Abstract
Narrowing down all that was previously presented to a sentence, the focus of this short book was the bottom-up applicability of swarm intelligence to solve multiple different problems, such as typical curve fitting, the relevant image segmentation process, and even the more technologically oriented swarm robotics. This final chapter summarizes the research covered around a novel PSO-based algorithm, denoted fractional-order Darwinian particle swarm optimization (FODPSO). After discussing the presented contributions, and considering their advantages and limitations, it points out perspectives on future research.
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
Couceiro, M. S., Rocha, R. P., Ferreira, N. M. F., & Machado, J. A. T. (2012). Introducing the fractional order Darwinian PSO. Signal, Image and Video Processing, 6(3), 343–350 (2012). doi: 10.1007/s11760-012-0316-2
Kennedy, J., & Eberhart, R. (1995). A new optimizer using particle swarm theory. In Proceedings of IEEE Sixth International Symposium on Micro Machine Human Science (Vol. 34, Issue 2008, pp. 39–43).
Wang, W., Zhang, Y., Li, Y., & Zhang, X. (2006). The global fuzzy c-means clustering algorithm. In Intelligent Control and Automation, pp. 3604–3607.
Author information
Authors and Affiliations
Corresponding author
Rights and permissions
Copyright information
© 2016 The Author(s)
About this chapter
Cite this chapter
Couceiro, M., Ghamisi, P. (2016). Conclusions. In: Fractional Order Darwinian Particle Swarm Optimization. SpringerBriefs in Applied Sciences and Technology. Springer, Cham. https://doi.org/10.1007/978-3-319-19635-0_6
Download citation
DOI: https://doi.org/10.1007/978-3-319-19635-0_6
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-19634-3
Online ISBN: 978-3-319-19635-0
eBook Packages: EngineeringEngineering (R0)