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Title:Prilagodljivi algoritem diferencialne evolucije z arhivom uspešnosti in linearnim zmanjševanjem populacije : diplomsko delo
Authors:ID Gartner, Aleš (Author)
ID Fister, Iztok (Mentor) More about this mentor... New window
ID Fister, Iztok (Co-mentor)
Files:.pdf UN_Gartner_Ales_2022.pdf (1008,89 KB)
MD5: DFED990331B959A61BCB98DAA2F8A7B8
 
Language:Slovenian
Work type:Bachelor thesis/paper
Typology:2.11 - Undergraduate Thesis
Organization:FERI - Faculty of Electrical Engineering and Computer Science
Abstract:V sklopu diplomskega dela predstavljamo delovanje prilagodljivega algoritma diferencialne evolucije z arhivom uspešnosti in linearnim zmanjševanjem populacije ter ga implementiramo v programskem jeziku Python. S statistično primerjavo rezultatov implementacije na testnih funkcijah smo pokazali, da smo algoritem uspešno implementirali. Algoritem smo vključili v Python knjižnico NiaPy ter primerjali njegovo učinkovitost z drugimi algoritmi diferencialne evolucije, implementiranimi v NiaPy. Z analizo rezultatov smo pokazali, da je naš implementirani algoritem resnično eden izmed najučinkovitejših verzij algoritma diferencialne evolucije.
Keywords:optimizacija, algoritmi po vzoru iz narave, diferencialna evolucija, NiaPy
Place of publishing:Maribor
Place of performance:Maribor
Publisher:[A. Gartner]
Year of publishing:2022
Number of pages:1 spletni vir (1 datoteka PDF (X, 29 f.))
PID:20.500.12556/DKUM-82948 New window
UDC:004.8.021(043.2)
COBISS.SI-ID:137305859 New window
Publication date in DKUM:24.10.2022
Views:302
Downloads:24
Metadata:XML RDF-CHPDL DC-XML DC-RDF
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:11.09.2022

Secondary language

Language:English
Title:Succes-History based adaptive differential evolution algorithm with linear population size reduction
Abstract:As part of our thesis, we have presented the operation of the Success-History based Adaptive Differential Evolution algorithm with Linear Population Size Reduction and implemented it in the Python programming language. Through statistical comparison of the results on test functions, we have demonstrated that the algorithm was successfully implemented. We merged the algorithm into the Python library NiaPy and compared its performance with other already existing differential evolution algorithms implemented in the same library. By analysing the results, we justify that our implemented algorithm is among the best preforming variants of the differential evolution algorithm.
Keywords:optimization, nature-inspired algorithms, differential evolution, NiaPy


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