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Mathematical Problems in Engineering
Volume 2016 (2016), Article ID 9635912, 13 pages
http://dx.doi.org/10.1155/2016/9635912
Research Article

Research on Preference Polyhedron Model Based Evolutionary Multiobjective Optimization Method for Multilink Transmission Mechanism Conceptual Design

College of Mechanical and Electrical Engineering, Nanjing University of Aeronautics and Astronautics, Nanjing 210016, China

Received 15 October 2015; Revised 4 March 2016; Accepted 31 March 2016

Academic Editor: Thomas Hanne

Copyright © 2016 Haihua Zhu 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. K. Deb, Multi-Objective Optimization Using Evolutionary Algorithms, Wiley-Interscience, New York, NY, USA, 2001. View at MathSciNet
  2. C. Coello, D. Veldhuizen, D. Van, and G. Lamont, Evolutionary Algorithms for Solving Multi-Objective Problems, Genetic Algorithms and Evolutionary Computation, Kluwer Academic, Norwell, Mass, USA, 2002.
  3. R. T. Marler and J. S. Arora, “Survey of multi-objective optimization methods for engineering,” Structural & Multidisciplinary Optimization, vol. 26, no. 6, pp. 369–395, 2004. View at Publisher · View at Google Scholar · View at Scopus
  4. R. Saravanan, S. Ramabalan, N. G. R. Ebenezer, and C. Dharmaraja, “Evolutionary multi criteria design optimization of robot grippers,” Applied Soft Computing Journal, vol. 9, no. 1, pp. 159–172, 2009. View at Publisher · View at Google Scholar · View at Scopus
  5. O. Castillo, L. Trujillo, and P. Melin, “Multiple objective genetic algorithms for path-planning optimization in autonomous mobile robots,” Soft Computing, vol. 11, no. 3, pp. 269–279, 2007. View at Publisher · View at Google Scholar · View at Scopus
  6. D. W. Gong, N. N. Qin, and X. Y. Sun, “Evolutionary optimization algorithm for multi-objective optimization problems with interval parameters,” in Proceedings of 5th IEEE International Conference on Bio-Inspired Computing: Theories and Applications, pp. 411–420, Changsha, China, September 2010.
  7. M. A. Abido, “Multiobjective evolutionary algorithms for electric power dispatch problem,” IEEE Transactions on Evolutionary Computation, vol. 10, no. 3, pp. 315–329, 2006. View at Publisher · View at Google Scholar · View at Scopus
  8. 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
  9. M. Emmerich, N. Beume, and B. Naujoks, “An EMO algorithm using the hypervolume measure as selection criterion,” in Evolutionary Multi-Criterion Optimization: Third International Conference, EMO 2005, Guanajuato, Mexico, March 9–11, 2005. Proceedings, vol. 3410 of Lecture Notes in Computer Science, pp. 62–76, Springer, Berlin, Germany, 2005. View at Publisher · View at Google Scholar
  10. S. Gunawan, A. Farhang-Mehr, and S. Azarm, “Multi-level multi-objective genetic algorithm using entropy to preserve diversity,” in Evolutionary Multi-Criterion Optimization: Second International Conference, EMO 2003, Faro, Portugal, April 8–11, 2003. Proceedings, vol. 2632 of Lecture Notes in Computer Science, pp. 148–161, Springer, Berlin, Germany, 2003. View at Publisher · View at Google Scholar
  11. R. Battiti and A. Passerini, “Brain-computer evolutionary multiobjective optimization: a genetic algorithm adapting to the decision maker,” IEEE Transactions on Evolutionary Computation, vol. 14, no. 5, pp. 671–687, 2010. View at Publisher · View at Google Scholar · View at Scopus
  12. J. Branke, K. Deb, K. Miettinen, and R. Slowinski, Multiobjective Optimization-Interactive and Evolutionary Approaches, Springer, Heidelberg, Germany, 2008.
  13. K. Deb, A. Sinha, P. J. Korhonen, and J. Wallenius, “An interactive evolutionary multiobjective optimization method based on progressively approximated value functions,” IEEE Transactions on Evolutionary Computation, vol. 14, no. 5, pp. 723–739, 2010. View at Publisher · View at Google Scholar · View at Scopus
  14. H. Eskandari, C. D. Geiger, and R. Bird, “Handling uncertainty in evolutionary multiobjective optimization: SPGA,” in Proceedings of the IEEE Congress on Evolutionary Computation (CEC '07), pp. 4130–4137, IEEE, Singapore, September 2007. View at Publisher · View at Google Scholar · View at Scopus
  15. J. W. Fowler, E. S. Gel, M. M. Köksalan, P. Korhonen, J. L. Marquis, and J. Wallenius, “Interactive evolutionary multi-objective optimization for quasi-concave preference functions,” European Journal of Operational Research, vol. 206, no. 2, pp. 417–425, 2010. View at Publisher · View at Google Scholar · View at Zentralblatt MATH · View at Scopus
  16. L. Rachmawati and D. Srinivasan, “Incorporating the notion of relative importance of objectives in evolutionary multiobjective optimization,” IEEE Transactions on Evolutionary Computation, vol. 14, no. 4, pp. 530–546, 2010. View at Publisher · View at Google Scholar · View at Scopus
  17. L. B. Said, S. Bechikh, and K. Ghédira, “The r-dominance: a new dominance relation for interactive evolutionary multicriteria decision making,” IEEE Transactions on Evolutionary Computation, vol. 14, no. 5, pp. 801–818, 2010. View at Publisher · View at Google Scholar · View at Scopus
  18. J. D. Schaffer, “Multiple objective optimization with vector evaluated genetic algorithms,” in Proceedings of the 1st International Conference on Genetic Algorithms, pp. 93–100, Hillsdale, NJ, USA, 1985.
  19. A. Sinha, P. Korhonen, J. Wallenius, and K. Deb, “An interactive evolutionary multi-objective optimization method based on polyhedral cones,” in Learning and Intelligent Optimization, C. Blum and R. Battiti, Eds., vol. 6073 of Lecture Notes in Computer Science, pp. 318–332, 2010. View at Publisher · View at Google Scholar
  20. J. Sun, D. Gong, and X. Sun, “Solving interval multi-objective optimization problems using evolutionary algorithms with preference polyhedron,” in Proceedings of the 13th Annual Conference on Genetic and Evolutionary Computation (GECCO '11), pp. 729–736, ACM, July 2011. View at Publisher · View at Google Scholar · View at Scopus
  21. K. Miettinen, “Survey of methods to visualize alternatives in multiple criteria decision making problems,” OR Spectrum, vol. 36, no. 1, pp. 3–37, 2014. View at Publisher · View at Google Scholar · View at MathSciNet · View at Scopus
  22. H. L. Trinkaus and T. Hanne, “KnowCube: a visual and interactive support for multicriteria decision making,” Computers and Operations Research, vol. 32, no. 5, pp. 1289–1309, 2005. View at Publisher · View at Google Scholar · View at Scopus
  23. C. M. Fonseca and P. J. Fleming, “Genetic algorithms for multiobjective optimization: formulation discussion and generalization,” in Proceedings of the 5th International Conference on Genetic Algorithms, pp. 416–423, 1993.
  24. D. E. Goldberg, Genetic Algorithms in Search, Optimization, and Machine Learning, Compact MRI for TB of the Distal Radius, 1989.
  25. N. Srinivas and K. Deb, “Multiobjective optimization using nondominated sorting in genetic algorithms,” Evolutionary Computation, vol. 2, no. 3, pp. 221–248, 1994. View at Publisher · View at Google Scholar