Advertisement

Seed Disperser Ant Algorithm: An Evolutionary Approach for Optimization

  • Wen Liang Chang
  • Jeevan KanesanEmail author
  • Anand Jayant Kulkarni
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9028)

Abstract

The Seed Disperser Ant Algorithm (SDAA) is inspired from the evolution of Seed Disperser Ant (Aphaenogaster senilis) colony. The ants in the colony are highly related siblings sharing average 75 % similarity in genotype. Hence, the genotype of every ant represents variables in binary form that are used to locally search for optimum solution. Once the colony matures, in other words a local optimum solution reached, nuptial flights take place where female genotype copies the male genotype originating from another colony. Once all colonies saturate new young queen emerges to establish new colonies. This diversifies the search for global optimum. The SDAA is validated by solving four 30 dimensional classical benchmark problems and six composite benchmark functions from CEC 2005 special session. The optimal results are found to be better than the selected state-of-the-art swarm intelligence based optimization.

Keywords

Seed disperser ant algorithm Evolutionary computation Optimization 

Notes

Acknowledgement

This work is supported by ER011-2013A, Ministry of Science, Technology and Innovation, Malaysia (MOSTI).

References

  1. 1.
    Eberhart, R.C., Kennedy, J.: A new optimizer using particle swarm theory. In: Proceedings of the sixth international symposium on micro machine and human science, New York, NY (1995)Google Scholar
  2. 2.
    Yang, X.-S., Deb, S.: Cuckoo search via Lévy flights. In: World Congress on Nature & Biologically Inspired Computing, 2009, NaBIC 2009. IEEE (2009)Google Scholar
  3. 3.
    Mirjalili, S., Mirjalili, S.M., Lewis, A.: Grey wolf optimizer. Adv. Eng. Softw. 69, 46–61 (2014)CrossRefGoogle Scholar
  4. 4.
    Miranda, V., Fonseca, N.: EPSO-evolutionary particle swarm optimization, a new algorithm with applications in power systems. In: Proceedings of the Asia Pacific IEEE/PES Transmission and Distribution Conference and Exhibition. Citeseer (2002)Google Scholar
  5. 5.
    Lee, T.-Y., Chen, C.-L.: Unit commitment with probabilistic reserve: An IPSO approach. Energy Convers. Manag. 48(2), 486–493 (2007)CrossRefGoogle Scholar
  6. 6.
    Jamian, J.J., et al.: Global particle swarm optimization for high dimension numerical functions analysis. J. Appl. Math. 2014, 14 (2014)CrossRefMathSciNetGoogle Scholar
  7. 7.
    Liu, L., Zhong, W.-M., Qian, F.: An improved chaos-particle swarm optimization algorithm. J. East China Univ. Sci. Technol. 36(2), 267–272 (2010)Google Scholar
  8. 8.
    Rashedi, E., Nezamabadi-Pour, H., Saryazdi, S.: GSA: a gravitational search algorithm. Inf. Sci. 179(13), 2232–2248 (2009)CrossRefzbMATHGoogle Scholar
  9. 9.
    Kulkarni, A.J., Durugkar, I.P., Kumar, M.: Cohort intelligence: A self supervised learning behavior. In: Systems, 2013 IEEE International Conference on Man, and Cybernetics (SMC). IEEE (2013)Google Scholar
  10. 10.
    Mitchell, M.: An Introduction to Genetic Algorithms. MIT press, Cambridge (1998)zbMATHGoogle Scholar
  11. 11.
    Goldberg, D.E., et al.: Genetic algorithms: A bibliography. Urbana 51, 61801 (1997)Google Scholar
  12. 12.
    Moscato, P.: On evolution, search, optimization, genetic algorithms and martial arts: Towards memetic algorithms. Caltech concurrent computation program, C3P Report, Vol. 826, p. 1989 (1989)Google Scholar
