CN116663660A - One-dimensional blanking method based on genetic evaluation genetic algorithm - Google Patents

Abstract

The present invention is a one-dimensional blanking method based on gene evaluation genetic algorithm. By randomly combining genetic materials, one can intuitively understand the composition of each gene, and at the same time know how to cut each steel bar, and spend a lot of money on decoding. The time is also reduced. By scoring each gene in the chromosome, and then selecting excellent genes for mutation, it is easier and faster to obtain the optimal solution; using the new mutation method, the evaluation time of the individual is reduced. decrease, which speeds up the convergence speed of the algorithm.

Description

A One-Dimensional Blanking Method Based on Gene Evaluation Genetic Algorithm

technical field

The method relates to the field of combination optimization of one-dimensional blanking, and more specifically relates to the research of genetic algorithm in the field of one-dimensional blanking.

Background technique

With the continuous advancement of China's industrialization and urbanization, more and more fields need the support of steel, especially the increasing use of steel bars. Rebar optimization cutting refers to the process of reducing the waste of rebar and improving the utilization rate of rebar through reasonable cutting of rebar length under the premise of meeting the structural design requirements. It should be specially explained here that the optimal cutting of steel bars belongs to one-dimensional cutting. The traditional method of optimizing the cutting of steel bars is to calculate manually. According to the length requirements on the design drawings, the length of each steel bar is manually calculated, and then cut. The disadvantages of this method are low efficiency, error-prone, time-consuming and high labor costs, and the optimal solution cannot be guaranteed. The modern one-dimensional blanking method uses computer technology and optimization algorithms to model and calculate the length, material, specification and other factors of steel bars, so as to obtain the optimal cutting plan and reduce the waste of steel bars.

In recent decades, for the one-dimensional blanking problem, a genetic algorithm was proposed in 1997 in China to solve the one-dimensional blanking problem, and then a hybrid genetic algorithm and an improved adaptive genetic algorithm were proposed. Among them, the improved adaptive genetic algorithm of Wei Liangliang et al. introduced the descending optimal adaptation strategy for the one-dimensional blanking problem, which improved the solution accuracy of the algorithm. The disadvantage is that the descending optimal adaptation strategy greatly increases the evaluation time for each individual. Later, domestic scholars such as Zhu Shenglan proved that the genetic algorithm is suitable for large-scale situations in the study of the optimization algorithm of the one-dimensional cutting problem, but the disadvantage is that it is insufficient in gene variation. In recent years, Li Bin, Peng Yaoyao and other scholars have published literatures such as improved hybrid genetic algorithm for solving one-dimensional blanking problem, and genetic algorithm based on expansion-shrinkage mechanism for solving one-dimensional blanking problem. The methods make the population samples more abundant and easy to obtain The optimal solution, but it also increases the consumption of computer memory resources.

Contents of the invention

In order to overcome the above-mentioned defects of the prior art, the present invention proposes a genetic algorithm based on gene evaluation to solve the one-dimensional blanking problem. The traditional one-dimensional blanking genetic algorithm only pays attention to the fitness evaluation of chromosomes. Each gene in the gene is scored, and then an excellent gene is selected for mutation, so as to solve the shortcomings in the above-mentioned background technology.

The technical scheme adopted in the present invention is:

A kind of one-dimensional blanking method based on gene evaluation genetic algorithm, described blanking method comprises the following steps:

S1. Input the raw material length Len, the length of the required reinforcement parts l ₁ ,l ₂ ,···,l _s , the quantity de ₁ ,de ₂ ,···,de _s , the number of iterations R, the crossover probability p _c , the mutation Probability p _m , convergence rate c, population size n, where the length and quantity of parts correspond one-to-one, the information on the length and quantity of rebar parts that can be used is called genetic material, and the length of parts is used as the element in the gene, de ₁ , de ₂ , ···, the value of de _s is less than or equal to Len, and s is the number of types of part length;

S2. Initialize the population according to the genetic material in S1, and perform random combination coding. The specific process is:

S21, d ₁ , d ₂ ,···,d _s represent the real-time quantities of parts of different lengths, the initial order d ₁ ,d ₂ ,···,d _s is equal to de ₁ ,de ₂ ,··· inputted in step S1 , de _s ; L represents the remaining length of the raw material, and L is equal to Len at the initial stage;

