20.500.12556/DKUM-82948 Prilagodljivi algoritem diferencialne evolucije z arhivom uspešnosti in linearnim zmanjševanjem populacije diplomsko delo Succes-History based adaptive differential evolution algorithm with linear population size reduction 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. 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. optimizacija algoritmi po vzoru iz narave diferencialna evolucija NiaPy optimization nature-inspired algorithms differential evolution NiaPy true true false [A. Gartner] Slovenski jezik Angleški jezik Diplomsko delo/naloga 2022-09-11 12:30:08 2022-10-24 13:55:27 2023-01-13 03:06:44 0000-00-00 00:00:00 2022 0 Maribor 0 Maribor 1 spletni vir (1 datoteka PDF (X, 29 f.)) 0000-00-00 NiDoloceno NiDoloceno NiDoloceno 0000-00-00 0000-00-00 0000-00-00 004.8.021(043.2) 137305859 UN_Gartner_Ales_2022.pdf UN_Gartner_Ales_2022.pdf 1 DFED990331B959A61BCB98DAA2F8A7B8 1a4e8669ddafd0619e1b167b5b08e0384ac9b2753f282b601d10177b05c5da7b 77547c15-3275-11ed-9b90-00155d000105 https://dk.um.si/Dokument.php?lang=slv&id=162358 Fakulteta za elektrotehniko, računalništvo in informatiko 0 0 0