Synchronous and asynchronous Pareto-based multi-objective Artificial Bee Colony algorithms | Journal of Global Optimization Skip to main content
Springer Nature Link
Log in

Synchronous and asynchronous Pareto-based multi-objective Artificial Bee Colony algorithms

  • Published:
Journal of Global Optimization Aims and scope Submit manuscript

Abstract

Pareto-based multi-objective optimization algorithms prefer non-dominated solutions over dominated solutions and maintain as much as possible diversity in the Pareto optimal set to represent the whole Pareto-front. This paper proposes three multi-objective Artificial Bee Colony (ABC) algorithms based on synchronous and asynchronous models using Pareto-dominance and non-dominated sorting: asynchronous multi-objective ABC using only Pareto-dominance rule (A-MOABC/PD), asynchronous multi-objective ABC using non-dominated sorting procedure (A-MOABC/NS) and synchronous multi-objective ABC using non-dominated sorting procedure (S-MOABC/NS). These algorithms were investigated in terms of the inverted generational distance, hypervolume and spread performance metrics, running time, approximation to whole Pareto-front and Pareto-solutions spaces. It was shown that S-MOABC/NS is more scalable and efficient compared to its asynchronous counterpart and more efficient and robust than A-MOABC/PD. An investigation on parameter sensitivity of S-MOABC/NS was presented to relate the behavior of the algorithm to the values of the control parameters. The results of S-MOABC/NS were compared to some state-of-the art algorithms. Results show that S-MOABC/NS can provide good approximations to well distributed and high quality non-dominated fronts and can be used as a promising alternative tool to solve multi-objective problems with the advantage of being simple and employing a few control parameters.

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

Access this article

Log in via an institution

Subscribe and save

Springer+ Basic
¥17,985 /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 includes VAT (Japan)

Instant access to the full article PDF.

Institutional subscriptions

Similar content being viewed by others

References

  1. Srinivas N., Deb K.: Muiltiobjective optimization using nondominated sorting in genetic algorithms. Evol. Comput. 2, 221–248 (1994)

    Article  Google Scholar 

  2. Coello Coello, C.A., Lamont, G.B.: Applications of Multi-objective Evolutionary Algorithms, Chapter an Introduction to Multi-objective Evolutionary Algorithms and their Applications, pp. 1–28. World Scientific, Singapore (2004)

  3. Zhang Q., Li H.: Moea/d: a multiobjective evolutionary algorithm based on decomposition. IEEE Trans. Evol. Comput. 11(6), 712–731 (2007)

    Article  Google Scholar 

  4. Sindhya, A., Sinha, K., Deb, K., Miettinen, K.: Local search based evolutionary multi-objective optimization algorithm for constrained and unconstrained problems. In: Proceedings of the Eleventh Conference on Congress on Evolutionary Computation (CEC’09), pp. 2919–2926, IEEE Press, Piscataway (2009)

  5. Schaffer, J.D.: Multiple objective optimization with vector evaluated genetic algorithms. In: Proceedings of the 1st International Conference on Genetic Algorithms, pp. 93–100. L. Erlbaum, Hillsdale (1985)

  6. Zitzler E., Thiele L.: Multiobjective evolutionary algorithms: a comparative case study and the strength Pareto approach. IEEE Trans. Evol. Comput. 3(4), 257–271 (1999)

    Article  Google Scholar 

  7. Zitzler E., Deb K., Thiele L.: Comparison of multiobjective evolutionary algorithms: empirical results. Evol. Comput. 8(2), 173–195 (2000)

    Article  Google Scholar 

  8. Coello Coello C.A., Veldhuizen D.A.V., Lamont G.B.: Evolutionary Algorithms for Solving Multi-objective Problems. Kluwer, Norwell (2002)

    Book  Google Scholar 

  9. Coello Coello, C.A.: List of references on evolutionary multiobjective optimization (2011). http://delta.cs.cinvestav.mx/~ccoello/EMOO/EMOObib.html

  10. Knowles, J.D., Corne, D.W.: The Pareto archived evolution strategy: a new baseline algorithm for multiobjective optimisation. In: 1999 Congress on Evolutionary Computation, pp. 98–105. IEEE Service Center, Washington (1999)

  11. Zitzler, E., Laumanns, M., Thiele, L.: SPEA2: improving the strength Pareto evolutionary algorithm. Technical Report 103, Computer Engineering and Networks Laboratory (TIK), Swiss Federal Institute of Technology (ETH) Zurich, Gloriastrasse 35, CH-8092 Zurich, Switzerland (2001)

