Skip to main content
SpringerLink
Log in

A Membrane-Inspired Evolutionary Algorithm Based on Artificial Bee Colony Algorithm

  • Conference paper
Book cover Bio-Inspired Computing - Theories and Applications

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 472))

Abstract

The paper presents a novel membrane-inspired evolutionary algorithm, named artificial bee colony algorithm based on P systems (ABCPS), which combines P systems and artificial bee colony algorithm (ABC). ABCPS uses the evolutionary rules of ABC, the one level membrane structure, and transformation or communication rules in P systems to design its algorithm. Experiments have been conducted on a set of 29 benchmark functions. The results demonstrate good performance of ABCPS in solving complex function optimization problems when compared with ABC.

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

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 39.99
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 54.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. Păun, G.: Computing with Membranes. Journal of Computer and System Sciences 61(1), 108–143 (2000)

    Article  MathSciNet  MATH  Google Scholar 

  2. Zhang, G.X., Cheng, J.X., Gheorghe, M.: Dynamic Behavior Analysis of Membrane-Inspired Evolutionary Algorithms. International Journal of Computers, Communications & Contorl 9(2), 227–242 (2014)

    Google Scholar 

  3. Zhang, G.X., Gheorghe, M., Pan, L.Q., Pérez-Jiménez, M.J.: Evolutionary membrane computing: A comprehensive survey and new results. Information Sciences (2014), http://dx.doi.org/10.1016/j.ins.2014.04.007

  4. Nishida, T.Y.: An application of P-system: A new algorithm for NP-complete optimization problems. In: 8th World Multi-Conference on Systems, Cybernetics and Informatics, V, Orlando, pp. 109–112 (2004)

    Google Scholar 

  5. Nishida, T.Y.: An approximate algorithm for NP-complete optimization problems exploiting P-systems. In: 6th International Workshop on Membrane Computing, Vienna, pp. 26–43 (2005)

    Google Scholar 

  6. Nishida, T.Y.: Membrane algorithms: Approximate algorithms for NP-complete optimization problems. Applications of Membrane Computing, pp. 303–314 (2006)

    Google Scholar 

  7. Leporati, A., Pagani, D.: A membrane algorithm for the min storage problem. In: Hoogeboom, H.J., Păun, G., Rozenberg, G., Salomaa, A. (eds.) WMC 2006. LNCS, vol. 4361, pp. 443–462. Springer, Heidelberg (2006)

    Chapter  Google Scholar 

  8. Huang, L., He, X.X., Wang, N., Xie, Y.: P systems based multi-objective optimization algorithm. Progress in Natural Science 17(4), 458–465 (2007)

    Article  MathSciNet  MATH  Google Scholar 

  9. Huang, L., Wang, N.: An optimization algorithm inspired by membrane computing. In: Jiao, L., Wang, L., Gao, X.-b., Liu, J., Wu, F. (eds.) ICNC 2006. LNCS, vol. 4222, pp. 49–52. Springer, Heidelberg (2006)

    Chapter  Google Scholar 

  10. Cheng, J.X., Zhang, G.X., Zeng, X.X.: A novel membrane algorithm based on differential evolution for numerical optimization. International Journal of Unconventional Computing 7(3), 159–183 (2011)

    Google Scholar 

  11. Zhang, G.X., Liu, C.X., Gheorghe, M., Ipate, F.: Solving satisability problems with membrane algorithm. In: 4th International Conference on Bio-Inspired Computing: Theories and Applications, Beijing, pp. 29–36 (2009)

    Google Scholar 

  12. Zhang, G.X., Gheorghe, M., Wu, C.Z.: A quantum-inspired evolutionary algorithm based on P systems for knapsack problem. Fundamenta Informaticae 87(1), 93–116 (2008)

    MathSciNet  MATH  Google Scholar 

  13. Liu, C.X., Zhang, G.X., Zhu, Y.H., Fang, C., Liu, H.W.: A quantum-inspired evolutionary algorithm based on P systems for radar emitter signals. In: 4th International Conference on Bio-Inspired Computing: Theories and Applications, Beijing, pp. 1–5 (2009)

