An Improved Artificial Fish Swarm Algorithm and Its Application

DOI

10.2991/meici-16.2016.6How to use a DOI?

Keywords

Artificial Fish Swarm Algorithm (AFSA); Parameters dynamic mechanism; Iterative adaptive mechanism; Local traversal algorithm

Abstract

To overcome the standard AFSA's slow convergence speed and limited optimizing accuracy problem, an improved AFSA is presented in this paper. For this improved algorithm, parameters dynamic mechanism is introduced to improve the accuracy. Besides, iterative adaptive mechanism and local algorithm were introduced to overcome invalid calculation and convergence shocks problems. Comparison experiments show that the improved AFSA is better than the standard algorithm on the convergence rate and optimization accuracy. Moreover, SVM parameter optimization also shows that the improved AFSA has a better optimization performance metric,time performance metric and robustness metric than traditional method.

Copyright

© 2016, the Authors. Published by Atlantis Press.

Open Access

This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).

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TY  - CONF
AU  - Mantao Wang
AU  - Haitao Tang
AU  - Jong Mu
AU  - Peng Wei
PY  - 2016/09
DA  - 2016/09
TI  - An Improved Artificial Fish Swarm Algorithm and Its Application
BT  - Proceedings of the 2016 6th International Conference on Management, Education, Information and Control (MEICI 2016)
PB  - Atlantis Press
SP  - 24
EP  - 33
SN  - 1951-6851
UR  - https://doi.org/10.2991/meici-16.2016.6
DO  - 10.2991/meici-16.2016.6
ID  - Wang2016/09
ER  -