ASGA: Improving the Ant System by Integration with Genetic Algorithms

1 Email: oppacher@scs.carleton.ca ABSTRACT This paper describes how the Ant System can be improved by selfadaptation of its controlling parameters. Adaptation is achieved by integrating a genetic algorithm with the ant system and maintaining a population of agents (ants) that have been used to generate solutions. These agents have behavior that is inspired by the foraging activities of ants, with each agent capable of simple actions. Problem solving is inherently distributed and arises as a consequence of the self-organization of a collection of agents, or swarm system. This paper applies the Ant System with Genetic Algorithm (ASGA) system to the problem of path finding in networks, demonstrating by experimentation that the hybrid algorithm exhibits improved performance when compared to the basic Ant System.

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