Advertisement

SOMA T3A for Solving the 100-Digit Challenge

Conference paper
  • 210 Downloads
Part of the Communications in Computer and Information Science book series (CCIS, volume 1092)

Abstract

In this paper, we address 10 basic test functions of the 100-Digit Challenge of the SEMCCO 2019 & FANCCO 2019 Competition by using team-to-team adaptive seft-organizing migrating algorithm - SOMA T3A with many improvements in the Organization, Migration, and Update process, as well as the linear adaptive PRT and the cosine-based adaptive Step. The results obtained from the algorithm on the 100-Digit Challenge are very promising with 93 points in total.

Keywords

Self-organizing migrating algorithm Optimization function Swarm intelligence SOMA T3A 

Notes

Acknowledgment

The following grants are acknowledged for the financial support provided for this research: Grant of SGS No. SP2019/137, VSB - Technical University of Ostrava. This work was also supported by the Ministry of Education, Youth and Sports of the Czech Republic within the National Sustainability Programme Project no. LO1303 (MSMT-7778/2014), further by the European Regional Development Fund under the Project CEBIA-Tech no. CZ.1.05/2.1.00/03.0089.

References

  1. 1.
    Agrawal, S., Singh, D.: Modified Nelder-Mead self organizing migrating algorithm for function optimization and its application. Appl. Soft Comput. 51, 341–350 (2017)CrossRefGoogle Scholar
  2. 2.
    Bao, D.Q., Zelinka, I.: Obstacle avoidance for Swarm robot based on self-organizing migrating algorithm. Procedia Comput. Sci. 150, 425–432 (2019)CrossRefGoogle Scholar
  3. 3.
    Davendra, D., Zelinka, I., Senkerik, R., Pluhacek, M.: Complex network analysis of discrete self-organising migrating algorithm. In: Zelinka, I., Suganthan, P.N., Chen, G., Snasel, V., Abraham, A., Rössler, O. (eds.) Nostradamus 2014: Prediction, Modeling and Analysis of Complex Systems. AISC, vol. 289, pp. 161–174. Springer, Cham (2014).  https://doi.org/10.1007/978-3-319-07401-6_16CrossRefzbMATHGoogle Scholar
  4. 4.
    Deep, K.: Dipti: a self-organizing migrating genetic algorithm for constrained optimization. Appl. Math. Comput. 198(1), 237–250 (2008)MathSciNetzbMATHGoogle Scholar
  5. 5.
    Deep, K., et al.: A new hybrid self organizing migrating genetic algorithm for function optimization. In: IEEE Congress on Evolutionary Computation 2007, CEC 2007, pp. 2796–2803. IEEE (2007)Google Scholar
  6. 6.
    Diep, Q.B.: Self-organizing migrating algorithm team to team adaptive-SOMA T3A. In: 2019 IEEE Congress on Evolutionary Computation (CEC), pp. 1182–1187. IEEE (2019)Google Scholar
  7. 7.
    Diep, Q.B., Zelinka, I., Das, S.: Self-organizing migrating algorithm for the 100-digit challenge. In: Proceedings of the Genetic and Evolutionary Computation Conference 2019 (GECCO 2019). ACM, New York (2019)Google Scholar
  8. 8.
    Diep, Q.B., Zelinka, I., Das, S.: Self-organizing migrating algorithm pareto. In: MENDEL, vol. 25, pp. 111–120 (2019)CrossRefGoogle Scholar
  9. 9.
    Diep, Q.B., Zelinka, I., Senkerik, R.: An algorithm for swarm robot to avoid multiple dynamic obstacles and to catch the moving target. In: Rutkowski, L., Scherer, R., Korytkowski, M., Pedrycz, W., Tadeusiewicz, R., Zurada, J.M. (eds.) ICAISC 2019. LNCS (LNAI), vol. 11509, pp. 666–675. Springer, Cham (2019).  https://doi.org/10.1007/978-3-030-20915-5_59CrossRefGoogle Scholar