S22. Determine which values among d ₁ , d ₂ ,...,d _s are greater than 0;

S23. Determine which values among l ₁ , l ₂ ,...,l _s are less than or equal to L;

S24. Select the genetic material that satisfies both step S22 and step S23;

S25. Randomly select an l _r from the results obtained in step S24, subtract 1 from its corresponding d _r and update the remaining length L of the raw material, that is, subtract the length of a selected part from the remaining length L of the raw material;

S26, followed by repeating S22-S25 until the remaining length L of the raw material is less than the minimum value of l ₁ , l ₂ ,...,l _s that is not 0 in d ₁ , d ₂ ,...,d _s or d ₁ , d ₂ ,···,d _s are all equal to 0, record the lengths of the parts randomly selected at this time, denoted as l _x ,ly _y ,···,l _z respectively;

S27. Combining the selected l _x , _{ly ,} . Set to False, the two flags will disappear after the gene is disassembled, but the elements in the gene will not disappear;

S28. Select multiple raw materials, and repeat S22-S27 until d ₁ , d ₂ ,...,d _s are all equal to 0, and the obtained genes are combined into individual chromosomes, realizing random combination coding;

S29. Execute n times of S21-S28 to complete the initialization of the population, where n is the population size;

S3. Perform a crossover operation according to the crossover probability p _c ;

S4. Score each gene of each individual chromosome in the population: find all genes whose stable flags are False in the individual, add and sum all elements in each gene whose stable flag is False to form suml, suml It is the gene score, select the gene with the maximum gene score for mutation operation, select the minimum value of the gene score in the individual chromosome, and use the minimum value of the gene score to calculate the fitness value of the individual, the fitness function f is:

f(k,minsuml)=(k-1)*Len+minsuml

Among them: k is the number of genes in the individual chromosome, minsuml is the smallest suml in the individual chromosome, and Len is the length of the raw material;

S5. If the evolution condition is met, the optimal solution is output. If the evolution condition is not met, the individual to be selected for the next round of iteration is selected according to the fitness value according to the selection probability. The smaller the fitness value, the greater the probability of being selected.

Further, the evolution condition is: whether the number of iterations R is reached or whether the stability flag and the replication flag of all genes of all individual chromosomes in the population are set to True.

Further, the step S3 includes the following sub-steps:

S31. Randomly generate a decimal number of 0-1. If the randomly generated decimal number is less than the crossover probability p _c , execute S32 and S33. If it is greater than or equal to p _c , do nothing;

S32. Randomly select two individuals from the population;

S33. Randomly select a gene whose stable flag is False from two individuals to perform crossover operation;

The crossover operation is as follows: if the A chromosome needs to obtain the b gene of the B chromosome, and the B chromosome needs to obtain the a gene of the A chromosome, then it is judged whether all the genes whose stable flags are False in the A chromosome and the a gene can be disassembled to form a If the b gene that crossed over can be dismantled in a small range, the genes that can form the b gene in the A chromosome will be disassembled. If the disassembled gene can form the b gene in the B chromosome, then the disassembly of the A chromosome will not continue; Then combine a b gene and set the stable flag and replication flag of the b gene to False, and combine the remaining genetic material according to steps S22 to S28 until the disassembled genetic material is used; if a cross cannot be formed The incoming b gene does nothing; similarly, it does the same for the B chromosome.

Further, the mutation operation includes:

After obtaining each gene score of an individual chromosome, arrange the gene scores from large to small; select the gene with the largest gene score, if there are multiple genes with the same maximum suml value, select one of the genes; judge the selected gene Whether the mutation condition is met, if the condition is met, the mutation operation is performed, otherwise nothing is done;

The mutation condition is: first judge whether the current iteration number r is less than or equal to R/10, if yes, then judge whether the suml value of the selected gene is equal to Len, if yes, perform the mutation operation; if the current iteration number r is greater than R/10, then judge whether the suml value of the selected gene is greater than or equal to L-c*r*(Len/10), if so, perform the mutation operation; do nothing in other cases;

The gene whose number of current iterations meets the mutation condition is the gene to be mutated, and the stable flag and the replication flag of the gene to be mutated are both set to True, and then all the genes whose stable flags are False in the individual chromosomes are dismantled, and then from the dismantled After decomposing, select the same genetic material as the element in the gene to be mutated to copy and set the stable flag and copy flag of the copied gene to True until it cannot be copied. Whether the gene to be mutated can be copied It mainly depends on whether the disassembled genetic material can be combined into a gene whose flag bits are all True. If it can be combined, it can be copied; Combine to form a new gene whose stability flag and replication flag are both False, until the disassembled genetic material is used up.

