Oppositional biogeography-based optimization for combinatorial problems

2011 IEEE Congress of Evolutionary Computation, CEC 2011

Volumn , Issue , 2011, Pages 1496-1503

  Ergezer, Mehmet ^a   Simon, Dan ^a  

^a CLEVELAND STATE UNIVERSITY   (United States)

Author keywords

Biogeography based optimization; combinatorics; discrete optimization; evolutionary algorithms; graph coloring problem; opposition; traveling salesman problem

Indexed keywords

BIOGEOGRAPHY-BASED OPTIMIZATIONS; COMBINATORICS; DISCRETE OPTIMIZATION; GRAPH-COLORING PROBLEM; OPPOSITION; TRAVELING SALESMAN;

COMBINATORIAL MATHEMATICS; COMBINATORIAL OPTIMIZATION; ECOLOGY; EVOLUTIONARY ALGORITHMS; PROBLEM SOLVING;

TRAVELING SALESMAN PROBLEM;

EID: 80052011880     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/CEC.2011.5949792     Document Type: Conference Paper

References (59)

1. J. Dayhoff and J. DeLeo, "Artificial neural networks", Cancer, vol. 91, no. S8, pp. 1615-1635, 2001.
2. Z. Michalewicz, Genetic algorithms + data structures = evolution programs (3rd ed.). London, UK: Springer-Verlag, 1996.
3. D. Simon, "Biogeography-based optimization", IEEE Transactions on Evolutionary Computation, vol. 12, no. 6, pp. 702-713, 2008.
4. D. Simon, M. Ergezer, and D. Du, "Markov models of biogeographybased optimization", IEEE Transactions on Systems, Man, and Cybernetics - Part B, vol. 41, pp. 299-306, 2011.
5. H. Tizhoosh, "Reinforcement learning based on actions and opposite actions", in International Conference on Artificial Intelligence and Machine Learning, 2005.
6. -, "Opposition-based reinforcement learning", Journal of Advanced Computational Intelligence and Intelligent Informatics, vol. 10, no. 4, pp. 578-585, 2006.
7. H. Tizhoosh and F. Sahba, "Quasi-global oppositional fuzzy thresholding", pp. 1346-1351, 2009.
8. H. Tizhoosh, "Opposite fuzzy sets with applications in image processing", in Proceedings of International Fuzzy Systems Association World Congress, Lisbon, Postugal, 2009, pp. 36-41.
9. M. Ventresca and H. Tizhoosh, "Improving the convergence of backpropagation by opposite transfer functions", in IEEE International Joint Conference on Neural Networks, 2006, pp. 9527-9534.
10. -, "Opposite transfer functions and backpropagation through time", in Foundations of Computational Intelligence, 2007. FOCI 2007. IEEE Symposium on. IEEE, 2007, pp. 570-577. (Pubitemid 47447791)
11. M. Shokri, H. Tizhoosh, and M. Kamel, "Opposition-based q (lambda) with non-markovian update", in Proc. IEEE Symposium on Approximate Dynamic Programming and Reinforcement Learning (ADPRL 2007), Hawaii, 2007, pp. 288-295.
12. M. Rashid and A. Baig, "Improved Opposition-Based PSO for Feedforward Neural Network Training", in Information Science and Applications (ICISA), 2010 International Conference on, 2010, pp. 1-6.
13. S. Rahnamayan, "Opposition-based differential evolution", Systems Design Engineering, University of Waterloo, 2007.
14. S. Rahnamayan, H. Tizhoosh, and M. Salama, "Opposition-based differential evolution", IEEE Transactions on Evolutionary Computation, vol. 12, no. 1, pp. 64-79, 2008. (Pubitemid 351330332)
15. S. Rahnamayan, H. R. Tizhoosh, and M. M. A. Salama, " Quasioppositional differential evolution", in Proc. IEEE Congress on Evolutionary Computation CEC 2007, 2007, pp. 2229-2236.
16. S. Rahnamayan and G. G. Wang, "Solving large scale optimization problems by opposition-based differential evolution (ode)", WSEAS Transactions on Computers, vol. 7, pp. 1792-1804, October 2008.
17. M. Ergezer, D. Simon, and D. Du, "Oppositional biogeography-based optimization", in IEEE International Conference on Systems, Man and Cybernetics, 2009, pp. 1009-1014.
