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Conclusions

  • Micael CouceiroEmail author
  • Pedram Ghamisi
Chapter
Part of the SpringerBriefs in Applied Sciences and Technology book series (BRIEFSAPPLSCIENCES)

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.

Keywords

FODPSO Contributions Discussion Future work 

References

  1. 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 CrossRefGoogle Scholar
  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).Google Scholar
  3. Wang, W., Zhang, Y., Li, Y., & Zhang, X. (2006). The global fuzzy c-means clustering algorithm. In Intelligent Control and Automation, pp. 3604–3607.Google Scholar

Copyright information

© The Author(s) 2016

Authors and Affiliations

  1. 1.Ingeniarius, LtdMealhadaPortugal
  2. 2.Institute of Systems and Robotics (ISR)University of CoimbraCoimbraPortugal
  3. 3.Faculty of Electrical and Computer EngUniversity of IcelandReykjavikIceland

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