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Title:Študija samoprilagajanja krmilnih parametrov pri algoritmu DEMOwSA
Authors:Zamuda, Aleš (Author)
Brest, Janez (Author)
Bošković, Borko (Author)
Žumer, Viljem (Author)
Files:URL http://www.dlib.si/details/URN:NBN:SI:DOC-85JJUCSM
Work type:Not categorized (r6)
Typology:1.01 - Original Scientific Article
Organization:FERI - Faculty of Electrical Engineering and Computer Science
Abstract:V članku predstavljamo študijo samoprilagodljivih krmilnih parametrov algoritma diferencialne evolucije za večkriterijsko optimizacijo, ki ga krmili samoprilagoditveni mehanizem, predstavljen v evolucijskih strategijah. Samoprilagajanje parametrov omogoča danemu evolucijskemu algoritmu učinkovitejše iskanje, saj se algoritem lahko prilagodi optimizacijskemu problemu, ki ga rešuje. Z eksperimentom prikažemo dejanske vrednosti in spreminjanje samoprilagodljivih krmilnih parametrov na znanih testnih funkcijah.
Keywords:evolucijsko računanje, diferencialna evolucija, večkriterijska optimizacija, samoprilagoditev, algoritmi
Year of publishing:2008
Publisher:Elektrotehniška zveza Slovenije
Number of pages:str. 223-228
Numbering:Letn. 75, št. 4
ISSN on article:0013-5852
COBISS_ID:12933654 Link is opened in a new window
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Record is a part of a journal

Title:Elektrotehniški vestnik
Publisher:Strokovna zadruga koncesijoniranih elektrotehnikov
COBISS.SI-ID:742916 New window

Secondary language

Title:A study in self-adaptation of control parameters in the DEMOwSA algorithm
Abstract:In this paper we present an experimental analysis showing that the self-adaptation of control parameters plays an important role in the multiobjective optimization process (refer to Figure I for notion of multiobjective optimality). Experimental results of a self-adaptive differential evolution algorithm are evaluated on the set of benchmark functions provided for the CEC 2007 Special session on Performance Assessment & Competition on Multi-objective Optimization Algorithms, as seen in Tables 2-7. Self-adaptation is proven to statistically outperform fixed parameters, using t-test on the empirical results in these tables. The values of control parameters are encoded in each individual (see Figure 2) and changed during the optimization process. They depend on the nature of the problem being solved, as can be seen in Table I and Figures 3 and 4 which show how using self-adaptation good control parameters are obtained to improve the search results.
Keywords:evolutionary computation, differential evolution, multi-objective optimization, self-adaptation


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