Skip to main content
Log in

DNA sequences design under many objective evolutionary algorithm

  • Published:
Cluster Computing Aims and scope Submit manuscript

Abstract

DNA computing is conducted through reactions between DNA molecules, the quality of DNA sequences is directly influence on reactions. Following previous works, there are five metrics to estimate quality of DNA sequences and one constraint to follow. Evolutionary algorithms are widely applied in this field, conventional frames are often using multi-objective strategies to solve this problem. However, multi-objective strategies loss its efficiency in solving high dimensional problems especially Pareto Front is irregular. In this article, a many-objective evolutionary algorithm, R2HCAEMOA, is introduced to tackle with increased objective dimension. To increase diversity from beginning, chaotic mapping is applied to initialize decision variables of population. Since purpose of many-objective optimization algorithms is to find evenly distributed solution set on Pareto Front, decision makers are faced difficulty in solution selection. A method for choosing the most interesting solution from solution set is determined. Besides, an incremental scheme to generate a DNA sequence set is applied to enforce stability of evolutionary environment. The average values on each metrics are {0, 0, 56.00, 42.85, 0.15}, which the metrics are {continuity, hairpin, H-measure, similarity, variance of melting temperature}. Running time of our frame is significantly reduced compared with previous works. The results have shown our work is competitive among previous works and incline balanced value on each objective dimension.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Subscribe and save

Springer+ Basic
$34.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11
Fig. 12
Fig. 13
Algorithm 1
Algorithm 2
Fig. 14
Fig. 15

Similar content being viewed by others

Data availability

The data used to support the findings of this study are available from the corresponding author upon request.

Notes

  1. https://github.com/AndreasGuo/LBSR2HCA_Materials.git.

References

  1. Liu, K., Wang, B., Lv, H., Wei, X., Zhang, Q.: A bpson algorithm applied to DNA codes design. IEEE Access 7, 88811–88821 (2019)

    Article  Google Scholar 

  2. Adleman, L.M.: Molecular computation of solutions to combinatorial problems. Science 266(5187), 1021–1024 (1994)

    Article  Google Scholar 

  3. Shu, J.-J., Wang, Q.-W., Yong, K.-Y.: DNA-based computing of strategic assignment problems. Phys. Rev. Lett. 106(18), 188702 (2011)

    Article  Google Scholar 

  4. Jiao, H., Zhong, Y., Zhang, L.: Artificial DNA computing-based spectral encoding and matching algorithm for hyperspectral remote sensing data. IEEE Trans. Geosci. Remote Sens. 50(10), 4085–4104 (2012)

    Article  Google Scholar 

  5. Yin, Z., Cui, J., Yang, J.: Integer programming problem based on plasmid DNA computing model. Chin. J. Electron. 26(6), 1284–1288 (2017)

    Article  Google Scholar 

  6. Namasudra, S., Chakraborty, R., Majumder, A., Moparthi, N.R.: Securing multimedia by using DNA-based encryption in the cloud computing environment. ACM Trans. Multimedia Comput. Commun. Appl. 16(3s), 1–19 (2020)

    Article  Google Scholar 

  7. Shin, S.-Y., Lee, I.-H., Kim, D., Zhang, B.-T.: Multiobjective evolutionary optimization of DNA sequences for reliable DNA computing. IEEE Trans. Evol. Comput. 9(2), 143–158 (2005)

    Article  Google Scholar 

  8. Yang, X., Zhou, C.: DNA sequences under multiple Guanine–Cytosine (G–C) base pairs constraint. IEEE Trans. NanoBiosci. 1, 1 (2023)

    Google Scholar 

  9. Deaton, R., Garzon, M.: Thermodynamic constraints on DNA-based computing. Comput. Bio-Mol. 1, 138–152 (1998)

    Google Scholar 

  10. Li, X., Wang, B., Lv, H., Yin, Q., Zhang, Q., Wei, X.: Constraining DNA sequences with a triplet-bases unpaired. IEEE Trans. Nanobiosci. 19(2), 299–307 (2020)

    Article  Google Scholar 

  11. Bano, S., Bashir, M., Younas, I.: A many-objective memetic generalized differential evolution algorithm for DNA sequence design. IEEE Access 8, 222684–222699 (2020)

    Article  Google Scholar 

  12. Zhu, D., Huang, Z., Liao, S., Zhou, C., Yan, S., Chen, G.: Improved bare bones particle swarm optimization for DNA sequence design. IEEE Trans. Nanobiosci. 22(3), 603–613 (2022)

    Article  Google Scholar 

  13. Xie, L., Wang, S., Zhu, D., Hu, G., Zhou, C.: DNA sequence optimization design of arithmetic optimization algorithm based on billiard hitting strategy. Interdiscip. Sci. Comput. Life Sci. 15(2), 231–248 (2023)

    Article  Google Scholar 

  14. Yang, G., Wang, B., Zheng, X., Zhou, C., Zhang, Q.: Iwo algorithm based on niche crowding for DNA sequence design. Interdiscip. Sci. Comput. Life Sci. 9, 341–349 (2017)