Further, the specific process of selecting individuals who need to undergo the next round of iteration according to the selection probability according to the fitness value is: arrange the fitness values from small to large, determine the selection probability according to the ranking with an exponential function, and the i-th individual Chromosome selection probability is pi, where pi=m·(1-m) ^i-1 , i=1,2,···,n, m is a constant less than 1, and individual chromosomes are selected for iteration according to the selection probability.

Said m=4/n.

Compared with prior art, the beneficial effect of the present invention is:

(1) The traditional one-dimensional blanking genetic algorithm only pays attention to the fitness evaluation of chromosomes. The present invention scores each gene in the chromosome, and then selects excellent genes for mutation, and finally obtains the best genes more easily and faster. Excellent solution.

(2) The present invention adopts a random combination coding method, improves the chromosome coding method in the traditional one-dimensional blanking genetic algorithm, and avoids the generation of illegal genes. The traditional coding method is: code all raw materials as Similarly, number all parts /> random from /> choose from numbers numbers to form the chromosome code of an individual, such as (3,5,2,...,3) is the chromosome code of an individual, indicating that the No. 1 part is arranged on the No. 3 raw material, and the No. 2 part is arranged on On the No. 5 raw material, the No. 3 part is arranged on the No. 2 raw material, ..., No. /> Part No. 3 ranks on Raw Material No. 3. The traditional coding method will cause a huge time consumption when decoding. For example, in the optimization of steel bar cutting, it is necessary to know the cutting method of each steel bar. At this time, the chromosome needs to be decoded, and the time complexity of decoding is related to the part number /> Proportional. In this application, by randomly combining genetic materials, one can intuitively understand the composition of each gene, and at the same time know how to cut each steel bar, and the time spent on decoding is also reduced.

(3) The present invention reduces the evaluation time of the individual through the proposed variation method, and accelerates the convergence speed of the algorithm.

Description of drawings

Fig. 1 is the flow chart of one-dimensional blanking method based on genetic algorithm of genetic evaluation;

Figure 2 is a schematic diagram of population chromosomes, in which the stable markers and replication markers are omitted;

Figure 3 is a schematic diagram of A chromosome crossover, in which the stable flag and the replication flag are omitted;

Figure 4 is a schematic diagram of B chromosome crossover, in which the stable flag and the replication flag are omitted;

Figure 5 is a schematic diagram of the mutation operation, in which the stable flag and the copy flag are omitted;

Fig. 6 is a schematic diagram of a gene in a chromosome.

Detailed ways

Specific embodiments of the present invention are described below so that those skilled in the art can understand the present invention, but it should be clear that the present invention is not limited to the scope of specific embodiments, and for those of ordinary skill in the art, as long as various changes Within the spirit and scope of the present invention defined and determined by the appended claims, these changes are obvious, and all inventions and creations using the concept of the present invention are included in the protection list.

As shown in Figure 1, the one-dimensional blanking method based on genetic evaluation genetic algorithm comprises steps S1-S8:

S1. Input the raw material length Len, the length of the required reinforcement parts l ₁ ,l ₂ ,···,l _s , the quantity de ₁ ,de ₂ ,···,de _s , the number of iterations R, the crossover probability p _c , the mutation Probability p _m , convergence rate c, population size n, where the length and quantity of parts correspond one by one, the length and quantity information of steel parts that can be used is called genetic material, and the length of parts is used as the element in the gene, de ₁ , de ₂ , ···, the value of de _s is less than or equal to the length of raw material Len (unit cm), and s is the number of types of part length.

