International Journal of Computational Intelligence Systems

Volume 6, Issue 6, December 2013, Pages 1094 - 1107

An Adaptive Differential Evolution Algorithm Based on New Diversity

Authors
Huan Lian, Yong Qin, Jing Liu
Corresponding Author
Huan Lian
Available Online 9 January 2017.
DOI
https://doi.org/10.1080/18756891.2013.816064How to use a DOI?
Keywords
Intelligent algorithm, Differential evolution, Population diversity, Adaptive parameter control
Abstract
A DE approach based on a new measure of population diversity and a novel parameter control mechanism is proposed with the aim of introducing a good behavior of the algorithm. The ratio of the new defined population diversity of different generations is equal to that of the population variance, therefore the adaption of parameter can use some theoretical results in. Combining with the method in, we can adjust the mutation factor and the crossover rate at each generation in the searching process. The performance of the proposed algorithm (DE-F&CR) is compared to the basic DE and other four DE algorithms over 25 standard numerical benchmarks provided by the IEEE Congress on Evolutionary Computation 2005 special session on real parameter optimization. The results and its statistical analysis show that the DE-F&CR generally outperforms the other algorithms in multi-modal optimization.
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This is an open access article distributed under the CC BY-NC license.

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Journal
International Journal of Computational Intelligence Systems
Volume-Issue
6 - 6
Pages
1094 - 1107
Publication Date
2017/01
ISSN (Online)
1875-6883
ISSN (Print)
1875-6891
DOI
https://doi.org/10.1080/18756891.2013.816064How to use a DOI?
Open Access
This is an open access article distributed under the CC BY-NC license.

Cite this article

TY  - JOUR
AU  - Huan Lian
AU  - Yong Qin
AU  - Jing Liu
PY  - 2017
DA  - 2017/01
TI  - An Adaptive Differential Evolution Algorithm Based on New Diversity
JO  - International Journal of Computational Intelligence Systems
SP  - 1094
EP  - 1107
VL  - 6
IS  - 6
SN  - 1875-6883
UR  - https://doi.org/10.1080/18756891.2013.816064
DO  - https://doi.org/10.1080/18756891.2013.816064
ID  - Lian2017
ER  -