The current state of ant colony optimisation applied to dynamic problems

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Title: The current state of ant colony optimisation applied to dynamic problems
Author(s): Angus, Daniel
Abstract: In recent years Ant Colony Optimisation (ACO) algorithms have been applied to more challenging and complex problem domains. One such domain" which is suggested in [1] is dynamic problems. The majority of dynamic problems addressed using biologically inspired computation techniques are from the general field of Operations Research and are usually modifications of popular static problem domains such as the travelling salesman problem and the vehicle routing problem. This document aims to summarise the current state of the field with regard to ACO algorithms and their application to dynamic problems. The document will attempt to answer questions such as: (1) What general algorithmic characteristics do these novel approaches deem most important when addressing dynamic problems? (2) What utility do these dynamic algorithms offer over standard approaches? To address these questions several recent research papers have been reviewed and relevant findings presented.
Publication type: Technical report
Research centre: Swinburne University of Technology. Faculty of Information and Communication Technologies. Centre for Intelligent Systems and Complex Processes
Source: Daniel Angus: technical reports
Publication year: 2006
Keyword(s): ACO; Ant-inspired algorithms; Ant colony optimisation; Artificial intelligence; Quadratic Assignment Problems; Travelling Salesman Problems
Publisher: Swinburne University of Technology
Publisher URL: http://www.itee.uq.edu.au/~uqdangus/research.html
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