In an optional example of the present invention, let Len=1000, l ₁ =512, l ₂ =321, l 3 =128, l ₄ =247, l ₅ =290, _{de 1 =} 6, de ₂ =8, de ₃ =5, de ₄ =10, de ₅ =4, p _c =0.2, p _m =0.2, n=50, R=300, c=0.01.

S2. Initialize the population according to the genetic material in S1.

In an optional example of the present invention, the initialization operation is performed based on the information in step S1, and the obtained initialization result can be shown in FIG. 2 .

Step S2 includes the following sub-steps:

S21, d ₁ , d ₂ ,···,d _s represent the real-time quantities of parts of different lengths, the initial order d ₁ ,d ₂ ,···,d _s is equal to de ₁ ,de ₂ ,··· inputted in step S1 , de _s ; L represents the remaining length of the raw material, and L is equal to Len at the initial stage;

In an alternative embodiment of the invention, initially there are 6 512, 8 321, 5 128, 10 247, 4 290 genetic material.

In an optional example of the present invention, the initial time L is equal to 1000.

S22. Determine which values among d ₁ , d ₂ ,...,d _s are greater than 0;

In an optional example of the present invention, 6, 8, 5, 10, and 4 are all greater than 0 initially.

S23. Determine which values among l ₁ , l ₂ ,...,l _s are less than or equal to L;

In an optional example of the present invention, 512, 321, 128, 247, and 290 are all less than 1000 initially.

S24. Select the genetic material that satisfies both step S22 and step S23;

In an optional example of the present invention, 6 512, 8 321, 5 128, 10 247, and 4 290 genetic materials are all selected initially.

S25. Randomly select an l _r from the results obtained in step S24, subtract 1 from its corresponding d _r and update the remaining length L of raw materials, that is, subtract the length of a selected part from the remaining length L of raw materials, and update the method is L=Ll _r ;

In an optional example of the present invention, 512 is selected initially, then d ₁ minus 1 becomes 5, and the remaining length L of the raw material minus 512 becomes 488. At this time, the genetic material becomes 5 512, 8 321, 5 128, 10 247, and 4 290.

S26, followed by repeating S22-S25 until the remaining length L of the raw material is less than the minimum value of l ₁ , l ₂ ,...,l _s that is not 0 in d ₁ , d ₂ ,...,d _s or d ₁ , d ₂ ,···,d _s are all equal to 0, record the lengths of the parts randomly selected at this time, denoted as l _x ,ly _y ,···,l _z respectively;

In an optional example of the present invention, step S22 judges that 5, 8, 5, 10, and 4 are all greater than 0, step S23 judges that 321, 128, 247, and 290 are less than 488, and step S24 judges that 8 321, 5 128, 10 genetic materials of 247 and 4 genetic materials of 290 are selected, step S25 selects 321, then d ₂ minus 1 becomes 7, and the remaining length of raw material L minus 321 becomes 167, at this time the genetic material becomes 5 512 and 7 321 , 5 128, 10 247, 4 290. Then S22 judges that 5, 7, 5, 10, and 4 are all greater than 0, step S23 judges that 128 is less than 167, step S24 judges that five genetic materials of 128 are selected, and step S25 selects 128, then _d3 minus 1 becomes 4, The remaining length L of the raw material minus 128 becomes 39. At this time, the genetic material becomes 5 pieces of 512, 7 pieces of 321, 4 pieces of 128, 10 pieces of 247, and 4 pieces of 290. At this time, the value of the remaining length L of the raw material is 39, 512, 321, 128, 247, and 290 are all greater than 39, and the selection of l _x , _ly , l _z of one gene is completed.

Here, the meaning that the remaining length L of the raw material is less than the minimum value among l ₁ , l ₂ , ..., l _s that are not 0 among d ₁ , d ₂ , ..., d _s is explained. If the remaining length L of raw materials is 250 at this time, and the genetic material is 2 pieces of 512, 0 pieces of 321, 0 pieces of 128, 0 pieces of 247, and 6 pieces of 290, then d ₁ , d ₂ ,...,d _s are not 0 The minimum value of l ₁ ,l ₂ ,···,l _s is 290, and if 290 is greater than 250, the process of selecting genes l _x ,ly _y ,···,l _z ends.

