Skip to main content
Log in

NSGA-II-RJG applied to multi-objective optimization of polymeric nanoparticles synthesis with silicone surfactants

  • Research Article
  • Published:
Central European Journal of Chemistry

Abstract

Polydimethylsiloxane nanoparticles were obtained by nanoprecipitation, using a siloxane surfactant as stabilizer. Two neural networks and a genetic algorithm were used to optimize this process, by minimizing the particle diameter and the polydispersity, finding in this way the optimum values for surfactant and polymer concentrations, and storage temperature. In order to improve the performance of the non-dominated sorting genetic algorithm, NSGA-II, a genetic operator was introduced in this study — the transposition operator — “real jumping genes”, resulting NSGA-II-RJG. It was implemented in original software and was applied to the multi-objective optimization of the polymeric nanoparticles synthesis with silicone surfactants. The multi-objective function of the algorithm included two fitness functions. One fitness function was calculated with a neural network modelling the variation of the particle diameter on the surfactant concentration, polymer concentration, and storage temperature, and the other was computed by a neural network modelling the dependence of polydispersity index on surfactant and polymer concentrations. The performance of the software program that implemented NSGA-II-RJG was highlighted by comparing it with the software implementation of NSGA-II. The results obtained from simulations showed that NSGA-II-RJG is able to find non-dominated solutions with a greater diversity and a faster convergence time than NSGA-II.

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

Access this article

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

Instant access to the full article PDF.

