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Journal of Heuristics

, Volume 25, Issue 4–5, pp 521–537 | Cite as

Diversification-based learning in computing and optimization

  • Fred Glover
  • Jin-Kao HaoEmail author
Article

Abstract

Diversification-based learning (DBL) derives from a collection of principles and methods introduced in the field of metaheuristics that have broad applications in computing and optimization. We show that the DBL framework goes significantly beyond that of the more recent opposition-based learning (OBL) framework introduced in Tizhoosh (in: Proceedings of international conference on computational intelligence for modelling, control and automation, and international conference on intelligent agents, web technologies and internet commerce (CIMCA/IAWTIC-2005), pp 695–701, 2005), which has become the focus of numerous research initiatives in machine learning and metaheuristic optimization. We unify and extend earlier proposals in metaheuristic search (Glover, in Hao J-K, Lutton E, Ronald E, Schoenauer M, Snyers D (eds) Artificial evolution, Lecture notes in computer science, Springer, Berlin, vol 1363, pp 13–54, 1997; Glover and Laguna Tabu search, Springer, Berlin, 1997) to give a collection of approaches that are more flexible and comprehensive than OBL for creating intensification and diversification strategies in metaheuristic search. We also describe potential applications of DBL to various subfields of machine learning and optimization.

Keywords

Learning-based optimization Diversification strategies Metaheuristic search 

Notes

Acknowledgements

We are grateful to the reviewers for their comments which helped us to improve the paper.