S27. Combining the selected l _x , _{ly ,} . If it is False, the two flags will disappear after the gene is disassembled, but the genetic materials such as l _x , _ly ,···,l _z will not disappear, where x,y,···,z are equal to 1,2, ···, the value in s. Two markers are produced when the gene is formed and disappear when the gene is dismantled. The sequence of l _x , _ly , ... , l _z in the gene is arranged according to the order of random selection.

In an optional example of the present invention, 512, 321, 128, a stable flag with a value of False, and a replication flag with a value of False are combined into one gene, as shown in FIG. 6 .

S28. Select a plurality of raw materials, and repeat S22-S27 until d ₁ , d ₂ ,···,d _s are all equal to 0, and the obtained multiple genes are combined to form individual chromosomes.

In an optional example of the present invention, one individual has 1 247 gene, 1 512, 321 gene, 1 321, 321, 247 gene, 1 321, 247, 247 gene, 1 290 gene in the chromosome of an individual , 128, 512 genes, 1 247, 247, 247, 247 genes, 1 247, 321, 128, 128 genes, 1 290, 321, 290 genes, 2 321, 128, 512 genes, 1 512, 247 genes, 1 512, 290 genes, these genes form an individual chromosome.

S29. Execute S21-S28 n times to complete the initialization of the population and realize random combination coding, wherein n is the population size.

S3. Perform a crossover operation according to the crossover probability p _c .

In an optional example of the present invention, the interleaving operation is as shown in FIG. 3 and FIG. 4 .

Step S3 includes the following sub-steps:

S31. Randomly generate a decimal number of 0-1. If the randomly generated decimal number is less than the crossover probability p _c , execute S32 and S33. If it is greater than or equal to p _c , do nothing;

In an optional example of the present invention, the randomly generated decimal this time is 0.18, and it is decided to continue to execute steps S32 and S33.

S32. Randomly select two individuals from the population;

S33. Randomly select a gene whose stable flag bit is False from two individuals to perform a crossover operation.

Wherein, the crossover operation is as follows: set the A chromosome (Fig. 3) to obtain the b gene of the B chromosome (Fig. 4), and the B chromosome to obtain the a gene of the A chromosome, then judge all genes and a genes whose stable flags are False in the A chromosome Can it be disassembled to form a cross-over b gene? If so, those genes that can form b gene will be disassembled in a small range, and the rest of the genes will not be disassembled. The small range means that the disassembly can be judged by scanning sequentially. If the selected gene to be disassembled can form the b gene, then there is no need to disassemble other genes, that is, only disassemble within a small range. Can. Then combine a b gene and set the stable flag and replication flag of the b gene to False, and combine the remaining genetic material according to steps S22 to S28 until the disassembled genetic material is used up. If a cross cannot be formed The b gene that comes over does nothing. Likewise, do the same for the B chromosome. The method of operation of this application will not produce unqualified genes.

In an optional example of the present invention, at a certain iteration round, genes 321, 247, and 247 in chromosome A are intended to be exchanged with genes 321, 321, and 321 in chromosome B. At this time, it is found that the stable flags of the two 321, 128, and 512 genes in the A chromosome are True, then these two genes are not disassembled, and the stable flags of the remaining genes are False. The 321, 247, and 247 genes in the A chromosome are found in a small range, and the 321, 321, and 247 genes can be combined into a 321, 321, and 321 gene. Therefore, the genes 321, 247, 247 and genes 321, 321, and 247 in the A chromosome are disassembled and combined into an exchanged gene 321, 321, and 321, and the remaining genetic material is combined into a gene 247, 247, and 247. The stable flag and replication flag of a gene are set to False, and the other genes in the A chromosome whose stable flag is False are not disassembled.

It is found that the stable flags of all genes on chromosome B are False, and genes 321, 321, 321, genes 512, 247, and genes 247, 247 can be combined into an exchanged gene 321, 247, 247 in a small range, so this The three genes are disassembled and combined into one 321, 247, 247 gene, one 321, 321 gene and one 512, 247 gene. The stability flag and replication flag of these three genes are set to False, and in the B chromosome Genes whose other stable flags are False do not operate.

S4. Scoring each gene of each individual chromosome in the population.