Similar content being viewed by others

References

  1. A.V. Kabanov, E.V. Batrakova, V.Y. Alakhov, J. Control. Release 82, 189 (2002)

    Article  CAS  Google Scholar 

  2. B.A. Pfeifer, J.A. Burdick, R. Langer, Biomaterials 26, 117 (2005)

    Article  CAS  Google Scholar 

  3. A. Sanchez, M. Tobio, L. Gonzales, A. Fabra, M.J. Alonso, Eur. J. Pharm. Sci. 18, 221 (2003)

    Article  CAS  Google Scholar 

  4. A. Guo, G. Liu, J. Tao, Macromolecules 29, 2487 (1996)

    Article  CAS  Google Scholar 

  5. A. Harada, K. Kataoka, Prog. Polym. Sci. 31, 949 (2006)

    Article  CAS  Google Scholar 

  6. F. Henselwood, G. Liu, Macromolecules 30, 488 (1997)

    Article  CAS  Google Scholar 

  7. M. Iijima, Y. Nagasaki, T. Okada, M. Kato, K. Kataoka, Macromolecules 32, 1140 (1999)

    Article  CAS  Google Scholar 

  8. C. Nardin, S. Thoeni, J. Widmer, M. Winterhalter, W. Meier, Chem. Commun. 15, 1433 (2000)

    Article  Google Scholar 

  9. C. Nardin, J. Widmer, M. Winterhalter, W. Meier, Eur. Phys. J. E 4, 403 (2001)

    Article  CAS  Google Scholar 

  10. N. Angelova, D. Hunkeler, Trends Biotechnol. 17, 409 (1999)

    Article  CAS  Google Scholar 

  11. H.R. Krikeldorf, Silicon in Polymer Synthesis (Springer, New York, 1996)

    Google Scholar 

  12. C.J. Zhou, R.F. Guan, S.Y. Feng, Eur. Polym. J. 40, 165 (2004)

    Article  CAS  Google Scholar 

  13. C. Racles, T. Hamaide, A. Ioanid, Appl. Organomet. Chem. 20, 235 (2006)

    Article  CAS  Google Scholar 

  14. H. Fessi, F. Puisieux, J. Ph. Devissaguet, N. Ammoury, S. Benita, Int. J. Pharm. 55, R1 (1989)

    Article  CAS  Google Scholar 

  15. D. Horn, J. Rieger, Angew. Chem. Int. Edit. 40, 4330 (2001)

    Article  CAS  Google Scholar 

  16. D. Quintanar-Guerrero, E. Allemann, H. Fessi, E. Doelker, Drug Dev. Ind. Pharm. 24, 1113 (1998)

    Article  CAS  Google Scholar 

  17. S. Stainmesse, A.M. Orecchioni, E. Nakache, F. Puisieux, H. Fessi, Colloid Polym. Sci. 273, 505 (1995)

    Article  CAS  Google Scholar 

  18. C. Racles, T. Hamaide, Macromol. Chem. Physic. 206, 1757 (2005)

    Article  CAS  Google Scholar 

  19. C. Racles, M. Cazacu, G. Hitruc, T. Hamaide, Colloid Polym. Sci. 287, 461 (2009)

    Article  CAS  Google Scholar 

  20. C.A. Coello Coello, G.B. Lamont, Applications of Multi-Objective Evolutionary Algorithms (World Scientific, Singapore, 2004)

    Google Scholar 

  21. C.A. Coello Coello, In: A. Abraham, L. Jain, R. Goldberg (Eds.), Recent trends in evolutionary multiobjective optimization (Springer-Verlag, London, 2005)

    Google Scholar 

  22. 7 C.A. Coello Coello, G.B. Lamont, D.A. Van Veldhuizen, Evolutionary Algorithms for Solving Multi-Objective Problems, 2nd edition (Springer, New York, 2007)

    Google Scholar 

  23. K.C. Tan, E.F. Khor, T.H. Lee, Multiobjective Evolutionary Algorithms and Applications (Springer-Verlag, London, 2005)

    Google Scholar 

  24. K. Deb, A. Pratap, S. Agarwal, T. Meyarivan, IEEE T. Evolut. Comput. 6, 182 (2002)

    Article  Google Scholar 

  25. K. Deb, Multiobjective Optimization using Evolutionary Algorithms (John Wiley & Sons, Chichester, 2001)

    Google Scholar 

  26. R. Furtuna, S. Curteanu, F. Leon, Pet. Gas Univ. Ploiesti Bull. 61, 161 (2009)

    Google Scholar 

  27. K. Mitra, S. Majumdar, S. Raha, Comput. Chem. Eng. 28, 2583 (2004)

    Article  CAS  Google Scholar 

  28. D. Sarkar, J.M. Modak, Chem. Eng. Sci. 60, 481 (2005)

    Article  CAS  Google Scholar 

  29. R.B. Kasat, S.K. Gupta, Comput. Chem. Eng. 27, 1785 (2003)

    Article  CAS  Google Scholar 

  30. B. McKlintock, The Discovery and Characterization of Transposable Elements (Garland, New York, 1987)

    Google Scholar 

  31. N. Agrawal, G.P. Rangaiah, A.K. Ray, S.K. Gupta, Chem. Eng. Sci. 62, 2346 (2007)

    Article  CAS  Google Scholar 

  32. R. Kachhap, C. Guria, Macromol. Theor. Simul. 14, 358 (2005)

    Article  CAS  Google Scholar 

  33. K.S. Nawaz Ripon, S. Kwong, K.F. Man, Inform. Sciences 177, 632 (2007)

    Article  Google Scholar 

  34. S.W. Kantor, W.T. Grubb, R.C. Osthoff, J. Am. Chem. Soc. 76, 5190 (1954)

    Article  CAS  Google Scholar 

  35. F. Herrera, M. Lozano, J. Verdegay, Artif. Intell. Rev. 12, 265 (1998)

    Article  Google Scholar 

  36. R. Furtuna, S. Curteanu, M. Cazacu, Int. J. Quantum Chem. 111, 539 (2011)

    Article  CAS  Google Scholar 

  37. S. Curteanu, F. Leon, Int. J. Quantum Chem. 108, 617 (2007)

    Article  Google Scholar 

  38. S. Curteanu, M. Cazacu, J. Macromol. Sci. A45, 23 (2007)

    Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Silvia Curteanu.

About this article

Cite this article

Furtuna, R., Curteanu, S. & Racles, C. NSGA-II-RJG applied to multi-objective optimization of polymeric nanoparticles synthesis with silicone surfactants. cent.eur.j.chem. 9, 1080–1095 (2011). https://doi.org/10.2478/s11532-011-0096-5

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.2478/s11532-011-0096-5

Keywords

Navigation