Using Complex Network Visualization and Analysis for Uncovering the Inner Dynamics of PSO Algorithm

  • Michal Pluhacek
  • Roman Senkerik
  • Adam Viktorin
  • Tomas Kadavy
  • Ivan Zelinka
Keywords: Swarm Intelligence, Particle Swarm Optimization, Complex Network, Metaheuristic

Abstract

In this study, we construct a complex network from the inner dynamic of Particle Swarm Optimization algorithm. The subsequent analysis of the network promises to provide useful information for better understanding the dynamic of the swarm that is not acquirable by other means. We present several network visualizations and numerical analysis. We discuss the observations and propose further directions for the research.

References

Kennedy J. and Eberhart R., “Particle swarm optimization,” in Proceedings of the IEEE International Conference on Neural Networks, 1995, pp. 1942–1948.

Shi Y. and Eberhart R., “A modified particle swarm optimizer,” in Proceedings of the IEEE International Conference on Evolutionary Computation (IEEE World Congress on Computational Intelligence), 1998, pp. 69–73.I. S.

Kennedy J., “The particle swarm: social adaptation of knowledge,” in Proceedings of the IEEE International Conference on Evolutionary Computation, 1997, pp. 303–308.¨

Nickabadi A., Ebadzadeh M. M., Safabakhsh R., A novel particle swarm optimization algorithm with adaptive inertia weight, Applied Soft Computing, Volume 11, Issue 4, June 2011, Pages 3658-3670, ISSN 1568-4946.

van den Bergh, F., Engelbrecht, A.P.: A study of particle swarm optimization particle trajectories. Information Sciences 176(8), 937-971 (2006).

Cleghorn, C. W., Engelbrecht, A. P. (2015). Particle swarm variants: standardized convergence analysis. Swarm Intelligence, 9(2-3), 177-203.

Zelinka, I., Davendra, D., Senkerik, R., Jasek, R.: Do Evolutionary Algorithm Dynamics Create Complex Network Structures? Complex Systems 2, 0891–2513, 20, 127–140

Zelinka, I. Investigation on relationship between complex network and evolutionary algorithms dynamics, AIP Conference Proceedings 1389 (1) 1011–1014 (2011).

Zelinka, I., Davendra, D.D., Chadli, M., Senkerik, R., Dao, T.T., Skanderova, L.: Evolutionary Dynamics as The Structure of Complex Networks. In: Zelinka, I.,Snasel, V., Abraham, A. (eds.) Handbook of Optimization. ISRL, vol. 38, pp. 215–243. Springer, Heidelberg (2013)

Davendra, D., Zelinka, I., Senkerik, R. and Pluhacek, M. Complex Network Analysis of Discrete Self-organising Migrating Algorithm, in: Zelinka, I. and Suganthan, P. and Chen, G. and Snasel, V. and Abraham, A. and Rossler, O. (Eds.) Nostradamus 2014: Prediction, Modeling and Analysis of Complex Systems, Advances in Intelligent Systems and Computing, Springer Berlin Heidelberg, pp. 161–174 (2014a).

Davendra, D., Zelinka, I, Metlicka, M., Senkerik, R., Pluhacek, M., "Complex network analysis of differential evolution algorithm applied to flowshop with no-wait problem," Differential Evolution (SDE), 2014 IEEE Symposium on , vol., no., pp.1,8, 9-12 Dec. (2014b)

Digalakis, J. G., Margaritis, K. G. "On benchmarking functions for genetic algorithms." International journal of computer mathematics 77.4 (2001): 481-506.

Dieterich, J.M., Hartke B.. "Empirical review of standard benchmark functions using evolutionary global optimization." arXiv preprint arXiv:1207.4318 (2012).

Wolfram Research, Inc., Mathematica, Version 11.1, Champaign, IL (2017).

Published
2017-06-01
How to Cite
[1]
Pluhacek, M., Senkerik, R., Viktorin, A., Kadavy, T. and Zelinka, I. 2017. Using Complex Network Visualization and Analysis for Uncovering the Inner Dynamics of PSO Algorithm. MENDEL. 23, 1 (Jun. 2017), 87-94. DOI:https://doi.org/10.13164/mendel.2017.1.087.
Section
Articles