Step S4 includes the following sub-steps:

S41. Select an individual in the population;

S42. Find all genes whose stable flags are False in the individual;

S43. Add the elements l _x , _ly , . . . , l _z values of the genes obtained in step S42 to obtain suml, suml is the gene score, and the larger the suml is, the easier it is for the gene to mutate. For the genes obtained in step S29 and the genes obtained after the crossover operation, their stable flags are all False, and the genes will be scored.

In an optional example of the present invention, gene 321, 128, 512, gene 321, 321, 247, gene 247, 247, 290 and other genes are scored, and it is found that the suml value of gene 321, 128, 512 is the largest, then select The gene mutates.

S44. Each individual in the population executes steps S42 and S43.

S5. Perform a mutation operation according to the mutation probability p _m .

In an optional example of the present invention, the mutation operation is shown in FIG. 5 .

Step S5 includes the following sub-steps:

S51. Select an individual in the population;

S52. Find all genes whose stable flags are False in the individual chromosome;

S53, arrange the suml of the genes obtained in step S52 from large to small;

S54, select the gene with the largest suml value, if there are multiple genes with the same largest suml value, then select one of the genes;

S55, judging whether the gene obtained in step S54 satisfies the mutation condition, if the condition is met, then perform the mutation operation, otherwise do nothing;

The mutation condition is: first judge whether the current iteration number r is less than or equal to R/10, if so, then judge whether the suml value of the selected gene is equal to Len, if yes, perform a mutation operation, if the current iteration number r is greater than R/10, then judge whether the suml value of the selected gene is greater than or equal to Len-c*r*(Len/10), if yes, perform a mutation operation, usually the larger the number of iterations R, the smaller the convergence rate c, and the rest The situation does nothing.

The mutation operation is as follows: set both the stable flag and the replication flag of the gene to be mutated to True, then dismantle all genes whose stable flags are False in the chromosome, and then select the gene to be mutated from the disassembled genetic material Copy the elements in and set the stable flag and replication flag of the copied gene to True until it cannot be copied, and the remaining genetic materials are combined according to steps S22 to S28 to form new stable flags and replication flags All genes whose bits are False until the disassembled genetic material is used up.

In an optional example of the present invention, the stable flag and the replication flag of the two 247, 247, 247, 247 genes have been mutated in the last round, and the stable flag and the replication flag have been set to True, In this round, genes 321, 128, and 512 are selected through step S54, and are found to meet the mutation conditions through step S55. At this point, set the stable flag and replication flag of genes 321, 128, and 512 to True. Therefore, except for these three genes, all the other genes in the chromosome were disassembled, and the dismantled genetic materials were 5 512, 7 321, 4 290, 2 247, and 4 128. Whether the gene to be mutated can be copied mainly depends on whether the disassembled genetic material can be combined into the gene to be mutated. If it can be combined, it can be copied. If it cannot be combined, it cannot be copied. After that, the genes 321, 128, 512 is copied according to the length with the least number, and the number of copies is the number of the length with the least number. In the optional embodiment, it is copied 4 times, and the stable flags and replication flags of the copied 4 321, 128, and 512 genes are also True At this time, the genetic material becomes 1 512, 3 321, 4 290, 2 247, and 0 128. The remaining genetic material is randomly combined into one 512, 321 gene, one 290, 290, 247 gene, one 321, 247, 290 gene, and one 290, 321 gene according to the conditions, but the stable markers of the genes composed and copy flag are False.

S56. Steps S52, S53, S54, and S55 are executed for each individual chromosome in the population.

S6. Evaluate the fitness of each individual in the population.

In an optional example of the present invention, there are 5 321, 128, 512 genes, 2 247, 247, 247, 247 genes, 1 512, 321 genes, 1 290, 290, 247 genes in the chromosome of Fig. 5 Genes, one 321, 247, 247 genes, one 290, 321 genes, the minsuml value is 611, there are 11 genes in this individual chromosome, k=11, and the fitness value is calculated as 10611.

Step S6 includes the following sub-steps:

S61. Select an individual in the population;

S62. Substituting the relevant data of the individual into the fitness function to obtain the fitness value, the fitness function is: f(k,minsuml)=(k-1)*Len+minsuml, wherein: k is the number of genes in the individual chromosome The number, minsuml is the smallest suml in the individual chromosome, and Len is the length of the raw material.

