Proceedings of the 2016 International Conference on Engineering and Advanced Technology

Research on the Population Migration Trend Algorithm based on Artificial Fish Swarm Algorithm

Authors
XiWen BI, Meng XU
Corresponding Author
XiWen BI
Available Online May 2016.
DOI
https://doi.org/10.2991/iceat-16.2017.15How to use a DOI?
Keywords
Population Migration Trend Algorithm,Artificial Fish Swarm Algorithm,Convergence
Abstract

Population migration trend is a new kind of evolutionary algorithm proposed recently, which simulates the principle of Population Migration. In this paper, the new search of mechanism is proposed to predict the population migration trend for the visual effect to the convergence of the ASFA(Artificial Fish Swarm Algorithm). the analysis of experiments is obvious that when searching area contracts, the convergence is improved greatly. The numerical experiments show that the mean iteration generation and the least successfully iteration generation of he randomness of population's movement in PMA is less than that of ASFA. And the convergence algorithm shows better local search ability and convergence stability.

Copyright
© 2017, 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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Volume Title
Proceedings of the 2016 International Conference on Engineering and Advanced Technology
Series
Advances in Engineering Research
Publication Date
May 2016
ISBN
978-94-6252-294-7
ISSN
2352-5401
DOI
https://doi.org/10.2991/iceat-16.2017.15How to use a DOI?
Copyright
© 2017, 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/).

Cite this article

TY  - CONF
AU  - XiWen BI
AU  - Meng XU
PY  - 2016/05
DA  - 2016/05
TI  - Research on the Population Migration Trend Algorithm based on Artificial Fish Swarm Algorithm
BT  - Proceedings of the 2016 International Conference on Engineering and Advanced Technology
PB  - Atlantis Press
SP  - 65
EP  - 69
SN  - 2352-5401
UR  - https://doi.org/10.2991/iceat-16.2017.15
DO  - https://doi.org/10.2991/iceat-16.2017.15
ID  - BI2016/05
ER  -