Abstract
In this initial study it is described the possible hybridization of advanced Particle Swarm Optimization (PSO) modification called MC-PSO and the Differential evolution (DE) algorithm. The advantage of hybridization of various evolutionary techniques is the shared benefit from various advantages of these methods. The motivation came from previous studies of the MC-PSO performance and behavior. The performance of the proposed method is tested on IEEE CEC 2013 benchmark set and compared with both PSO and DE.
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Acknowledgements
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. 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 and SP2015/141, VŠB - Technical University of Ostrava, Czech Republic and by Internal Grant Agency of Tomas Bata University under the project No. IGA/FAI/2015/057.
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Pluhacek, M., Senkerik, R., Zelinka, I., Davendra, D. (2015). MC-PSO/DE Hybrid with Repulsive Strategy – Initial Study. In: Onieva, E., Santos, I., Osaba, E., Quintián, H., Corchado, E. (eds) Hybrid Artificial Intelligent Systems. HAIS 2015. Lecture Notes in Computer Science(), vol 9121. Springer, Cham. https://doi.org/10.1007/978-3-319-19644-2_18
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DOI: https://doi.org/10.1007/978-3-319-19644-2_18
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