    Google Scholar 

  14. Liu, C., Zhang, G., Liu, H., Gheorghe, M., Ipate, F.: An improved membrane algorithm for solving time-frequency atom decomposition. In: Păun, G., Pérez-Jiménez, M.J., Riscos-Núñez, A., Rozenberg, G., Salomaa, A. (eds.) WMC 2009. LNCS, vol. 5957, pp. 371–384. Springer, Heidelberg (2010)

    Chapter  Google Scholar 

  15. Liu, C.X., Zhang, G.X., Liu, H.W.: A memetic algorithm based on P systems for IIR digital filter design. In: 8th IEEE International Conference on Pervasive Intelligence and Computing, Chengdu, pp. 330–334 (2009)

    Google Scholar 

  16. Huang, L., Suh, I.H.: Controller design for a marine diesel engine using membrane computing. International Journal of Innovative Computing Information and Control 5(4), 899–912 (2009)

    Google Scholar 

  17. Zhang, G.X., Liu, C.X., Rong, H.N.: Analyzing radar emitter signals with membrane algorithms. Mathematical and Computer Modelling 52(11-12), 1997–(2010)

    Article  Google Scholar 

  18. Yang, S.P., Wang, N.: A P systems based hybrid optimization algorithm for parameter estimation of FCCU reactor-regenerator model. Chemical Engineering Journal 211-212, 508–518 (2012)

    Article  Google Scholar 

  19. Zhang, G.X., Gheorghe, M., Li, Y.Q.: A membrane algorithm with quantuminspired subalgorithms and its application to image processing. Natural Computing 11(4), 701–717 (2012)

    Article  MathSciNet  Google Scholar 

  20. Zhang, G.X., Cheng, J.X., Gheorghe, M., Meng, Q.: A hybrid approach based on differential evolution and tissue membrane systems for solving constrained manufacturing parameter optimization problems. Applied Soft Computing 13(3), 1528–1542 (2013)

    Article  Google Scholar 

  21. Zhang, G.X., Zhou, F., Huang, X.L.: A Novel membrane algorithm based on particle swarm optimization for optimization for solving broadcasting problems. Journal of universal computer science 18(13), 1821–1841 (2012)

    Google Scholar 

  22. Tu, M., Wang, J., Song, X.X., Yang, F., Cui, X.R.: An artificial fish swarm algorithm based on P systems. ICIC Express Letters, Part B: Applications 4(3), 747–753 (2013)

    Google Scholar 

  23. Păun, G., Pérez-Jiménez, M.J.: Membrane computing: brief introduction, recent results and applications. Biosystems 85(1), 11–22 (2006)

    Article  Google Scholar 

  24. Păun, G.: Tracing some open problems in membrane computing. Romanian Journal of Information Science and Technology 10(4), 303–314 (2007)

    Google Scholar 

  25. Păun, G., Rozenberg, G.: A guide to membrane computing. Theoretical Computer Science 287(1), 73–100 (2002)

    Google Scholar 

  26. Karaboga, D.: An idea based on honey bee swarm for numerical optimization. Technical Report TR06, Erciyes University, Engineering Faculty, Computer Engineering Department (2005)

    Google Scholar 

  27. Tereshko, V., Loengarov, A.: Collective decision-making in honeybee foraging dynamics. Computing and Information Systems Journal 9(3), 1–7 (2005)

    Google Scholar 

  28. Karaboga, D., Basturk, B.: A powerful and efficient algorithm for numerical function optimization: artificial bee colony (ABC) algorithm. Journal of Global Optimization 39(3), 459–471 (2007)

    Article  MathSciNet  MATH  Google Scholar 

  29. Karaboga, D., Basturk, B.: On The performance of artificial bee colony (ABC) algorithm. Applied Soft Computing 8(1), 687–697 (2008)

