Dynamic problems and nature inspired meta-heuristics

Tim Hendtlass; Irene Moser; Marcus Randall

doi:10.1109/E-SCIENCE.2006.261195

Dynamic problems and nature inspired meta-heuristics

Tim Hendtlass, Irene Moser, Marcus Randall

Bond Business School

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › Research › peer-review

7 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 language	English
Title of host publication	2006 Second IEEE International Conference on e-Science and Grid Computing (e-Science'06)
Place of Publication	Ammsterdam
Publisher	IEEE Computer Society
Pages	1-6
Number of pages	6
ISBN (Print)	0-7695-2734-5
DOIs	https://doi.org/10.1109/E-SCIENCE.2006.261195
Publication status	Published - 2006
Event	IEEE International Conference on e-Science and Grid Computing - Amsterdam, Netherlands Duration: 4 Dec 2006 → 6 Dec 2006 Conference number: 2nd

Conference

Conference	IEEE International Conference on e-Science and Grid Computing
Abbreviated title	e-Science 06
Country/Territory	Netherlands
City	Amsterdam
Period	4/12/06 → 6/12/06

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10.1109/E-SCIENCE.2006.261195

Cite this

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title = "Dynamic problems and nature inspired meta-heuristics",

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.",

author = "Tim Hendtlass and Irene Moser and Marcus Randall",

year = "2006",

doi = "10.1109/E-SCIENCE.2006.261195",

language = "English",

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note = "IEEE International Conference on e-Science and Grid Computing, e-Science 06 ; Conference date: 04-12-2006 Through 06-12-2006",

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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). IEEE Computer Society, Ammsterdam, pp. 1-6, IEEE International Conference on e-Science and Grid Computing, Amsterdam, Netherlands, 4/12/06. https://doi.org/10.1109/E-SCIENCE.2006.261195

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T1 - Dynamic problems and nature inspired meta-heuristics

AU - Hendtlass, Tim

AU - Moser, Irene

AU - Randall, Marcus

N1 - Conference code: 2nd

PY - 2006

Y1 - 2006

N2 - 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.

AB - 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.

U2 - 10.1109/E-SCIENCE.2006.261195

DO - 10.1109/E-SCIENCE.2006.261195

M3 - Conference contribution

SN - 0-7695-2734-5

SP - 1

EP - 6

BT - 2006 Second IEEE International Conference on e-Science and Grid Computing (e-Science'06)

PB - IEEE Computer Society

CY - Ammsterdam

T2 - IEEE International Conference on e-Science and Grid Computing

Y2 - 4 December 2006 through 6 December 2006

ER -

Dynamic problems and nature inspired meta-heuristics

Abstract

Conference

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