S63. Each individual in the population executes step S62.

S7. Decide whether to continue the evolution. If it decides to continue the evolution, perform a selection operation, and then continue to execute S3, S4, S5, S6, and S7, otherwise directly execute S8.

In an optional example of the present invention, after 300 iterations, it is decided to abandon the evolution and output the optimal solution.

Further, step S7 includes evolution conditions and selection operations:

The evolution condition is: whether the number of iterations R is reached or whether the stable flag and the replication flag of all genes of all individual chromosomes in the population are set to True.

The selection operation is: arrange the obtained fitness values from small to large, and use an exponential function to determine the selection probability according to the ranking, where p _i =m·(1-m) ^i-1 , i=1,2,... , n, i represent the i-th individual after the ranking, p _i represents the selection probability of the i-th individual, where m=4/n, n is the population size, and the individual is selected for iteration according to the obtained probability.

S8. Outputting an optimal solution.

In an optional example of the present invention, the optimal solution of output is 1 290, 128, 290, 290 gene, 1 290, 321, 128, 128, 128 gene, 2 321, 321, 321 gene, 1 321, 512, 128 genes, 2 247, 247, 247, 247 genes, 2 512, 247 genes, 3 512 genes.

Those skilled in the art will appreciate that the examples described here are to help readers understand the principles of the present invention, and it should be understood that the protection scope of the present invention is not limited to such specific statements and examples. Those skilled in the art can make various other specific modifications and combinations based on the technical revelations disclosed in the present invention without departing from the essence of the present invention, and these modifications and combinations are still within the protection scope of the present invention.

What is not mentioned in the present invention is applicable to the prior art.

Claims (6)

