those of ordinary skill in the art that: the technical solutions described in the foregoing embodiments may still be modified, or some technical features may be equivalently replaced; and such modifications or substitutions do not depart from the spirit and scope of the corresponding technical solutions of the embodiments of the present invention.
Claims (10)
1. A method for collaborative optimization of production and transportation of a smelting plant based on a hybrid algorithm is characterized by comprising the following steps:
s1, initializing relevant parameters and populations of the BRKGA algorithm and the TSA algorithm; the related parameters comprise a population scale N, an elite individual proportion alpha, a variant individual proportion beta and a variant probability delta of the BRKGA algorithm, a tree species generation proportion gamma in an iterative operator based on the TSA algorithm is searched for a trend control parameter ST, and the maximum number Mit of new solutions generated in the operation process of the algorithm is calculated; the current batch number k of the aviation components is 1; start time of melting of kth lot of aviation Componentsk0; the number n of aviation components which need to be processed in a smelting workshop; the number m of the molds which can be placed in the cooling chamber loaded with the molds at most; size of mold for the first aerospace component slWherein, l is 1, 2.. and n; the crucible capacity V in the smelting furnace; cooling time per unit size plWherein, l is 1, 2.. and n; energy consumption c of the smelting plant in unit time;
s2, calculating fitness function values of all chromosomes in the current population, dividing alpha individuals with the best fitness function values into elite individual groups, and dividing the rest individuals into non-elite individual groups;
s3, reserving the elite individual and performing a cross operation based on a TSA algorithm on the non-elite individual group;
s4, calculating fitness function values of all chromosomes in the updated population, and executing variation operation based on the TSA algorithm on the updated non-elite individual groups with the updated fitness function values being in the number of beta N in the reciprocal;
s5, judging whether the number of new solutions generated by the current iteration is smaller than the maximum number Mit of new solutions, if so, turning to S2, otherwise, outputting the optimal solution XbestAnd the corresponding fitness function value fitbestAnd optimizing the scheduling scheme by corresponding coordination.
2. The method of claim 1, wherein, when initializing the population in S1, generating an initial solution for the population according to the random key coding rule, specifically comprises:
randomly initializing a population of the BRKGA algorithm, wherein the population contains N chromosomes X ═ Xi1, 2., N }, wherein the genome of the ith chromosome is Xi＝{xij∈(0,1)|j＝1,2,...,n}；
A number set a of n parts is defined, a { j | j ═ 1,2i＝{Xi1,Xi2,...,XinH, mixing XijCorresponding to the part number j one by one;
all elements in the number set A are according to the corresponding XiArranging the components in ascending order from small to large, taking the numbering sequence of the components in the reordered numbering set A as the smelting and transporting sequence of the components, and recording the sequence as the smelting and transporting sequence of the components
The corresponding set of mold sizes is { s }1,s2,...,snSet of cooling times per unit size as { p }1,p2,...,pn}。
3. The method of claim 1, wherein the S2 calculates fitness function values of respective chromosomes in the current population, and divides the α individuals having the best fitness function values into elite individual groups, and the remaining individuals into non-elite individual groups comprises:
s21, setting k to 1, and startingk＝0，
Wherein k represents the current batch number; starting timekRepresents the melting start time of the kth batch;
s22, order
And let k be k +1, and then let
wherein ,
finishkrepresents the cooling end time of the k-th batch;
n represents the number of aviation components which need to be processed in the smelting workshop;
m represents the number of the molds which can be placed in the cooling chamber with the molds at most at the same time;
sldenotes the mold size of the first aircraft component, l ═ 1, 2.., n;
v represents the crucible capacity in the smelting furnace;
r represents the time required for the melting of the metallic material of all the aerospace components;
pldenotes the cooling time per unit size, l ═ 1, 2.., n;
t represents the time required for the last part to be transported to the next stage from time 0;
