Abstract
This paper is an introduction to a novel method for visualizing the dynamics of evolutionary algorithms in the form of networks. The whole idea is based on the obvious similarity between interactions between individuals in a swarm and evolutionary algorithms and for example, users of social networks, linking between web pages, etc.
In this paper, two completely different areas of research are merged: (complex) networks and evolutionary computation. As already mentioned, interactions among the individuals in a swarm and evolutionary algorithms can be considered like user interactions in social networks or just people in society. This induces hypothesis whether interactions inside of EAs can be taken like interactions in society or swarm colonies.
The analogy between individuals in populations in an arbitrary evolutionary algorithm and vertices of a network is discussed, as well as between edges in a network and communication between individuals in a population.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Skanderova, L., Fabian, T., Zelinka, I. (2016). Small-world hidden in differential evolution. In Evolutionary Computation (CEC), 2016 IEEE Congress on (pp. 3354–3361).
Turing, A.: Intelligent machinery, unpublished report for National Physical Laboratory. In: Michie, D. (ed.) Machine Intelligence, vol. 7 (1969); Turing, A.M. (ed.): The Collected Works, vol. 3, Ince D. North-Holland, Amsterdam (1992).
Holland, J.: Adaptation in natural and artificial systems. Univ. of Michigan Press, Ann Arbor (1975).
Schwefel, H.: Numerische Optimierung von Computer-Modellen, PhD thesis (1974); Reprinted by Birkhauser (1977).
Rechenberg, I.: (1971) Evolutionsstrategie – Optimierung technischer Systeme nach Prinzipien der biologischen Evolution (PhD thesis), Printed in Fromman-Holzboog (1973).
Fogel, D.B.:Unearthinga Fossil from the History of Evolutionary Computation. Fundamenta Informaticae 35(1–4), 1‚Äì16 (1998).
Qin AK, Huang VL, Suganthan PN (2009) Differential Evolution Algorithm With Strategy Adaptation for Global Numerical Optimization. Evolutionary Computation, IEEE Transactions on 13 (2):398–417.
Mallipeddi R, Suganthan PN, Pan QK, Tasgetiren MF (2011) Differential evolution algorithm with ensemble of parameters and mutation strategies. Applied Soft Computing 11 (2):1679–1696.
Das S., Mullick S.S., Suganthan P. (2016) Recentadvancesindifferential evolution – An updated survey, Swarm and Evolutionary Computation, vol. 27, pp. 1–30.
Jabeen H, Jalil Z, Baig AR (2009) Opposition based initialization in particle swarm optimization (O-PSO). Paper presented at the Proceedings of the 11th Annual Conference Companion on Genetic and Evolutionary Computation Conference.
Engelbrecht A (2010) Heterogeneous Particle Swarm Optimization. In: Dorigo M, Birattari M, Di Caro G et al. (eds) Swarm Intelligence, vol 6234. Lecture Notes in Computer Science. Springer Berlin Heidelberg, pp 191–202.
Zelinka, I. (2016). SOMA—Self-organizing Migrating Algorithm. In Self-Organizing Migrating Algorithm (pp. 3–49). Springer International Publishing.
Zelinka, I. (2004). SOMA—self-organizing migrating algorithm. In New optimization techniques in engineering (pp. 167–217). Springer Berlin Heidelberg.
D. Karaboga, B. Basturk A powerful and efficient algorithm for numerical function optimization: artificial bee colony (ABC) algorithm, Journal of Global Optimization, 39 (3) (2007), pp. 459–471.
Yang, X.S.: Nature-inspired metaheuristic algorithms. Luniver Press, Frome, U.K. (2010).
Fister I., Fister I. Jr., Yang X.S, Brest J., A comprehensive review of firefly algorithms, Swarm and Evolutionary Computation, Volume 13, 2013, Pages 34–46.
Zelinka I, Davendra D, Lampinen J, Senkerik R, Pluhacek M Evolutionary algorithms dynamics and its hidden complex network structures. In: Evolutionary Computation (CEC), 2014 IEEE Congress on, 2014, pp 3246–3251.
Davendra D, Zelinka I, Metlicka M, Senkerik R, Pluhacek M Complex network analysis of differential evolution algorithm applied to flowshop with no-wait problem. In: Differential Evolution (SDE), 2014 IEEE Symposium on, 2014a, pp 1–8.
Davendra D, Zelinka I, Senkerik R, Pluhacek M (2014b) Complex Network Analysis of Evolutionary Algorithms Applied to Combinatorial Optimisation Problem. In: Kömer P, Abraham A, Snášel V (eds) Proceedings of the Fifth International Conference on Innovations in Bio-Inspired Computing and Applications IBICA 2014. Springer International Publishing, pp 141–150.
Skanderova L, Fabian T (2015) Differential evolution dynamics analysis by complex networks. Soft Computing:1–15.
Metlicka M, Davendra D Ensemble centralities based adaptive Artificial Bee algorithm. In: Evolutionary Computation (CEC), 2015 IEEE Congress on, 2015. pp 3370–3376.
