| bibtype |
J -
Journal Article
|
| ARLID |
0423196 |
| utime |
20240903204256.1 |
| mtime |
20140226235959.9 |
| title
(primary) (eng) |
Black-Box Optimization for Buildings and Its Enhancement by Advanced Communication Infrastructure |
| specification |
|
| serial |
| ARLID |
cav_un_epca*0423195 |
| ISSN |
2255-2863 |
| title
|
Advances in Distributed Computing and Artificial Intelligence Journal |
| volume_id |
1 |
| volume |
5 (2013) |
| page_num |
53-64 |
|
| keyword |
Evolutionary algorithms |
| keyword |
Black box modeling |
| keyword |
Simplification |
| keyword |
Refining |
| keyword |
HVAC |
| keyword |
Load shedding |
| keyword |
Communication infrastructure |
| author
(primary) |
| ARLID |
cav_un_auth*0292010 |
| name1 |
Macek |
| name2 |
Karel |
| full_dept (cz) |
Adaptivní systémy |
| full_dept (eng) |
Department of Adaptive Systems |
| department (cz) |
AS |
| department (eng) |
AS |
| institution |
UTIA-B |
| fullinstit |
Ústav teorie informace a automatizace AV ČR, v. v. i. |
|
| author
|
| ARLID |
cav_un_auth*0292027 |
| name1 |
Rojíček |
| name2 |
J. |
| country |
CZ |
|
| author
|
| ARLID |
cav_un_auth*0301156 |
| name1 |
Kontes |
| name2 |
G. |
| country |
GR |
|
| author
|
| ARLID |
cav_un_auth*0301157 |
| name1 |
Rovas |
| name2 |
D. V. |
| country |
GR |
|
| source |
|
| cas_special |
| project |
| project_id |
GA13-13502S |
| agency |
GA ČR |
| ARLID |
cav_un_auth*0292725 |
|
| abstract
(eng) |
The solution of repeated fixed-horizon trajectory optimization problems of processes that are either too difficult or too complex to be described by physicsbased models can pose formidable challenges. Very often, soft-computing methods - e.g. black-box modeling and evolutionary optimization - are used. These approaches are ineffective or even computationally intractable for searching high-dimensional parameter spaces. In this paper, a structured iterative process is described for addressing such problems: the starting point is a simple parameterization of the trajectory starting with a reduced number of parameters; after selection of values for these parameters so that this simpler problem is covered satisfactorily, a refinement procedure increases the number of parameters and the optimization is repeated. This continuous parameter refinement and optimization process can yield effective solutions after only a few iterations. |
| reportyear |
2014 |
| RIV |
BC |
| num_of_auth |
4 |
| inst_support |
RVO:67985556 |
| permalink |
http://hdl.handle.net/11104/0231499 |
| cooperation |
| ARLID |
cav_un_auth*0299369 |
| name |
Honeywell Prague Laboratory |
| country |
CZ |
|
| confidential |
S |
| arlyear |
2013 |
| mrcbU63 |
cav_un_epca*0423195 Advances in Distributed Computing and Artificial Intelligence Journal 2255-2863 2255-2863 Roč. 1 č. 5 2013 53 64 |
|