High-level synthesis by dynamic ant

In this article, a new algorithm called dynamic ant is introduced. It was a combination of ant colony optimization (ACO) techniques and the dynamic niche sharing scheme. The interesting point of this algorithm is that it is implemented easily and could be well matched with existing design algorithms by adding the heuristic weights to speed up the algorithm. The algorithm uses the problem state structure as in the reinforcement-learning algorithm, but the storage explosion is prevented by means of the pheromone trail. This algorithm was investigated for the data path design problem of high-level synthesis of which has a large number of design steps and design techniques. © 2004 Wiley Periodicals, Inc.

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