18. H. Wang, Y. Liu, S. Zeng, H. Li, and C. Li, "Opposition-based particle swarm algorithm with cauchy mutation", in IEEE Congress on Evolutionary Computation, Singapore, 2007, pp. 4750-4756.
19. F. Shahzad, A. Baig, S. Masood, M. Kamran, and N. Naveed, "Opposition-Based Particle Swarm Optimization with Velocity Clamping", Advances in Computational Intelligence, pp. 339-348, 2009.
20. J. Tang and X. Zhao, "An enhanced opposition-based particle swarm optimization", in Intelligent Systems, 2009. GCIS'09. WRI Global Congress on, vol. 1. IEEE, 2009, pp. 149-153.
21. Y. Chi and G. Cai, "Particle swarm optimization with opposition-based disturbance", in Informatics in Control, Automation and Robotics (CAR), 2010 2nd International Asia Conference on, vol. 2. IEEE, 2010, pp. 223-226.
22. A. R. Malisia, "Investigating the application of opposition-based ideas to ant algorithms", Master's thesis, University of Waterloo, 2007.
23. A. Malisia, "Improving the exploration ability of ant-based algorithms", in Oppositional Concepts in Computational Intelligence. Springer, 2008, pp. 121-142.
24. M. Ventresca and H. Tizhoosh, "Simulated annealing with opposite neighbors", in Foundations of Computational Intelligence, 2007. FOCI 2007. IEEE Symposium on. IEEE, 2007, pp. 186-192. (Pubitemid 47447733)
25. F. Khalvati, H. Tizhoosh, and M. Aagaard, "Opposition-based window memoization for morphological algorithms", in IEEE Symposium on Foundations of Computational Intelligence, Honolulu, Hawaii, USA, 2007, pp. 425-430. (Pubitemid 47431622)
26. S. Rahnamayan and H. Tizhoosh, "Image thresholding using micro opposition-based differential evolution (micro-ode)", in Evolutionary Computation, 2008. CEC 2008. (IEEE World Congress on Computational Intelligence). IEEE Congress on. IEEE, 2008, pp. 1409-1416.
27. D. Subudhi, B; Jena, "Nonlinear system identification using opposition based learning differential evolution and neural network techniques", IEEE Journal of Intelligent Cybernetic Systems, vol. 1, pp. 1-13, 2009.
28. B. Subudhi and D. Jena, "A differential evolution based neural network approach to nonlinear system identification", Applied Soft Computing, vol. 11, pp. 861-871, 2011.
29. D. Du, "Biogeography-based optimization: Synergies with evolutionary strategies, immigration refusal and Kalman filters", Master's thesis, Cleveland State University, 2009.
30. H. Mo and L. Xu, "Biogeography migration algorithm for traveling salesman problem", Advances in Swarm Intelligence, pp. 405-414, 2010.
31. Y. Song, M. Liu, and Z. Wang, "Biogeography-based optimization for the traveling salesman problems", in 2010 Third International Joint Conference on Computational Science and Optimization. IEEE, 2010, pp. 295-299.
32. C. Darwin, "On the origin of species by means of natural selection, or the preservation of favoured races in the struggle for life", New York: D. Appleton, 1859.
33. R. H. MacArthur and E. O. Wilson, The Theory of Island Biogeography. Princeton University Press, 1967.
34. R. Rarick, D. Simon, F. Villaseca, and B. Vyakaranam, "Biogeographybased optimization and the solution of the power flow problem", in IEEE International Conference on Systems, Man and Cybernetics, 2009. IEEE, 2009, pp. 1003-1008.
35. G. Lozovyy, P.; Thomas and D. Simon, "Biogeography-based optimization for robot controller tuning", in Computational Modeling and Simulation of Intellect: Current State and Future Perspectives. IGI Global, 2011.
36. H. Kundra and M. Sood, "Cross-Country Path Finding using Hybrid approach of PSO and BBO", International Journal, vol. 7, 2010.
37. A. Bhattacharya and P. Chattopadhyay, "Biogeography-based optimization for different economic load dispatch problems", IEEE Transactions on Power Systems, vol. 25, no. 2, pp. 1064-1077, 2010.
