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Title:
ANALIZA ALGORITMA PSO TER NJEGOVIH IZPELJANK
Authors:
ID
Robnik, Aleksander
(Author)
ID
Črepinšek, Matej
(Mentor)
More about this mentor...
Files:
MAG_Robnik_Aleksander_2016.pdf
(4,14 MB)
MD5: F1201972224D661DE9C55BB10B3BEE73
Language:
Slovenian
Work type:
Master's thesis/paper
Typology:
2.09 - Master's Thesis
Organization:
FERI - Faculty of Electrical Engineering and Computer Science
Abstract:
V magistrskem delu predstavimo inteligenco rojev ter opišemo algoritem optimizacije z rojem delcev. Pojasnimo izvor algoritmov inteligence rojev in njihov osnovni namen. Podrobneje raziščemo različne izpeljanke algoritma optimizacije z rojem delcev ter naredimo primerjavo med njimi. Med različnimi izpeljankami algoritma optimizacije z rojem delcev prihaja do majhnih sprememb v programski kodi, zato le te nazorno prikažemo. Analizo delovanja algoritmov izvedemo s pomočjo sistema EARS, s katerim lahko primerjamo moči posameznih evolucijskih algoritmov.
Keywords:
optimizacija z rojem delcev
,
inteligenca rojev
,
PSO
Place of publishing:
[Maribor
Publisher:
A. Robnik
Year of publishing:
2016
PID:
20.500.12556/DKUM-64039
UDC:
004.434:004.8(043.2)
COBISS.SI-ID:
20267286
NUK URN:
URN:SI:UM:DK:YCOEHBKZ
Publication date in DKUM:
27.10.2016
Views:
1489
Downloads:
221
Metadata:
Categories:
KTFMB - FERI
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Vancouver
:
ROBNIK, Aleksander, 2016,
ANALIZA ALGORITMA PSO TER NJEGOVIH IZPELJANK
[online]. Master’s thesis. Maribor : A. Robnik. [Accessed 24 April 2025]. Retrieved from: https://dk.um.si/IzpisGradiva.php?lang=eng&id=64039
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Secondary language
Language:
English
Title:
ANALYSIS OF THE PSO ALGORITHM AND ITS DERIVATIVES
Abstract:
In this thesis, we present swarm intelligence and describe particle swarm optimization algorithm. We explain the origin of swarm intelligence algorithms and their primary purpose. More specifically we investigate the different derivatives of the particle swarm optimization algorithms and make a comparison between them. Among various derivatives of particle swarm optimization algorithms comes to small differences in programming code. We make comparison among those differences. We make performance analysis of the algorithms with EARS system, which can compare the power of individual evolutionary algorithms.
Keywords:
particle swarm optimization
,
swarm intelligence
,
PSO
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