CN111339635B - DNA storage coding optimization method of multi-element universe algorithm based on damping factors - Google Patents
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Abstract
The invention discloses a DNA storage coding optimization method of a multi-element universe algorithm based on damping factors, which comprises the following steps: constructing an optimal DNA coding sequence meeting combination constraint conditions, firstly constructing a certain number of DNA sequences as an initial population, and evaluating and sequencing the fitness of the population. Secondly, the obtained DNA coding sequence is utilized, and an updated formula containing damping factors and Levy flight search are used for optimization, so that the DNA storage coding sequence with higher fitness is obtained. And then judging whether to add the alternative solution set according to the constraint by constraint comparison. And finally, outputting the optimal DNA storage coding sequence. The method can search out the DNA storage coding sequence with better quantity.
Description
Technical Field
The invention relates to a group intelligent optimization algorithm and DNA storage coding, in particular to a method for optimizing a DNA coding sequence by using a multi-element universe algorithm, damping factors and Levy flight, which belongs to the field of coding design in DNA storage.
Background
The massive growth of data has made it desirable to have new media that are more efficient in storage and have higher storage capacity. DNA acts as a high density storage medium and has long term stability as a viable solution. DNA consists of four nucleotides ATCG, the theoretical storage capacity being twice that of the binary system in a conventional electronic system. Studies have shown that DNA can be stored for over a thousand years under suitable conditions. DNA-based data storage is commonly used for pioneering work, for example Joe Davis proposes an pioneering "Microvenus" project, using bacteria as a storage medium for storing non-biological information. At the beginning of the century, bancroft et al proposed a simple coding method using codon triplets, showing the great potential of DNA as a storage medium. DNA storage has advantages over storage time, and DNA data storage can be maintained for years under the conditions of adaptation. However, the cost of reading and writing DNA data remains high. However, recently, DNA synthesis and sequencing methods have been rapidly developed, and DNA storage will be a very competitive storage solution in the future.
Disclosure of Invention
The application provides a DNA storage coding optimization method of a multi-element universe algorithm based on damping factors, which comprises the steps of firstly searching an initial solution set for an initial population by using a worm hole strategy in the multi-element universe algorithm; secondly, updating the universe set by using an updating formula with a damping disturbance factor; then, carrying out Levy flight operation on the optimal universe in the updated universe set; finally, comparing whether the obtained set meets constraint conditions or not, and adding an alternative solution set meeting the constraint; the method can construct a DNA storage coding sequence with a better quantity.
In order to achieve the above purpose, the technical scheme of the application is as follows: the DNA storage coding optimization method of the multi-element universe algorithm based on the damping factors comprises the following specific steps: constructing an optimal DNA coding sequence meeting combination constraint conditions, firstly constructing a certain number of DNA sequences as an initial population, and evaluating and sequencing the fitness of the population. Secondly, the obtained DNA coding sequence is utilized to optimize by using a damping factor and Levy flight, so as to obtain the DNA coding sequence with higher fitness. And then judging whether to add the alternative solution set according to the constraint by constraint comparison. Finally, the optimal DNA coding sequence is output.
By adopting the technical scheme, the invention can obtain the following technical effects:
1. the fitness calculation is carried out on the initial population by using a multi-universe algorithm, and the introduction of the black/white tunnel not only can randomly transmit substances to the best universe, but also can improve the average fitness of the initial population;
2. the update strategy of Levy flight is used for replacing random update based on the current global optimum, so that the influence of individuals with maximum and minimum values on an update mechanism is reduced, and the convergence speed is increased. When an undesirable state is reached in the optimizing process, the damping factor can be used for jumping out of the current state to try to search in a larger range, so that the algorithm can be prevented from being trapped into local optimum in the later stage;
3. the DNA sequence optimization algorithm of the multi-element universe algorithm of the damping factor provided by the invention can search out the DNA storage coding sequences with better quantity.
Drawings
FIG. 1 is a flow chart of an implementation of the present invention.
Detailed Description
The following description of the embodiments of the present invention will be made with reference to the accompanying drawings, in which it is to be understood that the examples described are merely some, but not all embodiments of the present invention. All other embodiments, which can be made by a person skilled in the art without making any inventive effort, are intended to fall within the scope of the present invention.
Four constraint conditions are respectively full discontinuity constraint, editing distance, GC content and address irrelevant constraint. And taking the sum of the editing distances in the constraint conditions as an objective function, and taking the other three terms as the constraint conditions. The fitness value of each individual is calculated for use in the second step of the claims. The total discontinuity constraint indicates that identical bases in a DNA sequence cannot occur consecutively when adjacent. The edit distance refers to the sum of the minimum number of single character edits (insertions, deletions, substitutions) between pairs of DNA sequences. GC content constraint means that the number of guanine (G) and cytosine (C) in any one sequence in the set of DNA sequences is a percentage of the number of bases in the entire sequence, which is constrained to 50% in this example. Address-independent constraints refer to pairs of DNA sequences L, H. The prefix of L cannot appear as a suffix of H and vice versa, the prefix length is set to 3 in the present application.
