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Title:
Vpliv pogostosti spreminjanja ocenitvene funkcije v dinamični optimizaciji z evolucijskimi algoritmi : diplomsko delo
Authors:
ID
Vac, Tadej
(
Author
)
ID
Mernik, Marjan
(
Mentor
)
More about this mentor...
Files:
VS_Vac_Tadej_2024.pdf
(3,40 MB)
MD5: 029F9BE90E10910B0934DA4BC9C0BA18
Language:
Slovenian
Work type:
Bachelor thesis/paper
Typology:
2.11 - Undergraduate Thesis
Organization:
FERI - Faculty of Electrical Engineering and Computer Science
Abstract:
V diplomskem delu smo raziskali optimizacijo algoritmov ABC, DE in PSO na dinamičnem problemu MPB. Namen dela je bil raziskati in preizkusiti strategije zaznavanja sprememb in prilagajanje algoritmov v dinamičnih okoljih. Delo je razdeljeno v tri glavne dele: pregled področja, predstavitev uporabljenih algoritmov in pristopov optimizacije ter analiza rezultatov. Najprej smo opisali osnove evolucijskih algoritmov in dinamične optimizacije. Nato smo predstavili dinamičen problem, algoritme in pristope optimizacije. Na koncu smo primerjali uspešnost algoritmov pri iskanju optimalnih rešitev v problemu MPB.
Keywords:
evolucijski algoritmi
,
dinamična optimizacija
,
zaznavanje sprememb
Place of publishing:
Maribor
Place of performance:
Maribor
Publisher:
[T. Vac]
Year of publishing:
2024
Number of pages:
1 spletni vir (1 datoteka PDF (X, 50 str.))
PID:
20.500.12556/DKUM-91340
UDC:
004.021:575.82(043.2)
COBISS.SI-ID:
227681283
Publication date in DKUM:
06.02.2025
Views:
153
Downloads:
33
Metadata:
Categories:
KTFMB - FERI
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Licences
License:
CC BY-NC-ND 4.0, Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International
Link:
http://creativecommons.org/licenses/by-nc-nd/4.0/
Description:
The most restrictive Creative Commons license. This only allows people to download and share the work for no commercial gain and for no other purposes.
Licensing start date:
13.12.2024
Secondary language
Language:
English
Title:
The influence of the frequency of changing the evaluation function in dynamic optimization with evolutionary algorithms
Abstract:
In this thesis, we explored the optimization of ABC, DE and PSO algorithms on the dynamic MPB problem. The aim of this work was to investigate and test change detection strategies and adapt algorithms to dynamic environments. The work is divided into three main sections: field overview, a presentation of the algorithms and optimization approaches, and an analysis of the results. Firstly we described the basics of evolutionary algorithms and dynamic optimization. Then, we presented the dynamic problem, algorithms and optimization strategies. Finally, we compared the performance of the algorithms in finding of the optimal solution in the MPB problem.
Keywords:
evoltionary algorithms
,
dynamic optimization
,
change detection
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