c represents the energy consumption of the smelting plant in unit time;
s23, determining whether (k-1) m ≦ n is true, if true, going to S22, otherwise, outputting Tc ═ finish ×, and using Tc ═ finish ≦ c as the fitness value of the chromosome, and calculating the fitness value fit ═ { fit ≦ of each chromosome in the current populationi|i＝1,2,...,N}；
S24, carrying out ascending order arrangement on chromosomes in the population according to fitness values, and marking the individuals corresponding to the chromosomes with the top alpha as elite individual groups
Individuals corresponding to the rest chromosomes are marked as non-elite individual groups
4. The method of claim 3, wherein said performing a TSA algorithm-based crossover operation on said group of non-elite individuals in S3 comprises:
s31, recording the current t-th generation non-elite individual group as
Setting i ═ α N + 1;
s32 for non-elite individuals
If the random number rand (0,1) is less than or equal to ST, then
Randomly generating a tree species
Otherwise according to
Randomly generating a tree species
Judging whether the solution of the tree species is superior to that of the original tree species or not, and replacing the solution of the original tree species if the solution of the tree species is superior to that of the original tree species;
wherein ST represents a search trend control parameter;
the j gene of any chromosome except the best chromosome in the group of elite individuals; alpha is alphaijIs a random scaling factor;
s33, repeating the operation gamma N times in the step S32;
wherein gamma represents the tree species generation proportion in the iterative operator based on the TSA algorithm; n represents the population scale of the BRKGA algorithm;
s34, if i > N, the loop is ended, otherwise, i is made to i +1 and S32 is proceeded to.
5. The method of claim 1, wherein said performing a TSA algorithm-based mutation operation on said updated group of non-elite individuals having updated fitness function values in the inverse β N at S4 comprises:
s41, will be whenThe first t-th generation reciprocal beta N non-elite individual is recorded
Setting i ═ (1- β) N + 1;
s42 for Xnep(t) any non-elite individual
Setting j to 1;
s43, if the random number rand (0,1) is less than or equal to 0.5, making
Otherwise make
wherein ,
the j gene of the current best chromosome;
gene j of any one of the elite group of individuals except the best chromosome;
and S44, making j equal to j +1, judging whether j is less than or equal to n, if so, turning to S43, and if not, ending the loop.
6. A smelt plant production and transportation collaborative optimization system based on a hybrid algorithm, the system comprising:
a processing unit for performing the steps of:
s1, initializing relevant parameters of a BRKGA algorithm and a TSA algorithm, and a population; the related parameters comprise a population scale N, an elite individual proportion alpha, a variant individual proportion beta and a variant probability delta of the BRKGA algorithm, a tree species generation proportion gamma in an iterative operator based on the TSA algorithm is searched for a trend control parameter ST, and the maximum number Mit of new solutions generated in the operation process of the algorithm is calculated; navigation deviceThe current batch number k of the empty parts is 1; start time of melting of kth lot of aviation Componentsk0; the number n of aviation components which need to be processed in a smelting workshop; the number m of the molds which can be placed in the cooling chamber loaded with the molds at most; size of mold for the first aerospace component slWherein, l is 1, 2.. and n; the crucible capacity V in the smelting furnace; cooling time per unit size plWherein, l is 1, 2.. and n; energy consumption c of the smelting plant in unit time;
s2, calculating fitness function values of all chromosomes in the current population, dividing alpha individuals with the best fitness function values into elite individual groups, and dividing the rest individuals into non-elite individual groups;
s3, reserving the elite individual and performing a cross operation based on a TSA algorithm on the non-elite individual group;
s4, calculating fitness function values of all chromosomes in the updated population, and executing variation operation based on the TSA algorithm on the updated non-elite individual groups with the updated fitness function values being in the number of beta N in the reciprocal;
s5, judging whether the number of new solutions generated by the current iteration is smaller than the maximum number Mit of new solutions or not, if so, turning to S2, otherwise, ending the algorithm execution;
an output unit for outputting the optimal solution XbestAnd corresponding fitbestAnd a corresponding scheme.
