Abstract
This contribution deals with the hybridization of complex network frameworks and metaheuristic algorithms. The population is visualized as an evolving complex network that exhibits non-trivial features. It briefly investigates the time and structure development of a complex network within a run of selected metaheuristic algorithms – i.e. PSO and Differential Evolution (DE). Two different approaches for the construction of complex networks are presented herein. It also briefly discusses the possible utilization of complex network attributes. These attributes include an adjacency graph that depicts interconnectivity, while centralities provide an overview of convergence and stagnation, and clustering encapsulates the diversity of the population, whereas other attributes show the efficiency of the network. The experiments were performed for one selected DE/PSO strategy and one simple test function.
This is a preview of subscription content, log in via an institution.
Buying options
Tax calculation will be finalised at checkout
Purchases are for personal use only
Learn about institutional subscriptionsReferences
Zelinka, I., Davendra, D., Lampinen, J., Senkerik, R., Pluhacek, M.: Evolutionary algorithms dynamics and its hidden complex network structures. In: 2014 IEEE Congress on Evolutionary Computation (CEC), pp. 3246–3251 (2014)
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: 2014 IEEE Symposium on Differential Evolution (SDE), pp. 1–8 (2014)
Davendra, D., Zelinka, I., Senkerik, R., Pluhacek, M.: 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. AISC, vol. 303, pp. 141–150. Springer, Cham (2014). doi:10.1007/978-3-319-08156-4_15
Skanderova, L., Fabian, T.: Differential evolution dynamics analysis by complex networks. Soft. Comput. 21, 1–15 (2015)
Metlicka, M., Davendra, D.: Ensemble centralities based adaptive Artificial Bee algorithm. In: 2015 IEEE Congress on Evolutionary Computation (CEC), pp. 3370–3376 (2015)
Gajdos, P., Kromer, P., Zelinka, I.: Network visualization of population dynamics in the differential evolution. In: 2015 IEEE Symposium Series on Computational Intelligence, pp. 1522–1528 (2015)
Price, K.V.: An introduction to differential evolution. In: Corne, D., Dorigo, M., Glover, F. (eds.) New Ideas in Optimization, pp. 79–108. McGraw-Hill Ltd. (1999)
Kennedy, J., Eberhart, R.: Particle swarm optimization. In: IEEE International Conference on Neural Networks, Nov/Dec 1995, pp. 1942–1948 (1995)
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)
Mallipeddi, R., Suganthan, P.N., Pan, Q.K., Tasgetiren, M.F.: Differential evolution algorithm with ensemble of parameters and mutation strategies. Appl. Soft Comput. 11(2), 1679–1696 (2011)
Jabeen, H., Jalil, Z., Baig, A.R.: 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 (2009)
Engelbrecht, A.P.: Heterogeneous particle swarm optimization. In: Dorigo, M., et al. (eds.) ANTS 2010. LNCS, vol. 6234, pp. 191–202. Springer, Heidelberg (2010). doi:10.1007/978-3-642-15461-4_17
Janostik, J., Pluhacek, M., Senkerik, R., Zelinka, I.: Particle swarm optimizer with diversity measure based on swarm representation in complex network. In: Abraham, A., Wegrzyn-Wolska, K., Hassanien, A.E., Snasel, V., Alimi, A.M. (eds.) Proceedings of the Second International Afro-European Conference for Industrial Advancement AECIA 2015. AISC, vol. 427, pp. 561–569. Springer, Cham (2016). doi:10.1007/978-3-319-29504-6_52
Das, S., Mullick, S.S., Suganthan, P.: Recent advances in differential evolution – an updated survey. Swarm Evol. Comput. 27, 1–30 (2016)
Acknowledgements
This work was supported by Grant Agency of the Czech Republic - GACR P103/15/06700S, further by the Ministry of Education, Youth and Sports of the Czech Republic within the National Sustainability Programme project No. LO1303 (MSMT-7778/2014) 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 of SGS No. SP2016/175 of VSB - Technical University of Ostrava, Czech Republic and by Internal Grant Agency of Tomas Bata University under the project No. IGA/CebiaTech/2016/007.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2017 Springer International Publishing AG
About this paper
Cite this paper
Senkerik, R., Zelinka, I., Pluhacek, M., Viktorin, A. (2017). Study on the Development of Complex Network for Evolutionary and Swarm Based Algorithms. In: Pichardo-Lagunas, O., Miranda-Jiménez, S. (eds) Advances in Soft Computing. MICAI 2016. Lecture Notes in Computer Science(), vol 10062. Springer, Cham. https://doi.org/10.1007/978-3-319-62428-0_12
Download citation
DOI: https://doi.org/10.1007/978-3-319-62428-0_12
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-62427-3
Online ISBN: 978-3-319-62428-0
eBook Packages: Computer ScienceComputer Science (R0)