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Title:Koevolucijski algoritem roja delcev z metodo rekurzivnega diferencialnega grupiranja za reševanje problemov velikih dimenzij : magistrsko delo
Authors:ID Berkovič, Klemen (Author)
ID Brest, Janez (Mentor) More about this mentor... New window
ID Bošković, Borko (Comentor)
Files:.pdf MAG_Berkovic_Klemen_2024.pdf (2,09 MB)
MD5: 6CF08C8A2FCB98FA6D9DE0632AFFC8DC
 
Language:Slovenian
Work type:Master's thesis/paper
Typology:2.09 - Master's Thesis
Organization:FERI - Faculty of Electrical Engineering and Computer Science
Abstract:Kooperativna koevolucija je podzvrst evolucijskega računanja, ki se uporablja kot ogrodje za optimizacijo problemov z velikim številom dimenzij preko pristopa deli in vladaj. Glavni izzivi uporabe ogrodja kooperativne koevolucije ležijo v dekompoziciji problema ter v uporabi primernega optimizacijskega algoritma. Dekompozicija se v glavnem ukvarja z deljenjem problema v manjše podprobleme, kjer je glavni izziv, kako ugotoviti povezave med komponentami problema. V našem delu smo razvili kooperativni koevolucijski algoritem, ki uporablja rekurzivne strategije diferencialnega grupiranja za dekompozicijo problema, ter algoritem roja delcev, kot optimizacijski algoritem. V delu smo analizirali šest optimizacijskih algoritmov roja delcev na naboru testnih funkcij iz CEC2013, ki spadajo v probleme z velikim številom dimenzij, ter je njihova dimenzionalnost 1000. Na podlagi te analize smo v naš predlagan kooperativni koevolucijski algoritem vključili optimizacijski algoritem roja delcev, ki se je najbolje izkazal na naboru izbranih funkcij. Izvedli smo primerjalno analizo med najboljšim algoritmom roja delcev in predlaganimi kooperativnimi koevolucijskimi algoritmi, kjer smo uporabili pet različnih strategij rekurzivnega diferencialnega grupiranja. Ugotovili smo, da kooperativni koevolucijski algoritem deluje boljše od algoritmov roja delcev, ki smo jih uporabili v našem delu. Prav tako smo ugotovili, da izbira strategije dekompozicije problema igra pomembno vlogo.
Keywords:roj delcev, rekurzivno diferencialno grupiranje, kooperativna koevolucija, optimizacija, veliko število dimenzij
Place of publishing:Maribor
Place of performance:Maribor
Publisher:[K. Berkovič]
Year of publishing:2024
Number of pages:1 spletni vir (1 datoteka PDF (XV, 84 f.))
PID:20.500.12556/DKUM-88983 New window
UDC:004.421(043.2)
COBISS.SI-ID:205822467 New window
Publication date in DKUM:01.07.2024
Views:352
Downloads:82
Metadata:XML DC-XML DC-RDF
Categories:KTFMB - FERI
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Licences

License:CC BY-NC-SA 4.0, Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International
Link:http://creativecommons.org/licenses/by-nc-sa/4.0/
Description:A Creative Commons license that bans commercial use and requires the user to release any modified works under this license.
Licensing start date:03.06.2024

Secondary language

Language:English
Title:Coevolution particle swarm algorithm with recursive differential grouping method for solving large-scale optimization problems
Abstract:Cooperative coevolution is a subgenre of evolutionary computation used as a framework for optimizing high-dimensional problems through a divide-and-conquer approach. The main challenges of using the framework of cooperative coevolution lie in the decomposition of the problem and the use of a suitable optimization algorithm. Decomposition is mainly concerned with dividing the problem into smaller sub-problems, where the main challenge is determining the connections between the components of the problem. In our work, we developed a cooperative coevolutionary algorithm that uses a recursive differential grouping algorithm for problem decomposition, and a particle swarm algorithm as an optimization algorithm. In the work, we analyzed six particle swarm optimization algorithms on a set of test functions from CEC2013, which belong to large-scale optimization problems that have a large number of dimensions, and their dimensionality is 1000. Based on this analysis, we included the particle swarm optimization algorithm in our proposed cooperative coevolution algorithm, which performed best on the selected set of functions. We conducted a comparative analysis between the best particle swarm algorithm and the proposed cooperative coevolutionary algorithm, where we used five different recursive differential grouping strategies. We found that the cooperative coevolution algorithm performs better than the best particle swarm algorithm used in this work. We also found that the choice of problem decomposition strategy plays an important role.
Keywords:particle swarm, recursive differential grouping, cooperative coevolution, optimization, large scale


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