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Self-organizing Migrating Algorithm with Non-binary Perturbation

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

Abstract

The self-organizing migrating algorithm (SOMA) is a popular population base metaheuristic. One of its key mechanisms is a perturbation of the individual movement with a binary-valued perturbation (PRT) vector. The goal of perturbation is to improve the diversity of the population and exploration of the search space. In this paper, we study a variant of the SOMA algorithm with non-binary PRT vector. We investigate the effect of introducing a third possible value, a negative (repulsive) element, into the PRT vector. The aim is to slow the population convergence and prolong the exploration phase. The inspiration is taken from previous successful implementations of repulsive mechanics in another swarm-based method: the Particle Swarm Optimization.

Keywords

Self-organizing migrating algorithm SOMA Repulsivity Perturbation 

Notes

Acknowledgements

This work was 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 and by Internal Grant Agency of Tomas Bata University under the Projects no. IGA/CebiaTech/2019/002. This work is also based upon support by COST (European Cooperation in Science & Technology) under Action CA15140, Improving Applicability of Nature-Inspired Optimisation by Joining Theory and Practice (ImAppNIO), and Action IC1406, High-Performance Modelling, and Simulation for Big Data Applications (cHiPSet). The work was further supported by resources of A.I.Lab at the Faculty of Applied Informatics, Tomas Bata University in Zlin (ailab.fai.utb.cz).

References

  1. 1.
    Eberhart, R., Kennedy, J.: Swarm Intelligence. The Morgan Kaufmann Series in Artificial Intelligence. Morgan Kaufmann, Burlington (2001)Google Scholar
  2. 2.
    Kennedy, J., Eberhart, R.: Particle swarm optimization. In: Proceedings of IEEE International Conference on Neural Networks, IV, pp. 1942–1948 (1995)Google Scholar
  3. 3.
    Dorigo, M.: Ant Colony Optimization and Swarm Intelligence. Springer, Heidelberg (2006).  https://doi.org/10.1007/11839088CrossRefGoogle Scholar
  4. 4.
    Parpinelli, R.S., Lopes, H.S.: New inspirations in swarm intelligence: a survey. Int. J. Bio-Inspired Comput. 3(1), 1–16 (2011)CrossRefGoogle Scholar
  5. 5.
    Zelinka, I., Jouni, L.: SOMA self-organizing migrating algorithm mendel. In: 6th International Conference on Soft Computing, Brno, Czech Republic (2000)Google Scholar
  6. 6.
    Zelinka, I.: SOMA - self organizing migrating algorithm. In: Babu, B.V., Onwubolu, G. (eds.) New Optimization Techniques in Engineering, vol. 33. Springer, Heidelberg (2004).  https://doi.org/10.1007/978-3-540-39930-8_7. ISBN 3-540-20167XCrossRefGoogle Scholar
  7. 7.
    Davendra, D., Zelinka, I.: Self-organizing Migrating Algorithm. Springer, Heidelberg (2016).  https://doi.org/10.1007/978-3-319-28161-2CrossRefzbMATHGoogle Scholar
  8. 8.
    Nolle, L., et al.: Comparison of an self-organizing migration algorithm with simulated annealing and differential evolution for automated waveform tuning. Adv. Eng. Softw. 36(10), 645–653 (2005)CrossRefGoogle Scholar
  9. 9.
    Davendra, D., et al.: Discrete self-organising migrating algorithm for flow-shop scheduling with no-wait makespan. Math. Comput. Model. 57(1–2), 100–110 (2013)MathSciNetCrossRefGoogle Scholar
  10. 10.
    Kadlec, P., Raida, Z.: A novel multi-objective self-organizing migrating algorithm. Radioengineering 20(4), 804–816 (2011)Google Scholar
  11. 11.
    Deep, K., Dipti: A self-organizing migrating genetic algorithm for constrained optimization. Appl. Math. Comput. 198(1), 237–250 (2008)MathSciNetCrossRefGoogle Scholar
  12. 12.
    Deep, K., et al.: A new hybrid self organizing migrating genetic algorithm for function optimization. In: IEEE Congress on Evolutionary Computation, CEC 2007, pp. 2796–2803. IEEE (2007)Google Scholar
  13. 13.
    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
  14. 14.
    Sharma, S.K., Jain, Y.K.: Self organizing migration algorithm with curvelet based non local means method for the removal of different types of noise. Int. J. Comput. Sci. Inf. Secur. (IJCSIS) 16(4), 320–330 (2018)Google Scholar
  15. 15.
    Fusek, R., Dobeš, P.: Pupil localization using self-organizing migrating algorithm. In: Zelinka, I., Brandstetter, P., Trong Dao, T., Hoang Duy, V., Kim, S.B. (eds.) AETA 2018. LNEE, vol. 554, pp. 207–216. Springer, Cham (2020).  https://doi.org/10.1007/978-3-030-14907-9_21CrossRefGoogle Scholar
  16. 16.
    Tomaszek, L., Lycka, P., Zelinka, I.: On the self-organizing migrating algorithm comparison by means of centrality measures. In: Zelinka, I., Brandstetter, P., Trong Dao, T., Hoang Duy, V., Kim, S.B. (eds.) AETA 2018. LNEE, vol. 554, pp. 335–343. Springer, Cham (2020).  https://doi.org/10.1007/978-3-030-14907-9_33CrossRefGoogle Scholar
  17. 17.
    Riget, J., Vesterstrøm, J.S.: A diversity-guided particle swarm optimizer - the ARPSO. Technical report 2, Department of Computer Science, University of Aarhus, Aarhus, Denmark (2002)Google Scholar
  18. 18.
    Pluhacek, M., Senkerik, R., Viktorin, A., Kadavy, T.: Particle swarm optimization with single particle repulsivity for multi-modal optimization. In: Rutkowski, L., Scherer, R., Korytkowski, M., Pedrycz, W., Tadeusiewicz, R., Zurada, J.M. (eds.) ICAISC 2018. LNCS (LNAI), vol. 10841, pp. 486–494. Springer, Cham (2018).  https://doi.org/10.1007/978-3-319-91253-0_45CrossRefGoogle Scholar
  19. 19.
    Dieterich, J.M., Hartke, B.: Empirical review of standard benchmark functions using evolutionary global optimization. arXiv preprint arXiv:1207.4318 (2012)

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© Springer Nature Switzerland AG 2020

Authors and Affiliations

  1. 1.Faculty of Applied InformaticsTomas Bata University in ZlinZlinCzech Republic

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