DocumentCode
2909157
Title
Dynamic Problems and Nature Inspired Meta-Heuristics
Author
Hendtlass, Tim ; Moser, Irene ; Randall, Marcus
Author_Institution
Swinburne University, Australia
fYear
2006
fDate
Dec. 2006
Firstpage
111
Lastpage
111
Abstract
Biological systems are, by their very nature, adaptive. However, the meta-heuristic search algorithms inspired by them have mainly been applied to static problems (i.e., problems that do not change while they are being solved). Recently, a greater body of work has been completed on the newer meta-heuristics, particularly ant colony optimisation, particle swarm optimisation and extremal optimisation. This survey paper examines representative works and methodologies of these techniques on this class of problems. Beyond this we outline the limitations of these methods.
Keywords
Ant colony optimization; Biological systems; Bonding; Cities and towns; Communications technology; Evolutionary computation; Genetic algorithms; Information technology; Particle swarm optimization; Testing; ant colony optimisation; evoluationary and adaptive dynamics; extremal optimisation.; particle swarm optimisation;
fLanguage
English
Publisher
ieee
Conference_Titel
e-Science and Grid Computing, 2006. e-Science '06. Second IEEE International Conference on
Conference_Location
Amsterdam, The Netherlands
Print_ISBN
0-7695-2734-5
Type
conf
DOI
10.1109/E-SCIENCE.2006.261195
Filename
4031084
Link To Document