Dynamic Ant Colony Optimisation

Authors:
Daniel Angus;Tim Hendtlass
Affiliations:
Centre for Intelligent Systems and Complex Processes, Swinburne University of Technology, Australia;Centre for Intelligent Systems and Complex Processes, Swinburne University of Technology, Australia
Venue:
Applied Intelligence
Year:
2005

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Abstract

Ant Colony optimisation has proved suitable to solve static optimisation problems, that is problems that do not change with time. However in the real world changing circumstances may mean that a previously optimum solution becomes suboptimal. This paper explores the ability of the ant colony optimisation algorithm to adapt from the optimum solution for one set of circumstances to the optimal solution for another set of circumstances. Results are given for a preliminary investigation based on the classical travelling salesman problem. It is concluded that, for this problem at least, the time taken for the solution adaption process is far shorter than the time taken to find the second optimum solution if the whole process is started over from scratch.