Dynamic problems and nature inspired meta-heuristics

Tim Hendtlass, Irene Moser, Marcus Randall

Research output: Chapter in Book/Report/Conference proceedingConference contributionResearchpeer-review

6 Citations (Scopus)

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.
Original languageEnglish
Title of host publication2006 Second IEEE International Conference on e-Science and Grid Computing (e-Science'06)
Place of PublicationAmmsterdam
PublisherIEEE Computer Society
Pages1-6
Number of pages6
ISBN (Print)0-7695-2734-5
DOIs
Publication statusPublished - 2006
EventIEEE International Conference on e-Science and Grid Computing - Amsterdam, Netherlands
Duration: 4 Dec 20066 Dec 2006
Conference number: 2nd

Conference

ConferenceIEEE International Conference on e-Science and Grid Computing
Abbreviated titlee-Science 06
CountryNetherlands
CityAmsterdam
Period4/12/066/12/06

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    Hendtlass, T., Moser, I., & Randall, M. (2006). Dynamic problems and nature inspired meta-heuristics. In 2006 Second IEEE International Conference on e-Science and Grid Computing (e-Science'06) (pp. 1-6). Ammsterdam: IEEE Computer Society. https://doi.org/10.1109/E-SCIENCE.2006.261195