38. V. Panchal, S. Goel, and M. Bhatnagar, "Biogeography based land cover feature extraction", in World Congress on Nature & Biologically Inspired Computing. IEEE, 2010, pp. 1588-1591.
39. M. Ergezer, B. E. Abali, and D. Simon, "Biogeography-based optimization identifies material coefficients as an inverse problem", January 2011, poster session presented at NSF CMMI Research and Innovation Conference, Atlanta, GA.
40. G. Tao and Z. Michalewicz, "Inver-over operator for the TSP", in Parallel Problem Solving from NaturePPSN V. Springer, 1998, p. 803.
41. D. Du and D. Simon, "Biogeography-based optimization for the traveling salesman problem", in Proceedings of the ASME 2011 International Design Engineering Technical Conferences & Computers and Information in Engineering Conference, Submitted.
42. J. Kruskal Jr, "On the shortest spanning subtree of a graph and the traveling salesman problem", Proceedings of the American Mathematical society, vol. 7, no. 1, pp. 48-50, 1956.
43. N. Christofides, A. Mingozzi, and P. Toth, "The vehicle routing problem", Revue Française d'Informatique et de Recherche Opérationnelle, vol. 10, no. 2, pp. 55-70, 1976.
44. G. Laporte, "The vehicle routing problem: An overview of exact and approximate algorithms", European Journal of Operational Research, vol. 59, no. 3, pp. 345-358, 1992.
45. P. Toth and D. Vigo, The vehicle routing problem. Society for Industrial Mathematics, 2002.
46. V. Magirou and A. Nicolitsas, "The efficient drilling of printed circuit boards", Interfaces, vol. 16, no. 4, pp. 13-23, 1986. (Pubitemid 16608247)
47. G. Onwubolu and M. Clerc, "Optimal path for automated drilling operations by a new heuristic approach using particle swarm optimization", International Journal of Production Research, vol. 42, no. 3, pp. 473-491, 2004.
48. G. Reinelt, "TSPLIB-A traveling salesman problem library", INFORMS Journal on Computing, vol. 3, no. 4, p. 376, 1991.
49. F. Leighton, "A graph coloring algorithm for large scheduling problems", Journal of Research of the National Bureau of Standards, vol. 84, no. 6, pp. 489-503, 1979.
50. G. Chaitin, M. Auslander, A. Chandra, J. Cocke, M. Hopkins, and P. Markstein, "Register allocation via coloring", Computer Languages, vol. 6, no. 1, pp. 47-57, 1981.
51. S. Even, O. Goldreich, S. Moran, and P. Tong, "On the NP-completeness of certain network testing problems", Networks, vol. 14, no. 1, pp. 1-24, 1984. (Pubitemid 14529972)
52. M. Carter, "A survey of practical applications of examination timetabling algorithms", Operations Research, vol. 34, no. 2, pp. 193-202, 1986. (Pubitemid 17461675)
53. D. Johnson, C. Aragon, L. McGeoch, and C. Schevon, "Optimization by simulated annealing: an experimental evaluation; part II, graph coloring and number partitioning", Operations research, pp. 378-406, 1991.
54. P. Galinier and J. Hao, "Hybrid evolutionary algorithms for graph coloring", Journal of Combinatorial Optimization, vol. 3, no. 4, pp. 379-397, 1999. (Pubitemid 129575323)
55. D. Costa and A. Hertz, "Ants can colour graphs", Journal of the Operational Research Society, vol. 48, no. 3, pp. 295-305, 1997. (Pubitemid 127608040)
56. C. Fleurent and J. Ferland, "Genetic and hybrid algorithms for graph coloring", Annals of Operations Research, vol. 63, no. 3, pp. 437-461, 1996. (Pubitemid 126729251)
57. M. Trick. (2010, August) Graph coloring instances. [Online]. Available: http://mat.gsia.cmu.edu/COLOR/instances.html
58. F. Leighton, "A graph coloring algorithm for large scheduling problems", Journal of Research of the National Bureau of Standards, vol. 84, no. 6, pp. 489-503, 1979.
59. D. Knuth, The Stanford GraphBase: a platform for combinatorial computing. ACM Press, 1993.