Gajdos P, Kromer P, Zelinka I Network Visualization of Population Dynamics in the Differential Evolution. In: Computational Intelligence, 2015 IEEE Symposium Series on, 2015. pp 1522–1528.
Janostik J, Pluhacek M, Senkerik R, Zelinka I (2016a) Particle Swarm Optimizer with Diversity Measure Based on Swarm Representation in Complex Network. In: Abraham A, Wegrzyn-Wolska K, Hassanien EA, Snasel V, Alimi MA (eds) Proceedings of the Second International Afro-European Conference for Industrial Advancement AECIA 2015. Springer International Publishing, Cham, pp 561–569.
Skanderova, L., Fabian, T., & Zelinka, I. (2017). Differential Evolution Dynamics Modeled by Longitudinal Social Network. Journal of Intelligent Systems, 26(3), 523–529.
Viktorin, A., Pluhacek, M., & Senkerik, R. (2016). Network based linear population size reduction in SHADE. In Intelligent Networking and Collaborative Systems (INCoS), 2016 International Conference on (pp. 86–93).
Senkerik, R., Viktorin, A., Pluhacek, M., Janostik, J., & Davendra, D. (2016). On the influence of different randomization and complex network analysis for differential evolution. In Evolutionary Computation (CEC), 2016 IEEE Congress on (pp. 3346–3353).
Viktorin, A., Senkerik, R., Pluhacek, M., & Kadavy, T. (2017). Towards better population sizing for differential evolution through active population analysis with complex network. In Conference on Complex, Intelligent, and Software Intensive Systems (pp. 225–235).
Pluhacek, M., Janostik, J., Senkerik, R., Zelinka, I., & Davendra, D. (2016a). PSO as complex network—capturing the inner dynamics—initial study. In Proceedings of the Second International Afro-European Conference for Industrial Advancement AECIA 2015 (pp. 551–559).
Pluhacek, M., Šenkeřík, R., Viktorin, A., & Kadavý, T. (2017a). Uncovering communication density in PSO using complex network. In Proceedings-31st European Conference on Modelling and Simulation, ECMS 2017. European Council for Modelling and Simulation.
Pluhacek, M., Viktorin, A., Senkerik, R., Kadavy, T., & Zelinka, I. (2017b). PSO with Partial Population Restart Based on Complex Network Analysis. In International Conference on Hybrid Artificial Intelligence Systems (pp. 183–192).
Pluhacek, M., Senkerik, R., Janostik, A. V. J., & Davendra, D. (2016b). Complex network analysis in PSO as an fitness landscape classifier. In Evolutionary Computation (CEC), 2016 IEEE Congress on (pp. 3332–3337).
Janostik, J., Pluhacek, M., Senkerik, R., Zelinka, I., & Spacek, F. (2016b). Capturing inner dynamics of firefly algorithm in complex network—initial study. In Proceedings of the Second International Afro-European Conference for Industrial Advancement AECIA 2015 (pp. 571–577).
Tomaszek, L., & Zelinka, I. (2016). On performance improvement of the SOMA swarm based algorithm and its complex network duality. In Evolutionary Computation (CEC), 2016 IEEE Congress on (pp. 4494–4500).
Krömer, P., Gajdo, P., & Zelinka, I. (2015). Towards a Network Interpretation of Agent Interaction in Ant Colony Optimization. In Computational Intelligence, 2015 IEEE Symposium Series on (pp. 1126–1132).
Skanderova, L., Zelinka, I., & Saloun, P. (2014). Complex Network Construction Based on SOMA: Vertices In-Degree Reliance on Fitness Value Evolution. In ISCS 2013: Interdisciplinary Symposium on Complex Systems (pp. 291–297). Springer Berlin Heidelberg.
Acknowledgments
This work was supported by Grant Agency of the Czech Republic, GACR P103/15/06700S, further by the financial support of research project NPU I No. MSMT-7778/2014 by the Ministry of Education of the Czech Republic and also by the European Regional Development Fund under the Project CEBIA-Tech No. CZ.1.05/2.1.00/03.0089, partially supported by Grant SGS 2017/134 of VSB-Technical University of Ostrava and by The Ministry of Education, Youth and Sports from the National Programme of Sustainability (NPU II) project “IT4Innovations excellence in science – LQ1602”.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2019 Springer Nature Switzerland AG
About this paper
Cite this paper
Zelinka, I., Šenkeřík, R. (2019). On Relation Between Swarm and Evolutionary Dynamics and Complex Networks. In: Georgiev, G., Smart, J., Flores Martinez, C., Price, M. (eds) Evolution, Development and Complexity. Springer Proceedings in Complexity. Springer, Cham. https://doi.org/10.1007/978-3-030-00075-2_9
Download citation
DOI: https://doi.org/10.1007/978-3-030-00075-2_9
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-00074-5
Online ISBN: 978-3-030-00075-2
eBook Packages: Physics and AstronomyPhysics and Astronomy (R0)