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Dynamic Problems and Nature Inspired Meta-Heuristics
Amsterdam, Netherlands December 04-December 06
DOI Bookmark: http://doi.ieeecomputersociety.org/10.1109/E-SCIENCE.2006.53Second IEEE International Conference ...
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Tim Hendtlass, Swinburne University, Australia
Irene Moser, Swinburne University, Australia
Marcus Randall, Bond University, Australia
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.
Index Terms:
evoluationary and adaptive dynamics, ant colony optimisation, particle swarm optimisation, extremal optimisation.
Citation:
Tim Hendtlass, Irene Moser, Marcus Randall, "Dynamic Problems and Nature Inspired Meta-Heuristics," e-science, pp.111, Second IEEE International Conference on e-Science and Grid Computing (e-Science'06), 2006
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