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Obravnavanje omejitev v večkriterijski optimizaciji : delo diplomskega seminarja
Erzin, Eva (Author), Orbanić, Alen (Mentor) More about this mentor... This link opens in a new window, Filipič, Bogdan (Co-mentor)

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Abstract
Večkriterijski optimizacijski problemi so del vsakdana. Včasih jih uspemo rešiti sami, včasih pa so prezahtevni in za to potrebujemo pomoč. Dober pristop k reševanju večkriterijskih optimizacijskih problemov so genetski algoritmi. V tem delu se ukvarjamo z večkriterijskimi optimizacijskimi problemi z omejitvami. Najprej jih definiramo in opišemo njihovo rešitev - Pareto optimalno množico. Nato predstavimo genetske algoritme, si podrobneje ogledamo dva izmed njih, NSGA-II in MOEA/D ter pregledamo obstoječe načine obravnavanja omejitev v večkriterijski optimizaciji, s katerimi lahko genetske algoritme za večkriterijsko optmizacijo prilagodimo tako, da lahko obravnavajo tudi probleme z omejitvami. Na koncu predstavimo še dva testna večkriterijska optimizacijska problema z omejitvami, na njima preizkusimo prej predstavljena algoritma ter dva izmed načinov obravnavanja omejitev in rezultate interpretiramo.

Language:Slovenian
Keywords:večkriterijska optimizacija z omejitvami, genetski algoritmi, NSGA-II, MOEA/D
Work type:Final seminar paper (mb14)
Tipology:2.11 - Undergraduate Thesis
Organization:FMF - Faculty of Mathematics and Physics
Year:2018
UDC:519.8
COBISS.SI-ID:18437465 Link is opened in a new window
Views:268
Downloads:125
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Secondary language

Language:English
Title:Constraint handling in multiobjective optimization
Abstract:
Multiobjective optimization problems are a part of everyday life. Sometimes we manage to solve them and other times they prove to be too difficult and we need help solving them. A good approach to solving multiobjective optimization problems are genetic algorithms. In this work we deal with constrained multiobjective problems. First we describe them and their solution - the Pareto front. Then we present genetic algorithms, desribe two of them, NSGA-II and MOEA/D, more in-depth and review existing constraint handling methods, that allow us to adapt existing multiobjective genetic algorithms for constrained multiobjective optimization. Finally we present two multiobjective constrained test problems, use them to test the beforementioned genetic algorithms and two of the constraint handling techniques, and interpret the results.

Keywords:constrained multiobjective optimization, genetic algorithms, NSGA-II, MOEA/D

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