Improved Differential Evolution Algorithm Based On Elite Group

DOI

10.2991/icence-16.2016.95How to use a DOI?

Keywords

DE, information entropy, average-distance-amongst-points, elite group.

Abstract

By introduce the information entropy and the average-distance-amongst-points to analysis the population distribution in the process of evolution, and figured out the cause of the DE/best/* premature convergence is the control function of the current optimal individual to decrease the population diversity of the algorithm. Based on the number of base vectors, improved the DE algorithm by setting up the elite group, the elite differential evolution algorithm is proposed. Finally, several typical test functions are used to test the performance. The results show that the elite differential evolution algorithm has a good performance in the search success rate and the global search capability.

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  - XiaoBo Gao
AU  - YouCai Wang
AU  - GuangZhao Yang
PY  - 2016/09
DA  - 2016/09
TI  - Improved Differential Evolution Algorithm Based On Elite Group
BT  - Proceedings of the 2nd International Conference on Electronics, Network and Computer Engineering (ICENCE 2016)
PB  - Atlantis Press
SP  - 499
EP  - 505
SN  - 2352-538X
UR  - https://doi.org/10.2991/icence-16.2016.95
DO  - 10.2991/icence-16.2016.95
ID  - Gao2016/09
ER  -