Advertisement

Abstract

In this paper we propose a new meta-heuristic algorithm called penguins Search Optimization Algorithm (PeSOA), based on collaborative hunting strategy of penguins. In recent years, various effective methods, inspired by nature and based on cooperative strategies, have been proposed to solve NP-hard problems in which, no solutions in polynomial time could be found. The global optimization process starts with individual search process of each penguin, who must communicate to his group its position and the number of fish found. This collaboration aims to synchronize dives in order to achieve a global solution (place with high amounts of food). The global solution is chosen by election of the best group of penguins who ate the maximum of fish. After describing the behavior of penguins, we present the formulation of the algorithm before presenting the various tests with popular benchmarks. Comparative studies with other meta-heuristics have proved that PeSOA performs better as far as new optimization strategy of collaborative and progressive research of the space solutions.

Keywords

Meta-heuristic Optimization Penguins Bio-inspiration NP-hard 

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Blum, C., Roli, A.: Metaheuristics in combinatorial optimization: Overview and conceptural comparision. ACM Comput. 35, 268–308 (2003)CrossRefGoogle Scholar
  2. 2.
    Bonabeau, E., Dorigo, M., Theraulaz, G.: Swarm Intelligence: From Natural to Artificial Systems. Oxford University Press (1999)Google Scholar
  3. 3.
    Bratton, D., Kennedy: Defining a standard for particle swarm optimization. Elsevier Publishing (2007)Google Scholar
  4. 4.
    Chattopadhyay, R.: A study of test functions for optimization algorithms. J. Opt. Theory Appl. 3, 231–236 (1971)MathSciNetCrossRefGoogle Scholar
  5. 5.
    Colorni, A., Dorigo, M., Maniezzo, M.: Distributed Optimization by Ant Colonies, pp. 134–142. Elsevier Publishing (1991)Google Scholar
  6. 6.
    Gardner, M.: Mathematical Games - The fantastic combinations of John Conway’s new solitaire game “life”, 120–123 (1970) (archived from the original on June 3, 2009)Google Scholar
  7. 7.
    Simpson, G.: Penguins: Past and Present, Here and There. Yale University Press (1976)Google Scholar
  8. 8.
    Green, K., Williams, R., Green, M.G.: Foraging ecology and diving behavior of Macaroni Penguins (Eudypteschrysolophus) at Heard Island. Arine Ornithology 26, 27–34 (1998)Google Scholar
  9. 9.
    Hanuise, N., Bost, C.A., Huin, W., Auber, A., Halsey, L.G., Handrich, Y.: Measuring foraging activity in a deep-diving bird: comparing wiggles, oesophageal temperatures and beak-opening angles as proxies of feeding. The Journal of Experimental Biology 213, 3874–3880 (2010)CrossRefGoogle Scholar
  10. 10.
    Holland, J.H.: Adaptation in Natural and Artificial Systems. University of Michigan Press, Ann Arbor (1975)Google Scholar
  11. 11.
    Houston, A., McNamara, J.M.: A general theory of central place foraging for single-prey loaders. Theoretical Population Biology 28, 233–262 (1985)MathSciNetzbMATHCrossRefGoogle Scholar
  12. 12.
    Jason, B: Clever Algorithms Nature-Inspired Programming Recipes. Lulu Enterprises (January 2011)Google Scholar
  13. 13.
    Liu, Y., Passino, K.: Biomimicry of social foraging bacteria for distributed optimization: Models, principles, and emergent behaviors. Journal of Optimization Theory and Applications 115(3), 603–628 (2002)MathSciNetzbMATHCrossRefGoogle Scholar
  14. 14.
    Rechenberg, I.: Cybernetic Solution Path of an Experimental Problem, Royal Aircraft. Establishment Library Translation (1965)Google Scholar
  15. 15.
    MacArthur, R.H., Pianka, E.: On optimal use of a patchy environment. The American Naturalist 100, 603–609 (1966)CrossRefGoogle Scholar
  16. 16.
    Mori, Y.: Optimal diving behavior for foraging in relation to body size. The American Naturalist 15, 269–276 (2002)Google Scholar
  17. 17.
    Robbins, H., Monro, S.: A Stochastic Approximation Method. Annals of Mathematical Statistics 22, 400–407 (1951)MathSciNetzbMATHCrossRefGoogle Scholar
  18. 18.
    Scilab Consortium (DIGITEO). SCILAB 5.3.2 (2010)Google Scholar
  19. 19.
    Schoen, F.: A wide class of test functions for global optimization. Global Optimization Journal 3, 133–137 (1993)MathSciNetzbMATHCrossRefGoogle Scholar
  20. 20.
    Shang, Y.W., Qiu, Y.H.: A note on the extended rosenrbock function. Evolutionary Computation 14, 119–126 (2006)CrossRefGoogle Scholar
  21. 21.
    Takahashi, A., Sato, K., Nishikawa, J., Watanuki, Y., Naito, Y.: Synchronous diving behavior of Adelie penguins. Journal of Ethology 22, 5–11 (2004)CrossRefGoogle Scholar
  22. 22.
    Yang, X.S.: Nature-Inspired Metaheuristic Algorithms. Luniver Press (2008)Google Scholar
  23. 23.
    Yang, X.S., Deb, S.: Cuckoo search via Levy flight, vol. 9, pp. 210–214. IEEE Publications (2009)Google Scholar
  24. 24.
    Yang, X.S.: Biology-derived algorithms in engineering optimization. In: Handbook of Bioinspired Algorithms and Applications, pp. 589–600 (2005)Google Scholar
  25. 25.
    Yang, X.S.: Bat algorithm for multi-objective optimization. IJBIC 5, 267–274 (2011)zbMATHGoogle Scholar
  26. 26.
    Yann, T., Yves, C.: Synchronous underwater foraging behavior in penguins. Cooper Ornithological Soc. 101, 179–185 (2005)Google Scholar
  27. 27.
    Yang, X.S.: Engineering Optimization: An Introduction with Metaheuristic Applications. Wiley (2010)Google Scholar
  28. 28.
    Wayen, L.: Penguins of the World. Firefly Books (October 1, 1997)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  • Youcef Gheraibia
    • 1
  • Abdelouahab Moussaoui
    • 2
  1. 1.Computing DepartmentMohamed Cherif Messadia UnivercitySouk AhrasAlgeria
  2. 2.Computing DepartmentFerhat Abbas UnivercitySetifAlgeria

Personalised recommendations