  13. 13.
    Moscato, P., Cotta, C., Mendes, A.: Memetic algorithms. In: New Optimization Techniques in Engineering, pp. 53–85. Springer, New York (2004)Google Scholar
  14. 14.
    Storn, R., Price, K.: Differential evolution–a simple and efficient heuristic for global optimization over continuous spaces. J. Global Optim. 11(4), 341–359 (1997)CrossRefzbMATHMathSciNetGoogle Scholar
  15. 15.
    Qin, A.K., Huang, V.L., Suganthan, P.N.: Differential evolution algorithm with strategy adaptation for global numerical optimization. IEEE Trans. Evol. Comput. 13(2), 398–417 (2009)CrossRefGoogle Scholar
  16. 16.
    Auger, A., Hansen, N.: A restart CMA evolution strategy with increasing population size. In: The 2005 IEEE Congress on Evolutionary Computation, 2005. IEEE (2005)Google Scholar
  17. 17.
    Cheron, B., et al.: Queen replacement in the monogynous ant Aphaenogaster senilis: supernumerary queens as life insurance. Anim. Behav. 78(6), 1317–1325 (2009)CrossRefGoogle Scholar
  18. 18.
    Ashton, M.C., et al.: Kin altruism, reciprocal altruism, and the Big Five personality factors. Evol. Hum. Behav. 19(4), 243–255 (1998)CrossRefGoogle Scholar
  19. 19.
    Osiński, J.: Kin altruism, reciprocal altruism and social discounting. Personality Individ. Differ. 47(4), 374–378 (2009)CrossRefGoogle Scholar
  20. 20.
    Kenne, M., Dejean, A.: Nuptial flight of myrmicaria opaciventris. Sociobiology 31(1), 41–50 (1998)Google Scholar
  21. 21.
    Queller, D.C., Strassmann, J.E.: Kin selection and social insects. Bioscience 48, 165–175 (1998)CrossRefGoogle Scholar
  22. 22.
    Adorio, E.P., Diliman, U.: Mvf-multivariate test functions library in c for unconstrained global optimization. Technical report, Department of Mathematics, UP Diliman (2005)Google Scholar
  23. 23.
    Molga, M., Smutnicki, C.: Test functions for optimization needs (2005). http://eccsia013.googlecode.com/svn/trunk/Ecc1/functions_benchmark.pdf
  24. 24.
    Shang, Y.-W., Qiu, Y.-H.: A note on the extended Rosenbrock function. Evol. Comput. 14(1), 119–126 (2006)CrossRefGoogle Scholar
  25. 25.
    Kulkarni, A.J., Tai, K.: Probability collectives: a decentralized, distributed optimization for multi-agent systems. In: Mehnen, J., Köppen, M., Saad, A., Tiwari, A. (eds.) Applications of Soft Computing. ASC, vol. 58, pp. 441–450. Springer, Heidelberg (2009)CrossRefGoogle Scholar
  26. 26.
    Xu, W., et al.: A piecewise linear chaotic map and sequential quadratic programming based robust hybrid particle swarm optimization. Inf. Sci. 218, 85–102 (2013)CrossRefzbMATHGoogle Scholar
  27. 27.
    Shi, Y., Eberhart, R.C.: Empirical study of particle swarm optimization. In: Proceedings of the 1999 Congress on Evolutionary Computation, 1999, CEC 1999. IEEE (1999)Google Scholar
  28. 28.
    Liang, J., Suganthan, P., Deb, K.: Novel composition test functions for numerical global optimization. In: Swarm Intelligence Symposium, 2005, SIS 2005, Proceedings 2005 IEEE. IEEE (2005)Google Scholar

Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

  • Wen Liang Chang
    • 1
  • Jeevan Kanesan
    • 1
    Email author
  • Anand Jayant Kulkarni
    • 2
  1. 1.Nature Inspired Meta-heuristic Group, Department of Electrical Engineering, Faculty of EngineeringUniversity MalayaKuala LumpurMalaysia
  2. 2.Department of Mechanical Engineering, Symbiosis Institute of TechnologySymbiosis International UniversityPuneIndia

Personalised recommendations