Self-adaptive differential evolution for dynamic environments with fluctuating numbers of optima

dc.contributor.authorDu Plessis, M.C. (Mathys Cornelius)
dc.contributor.authorEngelbrecht, Andries P.
dc.contributor.emailandries.engelbrecht@up.ac.zaen_US
dc.date.accessioned2014-05-09T12:28:45Z
dc.date.available2014-05-09T12:28:45Z
dc.date.issued2013
dc.description.abstractDespite the fact that evolutionary algorithms often solve static problems successfully, dynamic optimization problems tend to pose a challenge to evolutionary algorithms [21]. Differential evolution (DE) is one of the evolutionary algorithms that does not scale well to dynamic environments due to lack of diversity [35]. A significant body of work exists on algorithms for optimizing dynamic problems (see Section 1.3). Recently, several algorithms based on DE have been proposed [19][26][28][27][10]. Benchmarks used to evaluate algorithms aimed at dynamic optimization (like the moving peaks benchmark [5] and the generalized benchmark generator [17] [16]), typically focus on problems where a constant number of optima moves around a multi-dimensional search space. While some of these optima may be obscured by others, these benchmarks do not simulate problems where new optima are introduced, or current optima are removed from the search space. Dynamic Population DE (DynPopDE) [27] is a DE-based algorithm aimed at dynamic optimization problems where the number of optima fluctuates over time. This chapter describes the subcomponents of DynPopDE and then investigates the effect of hybridizing DynPopDE with the self-adaptive component of jDE [10] to form a new algorithm, Self-Adaptive DynPopDE (SADynPopDE). The following sections describe dynamic environments and the benchmark function used in this study. Related work by other researchers is presented in Section 1.3. Differential evolution is described in Section 1.4. The components of DynPopDE, the base algorithm used in this study, are described and motivated in Section 1.5. The incorporation of self-adaptive control parameters into DynPopDE to form SA- DynPopDE and the experimental comparison of these algorithms are described in Section 1.6. The main conclusions of this study are summarized in Section 1.7.en_US
dc.description.librarianhb2014en_US
dc.description.urihttp://www.springer.com/series/7092en_US
dc.identifier.citationDu Plessis, MC & Engelbrecht, AP 2013, 'Self-adaptive differential evolution for dynamic environments with fluctuating numbers of optima', Studies in Coputational Intelleigence, vol. 433, pp. 117-145.en_US
dc.identifier.issn1860-949X (print)
dc.identifier.urihttp://hdl.handle.net/2263/39751
dc.language.isoenen_US
dc.publisherSpringeren_US
dc.rights© Springer-Verlag 2014. The original publication is available at : http://www.springer.com/series/7092en_US
dc.subjectDynamic environmentsen_US
dc.subjectDifferential evolution (DE)en_US
dc.subjectAlgorithmsen_US
dc.subjectFluctuating numbersen_US
dc.subjectOptimaen_US
dc.titleSelf-adaptive differential evolution for dynamic environments with fluctuating numbers of optimaen_US
dc.typePostprint Articleen_US

Files

Original bundle

Now showing 1 - 1 of 1
Loading...
Thumbnail Image
Name:
DuPlessis_Self_2013.pdf
Size:
304.21 KB
Format:
Adobe Portable Document Format
Description:
Postprint Article

License bundle

Now showing 1 - 1 of 1
Loading...
Thumbnail Image
Name:
license.txt
Size:
1.71 KB
Format:
Item-specific license agreed upon to submission
Description: