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Mathematical Problems in Engineering
Volume 2017 (2017), Article ID 6439631, 20 pages
https://doi.org/10.1155/2017/6439631
Research Article

Latest Stored Information Based Adaptive Selection Strategy for Multiobjective Evolutionary Algorithm

Air Force Engineering University, Xi’an, China

Correspondence should be addressed to Jiale Gao; moc.361@dgk_elaijoag

Received 4 July 2017; Revised 18 October 2017; Accepted 15 November 2017; Published 17 December 2017

Academic Editor: Salvatore Alfonzetti

Copyright © 2017 Jiale Gao et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Linked References

  1. 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. View at Publisher · View at Google Scholar · View at Scopus
  2. A. Lara, G. Sanchez, C. A. C. Coello, and O. Schütze, “HCS: A new local search strategy for memetic multiobjective evolutionary algorithms,” IEEE Transactions on Evolutionary Computation, vol. 14, no. 1, pp. 112–132, 2010. View at Publisher · View at Google Scholar · View at Scopus
  3. D. J. Schaffer, “Multiple objective optimization with vector evaluated genetic algorithm,” in Proceedings of the First International Conference of Genetic Algorithms and Their Application, 1985.
  4. N. Srinivas and K. Deb, “Multiobjective function optimization using nondominated sorting genetic algorithms,” Evolutionary Computation, vol. 2, pp. 221–248, 1994. View at Publisher · View at Google Scholar
  5. J. Horn, N. Nafpliotis, and D. E. Goldberg, “A niched Pareto genetic algorithm for multiobjective optimization,” in Proceedings of the 1st IEEE Conference on Evolutionary Computation, pp. 82–87, June 1994. View at Scopus
  6. C. M. Fonseca and Fleming P. J., “Genetic algorithms for multiobjective optimization: formulation discussion and generalization,” in Proceedings of the 5th International Conference on Genetic Algorithms (ICGA '93), pp. 416–423, 1993. View at Publisher · View at Google Scholar
  7. 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. View at Publisher · View at Google Scholar · View at Scopus
  8. Z. Eckart, M. Laumanns, and L. Thiele, SPEA2: Improving the strength Pareto evolutionary algorithm, 2001.
  9. Q. Zhang and H. Li, “MOEA/D: a multiobjective evolutionary algorithm based on decomposition,” IEEE Transactions on Evolutionary Computation, vol. 11, no. 6, pp. 712–731, 2007. View at Publisher · View at Google Scholar · View at Scopus
  10. J.-K. Xiao, W.-M. Li, X.-R. Xiao, and C.-Z. Lv, “A novel immune dominance selection multi-objective optimization algorithm for solving multi-objective optimization problems,” Applied Intelligence, vol. 46, no. 3, pp. 739–755, 2017. View at Publisher · View at Google Scholar · View at Scopus
  11. M. Afzalirad and J. Rezaeian, “A realistic variant of bi-objective unrelated parallel machine scheduling problem: NSGA-II and MOACO approaches,” Applied Soft Computing, vol. 50, pp. 109–123, 2017. View at Publisher · View at Google Scholar · View at Scopus
  12. S. Santander-Jiménez and M. A. Vega-Rodríguez, “Using mixed mode programming to parallelize an indicator-based evolutionary algorithm for inferring multiobjective phylogenetic histories,” Soft Computing, pp. 1–20, 2016. View at Publisher · View at Google Scholar · View at Scopus
  13. K. Deb and R. B. Agrawal, “Simulated binary crossover for continuous search space,” Complex Systems, vol. 9, no. 2, pp. 115–148, 1995. View at Google Scholar · View at MathSciNet
  14. R. Storn and K. Price, “Differential Evolution - A Simple and Efficient Heuristic for Global Optimization over Continuous Spaces,” Journal of Global Optimization, vol. 11, no. 4, pp. 341–359, 1997. View at Publisher · View at Google Scholar · View at Scopus
  15. A. K. Qin, V. L. Huang, and P. N. Suganthan, “Differential evolution algorithm with strategy adaptation for global numerical optimization,” IEEE Transactions on Evolutionary Computation, vol. 13, no. 2, pp. 398–417, 2009. View at Publisher · View at Google Scholar · View at Scopus
  16. K. Li, A. Fialho, S. Kwong, and Q. Zhang, “Adaptive operator selection with bandits for a multiobjective evolutionary algorithm based on decomposition,” IEEE Transactions on Evolutionary Computation, vol. 18, no. 1, pp. 114–130, 2014. View at Publisher · View at Google Scholar · View at Scopus
  17. Q. Zhu, Q. Lin, Z. Du et al., “A novel adaptive hybrid crossover operator for multiobjective evolutionary algorithm,” Information Sciences, vol. 345, pp. 177–198, 2016. View at Google Scholar
  18. R. Mallipeddi, P. N. Suganthan, Q. K. Pan, and M. F. Tasgetiren, “Differential evolution algorithm with ensemble of parameters and mutation strategies,” Applied Soft Computing, vol. 11, no. 2, pp. 1679–1696, 2011. View at Publisher · View at Google Scholar · View at Scopus
  19. S. M. Islam, S. Das, S. Ghosh, S. Roy, and P. N. Suganthan, “An adaptive differential evolution algorithm with novel mutation and crossover strategies for global numerical optimization,” IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics, vol. 42, no. 2, pp. 482–500, 2012. View at Publisher · View at Google Scholar · View at Scopus
  20. S.-Z. Zhao, P. N. Suganthan, and Q. Zhang, “Decomposition-based multiobjective evolutionary algorithm with an ensemble of neighborhood sizes,” IEEE Transactions on Evolutionary Computation, vol. 16, no. 3, pp. 442–446, 2012. View at Publisher · View at Google Scholar · View at Scopus
  21. R. A. Gonçalves, C. P. Almeida, and A. Pozo, “Upper confidence bound (UCB) algorithms for adaptive operator selection in MOEA/D,” Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics): Preface, vol. 9018, pp. 411–425, 2015. View at Publisher · View at Google Scholar · View at Scopus
  22. L. P. Kaelbling, “Associative Reinforcement Learning: A Generate and Test Algorithm,” Machine Learning, vol. 15, no. 3, pp. 299–319, 1994. View at Publisher · View at Google Scholar · View at Scopus
  23. L. DaCosta, Á. Fialho, M. Schoenauer, and M. Sebag, “Adaptive operator selection with dynamic multi-armed bandits,” in Proceedings of the 10th Annual Genetic and Evolutionary Computation Conference, GECCO 2008, pp. 913–920, usa, July 2008. View at Scopus
  24. Á. Fialho, L. Da Costa, M. Schoenauer, and M. Sebag, “Analyzing bandit-based adaptive operator selection mechanisms,” Annals of Mathematics and Artificial Intelligence, vol. 60, no. 1-2, pp. 25–64, 2010. View at Publisher · View at Google Scholar · View at MathSciNet
  25. Á. Fialho, M. Schoenauer, and M. Sebag, “Toward comparison-based adaptive operator selection,” in Proceedings of the 12th Annual Genetic and Evolutionary Computation Conference, GECCO-2010, pp. 767–774, USA, July 2010. View at Publisher · View at Google Scholar · View at Scopus
  26. S. M. Venske, R. A. Gonçalves, and M. R. Delgado, “ADEMO/D: Multiobjective optimization by an adaptive differential evolution algorithm,” Neurocomputing, vol. 127, pp. 65–77, 2014. View at Publisher · View at Google Scholar · View at Scopus
  27. Q. Zhang, W. Liu, and H. Li, “The performance of a new version of MOEA/D on CEC09 unconstrained MOP test instances,” in Proceedings of the Proceeding of the IEEE Congress on Evolutionary Computation (CEC '09), pp. 203–208, Trondheim, Norway, May 2009. View at Publisher · View at Google Scholar · View at Scopus
  28. E. Zitzler, K. Deb, and L. Thiele, “Comparison of multiobjective evolutionary algorithms: empirical results,” Evolutionary Computation, vol. 8, no. 2, pp. 173–195, 2000. View at Publisher · View at Google Scholar · View at Scopus
  29. K. Deb, L. Thiele, M. Laumanns, and E. Zitzler, “Scalable multi-objective optimization test problems,” in Proceedings of the 2002 Congress on Evolutionary Computation, CEC 2002, pp. 825–830, usa, May 2002. View at Publisher · View at Google Scholar · View at Scopus
  30. 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,” special session on performance assessment of multi-objective optimization algorithms, technical report, University of Essex, Colchester, UK and Nanyang technological University, Singapore, 2008. View at Google Scholar
  31. H. Li and Q. Zhang, “Multiobjective optimization problems with complicated pareto sets, MOEA/D and NSGA-II,” IEEE Transactions on Evolutionary Computation, vol. 13, no. 2, pp. 284–302, 2009. View at Publisher · View at Google Scholar · View at Scopus
  32. K. Li, Q. Zhang, S. Kwong, M. Li, and R. Wang, “Stable matching-based selection in evolutionary multiobjective optimization,” IEEE Transactions on Evolutionary Computation, vol. 18, no. 6, pp. 909–923, 2014. View at Publisher · View at Google Scholar · View at Scopus
  33. Z. Wang, Q. Zhang, A. Zhou, M. Gong, and L. Jiao, “Adaptive Replacement Strategies for MOEA/D,” IEEE Transactions on Cybernetics, vol. 46, no. 2, pp. 474–486, 2016. View at Publisher · View at Google Scholar · View at Scopus
  34. R. Storn, “On the usage of differential evolution for function optimization,” in Proceedings of the Biennial Conference of the North American Fuzzy Information Processing Society (NAFIPS '96), pp. 519–523, June 1996. View at Scopus
  35. K. V. Price, R. M. Storn, and J. A. Lampinen, “The differential evolution algorithm,” Differential evolution: a practical approach to global optimization, pp. 37–134, 2005. View at Google Scholar
  36. A. W. Iorio and X. Li, “Solving rotated multi-objective optimization problems using differential evolution,” in AI 2004: Advances in artificial intelligence, vol. 3339 of Lecture Notes in Comput. Sci., pp. 861–872, Springer, Berlin, 2004. View at Publisher · View at Google Scholar · View at MathSciNet
  37. D. E. Goldberg, “Probability Matching, the Magnitude of Reinforcement, and Classifier System Bidding,” Machine Learning, vol. 5, no. 4, pp. 407–425, 1990. View at Publisher · View at Google Scholar · View at Scopus
  38. D. Thierens, “An adaptive pursuit strategy for allocating operator probabilities,” in Proceedings of the GECCO 2005 - Genetic and Evolutionary Computation Conference, pp. 1539–1546, USA, June 2005. View at Publisher · View at Google Scholar · View at Scopus
  39. P. Auer, N. Cesa-Bianchi, and P. Fischer, “Finite-time analysis of the multiarmed bandit problem,” Machine Learning, vol. 47, no. 2-3, pp. 235–256, 2002. View at Publisher · View at Google Scholar · View at Scopus
  40. J. M. Whitacre, T. Q. Pham, and R. A. Sarker, “Use of statistical outlier detection method in adaptive evolutionary algorithms track: Genetic algorithms,” in Proceedings of the 8th Annual Genetic and Evolutionary Computation Conference 2006, pp. 1345–1352, usa, July 2006. View at Scopus
  41. J. Maturana, F. Lardeux, and F. Saubion, “Autonomous operator management for evolutionary algorithms,” Journal of Heuristics, vol. 16, no. 6, pp. 881–909, 2010. View at Publisher · View at Google Scholar · View at Scopus
  42. K. Li, Á. Fialho, and S. Kwong, “Multi-objective Differential Evolution with adaptive control of parameters and operators,” Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics): Preface, vol. 6683, pp. 473–487, 2011. View at Publisher · View at Google Scholar · View at Scopus
  43. O. Schütze, V. A. S. Hernández, H. Trautmann, and G. Rudolph, “The hypervolume based directed search method for multi-objective optimization problems,” Journal of Heuristics, pp. 1–28, 2016. View at Publisher · View at Google Scholar · View at Scopus
  44. M. Li, S. Yang, K. Li, and X. Liu, “Evolutionary algorithms with segment-based search for multiobjective optimization problems,” IEEE Transactions on Cybernetics, vol. 44, no. 8, pp. 1295–1313, 2014. View at Publisher · View at Google Scholar · View at Scopus
  45. E. Zitzler, L. Thiele, M. Laumanns, C. M. Fonseca, and V. G. Da Fonseca, “Performance assessment of multiobjective optimizers: an analysis and review,” IEEE Transactions on Evolutionary Computation, vol. 7, no. 2, pp. 117–132, 2003. View at Publisher · View at Google Scholar · View at Scopus
  46. B. Akay, “Synchronous and asynchronous Pareto-based multi-objective Artificial Bee Colony algorithms,” Journal of Global Optimization, vol. 57, no. 2, pp. 415–445, 2013. View at Publisher · View at Google Scholar · View at Scopus
  47. O. Schütze, X. Esquivel, A. Lara, and C. A. C. Coello, “Using the averaged hausdorff distance as a performance measure in evolutionary multiobjective optimization,” IEEE Transactions on Evolutionary Computation, vol. 16, no. 4, pp. 504–522, 2012. View at Publisher · View at Google Scholar · View at Scopus