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Mathematical Problems in Engineering
Volume 2016, Article ID 4527402, 10 pages
http://dx.doi.org/10.1155/2016/4527402
Research Article

Simulation Experiment Exploration of Genetic Algorithm’s Convergence over the Relationship Advantage Problem

Department of Industrial Engineering, School of Mechanical and Electronic Engineering, Wuhan University of Technology, Wuhan 430070, China

Received 12 April 2016; Revised 14 June 2016; Accepted 15 June 2016

Academic Editor: László T. Kóczy

Copyright © 2016 Yabo Luo. 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.-B. Luo, “A virtual layout system integrated with polar coordinates-based genetic algorithm,” International Journal of Computer Applications in Technology, vol. 35, no. 2–4, pp. 122–127, 2009. View at Publisher · View at Google Scholar · View at Scopus
  2. N. H. Mui, V. D. Hoa, and L. T. Tuyen, “Convergence analysis of the new hybrid genetic algorithm for the job shop scheduling problem,” in Proceedings of the 12th IEEE International Symposium on Signal Processing and Information Technology (ISSPIT '12), pp. 7–12, IEEE, Ho Chi Minh City, Vietnam, December 2012. View at Publisher · View at Google Scholar · View at Scopus
  3. H. Pourvaziri and B. Naderi, “A hybrid multi-population genetic algorithm for the dynamic facility layout problem,” Applied Soft Computing Journal, vol. 24, pp. 457–469, 2014. View at Publisher · View at Google Scholar · View at Scopus
  4. C.-C. Tsai, H.-C. Huang, and C.-K. Chan, “Parallel elite genetic algorithm and its application to global path planning for autonomous robot navigation,” IEEE Transactions on Industrial Electronics, vol. 58, no. 10, pp. 4813–4821, 2011. View at Publisher · View at Google Scholar · View at Scopus
  5. A. N. Ünal and G. Kayakutlu, “A Partheno-genetic algorithm for dynamic 0-1 multidimensional knapsack problem,” RAIRO Operations Research, vol. 50, no. 1, pp. 47–66, 2016. View at Publisher · View at Google Scholar · View at MathSciNet · View at Scopus
  6. S. Nezhadhosein, A. Heydari, and R. Ghanbari, “A modified hybrid genetic algorithm for solving nonlinear optimal control problems,” Mathematical Problems in Engineering, vol. 2015, Article ID 139036, 21 pages, 2015. View at Publisher · View at Google Scholar · View at Scopus
  7. M. Khoeiniha, H. Zarabadipour, and A. Fakharian, “Nonlinear electrical circuit oscillator control based on backstepping method: a genetic algorithm approach,” Mathematical Problems in Engineering, vol. 2012, Article ID 597328, 14 pages, 2012. View at Publisher · View at Google Scholar · View at MathSciNet
  8. J. H. Holland, Adaptation in Natural and Artificial Systems, MIT Press, Cambridge, Mass, USA, 1992.
  9. M. Chandrasekaran, M. Muralidhar, C. M. Krishna, and U. S. Dixit, “Application of soft computing techniques in machining performance prediction and optimization: a literature review,” The International Journal of Advanced Manufacturing Technology, vol. 46, no. 5–8, pp. 445–464, 2010. View at Publisher · View at Google Scholar · View at Scopus
  10. T. Vidal, T. G. Crainic, M. Gendreau, and C. Prins, “A hybrid genetic algorithm with adaptive diversity management for a large class of vehicle routing problems with time-windows,” Computers and Operations Research, vol. 40, no. 1, pp. 475–489, 2013. View at Publisher · View at Google Scholar · View at Scopus
  11. A. S. Tasan and M. Gen, “A genetic algorithm based approach to vehicle routing problem with simultaneous pick-up and deliveries,” Computers and Industrial Engineering, vol. 62, no. 3, pp. 755–761, 2012. View at Publisher · View at Google Scholar · View at Scopus
