| | SLO | ENG | Cookies and privacy

Bigger font | Smaller font

Show document Help

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
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
COBISS.SI-ID:137305859 New window
Publication date in DKUM:24.10.2022
Categories:KTFMB - FERI
Copy citation
Average score:(0 votes)
Your score:Voting is allowed only for logged in users.
Share:Bookmark and Share

Hover the mouse pointer over a document title to show the abstract or click on the title to get all document metadata.


License:CC BY-NC-ND 4.0, Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International
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

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


Leave comment

You must log in to leave a comment.

Comments (0)
0 - 0 / 0
There are no comments!

Logos of partners University of Maribor University of Ljubljana University of Primorska University of Nova Gorica