    Article  Google Scholar 

  30. Karaboga, D., Akay, B.: A comparative study of artificial bee colony algorithm. Applied Mathematics and Computation 214(1), 108–132 (2009)

    Article  MathSciNet  MATH  Google Scholar 

  31. Singh, A.: An artificial bee colony algorithm for the leaf-constrained minimum spanning tree problem. Applied Soft Computing 9(2), 625–631 (2009)

    Article  Google Scholar 

  32. Kang, F., Li, J.J., Xu, Q.: Structural inverse analysis by hybrid simplex artificial bee colony algorithms. Computers & Sturctures 87(13-14), 861–870 (2009)

    Article  Google Scholar 

  33. Samrat, L., Udgata, S.K., Abraham, A.: Artificial bee colony algorithm for small signal model parameter extraction of MESFET. Engineering Applications of Artificial Intelligence 23(5), 689–694 (2010)

    Article  Google Scholar 

  34. Yao, X., Liu, Y., Lin, G.M.: Evolutionary programming made faster. IEEE Transactions on Evolutionary Computation 3(2), 82–102 (1999)

    Article  Google Scholar 

  35. Tang, K., Yao, X., Suganthan, P.N., MacNish, C., Chen, Y.P., Chen, C.M., Yang, Z.: Benchmark Functions for the CEC2008 Special Session and Competition on Large Scale Global Optimization, Technical Report, Nature Inspired Computation and Applications Laboratory, USTC, Hefei, China (2007)

    Google Scholar 

  36. Gao, W.F., Liu, S.Y., Huang, L.L.: A global best artificial bee colony algorithm for global optimization. Journal of Computational and Applied Mathematics 236(11), 2741–2753 (2012)

    Article  MathSciNet  MATH  Google Scholar 

  37. Gao, W.F., Liu, S.Y.: A modified artificial bee colony algorithm. Computers & Operations Research 39(3), 687–697 (2012)

    Article  MATH  Google Scholar 

  38. Zhu, G.P., Kwong, S.: Gbest-guided artificial bee colony algorithm for numericalfunction optimization. Applied Mathematics and Computation 217(7), 3166–3173 (2010)

    Article  MathSciNet  MATH  Google Scholar 

  39. Gao, W.F., Liu, S.Y.: Improved artificial bee colony algorithm for global optimization. Information Processing Letters 111(17), 871–882 (2011)

    Article  MathSciNet  MATH  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. School of Electrical and Information Engineering, Xihua University, Chengdu, Sichuan, P.R. China, 610039

    Xiaoxiao Song & Jun Wang

Authors
  1. Xiaoxiao Song
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Jun Wang
    View author publications

    You can also search for this author in PubMed Google Scholar

Editor information

Editors and Affiliations

  1. Huazhong University of Science and Technology, 430074, Wuhan, China

    Linqiang Pan

  2. Institute of Mathematics of the Romanian Academy, 014700, Bucuresti, Romania

    Gheorghe Păun

  3. Department of Computer Science and Artificial Intelligence, University of Sevilla, Avda. Reina Mercedes s/n., 41012, Sevilla, Spain

    Mario J. Pérez-Jiménez

  4. School of Automation, Huazhong University of Science and Technology, 430074, Wuhan, China

    Tao Song

Rights and permissions

Reprints and permissions

Copyright information

© 2014 Springer-Verlag Berlin Heidelberg

About this paper

Cite this paper

Song, X., Wang, J. (2014). A Membrane-Inspired Evolutionary Algorithm Based on Artificial Bee Colony Algorithm. In: Pan, L., Păun, G., Pérez-Jiménez, M.J., Song, T. (eds) Bio-Inspired Computing - Theories and Applications. Communications in Computer and Information Science, vol 472. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-662-45049-9_64

Download citation

  • DOI: https://doi.org/10.1007/978-3-662-45049-9_64

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-662-45048-2

  • Online ISBN: 978-3-662-45049-9

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation