Evolutionary Optimization of Dynamic Multiobjective Functions

Mehnen, Jörn; Rudolph, Günter; Wagner, Tobias

Evolutionary Optimization of Dynamic Multiobjective Functions

dc.contributor.author	Mehnen, Jörn	de
dc.contributor.author	Rudolph, Günter	de
dc.contributor.author	Wagner, Tobias	de
dc.date.accessioned	2009-05-12T16:00:21Z
dc.date.available	2009-05-12T16:00:21Z
dc.date.issued	2006-05	de
dc.description.abstract	Many real-world problems show both multiobjective as well as dynamic characteristics. In order to use multiobjective evolutionary optimization algorithms (MOEA) efficiently, a systematic analysis of the behavior of these algorithms in dynamic environments is necessary. Dynamic fitness functions can be classified into problems with moving Pareto fronts and Pareto sets having varying speed, shape, and structure. The influence of the dimensions of the objective and decision space is considered. The analysis will focus on standard benchmark functions and newly designed test functions. Convergence and solution distribution features of modern MOEA, namely NSGA-II, SPEA 2 and MSOPS using different variation operators (SBX and Differential Evolution), will be characterized using Pareto front metrics. A new path integral metric is introduced. Especially the ability of the algorithms to use historically evolved population properties will be discussed.	en
dc.identifier.uri	http://hdl.handle.net/2003/26112
dc.identifier.uri	http://dx.doi.org/10.17877/DE290R-631
dc.language.iso	en	de
dc.relation.ispartofseries	Reihe CI; 204-06	de
dc.subject.ddc	004	de
dc.title	Evolutionary Optimization of Dynamic Multiobjective Functions	en
dc.type	Text	de
dc.type.publicationtype	report	de
dcterms.accessRights	open access

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