Please use this identifier to cite or link to this item: http://hdl.handle.net/1959.3/25412

Download PDF (Published version) (Adobe Acrobat PDF, -1 bytes)

Title

Dynamic problems and nature inspired meta-heuristics

Author(s)

Hendtlass, Tim;  Moser, Irene;  Randall, Marcus

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.

Publication type

Conference paper

Research centre

Swinburne University of Technology. Faculty of Information and Communication Technologies. Centre for Information Technology Research

Source

Proceedings of the 2nd IEEE International Conference on e-Science and Grid Computing (e-Science '06), Amsterdam, Netherlands, 04-06 December 2006, pp. 111-116

Publication year

2006

Keyword(s)

ACO;  Adaptive dynamics;  Ant colony optimisation;  Evolutionary dynamics;  Extremal optimisation;  Particle swarm optimisation

Publisher

IEEE Computer Society

ISBN

0769527345

Publisher URL

http://dx.doi.org/10.1109/E-SCIENCE.2006.261195

Copyright

Copyright © 2006 IEEE. Paper reproduced here in accordance with the copyright policy of the publisher. Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works must be obtained from the IEEE.

Full text

Peer reviewed