  12. Deb, K., Agrawal, S., Pratap, A., Meyarivan, T.: A fast Elitist non-dominated sorting genetic algorithm for multi-objective optimization: NSGA-II. KanGAL report 200001, Indian Institute of Technology, Kanpur (2000)

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

  14. Karaboga D., Basturk B.: A powerful and efficient algorithm for numerical function optimization: artificial bee colony (abc) algorithm. J. Global Optim. 39(3), 459–471 (2007)

    Article  Google Scholar 

  15. Karaboga D., Akay B.: A comparative study of artificial bee colony algorithm. Appl. Math. Comput. 214, 108–132 (2008)

    Article  Google Scholar 

  16. Karaboga D., Akay B.: A modified artificial bee colony (abc) algorithm for constrained optimization problems. Appl. Soft Comput. 11(3), 3021–3031 (2011)

    Article  Google Scholar 

  17. Omkar S.N., Senthilnath J., Khandelwal R., Narayana Naik G., Gopalakrishnan S.: Artificial bee colony (abc) for multi-objective design optimization of composite structures. Appl. Soft Comput. 11(1), 489–499 (2011)

    Article  Google Scholar 

  18. Huang, V.L., Zhao, S.Z., Mallipeddi, R., Suganthan, P.N.: Multi-objective optimization using self-adaptive differential evolution algorithm. In: Proceedings of the Eleventh conference on Congress on Evolutionary Computation (CEC’09), pp. 190–194. IEEE Press, Piscataway (2009)

  19. Karaboga D.: Artificial bee colony algorithm. Scholarpedia 5(3), 6915 (2010)

    Article  Google Scholar 

  20. Karaboga D., Akay B.: A survey: algorithms simulating bee swarm intelligence. Artif. Intell. Rev. 31(1), 61–85 (2009)

    Article  Google Scholar 

  21. Zou, X., Liu, M., Kang, L., He, J.: A high performance multi-objective evolutionary algorithm based on the principles of thermodynamics. In: Yao, X., Burke, E., Lozano, J., Smith, J., Merelo-Guervs, J., Bullinaria, J., Rowe, J., Tino, P., Kabn, A., Schwefel, H.-P. (Eds.) Parallel Problem Solving from Nature—PPSN VIII, volume 3242 of Lecture Notes in Computer Science, pp. 922–931. Springer, Berlin (2004)

  22. Zou X., Chen Y., Liu M., Kang L.: A new evolutionary algorithm for solving many-objective optimization problems. IEEE Trans. Syst. Man Cybern. Part B Cybern. 38(5), 1402–1412 (2008)

    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. Corne, D., Knowles, J.: Some multiobjective optimizers are better than others. In: Proceedings of the 2003 Congress on Evolutionary Computation (CEC’2003), volume 4, pp. 2506–2512. IEEE Press, Canberra (2003)

  25. 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 

  26. Deb K.: Multi-objective genetic algorithms: problem difficulties and construction of test problems. Evol. Comput. 7, 205–230 (1999)

    Article  Google Scholar 

  27. Zhang, Q., Zhou, S., Zhao, A., Suganthan, P.N., Liu, W., Tiwariz, S.: Ces-487: multiobjective optimization test instances for the cec 2009 special session and competition. Technical report, University of Essex, The School of Computer Science and Electronic Engieering (2009)

  28. Zitzler E., Thiele L., Laumanns M., Fonseca C.M., da Fonseca V.G.: Performance assessment of multiobjective optimizers: an analysis and review. IEEE Trans. Evol. Comput. 7(2), 117–132 (2003)

    Article  Google Scholar 

  29. Van Veldhuizen, D.A.: Multiobjective evolutionary algorithms: classifications, analyses, and new innovations. PhD thesis, Wright Patterson AFB, OH (1999). AAI9928483

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

  31. Li H., Zhang Q.: Multiobjective optimization problems with complicated pareto sets, moea/d and nsga-ii. IEEE Trans. Evol. Comput. 13(2), 284–302 (2009)

    Article  Google Scholar 

  32. Deb K.: Multi-objective Optimization Using Evolutionary Algorithms. Wiley-Interscience Series in Systems and Optimization. Wiley, Chichester (2001)

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Department of Computer Engineering, Erciyes University, 38039, Melikgazi, Kayseri, Turkey

    Bahriye Akay

Authors
  1. Bahriye Akay
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Bahriye Akay.

Rights and permissions

Reprints and permissions

About this article

Cite this article

Akay, B. Synchronous and asynchronous Pareto-based multi-objective Artificial Bee Colony algorithms. J Glob Optim 57, 415–445 (2013). https://doi.org/10.1007/s10898-012-9993-1

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10898-012-9993-1

Keywords

Search

Navigation