Dynamic problems and nature inspired meta-heuristics

Tim Hendtlass*, Irene Moser, Marcus Randall

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingChapterResearchpeer-review

1 Citation (Scopus)

Abstract

Biological systems have often been used as the inspiration for search techniques to solve continuous and discrete combinatorial optimisation problems. One of the key aspects of biological systems is their ability to adapt to changing environmental conditions. Yet, biologically inspired optimisation techniques are mostly used to solve static problems (problems that do not change while they are being solved) rather than their dynamic counterparts. This is mainly due to the fact that the incorporation of temporal search control is a challenging task. Recently, however, a greater body of work has been completed on enhanced versions of these biologically inspired meta-heuristics, particularly genetic algorithms, ant colony optimisation, particle swarm optimisation and extremal optimisation, so as to allow them to solve dynamic optimisation problems. This survey chapter examines representative works and methodologies of these techniques on this important class of problems.

Original languageEnglish
Title of host publicationBiologically-Inspired Optimisation Methods: Parallel Algorithms, Systems and Applications
Pages79-109
Number of pages31
Volume210
DOIs
Publication statusPublished - 2009

Publication series

NameStudies in Computational Intelligence
Volume210
ISSN (Print)1860949X

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Hendtlass, T., Moser, I., & Randall, M. (2009). Dynamic problems and nature inspired meta-heuristics. In Biologically-Inspired Optimisation Methods: Parallel Algorithms, Systems and Applications (Vol. 210, pp. 79-109). (Studies in Computational Intelligence; Vol. 210). https://doi.org/10.1007/978-3-642-01262-4_4