Abstract
In this study the promising Multiple-choice strategy for PSO (MC-PSO) is enhanced with the blind search based single dimensional mutation. The MC-PSO utilizes principles of heterogeneous swarms with random behavior selection. The performance previously tested on both large-scale and fast optimization is significantly improved by this approach. The newly proposed algorithm is more robust and resilient to premature convergence than both original PSO and MC-PSO. The performance is tested on four typical benchmark functions with variety of dimension settings.
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 and SP2015/141 of VSB - 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). Multiple Choice Strategy for PSO Algorithm Enhanced with Dimensional Mutation. In: Rutkowski, L., Korytkowski, M., Scherer, R., Tadeusiewicz, R., Zadeh, L., Zurada, J. (eds) Artificial Intelligence and Soft Computing. ICAISC 2015. Lecture Notes in Computer Science(), vol 9119. Springer, Cham. https://doi.org/10.1007/978-3-319-19324-3_34
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DOI: https://doi.org/10.1007/978-3-319-19324-3_34
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