The detailed steps are as follows:
step 1: generating an initial universe population, wherein parameters TDR, WEP, MAXIter, C1 and WEP required by an initialization algorithm are the existence probability of a worm hole, WEP is a travel distance rate, and MAXtime is the maximum iteration number; c1 is a damping disturbance factor;
step 2: calculating fitness (expansion rate) of each universe, updating a parameter best_universe, namely the current Best universe, sequencing an initial universe population by using a worm hole strategy in a multi-universe algorithm, selecting an optimal fitness universe, and taking the current fitness universe as an initial universe set;
step 3: generating random number r 1 Sequentially selecting universe through roulette to generate white holes, and exchanging substances with other universe through the white holes by the optimal universe;
step 4: for each cosmic individual, a random number r is generated 2 For r 2 Judging the existence probability WEP of the worm hole, if r 2 If the probability of existence of the white hole is smaller than the probability WEP, executing the step 5, otherwise executing the step 7;
step 5: generating two random numbers r 3 、r 4 And based on random number r 4 And the damping disturbance factor C1 and the travel distance rate TDR to update the cosmic substance, if r 3 <0.5 executing the update formula 2, otherwise executing the update formula 3;
step 6: taking the updated result as input of Levy flight search, and carrying out Levy flight search operation on the optimum adaptability universe as the center;
step 7: calculating whether other universe and initial universe meet constraint conditions, and adding an initial universe set if the constraint conditions are met;
step 8: judging whether the maximum iteration times are reached, if yes, carrying out the step 9, otherwise, returning to the step 2;
step 9: and counting the results, and outputting the maximum number of sequences.
Example 1
The embodiment of the invention is implemented on the premise of the technical scheme of the invention, and a detailed implementation mode and a specific operation process are provided, but the protection scope of the invention is not limited to the following embodiment. In the example, the DNA coding length n is 8, the editing distance constraint is d is more than or equal to 5, and the total discontinuity constraint, the GC content constraint and the address uncorrelated constraint are as described above.
Step 1: the population was initialized to generate 1000 DNA coding sequences of length 8. The method comprises the steps that related parameters required by an algorithm are initialized, min in the worm hole existence probability WEP is 0.2, max is 1, and p in the travel distance rate TDR is 6;
step 2: searching an initial population by using a worm hole strategy in a multi-element universe algorithm, firstly initializing the fitness of the universe population, sequencing the fitness of the universe, selecting the optimal fitness, and taking the current fitness as an initial universe set. Simulation experiments are carried out by MATLAB on the embodiment, and initial set of 29 is obtained through GC content, total discontinuity and address uncorrelated constraint;
step 3: and (3) continuing optimizing the 29 8-dimensional DNA sequences obtained in the step (2) through constraint by using a multi-universe algorithm. In the embodiment, the result of sequencing the fitness of universe individuals is obtained by using the sort () function in MATLAB, and a random number r is used 1 The universe is selected to generate white holes through roulette in turn, substances are exchanged with other universe, and the universe substances are updated in the following ways:
wherein X is ij Represents the jth substance, X in the ith universe vj And the same is true. U (U) i The i-th universe is represented, so NI (Ui) is the standard expansion rate of the i-th universe. Selecting the jth substance of the jth universe as X through a roulette mechanism wj . Wherein r is 1 Is [0,1]Random numbers in between;
step 4: for each universe, a random number r is generated 2 For r 2 Judging the existence probability WEP of the worm hole, if r 2 If the probability of existence of the hole is smaller than WEP, executing the step 5, otherwise executing the step 7;
step 5: at [0,1]Interval generation of two random numbers r 3 ，r 4 And according to the random number r 4 And travel distance rate TDR to update the cosmic substance if r 3 <0.5 executing the update formula 2, otherwise executing the update formula 3;
x ij ＝X j +C1*TDR×((ub j -lb j )×r 4 +lb j ) (2)
x ij ＝X j -C1*TDR×((ub j -lb j )×r 4 +lb j ) (3)
wherein X is ij Represents the jth substance, X in the ith universe j The jth substance representing the best universe created at present has boundaries of ub j And lb j C1 is the damping disturbance factor calculated by equation 4, TDR is the adaptive parameter travel distance rate, r4 is [0,1]Random numbers of intervals.
Step 6: taking the updated result as input of Levy flight search, and carrying out Levy flight search operation on the universe with the optimal fitness;
step 7: calculating whether other universe and initial universe meet constraint conditions, and adding initial universe set newDNA if the constraint conditions are met;
step 8: judging whether the maximum iteration times reach 1000 generations, if yes, carrying out the step 9, otherwise, returning to the step 2;
step 9: counting the results and outputting the maximum number of sequences;
the invention provides a DNA storage coding optimization method of a multi-element universe algorithm based on damping factors, which searches an initial population by using the multi-element universe algorithm based on the damping factors. Screening out DNA sequences meeting requirements through GC, address uncorrelated constraint and total discontinuity constraint, continuously updating according to a multi-element universe algorithm updating formula with damping factors based on the sequences, performing Levy flight search operation on the optimal universe after each updating, evaluating the optimal fitness, entering the next iteration, and finally taking the obtained maximum DNA sequence coding set as an output result. In the invention, under the running environment of Intel (R) CPU3.6GHz, 4.0GB memory and Windows10, the simulation experiment is carried out on the algorithm by means of MATLAB, and the experimental result shows that the method result of the example is superior to the experimental results of other algorithms.
