AAAI Publications, 2011 AAAI Fall Symposium Series

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Ant Colony Optimization in a Changing Environment
John Jefferson Seymour, Joseph Tuzo, Marie desJardins

Last modified: 2011-11-03

Abstract


Ant colony optimization (ACO) algorithms are computational problem-solving methods that are inspired by the complex behaviors of ant colonies; specifically, the ways in which ants interact with each other and their environment to optimize the overall performance of the ant colony. Our eventual goal is to develop and experiment with ACO methods that can more effectively adapt to dynamically changing environments and problems. We describe biological ant systems and the dynamics of their environments and behaviors. We then introduce a family of dynamic ACO algorithms that can handle dynamic modifications of their inputs. We report empirical results, showing that dynamic ACO algorithms can effectively adapt to time-varying environments.

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