Skip to content
Research Article Peer ReviewedAt least two reviewers Open Access

MOEA/D Algoritmasının En Önemli İki Ayrıştırma Yöntemi Olan PBI ve TCH’ın Analizi

1Tokat Gaziosmanpaşa University
2Konya Technical University
✉ Corresponding author: Mustafa Altıok · mustafa.altiok@gop.edu.tr
PDF BibTeX RIS

References

  1. A. OZKIS, "Girdap arama ve yapay alg algoritmalarının çok amaçli optimzasyon problemlerine uyarlanmasi," phD, Computer Engineering, Selcuk Universty, KONYA, 2018.
  2. A. Özkış and A. Babalık, "A novel metaheuristic for multi-objective optimization problems: The multi-objective vortex search algorithm," Information Sciences, vol. 402, pp. 124-148, 2017.
  3. J. Schaffer, "Multiple objective optimization with vector evaluated," ph.D., Genetic Algorithms, Vanderbilt Universty, 1984.
  4. K. Deb, A. Pratap, S. Agarwal, and T. Meyarivan, "A fast and elitist multiobjective genetic algorithm: NSGA-II," IEEE transactions on evolutionary computation, vol. 6, no. 2, pp. 182-197, 2002.
  5. C. A. C. Coello, G. T. Pulido, and M. S. Lechuga, "Handling multiple objectives with particle swarm optimization," IEEE Transactions on evolutionary computation, vol. 8, no. 3, pp. 256-279, 2004.
  6. E. Zitzler and S. Künzli, "Indicator-based selection in multiobjective search," in International Conference on Parallel Problem Solving from Nature, 2004, pp. 832-842: Springer.
  7. Q. F. Zhang and H. Li, "MOEA/D: A multiobjective evolutionary algorithm based on decomposition," (in English), Ieee Transactions on Evolutionary Computation, vol. 11, no. 6, pp. 712-731, Dec 2007.
  8. J. Bai and H. Liu, "Multi-objective artificial bee algorithm based on decomposition by PBI method," Applied Intelligence, vol. 45, no. 4, pp. 976-991, 2016.
  9. E. ERGUL, "Çok amaçlı genetik algoritmalar: Temelleri ve Uygulamaları," ph.D., Multi Objective Optimization, Ondokuz Mayıs University, SAMSUN, 2010.
  10. A. J. Nebro and J. J. Durillo, "A study of the parallelization of the multi-objective metaheuristic MOEA/D," in International Conference on Learning and Intelligent Optimization, 2010, pp. 303-317: Springer.
  11. J. J. Durillo and A. J. Nebro, "jMetal: A Java framework for multi-objective optimization," Advances in Engineering Software, vol. 42, no. 10, pp. 760-771, 2011.
  12. E. Zitzler, K. Deb, and L. Thiele, "Comparison of multiobjective evolutionary algorithms: Empirical results," Evolutionary computation, vol. 8, no. 2, pp. 173-195, 2000.
  13. K. Deb, L. Thiele, M. Laumanns, and E. Zitzler, "Scalable test problems for evolutionary multiobjective optimization," in Evolutionary multiobjective optimization: Springer, 2005, pp. 105-145.
  14. Q. Zhang, A. Zhou, S. Zhao, P. N. Suganthan, W. Liu, and S. Tiwari, "Multiobjective optimization test instances for the CEC 2009 special session and competition," University of Essex, Colchester, UK and Nanyang technological University, Singapore, special session on performance assessment of multi-objective optimization algorithms, technical report, vol. 264, 2008.
  15. A. Babalik, A. Ozkis, S. A. Uymaz, and M. S. Kiran, "A multi-objective artificial algae algorithm," (in English), Applied Soft Computing, vol. 68, pp. 377-395, Jul 2018.
  16. Y. Xiang, Y. Zhou, and H. Liu, "An elitism based multi-objective artificial bee colony algorithm," European Journal of Operational Research, vol. 245, no. 1, pp. 168-193, 2015.
  17. D. H. Wolpert and W. G. Macready, "No free lunch theorems for optimization," IEEE transactions on evolutionary computation, vol. 1, no. 1, pp. 67-82, 1997.

Cited by

0 — Crossref — cited-by count (retrieved 2 Oct 2026, source)

Citation counts are shown with their source and retrieval date. They are not an Impact Factor or any journal-level metric.

Research Contributions (2019+)Peer reviewed by at least two reviewers
Open AccessCC BY 4.0
Open Discovery Metadata · DOI · OAI-PMH
Global Reachresearchers worldwide