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
Versions
Version Filter Type
Access Statistics: 220 Abstract Views  -  Detailed Statistics
Created: Mon, 04 Jan 2010, 08:16:50 EST by Catalyst Administrator
© 2014 RMIT Research Repository • Powered by Fez SoftwareContact us