Table 1 shows the initial DNA sequences
Table 2 shows the optimal DNA sequence set for n=8 and d.gtoreq.5
The above description is only of the preferred embodiments of the present invention and is not intended to limit the present invention, and it should be noted that it is possible for those skilled in the art to make several improvements and modifications without departing from the technical principle of the present invention, and these improvements and modifications should also be regarded as the protection scope of the present invention.
Claims (2)
1. The DNA storage coding optimization method of the multi-element universe algorithm based on the damping factors is characterized by comprising the following specific steps:
step 1: generating an initial universe population and initializing required parameters;
step 2: calculating fitness of each universe, updating a parameter best_universe, namely the current Best universe, sequencing an initial universe population by using a worm hole strategy in a multi-universe algorithm, selecting an optimal fitness universe, and taking the current fitness universe as an initial universe set;
step 3: generating random number r 1 Sequentially selecting universe through roulette to generate white holes, and exchanging substances with other universe through the white holes by the optimal universe;
step 4: for each cosmic individual, a random number r is generated 2 For r 2 Judging the existence probability WEP of the worm hole, if r 2 If the probability of existence of the white hole is smaller than the probability WEP, executing the step 5, otherwise executing the step 7;
step 5: generating two random numbers r 3 、r 4 And based on random number r 4 Damping disturbance factor C1 and travelThe distance rate TDR updates the cosmic substance if r 3 <0.5 executing the update formula 2, otherwise executing the update formula 3;
step 6: taking the updated result as input of Levy flight search, and carrying out Levy flight search operation on the universe with the optimal fitness;
step 7: calculating whether other universe and initial universe meet constraint conditions, and adding an initial universe set if the constraint conditions are met;
step 8: judging whether the maximum iteration times are reached, if yes, carrying out the step 9, otherwise, returning to the step 2;
step 9: the results are statistically summarized, and a sequence set and the maximum number of sequences are output;
the update mode of the cosmic substance in the step 3 is as follows:
wherein x is ij Represents the jth substance in the ith universe, U i Representing the ith universe, NI (U i ) Is the standard fitness value of the ith universe; selecting the jth substance of the jth universe as x by a roulette mechanism vj The method comprises the steps of carrying out a first treatment on the surface of the Wherein r is 1 Is [0,1]Random numbers in between;
update formula 2 and update formula 3 are respectively:
x ij ＝X j +C1*TDR×((ub j -lb j )×r 4 +lb j ) (2)
x ij ＝X j -C1*TDR×((ub j -lb j )×r 4 +lb j ) (3)
wherein X is j The jth substance representing the best universe created at present has boundaries of ub j And lb j C1 is the damping disturbance factor and TDR is the adaptationThe corresponding parameter travel distance rate, r 4 Is [0,1 ]]The random number of the interval, time is the current running algebra, max_time is the maximum running algebra.
2. The method for optimizing DNA storage coding of a damping factor-based multivariate algorithm of claim 1, wherein the parameters required in step 1 comprise TDR, WEP, MAXIter, C1, wherein WEP is the probability of existence of a worm hole and MAXIter is the maximum number of iterations; c1 is the damping perturbation factor.
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Citations (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN109300507A (en) * | 2018-09-04 | 2019-02-01 | 大连大学 | The DNA encoding sequence optimisation method of chaos invasive weed algorithm based on population |
| CN109389206A (en) * | 2018-09-26 | 2019-02-26 | 大连大学 | The DNA encoding sequence optimisation method of mixing bat algorithm based on non-dominated ranking |
| CN110533096A (en) * | 2019-08-27 | 2019-12-03 | 大连大学 | The DNA of multiverse algorithm based on K-means cluster stores Encoding Optimization |
-
2020
- 2020-01-17 CN CN202010051588.9A patent/CN111339635B/en active Active
Patent Citations (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN109300507A (en) * | 2018-09-04 | 2019-02-01 | 大连大学 | The DNA encoding sequence optimisation method of chaos invasive weed algorithm based on population |
| CN109389206A (en) * | 2018-09-26 | 2019-02-26 | 大连大学 | The DNA encoding sequence optimisation method of mixing bat algorithm based on non-dominated ranking |
| CN110533096A (en) * | 2019-08-27 | 2019-12-03 | 大连大学 | The DNA of multiverse algorithm based on K-means cluster stores Encoding Optimization |
Non-Patent Citations (2)
| Title |
|---|
| G.M.Viswanathan 等.Optimizing the sucess of random searches.《NATURE》.1999,第401卷全文. * |
| 张强 等.基于动态遗传算法的DNA 序列集合设计.《计算机学报》.2008,第31卷(第12期),全文. * |
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