Egyptian Vulture Optimization Algorithm – A New Nature Inspired Meta-heuristics for Knapsack Problem

Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 209)

Abstract

In this paper we have introduced for the first time a new nature inspired meta-heuristics algorithm called Egyptian Vulture Optimization Algorithm which primarily favors combinatorial optimization problems. The algorithm is derived from the nature, behavior and key skills of the Egyptian Vultures for acquiring food for leading their livelihood. These spectacular, innovative and adaptive acts make Egyptian Vultures as one of the most intelligent of its kind among birds. The details of the bird’s habit and the mathematical modeling steps of the algorithm are illustrated demonstrating how the meta-heuristics can be applied for global solutions of the combinatorial optimization problems and has been studied on the traditional 0/1 Knapsack Problem (KSP) and tested for several datasets of different dimensions. The results of application of the algorithm on KSP datasets show that the algorithm works well w.r.t optimal value and provide the scope of utilization in similar kind of problems like path planning and other combinatorial optimization problems.

Keywords

Egyptian vulture optimization algorithm combinatorial optimization graph based problems knapsack problem nature inspired meta-heuristics 

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References

  1. 1.
  2. 2.
  3. 3.
    Blum, C., Roli, A.: Metaheuristics in combinatorial optimization: Overview and conceptual comparison. ACM Comput. 35(3), 268–308 (2003)CrossRefGoogle Scholar
  4. 4.
    Kennedy, J., Eberhart, R.: Particle swarm optimization. In: Proceedings of IEEE International Conference on Neural Networks, vol. 4, pp. 1942–1948 (November/December 1995)Google Scholar
  5. 5.
    Karaboga, D.: An idea based on honey bee swarm for numerical optimization. Technical Report TR06, Erciyes University (October 2005)Google Scholar
  6. 6.
    Kashan, H.A.: League Championship Algorithm: A New Algorithm for Numerical Function Optimization. In: Proceedings of the 2009 International Conference of Soft Computing and Pattern Recognition (SOCPAR 2009), pp. 43–48. IEEE Computer Society, Washington, DC (2009)CrossRefGoogle Scholar
  7. 7.
    Yang, X.-S., Deb, S.: Cuckoo search via Levy flights. In: World Congress on Nature & Biologically Inspired Computing (NaBIC 2009), pp. 210–214. IEEE Publication, USA (2009)CrossRefGoogle Scholar
  8. 8.
    Yang, X.-S.: A New Metaheuristic Bat-Inspired Algorithm. In: González, J.R., Pelta, D.A., Cruz, C., Terrazas, G., Krasnogor, N. (eds.) NICSO 2010. SCI, vol. 284, pp. 65–74. Springer, Heidelberg (2010)CrossRefGoogle Scholar
  9. 9.
    Kirkpatrick, S., Gelatt Jr., C.D., Vecchi, M.P.: Optimization by Simulated Annealing. Science 220(4598), 671–680 (1983)MathSciNetMATHCrossRefGoogle Scholar
  10. 10.
    Storn, R., Price, K.: Differential evolution - a simple and efficient heuristic for global optimization over continuous spaces. Journal of Global Optimization 11(4), 341–359 (1997)MathSciNetMATHCrossRefGoogle Scholar
  11. 11.
    Farmer, J.D., Packard, N., Perelson, A.: The immune system, adaptation and machine learning. Physica D 22(1-3), 187–204 (1986)MathSciNetCrossRefGoogle Scholar
  12. 12.
    Geem, Z.W., Kim, J.H., Loganathan, G.V.: A new heuristic optimization algorithm: harmony search. Simulation 76(2), 60–68 (2001)CrossRefGoogle Scholar
  13. 13.
    Krishnanand, K., Ghose, D.: Glowworm swarm optimization for simultaneous capture of multiple local optima of multimodal functions. Swarm Intelligence 3(2), 87–124 (2009)CrossRefGoogle Scholar
  14. 14.
    Haddad, O.B., Afshar, A., Mariño, M.A., et al.: Honey-bees mating optimization (HBMO) algorithm: a new heuristic approach for water resources optimization. Water Resources Management 20(5), 661–680 (2006)CrossRefGoogle Scholar
  15. 15.
    Tamura, K., Yasuda, K.: Primary Study of Spiral Dynamics Inspired Optimization. IEEJ Transactions on Electrical and Electronic Engineering 6 (S1), S98–S100 (2011)Google Scholar
  16. 16.
    Shah-Hosseini, H.: The intelligent water drops algorithm: a nature-inspired swarm-based optimization algorithm. International Journal of Bio-Inspired Computation 1(1/2), 71–79 (2009)CrossRefGoogle Scholar
  17. 17.
    Civicioglu, P.: Transforming geocentric cartesian coordinates to geodetic coordinates by using differential search algorithm. Computers & Geosciences 46, 229–247 (2012)CrossRefGoogle Scholar
  18. 18.
    Tayarani-N, M.H., Akbarzadeh-T, M.R.: Magnetic Optimization Algorithms a new synthesis. In: IEEE Congress on Evolutionary Computation, CEC 2008, IEEE World Congress on Computational Intelligence, June 1-6, pp. 2659–2664 (2008) Google Scholar
  19. 19.
    Reynolds, C.W.: Flocks, herds and schools: A distributed behavioral model. Computer Graphics 21(4), 25–34 (1987)CrossRefGoogle Scholar
  20. 20.
    Kaveh, A., Talatahari, S.: A Novel Heuristic Optimization Method: Charged System Search. Acta Mechanica 213(3-4), 267–289 (2010)MATHCrossRefGoogle Scholar
  21. 21.
    Gandomi, A.H., Alavi, A.H.: Krill Herd Algorithm: A New Bio-Inspired Optimization Algorithm. Communications in Nonlinear Science and Numerical Simulation (2012)Google Scholar
  22. 22.
    Tamura, K., Yasuda, K.: Spiral Dynamics Inspired Optimization. Journal of Advanced Computational Intelligence and Intelligent Informatics 15(8), 1116–1122 (2011)Google Scholar
  23. 23.
    Wolpert, D.H., Macready, W.G.: No free lunch theorems for optimization. IEEE Transactions on Evolutionary Computation 1(1), 67–82 (1997)CrossRefGoogle Scholar
  24. 24.
    Liang, Y.-C., Josue, R.C.: Virus Optimization Algorithm for Curve Fitting Problems. In: IIE Asian Conference (2011)Google Scholar
  25. 25.
  26. 26.
    Dorigo, M., Gambardella, L.M.: Ant Colony System: A Cooperative Learning Approach to the Traveling Salesman Problem. IEEE Transactions on Evolutionary Computation 1, 53–66 (1997)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  1. 1.Soft Computing and Expert System LaboratoryABV-Indian Institute of Information Technology & ManagementGwaliorIndia

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