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Title:
Reševanje igre nurikabe s kolonijami mravelj : diplomsko delo
Authors:
ID
Bešenič, Tadej
(
Author
)
ID
Bošković, Borko
(
Mentor
)
More about this mentor...
ID
Brest, Janez
(
Comentor
)
Files:
VS_Besenic_Tadej_2022.pdf
(1,08 MB)
MD5: C75B115BCF24C2C1CF3E1524904FE6FC
Language:
Slovenian
Work type:
Bachelor thesis/paper
Typology:
2.11 - Undergraduate Thesis
Organization:
FERI - Faculty of Electrical Engineering and Computer Science
Abstract:
V nalogi predstavljamo učinkovitost optimizacije s kolonijami mravelj za reševanje japonske miselne igre Nurikabe. Razložimo pravila igre in njene osnovne lastnosti ter optimizacijo s kolonijami mravelj. Pojasnimo lastno implementacijo metode in metode iskanja v globino. Analiziramo krmilne parametre optimizacije s kolonijami mravelj. Vse algoritme primerjamo med seboj. Iz dobljenih rezultatov smo ugotovili, da je iskanje v globino časovno zahtevno. Optimizacija s kolonijami mravelj dosega dobre rezultate in omogoča reševanje zahtevnejših problemov.
Keywords:
Nurikabe
,
optimizacija s kolonijami mravelj
,
iskanje v globino
,
miselne igre
Place of publishing:
Maribor
Place of performance:
Maribor
Publisher:
[T. Bešenič]
Year of publishing:
2022
Number of pages:
1 spletni vir (1 datoteka PDF (XI, 34 f.))
PID:
20.500.12556/DKUM-83051
UDC:
004.932(043.2)
COBISS.SI-ID:
139149059
Publication date in DKUM:
25.10.2022
Views:
652
Downloads:
81
Metadata:
Categories:
KTFMB - FERI
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Licences
License:
CC BY-NC 4.0, Creative Commons Attribution-NonCommercial 4.0 International
Link:
http://creativecommons.org/licenses/by-nc/4.0/
Description:
A creative commons license that bans commercial use, but the users don’t have to license their derivative works on the same terms.
Licensing start date:
14.09.2022
Secondary language
Language:
English
Title:
Solving nurikabe game with ant colony optimization
Abstract:
In this thesis, the efficiency of the ant colony optimization method for solving the Japanese logic puzzle Nurikabe is presented. First, the rules and fundamental properties of the puzzle and the ant colony optimization technique are explained. Our implementation of the method as well as our depth-first search method are described. The input parameters of the algorithm are analyzed. The results of all the algorithms are compared. The depth-first search method is time consuming. Ant colony optimization brings better results and makes it possible to solve more difficult problems.
Keywords:
Nurikabe
,
ant colony optimization
,
depth-first search
,
logic puzzles
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