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Abstract:
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Ant colony optimization (ACO) is a metaheuristic that was introduced in the early 90's, inspired by the foraging behaviour of real ant colonies. This foraging behaviour is based on indirect communication between the ants via chemical pheromone trails. The indirect communication allows an ant colony to find shortest paths between its nest and food sources. This is exploited in artificial ant colonies for solving discrete (and continuous) optimization problems. The first part of the tutorial will be concerned with the basics of ACO algorithms. First, the origins of ACO algorithms in swarm intelligence will be presented. Then we will show how to transfer the foraging behaviour of real ants into a technical algorithm. Finally, the basic components of ACO algorithms will be explained by means of example applications. The second part of the tutorial will deal with two important topics: the hybridization of ACO algorithms with other techniques for optimization, and the occurance of negative search bias in the search process. The tutorial will be concluded with an example of how to apply ACO to continuous optimization problems.
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