  10. 10.
    Zelinka, I.: SOMA—self-organizing migrating algorithm. In: Davendra, D., Zelinka, I. (eds.) Self-Organizing Migrating Algorithm. SCI, vol. 626, pp. 3–49. Springer, Cham (2016).  https://doi.org/10.1007/978-3-319-28161-2_1CrossRefzbMATHGoogle Scholar
  11. 11.
    Lin, Z., Juan Wang, L.: Hybrid self-organizing migrating algorithm based on estimation of distribution. In: 2014 International Conference on Mechatronics, Electronic, Industrial and Control Engineering (MEIC-14). Atlantis Press (2014)Google Scholar
  12. 12.
    Mohamed, A.W.: Solving large-scale global optimization problems using enhanced adaptive differential evolution algorithm. Complex Intell. Syst. 3(4), 205–231 (2017)CrossRefGoogle Scholar
  13. 13.
    Pospíšilík, M., Kouřil, L., Motỳl, I., Adámek, M.: Single and double layer spiral planar inductors optimisation with the aid of self-organising migrating algorithm. In: Proceedings of the 11th WSEAS International Conference on Signal Processing, Computational Geometry and Artificial Vision, pp. 272–277. WSEAS Press (IT), Venice (2011)Google Scholar
  14. 14.
    Price, K.V., Awad, N.H., Ali, M.Z., Suganthan, P.N.: Problem definitions and evaluation criteria for the 100-digit challenge special session and competition on single objective numerical optimization. In: Technical report, Nanyang Technological University, Singapore, November 2018Google Scholar
  15. 15.
    dos Santos Coelho, L., Alotto, P.: Electromagnetic optimization using a cultural self-organizing migrating algorithm approach based on normative knowledge. IEEE Trans. Magn. 45(3), 1446–1449 (2009)CrossRefGoogle Scholar
  16. 16.
    dos Santos Coelho, L., Mariani, V.C.: An efficient cultural self-organizing migrating strategy for economic dispatch optimization with valve-point effect. Energy Convers. Manag. 51(12), 2580–2587 (2010)CrossRefGoogle Scholar
  17. 17.
    Singh, D., Agrawal, S.: Hybridization of self organizing migrating algorithm with quadratic approximation and non uniform mutation for function optimization. In: Das, K.N., Deep, K., Pant, M., Bansal, J.C., Nagar, A. (eds.) Proceedings of Fourth International Conference on Soft Computing for Problem Solving. AISC, vol. 335, pp. 373–387. Springer, New Delhi (2015).  https://doi.org/10.1007/978-81-322-2217-0_32CrossRefGoogle Scholar
  18. 18.
    Singh, D., Agrawal, S.: Self organizing migrating algorithm with quadratic interpolation for solving large scale global optimization problems. Appl. Soft Comput. 38, 1040–1048 (2016)CrossRefGoogle Scholar
  19. 19.
    Zelinka, I.: SOMA-self-organizing migrating algorithm. In: Onwubolu, G.C., Babu, B.V. (eds.) New Optimization Techniques in Engineering, pp. 167–217. Springer, Heidelberg (2004).  https://doi.org/10.1007/978-3-540-39930-8_7CrossRefGoogle Scholar
  20. 20.
    Zelinka, I., Jouni, L.: SOMA-self-organizing migrating algorithm mendel. In: 6th International Conference on Soft Computing, Brno, Czech Republic (2000)Google Scholar

Copyright information

© Springer Nature Switzerland AG 2020

Authors and Affiliations

  1. 1.Faculty of Electrical Engineering and Computer ScienceVSB - Technical University of OstravaOstrava-PorubaCzech Republic
  2. 2.Electronics and Communication Sciences UnitIndian Statistical InstituteKolkataIndia
  3. 3.Faculty of Applied InformaticsTomas Bata University in ZlinZlinCzech Republic

Personalised recommendations