Abstract
| Original language | English |
|---|---|
| Title of host publication | 2015 IEEE Congress on Evolutionary Computation, CEC 2015 - Proceedings |
| Pages | 632-639 |
| Number of pages | 8 |
| DOIs | |
| Publication status | Published - 10 Sep 2015 |
| Event | IEEE Congress on Evolutionary Computation, CEC 2015 - Sendai, Japan Duration: 25 May 2015 → 28 May 2015 |
Conference
| Conference | IEEE Congress on Evolutionary Computation, CEC 2015 |
|---|---|
| Country | Japan |
| City | Sendai |
| Period | 25/05/15 → 28/05/15 |
Keywords
- global optimization
- differential evolution
- multi-population algorithm
- adaptive algorithm
Cite this
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Multi-population inflationary differential evolution algorithm with adaptive local restart. / Di Carlo, Marilena; Vasile, Massimiliano; Minisci, Edmondo.
2015 IEEE Congress on Evolutionary Computation, CEC 2015 - Proceedings. 2015. p. 632-639 7256950.Research output: Chapter in Book/Report/Conference proceeding › Conference contribution book
TY - GEN
T1 - Multi-population inflationary differential evolution algorithm with adaptive local restart
AU - Di Carlo, Marilena
AU - Vasile, Massimiliano
AU - Minisci, Edmondo
PY - 2015/9/10
Y1 - 2015/9/10
N2 - In this paper a Multi-Population Inflationary Differential Evolution algorithm with Adaptive Local Restart is presented and extensively tested over more than fifty test functions from the CEC 2005, CEC 2011 and CEC 2014 competitions. The algorithm combines a multi-population adaptive Differential Evolution with local search and local and global restart procedures. The proposed algorithm implements a simple but effective mechanism to avoid multiple detections of the same local minima. The novel mechanism allows the algorithm to decide whether to start or not a local search. The local restart of the population, which follows the local search, is, therefore, automatically adapted.
AB - In this paper a Multi-Population Inflationary Differential Evolution algorithm with Adaptive Local Restart is presented and extensively tested over more than fifty test functions from the CEC 2005, CEC 2011 and CEC 2014 competitions. The algorithm combines a multi-population adaptive Differential Evolution with local search and local and global restart procedures. The proposed algorithm implements a simple but effective mechanism to avoid multiple detections of the same local minima. The novel mechanism allows the algorithm to decide whether to start or not a local search. The local restart of the population, which follows the local search, is, therefore, automatically adapted.
KW - global optimization
KW - differential evolution
KW - multi-population algorithm
KW - adaptive algorithm
UR - http://www.scopus.com/inward/record.url?scp=84963542820&partnerID=8YFLogxK
UR - http://sites.ieee.org/cec2015/
U2 - 10.1109/CEC.2015.7256950
DO - 10.1109/CEC.2015.7256950
M3 - Conference contribution book
SN - 9781479974924
SP - 632
EP - 639
BT - 2015 IEEE Congress on Evolutionary Computation, CEC 2015 - Proceedings
ER -