References

  1. Al-Qunaieer, F.S., Tizhoosh, H.R., Rahnamayan, S.: Opposition based computing–a survey. In: Proceedings of International Joint Conference on Neural Networks (IJCNN-2010), pp. 1–7 (2010)Google Scholar
  2. Duarte, A., Sánchez-Oro, J., Resende, M.G.C., Glover, F., Marti, R.: Greedy randomized search procedure with exterior path relinking for differential dispersion minimization. Inf. Sci. 296, 46–60 (2015)MathSciNetCrossRefGoogle Scholar
  3. Ergezer, M., Sikder, I.: Survey of oppositional algorithms. In: Proceedings of International Conference on Computer and Information Technology, 22–24 December, Dhaka, Bangladesh, pp. 623–628 (2011)Google Scholar
  4. Ergezer, M., Simon, D., Du, D.W.: Oppositional biogeography-based optimization. In: Proceedings of IEEE International Conference on Systems, Man and Cybernetics, San Antonio, USA, pp. 1009–1014 (2009)Google Scholar
  5. Ergezer, M., Simon, D.: Oppositional biogeography-based optimization for combinatorial problems. In: Proceedings of Congress on Evolutionary Computation (CEC-2011), pp. 1496–1503 (2011)Google Scholar
  6. Glover, F.: Heuristics for integer programming using surrogate constraints. Decis. Sci. 8(1), 156–166 (1977)CrossRefGoogle Scholar
  7. Glover, F.: Tabu search for nonlinear and parametric optimization (with links to genetic algorithms). Discret. Appl. Math. 49, 231–255 (1994)MathSciNetCrossRefzbMATHGoogle Scholar
  8. Glover, F.: A template for scatter search and path relinking. In: Hao, J.-K., Lutton, E., Ronald, E., Schoenauer, M., Snyers, D. (eds.) Artificial Evolution. Lecture Notes in Computer Science, vol. 1363, pp. 13–54. Springer, Berlin (1997)Google Scholar
  9. Glover, F.: Parametric Tabu search for mixed integer programs. Comput. Oper. Res. 33(9), 2449–2494 (2006)MathSciNetCrossRefzbMATHGoogle Scholar
  10. Glover, F.: Exterior path relinking for zero-one optimization. Int. J. Appl. Metaheur. Comput. 5(3), 1–8 (2014)CrossRefGoogle Scholar
  11. Glover, F.: Pseudo-centroid clustering. Soft. Comput. 21(22), 6571–6592 (2016)CrossRefGoogle Scholar
  12. Glover, F., Laguna, M.: Tabu search. In: Reeves, C. (ed.) Modern Heuristic Techniques for Combinatorial Problems, pp. 71–140. Blackwell Scientific Publishing, New York (1993)Google Scholar
  13. Glover, F., Laguna, M.: Tabu Search. Springer, Berlin (1997)CrossRefzbMATHGoogle Scholar
  14. Glover, F., Glover, R., Martinson, F.: A netform system for resource planning in the U.S. bureau of land management. J. Oper. Res. Soc. 35(7), 605–616 (1984)CrossRefGoogle Scholar
  15. Han, L., He, X.S.: A novel opposition-based particle swarm optimization for noisy problems. In: Proceedings of International Conference on Natural Computation, 24–27 August, Haikou, China, pp. 624–629 (2007)Google Scholar
  16. Kelly, J.P., Laguna, M., Glover, F.: A study of diversification strategies for the quadratic assignment problem. Comput. Oper. Res. 21(8), 885–893 (1994)CrossRefzbMATHGoogle Scholar
  17. Rahnamayan, S., Tizhoosh, H.R., Salama, M.: Opposition-based differential evolution. IEEE Trans. Evolut. Comput. 12(1), 64–79 (2008a)CrossRefGoogle Scholar
  18. Rahnamayan, S., Tizhoosh, H.R., Salama, M.M.: Opposition versus randomness in soft computing techniques. Appl. Soft Comput. 8(2), 906–918 (2008b)CrossRefGoogle Scholar
  19. Rahnamayan, S., Wang, G.G.: Center-based sampling for population-based algorithms. In: Proceedings of IEEE Congress on Evolutionary Computation, Trondheim, Norway, pp. 933–938 (2009)Google Scholar
  20. Tizhoosh, H.R.: Opposition-based learning: a new scheme for machine intelligence. In: Proceedings of International Conference on Computational Intelligence for Modelling, Control and Automation, and International Conference on Intelligent Agents, Web Technologies and Internet Commerce (CIMCA/IAWTIC-2005), pp. 695–701 (2005)Google Scholar
  21. Tizhoosh, H.R.: Reinforcement learning based on actions and opposite actions. J. Adv. Comput. Intell. Intell. Inform. 10(4), 578–585 (2006)MathSciNetCrossRefGoogle Scholar
  22. Ventresca, M., Tizhoosh, H.R.: A diversity maintaining population-based incremental learning algorithm. Inf. Sci. 178(21), 4038–4056 (2008)MathSciNetCrossRefzbMATHGoogle Scholar
  23. Ventresca, M., Tizhoosh, H.R.: Improving gradient-based learning algorithms for large scale feed forward networks. In: Proceedings of the International Joint Conference on Neural Networks, 14–19 June, Atlanta, USA, pp. 3212–3219 (2009)Google Scholar
  24. Wang, H., Wu, Z.J., Liu, Y., Wang, J., Jiang, D.Z., Chen, L.L.: Space transformation search: a new evolutionary technique. In: Proceedings of ACM/SIGEVO Summit on Genetic and Evolutionary Computation, Shanghai China, pp. 537-544 (2009)Google Scholar
  25. Xu, Q., Wang, L., He, B.M., Wang, N.: Opposition-based differential evolution using the current optimum for function optimization. J. Appl. Sci. 29, 308–315 (2011)Google Scholar
  26. Xu, Q., Wang, L., Wang, N., Hei, X., Zhao, L.: A review of opposition based learning from 2005 to 2012. Eng. Appl. Artif. Intell. 29, 1–12 (2014a)CrossRefGoogle Scholar
  27. Xu, Q., Guo, L., Wang, N., He, Y.: COOBBO: a novel opposition-based soft computing algorithm for TSP problems. Algorithms 7, 663–684 (2014b)CrossRefzbMATHGoogle Scholar
  28. Zhou, Y., Hao, J.-K., Duval, B.: Opposition-based memetic search for the maximum diversity problem. IEEE Trans. Evol. Comput. 21(5), 731–745 (2017)CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2018

Authors and Affiliations

  1. 1.ECEE-College of Engineering and Applied Science University of Colorado – BoulderBoulderUSA
  2. 2.OptTek SystemsBoulderUSA
  3. 3.LERIA, Université d’AngersAngersFrance
  4. 4.Institut Universitaire de FranceParisFrance

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