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 :
بازگشت