1. a one-dimensional blanking method based on gene evaluation genetic algorithm, it is characterized in that, described blanking method comprises the following steps: S1. Input the raw material length Len, the length of the required reinforcement parts l ₁ ,l ₂ ,···,l _s , the quantity de ₁ ,de ₂ ,···,de _s , the number of iterations R, the crossover probability p _c , the mutation Probability p _m , convergence rate c, population size n, where the length and quantity of parts correspond one-to-one, the information on the length and quantity of rebar parts that can be used is called genetic material, and the length of parts is used as the element in the gene, de ₁ , de ₂ , ···, the value of de _s is less than or equal to Len, and s is the number of types of part length; S2. Initialize the population according to the genetic material in S1, and perform random combination coding. The specific process is: S21, d ₁ , d ₂ ,···,d _s represent the real-time quantities of parts of different lengths, the initial order d ₁ ,d ₂ ,···,d _s is equal to de ₁ ,de ₂ ,··· inputted in step S1 , de _s ; L represents the remaining length of the raw material, and L is equal to Len at the initial stage; S22. Determine which values among d ₁ , d ₂ ,...,d _s are greater than 0; S23. Determine which values among l ₁ , l ₂ ,...,l _s are less than or equal to L; S24. Select the genetic material that satisfies both step S22 and step S23; S25. Randomly select an l _r from the results obtained in step S24, subtract 1 from its corresponding d _r and update the remaining length L of the raw material, that is, subtract the length of a selected part from the remaining length L of the raw material; S26, followed by repeating S22-S25 until the remaining length L of the raw material is less than the minimum value of l ₁ , l ₂ ,...,l _s that is not 0 in d ₁ , d ₂ ,...,d _s or d ₁ , d ₂ ,···,d _s are all equal to 0, record the lengths of the parts randomly selected at this time, denoted as l _x ,ly _y ,···,l _z respectively; S27. Combining the selected l _x , _{ly ,} . Set to False, the two flags will disappear after the gene is disassembled, but the elements in the gene will not disappear; S28. Select multiple raw materials, and repeat S22-S27 until d ₁ , d ₂ ,...,d _s are all equal to 0, and the obtained genes are combined into individual chromosomes, realizing random combination coding; S29. Execute n times of S21-S28 to complete the initialization of the population, where n is the population size; S3. Perform a crossover operation according to the crossover probability p _c ; S4. Score each gene of each individual chromosome in the population: find all genes whose stable flags are False in the individual, add and sum all elements in each gene whose stable flag is False to form suml, suml It is the gene score, select the gene with the maximum gene score for mutation operation, select the minimum value of the gene score in the individual chromosome, and use the minimum value of the gene score to calculate the fitness value of the individual, the fitness function f is: f(k,min suml)=(k-1)*Len+min suml Among them: k is the number of genes in the individual chromosome, min suml is the smallest suml in the individual chromosome, and Len is the length of the raw material; S5. If the evolution condition is met, the optimal solution is output. If the evolution condition is not met, the individual to be selected for the next round of iteration is selected according to the fitness value according to the selection probability. The smaller the fitness value, the greater the probability of being selected. 2. the one-dimensional blanking method based on gene evaluation genetic algorithm according to claim 1, is characterized in that, described evolutionary condition is: whether has reached the stable flag position of all genes of all individual chromosomes in the number of iterations R or population and copy flags are both set to True. 3. the one-dimensional blanking method based on gene evaluation genetic algorithm according to claim 1, is characterized in that, described step S3 comprises following sub-steps: S31. Randomly generate a decimal number of 0-1. If the randomly generated decimal number is less than the crossover probability p _c , execute S32 and S33. If it is greater than or equal to p _c , do nothing; S32. Randomly select two individuals from the population; S33. Randomly select a gene whose stable flag is False from two individuals to perform crossover operation; The crossover operation is as follows: if the A chromosome needs to obtain the b gene of the B chromosome, and the B chromosome needs to obtain the a gene of the A chromosome, then it is judged whether all the genes whose stable flags are False in the A chromosome and the a gene can be disassembled to form a If the b gene that crossed over can be dismantled in a small range, the genes that can form the b gene in the A chromosome will be disassembled. If the disassembled gene can form the b gene in the B chromosome, then the disassembly of the A chromosome will not continue; Then combine a b gene and set the stable flag and replication flag of the b gene to False, and combine the remaining genetic material according to steps S22 to S28 until the disassembled genetic material is used; if a cross cannot be formed The incoming b gene does nothing; similarly, it does the same for the B chromosome. 4. the one-dimensional blanking method based on gene evaluation genetic algorithm according to claim 1, is characterized in that, described variation operation comprises: After obtaining each gene score of an individual chromosome, arrange the gene scores from large to small; select the gene with the largest gene score, if there are multiple genes with the same maximum suml value, select one of the genes; judge the selected gene Whether the mutation condition is met, if the condition is met, the mutation operation is performed, otherwise nothing is done; The mutation condition is: first judge whether the current iteration number r is less than or equal to R/10, if yes, then judge whether the suml value of the selected gene is equal to Len, if yes, perform the mutation operation; if the current iteration number r is greater than R/10, then judge whether the suml value of the selected gene is greater than or equal to L-c*r*(Len/10), if so, perform the mutation operation; do nothing in other cases; The gene whose number of current iterations meets the mutation condition is the gene to be mutated, and the stable flag and the replication flag of the gene to be mutated are both set to True, and then all the genes whose stable flags are False in the individual chromosomes are dismantled, and then from the dismantled After decomposing, select the same genetic material as the element in the gene to be mutated to copy and set the stable flag and copy flag of the copied gene to True until it cannot be copied. Whether the gene to be mutated can be copied It mainly depends on whether the disassembled genetic material can be combined into a gene whose flag bits are all True. If it can be combined, it can be copied; Combine to form a new gene whose stability flag and replication flag are both False, until the disassembled genetic material is used up. 5. the one-dimensional blanking method based on gene evaluation genetic algorithm according to claim 1, is characterized in that, the specific process of selecting the individual who needs to carry out the next round of iteration according to the selection probability according to the fitness value is: Degree values are arranged from small to large, and the selection probability is determined with an exponential function according to the ranking. The selection probability of the i-th individual chromosome is p _i , where p _i =m·(1-m) ^i-1 , i=1,2 ,···, n, m are constants less than 1, and individual chromosomes are selected for iteration according to the selection probability. 6. The one-dimensional blanking method based on genetic evaluation genetic algorithm according to claim 1, characterized in that, said m=4/n.