A Dynamic Ant Colony Optimization Algorithm for 3D NoC Mapping

Considering that large number of nodes and tasks are involved during the mapping process of 3D NoC,traditional mapping algorithms are inefficient.To save the execution time and improve the optimization capacity,the dynamic ant colony algorithm(DACA) is proposed based on the ant colony algorithm(ACA) in this paper.In DACA,parameters α,β and the ant number M are all adjusted dynamically during the iteration by the logistic sigmoid function.Experimental results show that DACA can save the execution time and improve the performance compared with ACA.For random generated tasks,the improvement of the optimization capacity per second can reach 38.2%~65.9%.And,for a multimedia system,it can achieve an improvement of 25.3%~32.7%.