    Article  Google Scholar 

  15. Chaves-Gonzalez, J.M., Vega-Rodriguez, M.A.: DNA strand generation for DNA computing by using a multi-objective differential evolution algorithm. Biosystems 116, 49–64 (2014)

    Article  Google Scholar 

  16. Chaves-González, J.M., Martínez-Gil, J.: An efficient design for a multi-objective evolutionary algorithm to generate DNA libraries suitable for computation. Interdiscip. Sci. Comput. Life Sci. 11, 542–558 (2019)

    Article  Google Scholar 

  17. Zitzler, E., Thiele, L.: Multiobjective optimization using evolutionary algorithms—a comparative case study. In: International Conference on Parallel Problem Solving from Nature, pp. 292–301 (1998). Springer

  18. Tang, W., Liu, H.-L., Chen, L., Tan, K.C., Cheung, Y.-M.: Fast hypervolume approximation scheme based on a segmentation strategy. Inf. Sci. 509, 320–342 (2020)

    Article  MathSciNet  Google Scholar 

  19. Shang, K., Ishibuchi, H., Ni, X.: R2-based hypervolume contribution approximation. IEEE Trans. Evol. Comput. 24(1), 185–192 (2019)

    Article  Google Scholar 

  20. Shang, K., Ishibuchi, H.: A new hypervolume-based evolutionary algorithm for many-objective optimization. IEEE Trans. Evol. Comput. 24(5), 839–852 (2020)

    Article  Google Scholar 

  21. Varol Altay, E., Alatas, B.: Bird swarm algorithms with chaotic mapping. Artif. Intell. Rev. 53(2), 1373–1414 (2020)

    Article  Google Scholar 

  22. Rasool, A., Hong, J., Jiang, Q., Chen, H., Qu, Q.: Bo-dna: biologically optimized encoding model for a highly-reliable DNA data storage. Comput. Biol. Med. 165, 107404 (2023). https://doi.org/10.1016/j.compbiomed.2023.107404

    Article  Google Scholar 

  23. Deb, K., Pratap, A., Agarwal, S., Meyarivan, T.: A fast and elitist multiobjective genetic algorithm: Nsga-ii. IEEE Trans. Evol. Comput. 6(2), 182–197 (2002)

    Article  Google Scholar 

  24. Zhang, W., Zhu, D., Huang, Z., Zhou, C.: Improved multi-strategy matrix particle swarm optimization for DNA sequence design. Electronics 12(3), 547 (2023)

    Article  Google Scholar 

  25. Ishibuchi, H., Tsukamoto, N., Hitotsuyanagi, Y., Nojima, Y.: Effectiveness of scalability improvement attempts on the performance of NSGA-II for many-objective problems. In: Proceedings of the 10th Annual Conference on Genetic and Evolutionary Computation, pp. 649–656 (2008)

  26. Purshouse, R.C., Fleming, P.J.: On the evolutionary optimization of many conflicting objectives. IEEE Trans. Evol. Comput. 11(6), 770–784 (2007)

    Article  Google Scholar 

  27. Khare, V., Yao, X., Deb, K.: Performance scaling of multi-objective evolutionary algorithms. In: International Conference on Evolutionary Multi-criterion Optimization, pp. 376–390 (2003). Springer

  28. Yao, X.: How well do multi-objective evolutionary algorithms scale to large problems. In: 2007 IEEE Congress on Evolutionary Computation, pp. 3959–3966 (2007). IEEE

  29. Hadka, D., Reed, P.: Diagnostic assessment of search controls and failure modes in many-objective evolutionary optimization. Evol. Comput. 20(3), 423–452 (2012)

    Article  Google Scholar 

  30. Wagner, T., Beume, N., Naujoks, B.: Pareto-, aggregation-, and indicator-based methods in many-objective optimization. In: International Conference on Evolutionary Multi-criterion Optimization, pp. 742–756 (2007). Springer

  31. Geem, Z.W., Kim, J.H., Loganathan, G.V.: A new heuristic optimization algorithm: harmony search. Simulation 76(2), 60–68 (2001)

    Article  Google Scholar 

Download references

Funding

This work was supported in part by the National Natural Science Foundation of China under Grant Numbers 62272418 and 62102058, Basic public welfare research program of Zhejiang Province (No. LGG18E050011).

Author information

Authors and Affiliations

Authors

Contributions

Conceptualization: Zhou, Zhu; Methodology: Guo, Zhou; Formal analysis and investigation: Zhou, Zhu, Guo; Writing—original draft preparation: Guo, Zhu; Writing—review and editing: Zhu; Resources: Zhu, Zou; Supervision: Zhou, Zou;

Corresponding author

Correspondence to Changjun Zhou.

Ethics declarations

Conflict of interest

The authors declare no conflict on interest.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Guo, H., Zhu, D., Zhou, C. et al. DNA sequences design under many objective evolutionary algorithm. Cluster Comput 27, 14167–14183 (2024). https://doi.org/10.1007/s10586-024-04675-1

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10586-024-04675-1

Keywords

Navigation