7. The system of claim 6, wherein the processing unit, in initializing the population in execution of S1, generates an initial solution for the population according to the random key encoding rule, comprising:
randomly initializing a population of the BRKGA algorithm, wherein the population contains N chromosomes X ═ Xi1, 2., N }, wherein the genome of the ith chromosome is Xi＝{xij∈(0,1)|j＝1,2,...,n}；
A number set a of n parts is defined, a { j | j ═ 1,2i＝{Xi1,Xi2,...,XinH, mixing XijCorresponding to the part number j one by one;
all elements in the number set A are according to the corresponding XiArranging the components in ascending order from small to large, taking the numbering sequence of the components in the reordered numbering set A as the smelting and transporting sequence of the components, and recording the sequence as the smelting and transporting sequence of the components
The corresponding set of mold sizes is { s }1,s2,...,snSet of cooling times per unit size as { p }1,p2,...,pn}。
8. The system of claim 6, wherein the processing unit when executing S2, calculates fitness function values for individual chromosomes within the current population, and divides the α individuals with the best fitness function values into a group of elite individuals, and the division of the remaining individuals into a group of non-elite individuals comprises:
s21, setting k to 1, and startingk＝0，
Wherein k represents the current batch number; starting timekRepresents the melting start time of the kth batch;
s22, order
And let k be k +1, and then let
wherein ,
finishkrepresents the cooling end time of the k-th batch;
n represents the number of aviation components which need to be processed in the smelting workshop;
m represents the number of the molds which can be placed in the cooling chamber with the molds at most at the same time;
sldenotes the mold size of the first aircraft component, l ═ 1, 2.., n;
v represents the crucible capacity in the smelting furnace;
r represents the time required for the melting of the metallic material of all the aerospace components;
pldenotes the cooling time per unit size, l ═ 1, 2.., n;
t represents the time required for the last part to be transported to the next stage from time 0;
c represents the energy consumption of the smelting plant in unit time;
s23, determining whether (k-1) m ≦ n is true, if true, going to S22, otherwise, outputting Tc ═ finish ×, and using Tc ═ finish ≦ c as the fitness value of the chromosome, and calculating the fitness value fit ═ { fit ≦ of each chromosome in the current populationi|i＝1,2,...,N}；
S24, carrying out ascending order arrangement on chromosomes in the population according to fitness values, and marking the individuals corresponding to the chromosomes with the top alpha as elite individual groups
The remaining chromosomes are scored as non-elite cohorts
9. The system of claim 8, wherein the processing unit when executing S3, performing a TSA algorithm-based crossover operation on the group of non-elite individuals comprises:
s31, recording the current t-th generation non-elite individual group as
Setting i ═ α N + 1;
s32 for non-elite individuals
If the random number rand (0,1) is less than or equal to ST, then
Randomly generating a tree species
Otherwise according to
Randomly generating a tree species
Judging whether the tree seed solution is superior to the original tree seed solution or not, and replacing the original tree seed solution if the tree seed solution is superior to the original tree seed solution;
wherein ST represents a search trend control parameter;
the j gene of any chromosome except the best chromosome in the group of elite individuals; alpha is alphaijIs a random scaling factor;
s33, repeating the operation gamma N times in the step S32;
wherein gamma represents the tree species generation proportion in the iterative operator based on the TSA algorithm; n represents the population scale of the BRKGA algorithm;
s34, if i > N, the loop is ended, otherwise, i is made to i +1 and S32 is proceeded to.
10. The system of claim 6, wherein the processing unit when executing S4, performing a TSA algorithm-based mutation operation on the updated group of non-elite individuals having updated fitness function values in the inverse β N number comprises:
s41, recording the beta N non-elite individuals of the current t-th generation reciprocal
Setting i ═ (1- β) N + 1;
s42 for Xnep(t) any non-elite individual
Setting j to 1;
s43, if the random number rand (0,1) is less than or equal to 0.5, making
Otherwise make
wherein ,
the j gene of the current best chromosome;
gene j of any one of the elite group of individuals except the best chromosome;
and S44, making j equal to j +1, judging whether j is less than or equal to n, if so, turning to S43, and if not, ending the loop.
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