Abstract:
In this research an algorithm based on Ant Colony Optimization techniques called Ants on a Tree (AOT) is proposed. This algorithm can integrate many algorithms together to solve a single problem. The strength of AOT is demonstrated by solving a High-Level Synthesis problem. A High-Level Synthesis problem consists of many design steps and many algorithms to solve each of them. AOT can easily integrate these algorithms to limit the search space and use them as heuristic weights to guide the search. During the search, AOT generates a dynamic decision tree. A boosting technique similar to branch and bound algorithms is applied to guide the search in the decision tree. The storage explosion problem is eliminated by the evaporation of pheromone trail generated by ants the inherent property of our search algorithm. The algorithm was tested with the Elliptical Wave Filter (EWF) benchmark, and found that it is practically to be used. By allocating the resources at the early design state, the Fixed-resource Mobility could be integrated to further improve the performance of the algorithm
