Abstract
In this paper a alternative approach to the diversity guided particle swarm optimization (PSO) is investigated. The PSO shows acceptable performance on well-known test problems, however tends to suffer from premature convergence on multi-modal test problems. This premature convergence can be avoided by increasing diversity in search space. In this paper we introduce diversity measure based on graph representation of swam evolution and we discuss possibilities of graph representation of swarm population in adaptive control of PSO algorithm. Based on our findings we concluded, that network representation of evolution population and its subsequent analysis can be used in adaptive control, in various degrees of success.
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Acknowledgments
This work was supported by Grant Agency of the Czech Republic - GACR P103/15/06700S, further by 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 of SGS No. SP2015/142, VŠB - Technical University of Ostrava, Czech Republic and by Internal Grant Agency of Tomas Bata University under the project No. IGA/FAI/2015/057 and IGA/FAI/2015/061.
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Janostik, J., Pluhacek, M., Senkerik, R., Zelinka, I. (2016). Particle Swarm Optimizer with Diversity Measure Based on Swarm Representation in Complex Network. In: Abraham, A., Wegrzyn-Wolska, K., Hassanien, A., Snasel, V., Alimi, A. (eds) Proceedings of the Second International Afro-European Conference for Industrial Advancement AECIA 2015. Advances in Intelligent Systems and Computing, vol 427. Springer, Cham. https://doi.org/10.1007/978-3-319-29504-6_52
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DOI: https://doi.org/10.1007/978-3-319-29504-6_52
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