  12. J. F. Gonçalves and M. G. C. Resende, “A parallel multi-population biased random-key genetic algorithm for a container loading problem,” Computers & Operations Research, vol. 39, no. 2, pp. 179–190, 2012. View at Publisher · View at Google Scholar · View at Scopus
  13. M. H. Moradi and M. Abedini, “A combination of genetic algorithm and particle swarm optimization for optimal DG location and sizing in distribution systems,” International Journal of Electrical Power & Energy Systems, vol. 34, no. 1, pp. 66–74, 2012. View at Publisher · View at Google Scholar · View at Scopus
  14. R. Yusof, M. Khalid, G. T. Hui, S. Md Yusof, and M. F. Othman, “Solving job shop scheduling problem using a hybrid parallel micro genetic algorithm,” Applied Soft Computing Journal, vol. 11, no. 8, pp. 5782–5792, 2011. View at Publisher · View at Google Scholar · View at Scopus
  15. O. Engin, G. Ceran, and M. K. Yilmaz, “An efficient genetic algorithm for hybrid flow shop scheduling with multiprocessor task problems,” Applied Soft Computing, vol. 11, no. 3, pp. 3056–3065, 2011. View at Publisher · View at Google Scholar · View at Scopus
  16. R. Zhang, P.-C. Chang, and C. Wu, “A hybrid genetic algorithm for the job shop scheduling problem with practical considerations for manufacturing costs: investigations motivated by vehicle production,” International Journal of Production Economics, vol. 145, no. 1, pp. 38–52, 2013. View at Publisher · View at Google Scholar · View at Scopus
  17. L. Yabo, Theory and Method of Job Shop Scheduling Optimization, Huazhong University of Science and Technology Press, 2011.
  18. C.-L. Yang, S.-P. Chuang, and T.-S. Hsu, “A genetic algorithm for dynamic facility planning in job shop manufacturing,” The International Journal of Advanced Manufacturing Technology, vol. 52, no. 1–4, pp. 303–309, 2011. View at Publisher · View at Google Scholar · View at Scopus
  19. S. Selvakumar, K. P. Arulshri, and K. P. Padmanaban, “Machining fixture layout optimisation using genetic algorithm and artificial neural network,” International Journal of Manufacturing Research, vol. 8, no. 2, pp. 171–195, 2013. View at Publisher · View at Google Scholar · View at Scopus
  20. K. Izui, Y. Murakumo, I. Suemitsu, S. Nishiwaki, A. Noda, and T. Nagatani, “Multiobjective layout optimization of robotic cellular manufacturing systems,” Computers and Industrial Engineering, vol. 64, no. 2, pp. 537–544, 2013. View at Publisher · View at Google Scholar · View at Scopus
  21. S. Das and S. Sil, “Kernel-induced fuzzy clustering of image pixels with an improved differential evolution algorithm,” Information Sciences, vol. 180, no. 8, pp. 1237–1256, 2010. View at Publisher · View at Google Scholar · View at MathSciNet · View at Scopus
  22. K. Trist, V. Ciesielski, and P. Barile, “Can't see the forest: using an evolutionary algorithm to produce an animated artwork,” in Arts and Technology, vol. 30 of Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, pp. 255–262, 2009. View at Publisher · View at Google Scholar
  23. Y. Peng, X. Luo, and W. Wei, “A new fuzzy adaptive simulated annealing genetic algorithm and its convergence analysis and convergence rate estimation,” International Journal of Control, Automation and Systems, vol. 12, no. 3, pp. 670–679, 2014. View at Publisher · View at Google Scholar · View at Scopus
  24. S. Gupta and M. L. Garg, “Binary trie coding scheme: an intelligent genetic algorithm avoiding premature convergence,” International Journal of Computer Mathematics, vol. 90, no. 5, pp. 881–902, 2013. View at Publisher · View at Google Scholar · View at Scopus
  25. Y. Zhu and X. Cai, “Convergence and calculation speed of genetic algorithm in structural engineering optimization,” Metallurgical and Mining Industry, vol. 7, no. 8, pp. 259–263, 2015. View at Google Scholar · View at Scopus