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High‐level synthesis by dynamic ant
Author(s) -
Keinprasit Rachaporn,
Chongstitvatana Prabhas
Publication year - 2004
Publication title -
international journal of intelligent systems
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.291
H-Index - 87
eISSN - 1098-111X
pISSN - 0884-8173
DOI - 10.1002/int.10148
Subject(s) - ant colony optimization algorithms , computer science , heuristic , path (computing) , mathematical optimization , reinforcement learning , ant colony , algorithm , scheme (mathematics) , metaheuristic , artificial intelligence , mathematics , mathematical analysis , programming language
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.