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Title:
OPTIMIZACIJA S KOLONIJAMI MRAVELJ
Authors:
ID
Thuma, Marko
(Author)
ID
Strnad, Damjan
(Mentor)
More about this mentor...
ID
Guid, Nikola
(Comentor)
Files:
VS_Thuma_Marko_2013.pdf
(2,04 MB)
MD5: AEC5F5C4B2EB56BD982692F97CEE5A88
Language:
Slovenian
Work type:
Bachelor thesis/paper
Typology:
2.11 - Undergraduate Thesis
Organization:
FERI - Faculty of Electrical Engineering and Computer Science
Abstract:
V tem diplomskem delu predstavljamo naše izboljšave na simulatorju evakuacije v primeru poplav z optimizacijo s kolonijami mravelj. Simulator s pomočjo genetskega algoritma optimizira evakuacijski načrt, ki določa zaporedje reševanj. Za ocenitev evakuacijskega načrta potrebujemo najkrajše poti od evakuacijske točke do posameznih hiš. V osnovni različici za te poti privzamemo zračne linije, v optimizirani pa jih pridobimo z algoritmom sistema kolonije mravelj. V diplomskem delu primerjamo učinkovitost obeh rešitev.
Keywords:
mravlje
,
feromon
,
verjetnost
,
algoritem
,
najkrajša pot
Place of publishing:
Maribor
Publisher:
[M. Thuma]
Year of publishing:
2013
PID:
20.500.12556/DKUM-42762
UDC:
004.8.021:004.94(043.2)
COBISS.SI-ID:
17312534
NUK URN:
URN:SI:UM:DK:XNZBNHNN
Publication date in DKUM:
17.10.2013
Views:
2190
Downloads:
207
Metadata:
Categories:
KTFMB - FERI
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:
THUMA, Marko, 2013,
OPTIMIZACIJA S KOLONIJAMI MRAVELJ
[online]. Bachelor’s thesis. Maribor : M. Thuma. [Accessed 16 March 2025]. Retrieved from: https://dk.um.si/IzpisGradiva.php?lang=eng&id=42762
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Hover the mouse pointer over a document title to show the abstract or click on the title to get all document metadata.
Secondary language
Language:
English
Title:
ANT COLONY OPTIMIZATION
Abstract:
In this diploma thesis we present our improvements for the simulator of evacuation in case of floods using ant colony optimization. The simulator is using a genetic algorithm to optimize evacuation plans which provide the progression of rescue. To assess the evacuation plan we need the shortest paths from the evacuation point to the individual houses. In the basic version, we assume air distances for the paths, but in an optimized algorithm we obtaine the distances using the ant colony system. In the thesis we compare the efficiency of both solutions.
Keywords:
ants
,
pheromone
,
probability
,
algorithm
,
shortest path
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