Efficient differential evolution using speciation for multimodal function optimization

Efficient differential evolution using speciation for multimodal function optimization

Li, X 2005, 'Efficient differential evolution using speciation for multimodal function optimization', in H Beyer and U O'Reilly (ed.) Genetic and Evolutionary Computation (GECCO 2005), Washington DC, USA, 25-29 June 2005.

Document type:	Conference Paper
Collection:	Conference Papers

Title	Efficient differential evolution using speciation for multimodal function optimization
Author(s)	Li, X
Year	2005
Conference name	Genetic and Evolutionary Computation Conference
Conference location	Washington DC, USA
Conference dates	25-29 June 2005
Proceedings title	Genetic and Evolutionary Computation (GECCO 2005)
Editor(s)	H Beyer U O'Reilly
Publisher	ACM Press
Place of publication	USA
Abstract	In this paper differential evolution is extended by using the notion of speciation for solving multimodal optimization problems. The proposed species-based DE (SDE) is able to locate multiple global optima simultaneously through adaptive formation of multiple species (or subpopulations) in a DE population at each iteration step. Each species functions as a DE by itself. Successive local improvements through species formation can eventually transform into global improvements in identifying multiple global optima. In this study the performance of SDE is compared with another recently proposed DE variant Crowding DE. The computational complexity of SDE, the effect of population size and species radius on SDE are investigated. SDE is found to be more computationally e cient than CrowdingDE over a number of benchmark multimodal test functions.
Subjects	Neural, Evolutionary and Fuzzy Computation
Keyword(s)	evolutionary computation differential evolution multimodal function optimization
Copyright notice	© 2005 ACM

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