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 suboptimial. This paper explores the ability of the ant colony optimisation algorithm to adapt from the optimum solution to one set of circumstances to the optimal solution to another set of circumstances. Results are given for a preliminary investigation based on the classical travelling salesperson 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.
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Angus, D., Hendtlass, T. (2002). Ant Colony Optimisation Applied to a Dynamically Changing Problem. In: Hendtlass, T., Ali, M. (eds) Developments in Applied Artificial Intelligence. IEA/AIE 2002. Lecture Notes in Computer Science(), vol 2358. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-48035-8_60
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DOI: https://doi.org/10.1007/3-540-48035-8_60
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