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Dynamic Problems and Nature Inspired Meta-heuristics

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Biologically-Inspired Optimisation Methods

Part of the book series: Studies in Computational Intelligence ((SCI,volume 210))

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.

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Authors and Affiliations

  1. Centre for Information Technology Research School of Information and Communication Technologies, Swinburne University, VIC 3001

    Tim Hendtlass & Irene Moser

  2. School of Information Technology, Bond University, QLD 4229, Australia

    Marcus Randall

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  2. Irene Moser
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  1. Institute for Integrated and Intelligent Systems, Griffith University, Nathan Campus, 4111, Brisbane, Queensland, Australia

    Andrew Lewis

  2. Institut für Angewandte Informatik und Formale Beschreibungsverfahren - AIFB, Universität Karlsruhe, 76128, Karlsruhe, Germany

    Sanaz Mostaghim

  3. Faculty of Business, Technology and Sustainable Development, Bond University, 4229, Gold Coast, Queensland, Australia

    Marcus Randall

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Hendtlass, T., Moser, I., Randall, M. (2009). Dynamic Problems and Nature Inspired Meta-heuristics. In: Lewis, A., Mostaghim, S., Randall, M. (eds) Biologically-Inspired Optimisation Methods. Studies in Computational Intelligence, vol 210. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-01262-4_4

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